Methods, systems, and kits for the treatment of inflammatory diseases by targeting TL1A

Abstract

Methods, systems, and kits are provided for selecting a subject for treatment with an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression based on the presence of one or more genotypes associated with a positive therapeutic response to an inhibitor of TL1A. Methods, systems, and kits are also provided for detecting one or more of the genotypes described herein.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 384,925, filed November 23, 2022, U.S. Provisional Patent Application No. 63 / 385,622, filed November 30, 2022, U.S. Provisional Patent Application No. 63 / 478,865, filed January 6, 2023, and U.S. Provisional Patent Application No. 63 / 487,853, filed March 1, 2023.

[0002] Reference to an electronically submitted sequence listing This application contains a Sequence Listing that has been submitted electronically in XML format, and is incorporated herein by reference in its entirety. The XML file, created on November 13, 2023, has the filename 25747-WO-PCT_SL.xml and is 1,647,264 bytes in size. [Background technology]

[0003] Inflammatory, fibrostenosing, and fibrotic diseases affect a vast number of individuals and pose a significant health burden worldwide due to heterogeneous disease etiologies and diverse clinical manifestations. One such disease is inflammatory bowel disease (IBD), which has two common forms: Crohn's disease (CD) and ulcerative colitis (UC). IBD is a chronic, relapsing inflammatory disorder of the gastrointestinal tract. The occurrence of IBD is common, affecting nearly 3 million individuals in the United States alone.

[0004] Few treatment options are available for patients suffering from inflammatory, fibrostenosing, and fibrotic diseases. Existing anti-inflammatory therapies, such as steroids and tumor necrosis factor (TNF) inhibitors, are typically used as first-line treatments for IBD. Unfortunately, a significant number of patients experience a lack of response or loss of response to existing anti-inflammatory therapies, particularly TNF inhibitors. Patients are treated with ineffective anti-inflammatory therapies, but the disease worsens. Surgery, in the form of reconstruction (intestinal remodeling) or resection (intestinal removal), is the only treatment option for patients who do not respond to first-line treatments. Surgical treatment of IBD is invasive and poses postoperative risks, such as anastomotic leakage, infection, and bleeding, in an estimated one-third of patients undergoing surgery.

[0005] The pathogenesis of inflammatory, fibrostenosing, and fibrotic diseases, such as IBD, is thought to involve an uncontrolled immune response that can be triggered by specific environmental factors in genetically susceptible individuals. The heterogeneity of disease etiology and clinical course, coupled with variable response to treatment and its associated side effects, suggests that a personalized medicine approach to treating these diseases is the best therapeutic strategy. However, there are very few personalized treatments available for patients. Therefore, there is a need to identify targeted therapeutic approaches or treatments for inflammatory, fibrostenosing, and fibrotic diseases and their subclinical phenotypes. There is an even greater need to develop reliable methodologies for identifying patients who may respond to any given therapeutic approach based on their genotype. The necessary methodology would also identify undiagnosed subjects at risk of developing the disease, thereby allowing preventative interventions to be prescribed to reduce the growing health burden. Summary of the Invention

[0006] The models and genotypes described herein are associated (individually or together) with (i) an increase in TNFSF15 (TL1A) protein expression levels in a sample obtained from a subject or patient compared to a baseline level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual), (ii) an increase in IBD-enriched cell types in IBD-affected tissue compared to baseline levels in non-IBD-affected tissue, (iii) a decrease in IBD-depleted cell types in IBD-affected tissue compared to baseline levels in non-IBD-affected tissue, and / or (iv) an increase in the positive therapeutic response of IBD patients to treatment with a TL1A inhibitor compared to baseline levels of response in patients not selected by genotype or model. More specifically, the models and genotypes described herein are associated (individually or together) with (i) alone. (ii) alone; (iii) alone; (iv) alone; (i) and (ii) together; (i) and (iii) together; (i) and (iv) together; (ii) and (iii) together; (ii) and (iv) together; (iii) and (iv) together; (i), (ii) and (iii) together; (i), (ii) and (iv) together; (ii), (iii) and (iv) together; or (i), (ii), (iii), and (iv) together, where (i), (ii), (iii), and (iv) correspond to numbered items (i), (ii), (iii), and (iv) in the section preceding this paragraph. Thus, by way of example, the models and genotypes described herein are associated (individually or together) with an increased level of TNFSF15 (TL1A) protein expression in a sample obtained from a subject or patient, compared to a reference level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual). Furthermore, the models and genotypes described herein are associated (individually or together) with an increase in IBD-enriched cell types in IBD-affected tissue, compared to a reference level in non-IBD-affected tissue. Alternatively, the models and genotypes described herein are associated (individually or together) with a decrease in IBD-depleted cell types in IBD-affected tissue, compared to a reference level in non-IBD-affected tissue.Furthermore, the models and genotypes described herein are associated (individually or together) with an increased positive therapeutic response in IBD patients to treatment with an inhibitor of TL1A activity or expression, compared to a reference level of response in patients not selected by the genotype or model. The models provided herein, such as models for calculating a predicted response index (PRI), use the genotypes described herein and apply a mathematical function to the genotype to generate a PRI, as described in Sections 5.2 and 7. The genotypes disclosed herein are located at loci or loci directly or indirectly involved in TL1A-mediated or T cell-dependent inflammatory pathways. Furthermore, some of the genotypes provided herein are also significantly associated with inflammatory bowel disease (IBD), such as Crohn's disease (CD). The genotypes are useful for selecting patients or subjects for treatment with an inhibitor of TL1A activity or expression. The patient may be diagnosed with IBD, CD, or both. The subject may be suspected of having IBD, CD, or both.

[0007] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) greater than a cutoff, the PRI being calculated from a combination of polymorphisms determined from a sample from the subject, and wherein a PRI greater than the cutoff predicts the subject's positive therapeutic response to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 29%.

[0008] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0009] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating the PRI from the combination of polymorphisms; wherein a PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression. Provided herein are methods for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition.

[0010] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0011] In some embodiments, the method further comprises preparing DNA from the sample.

[0012] In one aspect, there is provided a computer-implemented method for determining a response probability score (RPS) for a subject, comprising: (a) receiving genotype data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, the genotype data including a combination of polymorphisms; (b) Genotype data, (i) assigning a weighted numerical value to each polymorphism in the combination of polymorphisms to generate a plurality of weighted values; and (ii) summing multiple weighted values; analyzing the subject with a first statistical algorithm configured to create a model risk score (MRS) for the subject by performing operations including: (c) providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); (d) applying a cutoff to the RPS, wherein the RPS for the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition; Provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, comprising:

[0013] In another aspect, there is provided a computer-implemented method for determining a response probability score (RPS) for a subject, comprising: (a) obtaining a plurality of multi-single nucleotide polymorphism (multi-SNP) models, each multi-SNP model predicting a positive therapeutic response to an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject; (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from the subject; and (c) calculating a model risk score (MRS) utilizing one or more statistical algorithms configured to perform operations including: (i) assigning a weighted value to each polymorphism of the plurality of polymorphisms to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; (d) applying a logarithmic scale and cutoff to the MRS to generate a response probability score (RPS); Provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, comprising:

[0014] In some embodiments, the PRI is a response probability score (RPS). In some embodiments, the PRI has a positive correlation coefficient with the RPS. In some embodiments, the correlation coefficient between the PRI and the RPS is a Pearson correlation coefficient or a Spearman correlation coefficient. In some embodiments, the positive correlation coefficient between the PRI and the RPS is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1. In some embodiments, the RPS ranges from 0 to 1. In some embodiments, the cutoff is 0.5. In some embodiments, the RPS is 1 / (1+e (-MRS) ) and MRS is

number

[0015] In some embodiments, the PRI is a Model Risk Score (MRS). In some embodiments, the PRI has a positive correlation coefficient with the MRS. In some embodiments, the correlation coefficient between the PRI and the MRS is a Pearson correlation coefficient or a Spearman correlation coefficient. In some embodiments, the positive correlation coefficient between the PRI and the MRS is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1. In some embodiments, the MRS is

number

[0016] In some embodiments, the SNPs in the model are: (i) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 2 for homozygous alternative allele; (ii) 1 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 0 for homozygous alternative allele; (iii) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (iv) 0 for homozygous reference allele, 0 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (v) 1 for the homozygous reference allele, 0 for the heterozygous reference allele and the alternative allele, and 0 for the homozygous alternative allele; and / or (vi) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, 0 for homozygous alternative allele Like χ i It is mathematically expressed by

[0017] In some embodiments, the combination of polymorphisms comprises one or more polymorphisms selected from Table 27, or a polymorphism with an R of at least 0.85. 2 or a combination thereof.

[0018] In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with at least about a 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% accuracy. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in a subject treated with an inhibitor of TL1A activity or expression with at least about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy.In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% specificity. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% specificity. In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% negative predictive value. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% sensitivity. In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% positivity. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-enriched cell types with at least about a 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% positive rate. In some embodiments, the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with at least about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy. In some embodiments, the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-enriched cell types with at least about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy.

[0019] In some embodiments, the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from the group consisting of activated fibroblasts, monocyte-derived dendritic cells (moDCs), and CD36+ endothelial cells, enterocytes and colonocytes, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

[0020] In some embodiments, the one or more IBD-depleted cell types comprise one or two cell types selected from the group consisting of tuft cells and BEST4+ epithelial cells.

[0021] In some embodiments, the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

[0022] In some embodiments, the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

[0023]

[0013] In one aspect, a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predicted response index (PRI) and (1) or (2): (1) If the PRI is positively correlated with the Response Probability Score (RPS), subjects are selected if the PRI is above the cutoff, or (2) If PRI is negatively correlated with RPS, subjects will be selected if PRI is below the cutoff; are selected based on comparison with the cutoffs A PRI is calculated from a combination of polymorphisms determined from a sample from the subject and a comparison of the predicted response index (PRI) according to (1) or (2) with a cutoff, and predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 29%. Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject.

[0024] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from a combination of polymorphisms, wherein the subject is (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0025] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from a combination of polymorphisms, the comparison being (1) or (2) (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0026] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) PRI and (1) or (2): (1) If PRI is positively correlated with RPS, select subjects if PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0027] In some embodiments, the method further comprises preparing DNA from the sample.

[0028] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression, based at least in part on a predicted response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, and determining a comparison of the PRI with a cutoff to predict a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression.

[0029] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) detecting the presence of a combination of polymorphisms in a sample from a subject; (b) applying a statistical algorithm to the combination of polymorphisms detected in step (a) to generate PRIs; (c) determining a comparison of the PRI with the cutoff; and Provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering an inhibitor of TL1A activity or expression to a subject who is predicted to have a positive therapeutic response to the inhibitor of TL1A activity or expression, as determined by a predicted response index (PRI) calculated by:

[0030] In a further aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, where the PRI is further determined in comparison to a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0031] In some embodiments, the method further comprises preparing DNA from the sample.

[0032] In some embodiments, the comparison of PRI to the cutoff is determined according to (1) or (2): (1) if PRI is positively correlated with RPS, determine the subject's PRI if PRI is above the cutoff, or (2) if PRI is negatively correlated with RPS, determine the subject's PRI if PRI is below the cutoff.

[0033] In some embodiments, the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

[0034] In some embodiments, when PRI is positively correlated with RPS, the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1; when PRI is negatively correlated with RPS, the negative correlation coefficient is at most about −0.6, at most about −0.65, at most about −0.7, at most about −0.75, at most about −0.8, at most about −0.85, at most about −0.95, at most about −0.99, or 1.

[0035] In some embodiments, RPS ranges from 0 to 1.

[0036] In some embodiments, if PRI is positively correlated with RPS, the cutoff is 0.5, or if PRI is negatively correlated with RPS, the cutoff is −0.5.

[0037] In some embodiments, RPS is calculated as 1 / (1+e(-MRS)), and MRS is

number

[0038] In some embodiments, the SNPs in the model are: (i) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 2 for homozygous alternative allele; (ii) 1 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 0 for homozygous alternative allele; (iii) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (iv) 0 for homozygous reference allele, 0 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (v) 1 for the homozygous reference allele, 0 for the heterozygous reference allele and the alternative allele, and 0 for the homozygous alternative allele; and / or (vi) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, 0 for homozygous alternative allele This is mathematically represented by χi as follows:

[0039] In some embodiments, the combination of polymorphisms comprises one or more polymorphisms selected from Table 27, or surrogate polymorphisms in linkage disequilibrium as determined by an R2 of at least 0.85, or a combination thereof.

[0040] In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a subject's positive therapeutic response to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a subject's positive therapeutic response to treatment with an inhibitor of TL1A activity or expression with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of the PRI to the cutoff according to (1) or (2) predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of the PRI to the cutoff according to (1) or (2) predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of the PRI to the cutoff according to (1) or (2) predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with at least about a 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% accuracy. In some embodiments, the cutoff is such that a comparison of the PRI to the cutoff according to (1) or (2) predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with at least about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy.In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% specificity. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a decrease in one or more IBD-enriched cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% specificity. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% negative predictive value. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% sensitivity. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% positivity. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with at least about a 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% accuracy. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with at least about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy. In some embodiments, the cutoff is such that a comparison of PRI to the cutoff according to (1) or (2) predicts a reduction in one or more IBD-depleted cell types with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% accuracy. In some embodiments, (1) and (2) referenced in this paragraph are (1) and (2) described in this section (Section 2), e.g., in the applicable previous paragraph.

[0041] In some embodiments, the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from the group consisting of activated fibroblasts, monocyte-derived dendritic cells (moDCs), and CD36+ endothelial cells, enterocytes and colonocytes, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

[0042] In some embodiments, the one or more IBD-depleted cell types comprise one or two cell types selected from the group consisting of tuft cells and BEST4+ epithelial cells.

[0043] In some embodiments, the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

[0044] In some embodiments, the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

[0045] In some embodiments, (i) the PRI is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; (ii) the MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; and / or (iii) the RPS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31.

[0046] In some embodiments, the combination of polymorphisms is detected in a sample by subjecting the sample to an assay configured to detect the presence of at least three nucleotides corresponding to nucleic acid position 501 in at least three of SEQ ID NOs: 2001-2048 and 2057-2059.

[0047] In one aspect, a computer-implemented system includes at least one processor; (a) receiving genotype data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, the genotype data including a combination of polymorphisms; (b) applying a first statistical algorithm to the genotype data, the first statistical algorithm comprising: (i) assigning a weighted numerical value to each polymorphism in the combination of polymorphisms to generate a plurality of weighted values; and (ii) summing multiple weighted values; generating a model risk score (MRS) for the subject by performing operations including: (c) applying a second statistical algorithm to the MRS, the second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); (d) applying a cutoff to the RPS, wherein the RPS for the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition; instructions executable by at least one processor to provide an application configured to determine a response probability score (RPS) of a subject by performing operations including: Provided herein is a computer-implemented system that includes:

[0048] In another aspect, a computer-implemented system includes at least one processor; (a) receiving a plurality of multi-single nucleotide polymorphism (multi-SNP) models, each multi-SNP model predicting a positive therapeutic response to an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject; (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from the subject; and (c) calculating a model risk score (MRS) utilizing one or more statistical algorithms configured to perform an operation including: (i) assigning a weighted value to each polymorphism of the plurality of polymorphisms to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; (d) applying a logarithmic scale and cutoff to the MRS to generate a response probability score (RPS); instructions executable by at least one processor to provide an application configured to determine a response probability score (RPS) of a subject by performing operations including: Provided herein is a computer-implemented system that includes:

[0049] In some embodiments of the computer-implemented system, the RPS ranges from 0 to 1.

[0050] In some embodiments of the computer-implemented system, the cutoff is 0.5.

[0051] In some embodiments of the computer-implemented system, the genotype data is a combination of single nucleotide polymorphisms (SNPs).

[0052] In some embodiments of the computer-implemented system, the RPS is 1 / (1+e (-MRS) ) and MRS is

number

[0053] In some embodiments of the computer-implemented system, the MRS

number

[0054] In some embodiments of the computer-implemented system, the SNPs in the model are: (i) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 2 for homozygous alternative allele; (ii) 1 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 0 for homozygous alternative allele; (iii) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (iv) 0 for homozygous reference allele, 0 for heterozygous reference allele and alternative allele, and 1 for homozygous alternative allele; (v) 1 for the homozygous reference allele, 0 for the heterozygous reference allele and the alternative allele, and 0 for the homozygous alternative allele; and / or (vi) 0 for homozygous reference allele, 1 for heterozygous reference allele and alternative allele, 0 for homozygous alternative allele Like χ i It is mathematically expressed by

[0055] In some embodiments of the computer-implemented system, the combination of polymorphisms comprises one or more polymorphisms selected from Table 27, or a polymorphism with an R of at least 0.85. 2 or a proxy polymorphism in linkage disequilibrium therewith, as determined by

[0056] In some embodiments of the computer-implemented system, the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

[0057] In some embodiments of the computer-implemented system, the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

[0058] In some embodiments of the computer-implemented system, (i) the MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31, and / or (ii) the RPS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31.

[0059] In some embodiments of the computer-implemented system, combinations of polymorphisms are detected in a sample by subjecting the sample to an assay configured to detect the presence of at least three nucleotides corresponding to at least three nucleic acid positions 501 of SEQ ID NOs: 2001-2048 and 2057-2059.

[0060] In some embodiments of the methods or computer-implemented systems provided herein, the subject has been treated with an advanced IBD therapy prior to treatment with the inhibitor of TL1A activity or expression. In some embodiments of the methods or computer-implemented systems provided herein, the subject has not been treated with an advanced IBD therapy prior to treatment with the inhibitor of TL1A activity or expression. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the advanced IBD therapy comprises one or more selected from the group consisting of a biologic therapeutic agent for IBD, an S1P1 modulator, or a JAK inhibitor. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the biologic therapeutic agent for IBD comprises an anti-TNFα antibody, an anti-IL23 antibody, or an anti-integrin antibody. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the anti-TNFα comprises adalimumab, infliximab, golimumab, certolizumab, or etanercept. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the anti-IL23 antibody comprises ustekinumab, guselkumab, risankizumab, brazikumab, mirikizumab, tildrakizumab, or briakinumab. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the anti-integrin antibody comprises etrolizumab, vedolizumab, natalizumab, or ontamalimab. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the S1P1 modulator comprises fingolimod, siponimod, tetrasimod, ozanimod, ponesimod, amiselimod, selalifimod, or moclavimod. In some embodiments of the methods or computer-implemented systems provided herein, including this paragraph, the JAK inhibitor comprises tofacitinib, abrocitinib, baricitinib, upadacitinib, or filgotinib.

[0061] In some embodiments, the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof (anti-TL1A antibody or antigen-binding fragment) that binds to TL1A, and the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable region comprising (a) an HCDR1 having the amino acid sequence set forth in SEQ ID NO: 1, (b) an HCDR2 having the amino acid sequence set forth in any one of SEQ ID NOs: 2 to 5, and (c) an HCDR3 having the amino acid sequence set forth in any one of SEQ ID NOs: 6 to 9, and a light chain variable region comprising (d) an LCDR1 having the amino acid sequence set forth in SEQ ID NO: 10, (e) an LCDR2 having the amino acid sequence set forth in SEQ ID NO: 11, and (f) an LCDR3 having the amino acid sequence set forth in any one of SEQ ID NOs: 12 to 15.

[0062] In some embodiments, the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, and the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable domain comprising an amino acid sequence at least about 90% identical to any one of SEQ ID NOs: 101-135 or 310-302, and a light chain variable domain comprising an amino acid sequence at least about 90% identical to any one of SEQ ID NOs: 201-206 or 303. In some embodiments, the heavy chain variable domain comprises an amino acid sequence at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 101-135 or 310-302. In some embodiments, the light chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 201-206 or 303.

[0063] In some embodiments, the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, wherein the anti-TL1A antibody or antigen-binding fragment comprises (a) a heavy chain variable framework region comprising a human IGHV1-46*02 framework or a modified human IGHV1-46*02 framework, and (b) a light chain variable framework region comprising a human IGKV3-20 framework or a modified human IGKV3-20 framework, wherein the heavy chain variable framework region and the light chain variable framework region collectively comprise fewer than about 14 amino acid modifications from the human IGHV1-46*02 framework and the human IGKV3-20 framework. In some embodiments, the amino acid modifications of the fewer than 14 amino acid modifications are: (a) the amino acid modification is at position 47 of the heavy chain variable region and the amino acid at position 47 is R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V; (b) the amino acid modification is at position 45 of the heavy chain variable region and the amino acid at position 45 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P; , S, T, W, Y, or V; (c) the amino acid modification is at position 55 of the heavy chain variable region and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; (d) the amino acid modification is at position 78 of the heavy chain variable region and the amino acid at position 78 is A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, or Y; (e (f) the amino acid modification is at position 82 of the heavy chain variable region and the amino acid at position 82 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V; (g) the amino acid modification is at position 89 of the heavy chain variable region and the amino acid at position 89 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; or (h) the amino acid modification is at position 91 of the heavy chain variable region and the amino acid at position 91 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, or Y, or a combination of two or more modifications selected from (a) to (h).In some embodiments, the amino acid modifications of the fewer than 14 amino acid modifications include A47R, R45K, M55I, V78A, M80I, R82T, V89A, M91L in the heavy chain variable region according to Aho or Kabat numbering. In some embodiments, the amino acid modifications of the fewer than 14 amino acid modifications include (a) a modification at amino acid position 54 in the light chain variable region, and / or (b) a modification at amino acid position 55 in the light chain variable region according to Aho or Kabat numbering. In some embodiments, the amino acid modifications of the fewer than 14 amino acid modifications include (a) the amino acid modification is at position 54 of the light chain variable region, where the amino acid at position 54 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V, and / or (b) the amino acid modification is at position 55 of the light chain variable region, where the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V. In some embodiments, the amino acid modifications of the fewer than 14 amino acid modifications include L54P and / or L55W in the light chain variable region according to Aho or Kabat numbering.

[0064] In some embodiments, the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A, and comprises a heavy chain variable region comprising SEQ ID NO: 301 X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYCAR[HCDR3]WGQGTTVTVSS; a light chain variable region comprising SEQ ID NO: 303 EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FGGGTKLEIK; and each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V. In some embodiments, the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A, and comprises a heavy chain variable region comprising SEQ ID NO: 302 X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYC[HCDR3]WGQGTTVTVSS; a light chain variable region comprising SEQ ID NO: 303 EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FGGGTKLEIK; and each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V. In some embodiments, (a) X1 is Q or E, (b) X2 is R or K, (c) X3 is A or R; (d) X4 is M or I; (e) X5 is V or A; (f) X6 is M or I; (g) X7 is R or T; (h) X8 is V or A; (i) X9 is M or L, (j) X10 is L or P; (k) X11 is L or W; or (l) X1-X11 are any combination of (a)-(k).

[0065] In some embodiments, the antibody or antigen-binding fragment comprises a heavy chain CDR1 represented by SEQ ID NO: 1, a heavy chain CDR2 represented by any one of SEQ ID NOs: 2 to 5, a heavy chain CDR3 represented by any one of SEQ ID NOs: 6 to 9, a light chain CDR1 represented by SEQ ID NO: 10, a light chain CDR2 represented by SEQ ID NO: 11, and a light chain CDR3 represented by any one of SEQ ID NOs: 12 to 15.

[0066] In some embodiments, the antibody or antigen-binding fragment comprises a heavy chain framework (FR) 1 set forth in SEQ ID NO: 304, a heavy chain FR2 set forth in SEQ ID NO: 305 or SEQ ID NO: 313, a heavy chain FR3 set forth in any one of SEQ ID NOs: 306, 307, 314, or 315, a heavy chain FR4 set forth in SEQ ID NO: 308, a light chain FR1 set forth in SEQ ID NO: 309, a light chain FR2 set forth in SEQ ID NO: 310, a light chain FR3 set forth in SEQ ID NO: 311, or a light chain FR4 set forth in SEQ ID NO: 312, or a combination thereof.

[0067] In some embodiments, the antibody or antigen-binding fragment is selected from the group consisting of: (a) 297A, 297Q, 297G, or 297D, (b) 279F, 279K, or 279L, (c) 228P, (d) 235A, 235E, 235G, 235Q, 235R, or 235S, (e) 237A, 237E, 237K, 237N, or 237R, (f) 234A, 234V, or 234F, (g) 233P, (h) 328A, (i) 327Q or 327T, (j) 329A, 329G, 329Y, or 329R, (k) 331S, (l) 236F or 236R, according to the Kabat numbering system. 6R, (m) 238A, 238E, 238G, 238H, 238I, 238V, 238W, or 238Y, (n) 248A, (o) 254D, 254E, 254G, 254H, 254I, 254N, 254P, 254Q, 254T, or 254V, (p) 255N, (q) 256H, 256K, 256R, or 256V, (r) 264S, (s) 265H, 265K, 265S, 265Y, or 265A, (t) 267G, 267H, 267I, or 267K, (u) 268K, (v) 269N or 269Q, (w) 270A, 270G, 270M, or 2 70N, (x) 271T, (y) 272N, (z) 292E, 292F, 292G, or 292I, (aa) 293S, (bb) 301W, (cc) 304E, (dd) 311E, 311G, or 311S, (ee) 316F, (ff) 328V, (gg) 330R, (hh) 339E or 339L, (ii) 343I or 343V, (jj) 373A, 373G, or 373S, (kk) 376E, 376W, or 376Y, (ll) 380D, (mm) 382D or 382P, (nn) 385P, (oo) 424H, 424M, or 424V, (pp) 434I, (qq)438G, (rr)439E, 439H, or 439Q, (ss)440A, 440D, 440E, 440F, 440M, 440T, or 440V, (tt)E233P, (uu)L235E, (vv)L234A and L235A, (ww)L234A, L235A, and G237A, (xx)L234A, L235A, and P329G, (yy)L234F, L235E, and P331S, (zz)L234A, L235E, and G237A, (aaa), L234A, L235E, G237A, and P331S(bbb)L234A,The human IgG1 Fc region further comprises L235A, G237A, P238S, H268A, A330S, and P331S (IgG1σ, (ccc) L234A, L235A, and P329A, (ddd) G236R and L328R, (eee) G237A, (fff) F241A, (ggg) V264A, (hhh) D265A, (iii) D265A and N297A, (jjj) D265A and N297G, (kkk) D270A, (lll) A330L, (mmm) P331A or P331S, or (nnn) any combination of two or more selected from (a) to (uu).

[0068] In some embodiments, the antibody or antigen-binding fragment comprises a human IgG4 Fc region. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region comprising a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 320-362. In some embodiments, the antibody of the antigen-binding fragment comprises a fragment crystallizable (Fc) region comprising reduced antibody-dependent cell-mediated cytotoxicity (ADCC) function compared to human IgG1 and / or reduced complement-dependent cytotoxicity (CDC) compared to human IgG1. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc comprises human IgG1 and comprises SEQ ID NO: 320. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein ADCC function of the Fc region, including reduced ADCC, is reduced by at least about 50% compared to human IgG1. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the CDC function of the Fc region comprising reduced CDC is reduced by at least about 50% compared to human IgG1. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc comprises (i) a human IgG4 Fc region, or (ii) a human IgG4 Fc region comprising (a) S228P, (b) S228P and L235E, or (c) S228P, F234A and L235A, according to Kabat numbering. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc comprises a human IgG2 Fc region; an IgG2-IgG4 cross-subclass Fc region; an IgG2-IgG3 cross-subclass Fc region; an IgG2 comprising H268Q, V309L, A330S, P331S (IgG2m4); or an IgG2 comprising V234A, G237A, P238S, H268A, V309L, A330S, P331S (IgG2σ).In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc region is one of the following amino acids according to the Kabat numbering: 329A, 329G, 329Y, 331S, 236F, 236R, 238A, 238E, 238G, 238H, 238I, 238V, 238W, 238Y, 248A, 254D, 254E, 254G, 254H, 254I, 254N , 254P, 254Q, 254T, 254V, 264S, 265H, 265K, 265S, 265Y, 265A, 267G, 267H, 267I, 267K, 434I, 438G, 439E, 439H, 439Q, 440A, 440D, 440E, 440F, 440M, 440T, and 440V.

[0069] In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc comprises a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 320-362. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region, wherein the Fc comprises any one of SEQ ID NOs: 401-413, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs: 401-413. In some embodiments, the antibody or antigen-binding fragment comprises a heavy chain comprising any one of SEQ ID NOs: 501-513, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs: 501-513. In some embodiments, the antibody or antigen-binding fragment comprises a light chain comprising any one of SEQ ID NOs: 514, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs: 514.

[0070] In some embodiments, the polymorphism combination is detected in the sample by subjecting the sample to an assay configured to detect the presence of a nucleotide combination corresponding to nucleic acid position 501 in a sequence combination selected from SEQ ID NOs: 2001-2041 and 2057-2059.

[0071] In some embodiments, the inflammatory, fibrotic, or fibrostenosing disease or condition comprises inflammatory bowel disease, Crohn's disease, obstructive Crohn's disease, ulcerative colitis, intestinal fibrosis, intestinal fibrostenosis, rheumatoid arthritis, or primary sclerosing cholangitis. In some embodiments, the Crohn's disease is ileal, ileocolonic, or colonic Crohn's disease. In some embodiments, the subject has or is at risk of developing non-response or loss of response to standard therapy, including glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy, anti-IL12p40 therapy, or a combination thereof.

[0072] In some embodiments, the method further comprises determining whether a subject with an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression, based at least in part on at least three polymorphisms detected in the sample. In some embodiments, the at least three polymorphisms are detected by using an assay comprising quantitative polymerase chain reaction (qPCR), nucleic acid sequencing, or genotyping array.

[0073] In some embodiments, the combination of polymorphisms comprises or consists of any combination of polymorphisms listed in row x of column 2 of Table 31, where x is any number between 2 and 1374.

[0074] In some embodiments, the combination of polymorphisms comprises or consists of any combination of polymorphisms set forth in row x of column 2 of Table 31, where x is any number between 2 and 1374, and the polymorphisms in the combination of polymorphisms have beta coefficients set forth in row x of column 1 of Table 1, and the polymorphisms in the combination of polymorphisms are numerically coded as set forth in row x of column 2 of Table 1.

[0075] In further embodiments, the at least three polymorphisms are at least eight polymorphisms. In some embodiments, at least eight polymorphisms are provided in the 8-SNP model of Table 25.

[0076] In some embodiments, the method further comprises providing a sample for determining the PRI, MRS, or RPS. In some embodiments, the method further comprises selecting subjects according to the PRI, MRS, or RPS.

[0077] In some embodiments, the method includes contacting genetic material in a sample with one or more nucleic acid primer pairs having a forward primer and a reverse primer capable of hybridizing to one or more target nucleic acid sequences, wherein the one or more target nucleic acid sequences collectively comprise the chromosomal location of the polymorphism in row x of column 2 of Table 31, where x is any number between 2 and 1374; amplifying the target nucleic acid sequences by polymerase chain reaction with the nucleic acid primer pairs of the contacting step; inputting the results of the amplification step into a computer system; and analyzing the results via the computer system to determine a PRI, an RPS, or an MRS, wherein the computer system comprises a memory unit configured to store a parameter in row y of column 1 of Table 31, where y is the same as x in the contacting step.

[0078] In some embodiments, the β used to calculate the MRS is about 0.0077127943934849 or about 0.008. In some embodiments, the cutoff for the MRS is about 0.0322446725024791 or about 0.03.

[0079]

[0013] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

[0080] 3. Incorporation by Reference All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained herein, the present specification is intended to supersede and / or supersede such conflicting material.

[0081] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "FIG" and "FIG"). [Brief explanation of the drawings]

[0082] [Figure 1] FIG. 1 illustrates a workflow according to one embodiment of the present disclosure for processing a biological sample obtained from a subject to inform the selection of a therapeutic agent for treating a disease or condition in the subject. [Figure 2] FIG. 1 illustrates a computer-implemented workflow according to one embodiment of the present disclosure for generating an electronic report to a user, such as a physician, that includes a subject's TNFSF15 profile based on an analysis of genotype data from the subject. [Figure 3] FIG. 1 illustrates a computer system programmed or otherwise configured to perform the methods provided herein. [Figure 4] FIG. 1 illustrates a computer-implemented workflow according to one embodiment of the present disclosure for generating a TNFSF15 profile. [Figure 5A]

[0023] Figures 5A, 5B, and 5C show clustering analysis within a TL1A companion diagnostic (CDx) dataset according to some embodiments herein. Figure 5A shows cluster 1, cluster 2, and cluster 3 from the TL1A CDx dataset. [Figure 5B]

[0023] Figures 5A, 5B, and 5C show clustering analysis within a TL1A companion diagnostic (CDx) dataset according to some embodiments herein. Figure 5A shows cluster 1, cluster 2, and cluster 3 from the TL1A CDx dataset. [Figure 5C]

[0023] Figures 5A, 5B, and 5C show clustering analysis within a TL1A companion diagnostic (CDx) dataset according to some embodiments herein. Figure 5A shows cluster 1, cluster 2, and cluster 3 from the TL1A CDx dataset. [Figure 6] FIG. 5 shows that the three clusters from FIGS. 5A to 5C were collapsed into two clusters: a high TL1A expression cluster shown on the left and a low TL1A expression cluster on the right. [Figure 7A] Figure 7 shows analytical size exclusion chromatography chromatograms of anti-TL1A antibodies. Figure 7A shows analytical size exclusion chromatography chromatograms of antibodies A193, A194, and A195. Figure 7B shows analytical size exclusion chromatography chromatograms of antibodies A196, A197, and A198. Figure 7C shows analytical size exclusion chromatography chromatograms of antibodies A199, A200, and A201. [Figure 7B]Figure 7 shows analytical size exclusion chromatography chromatograms of anti-TL1A antibodies. Figure 7A shows analytical size exclusion chromatography chromatograms of antibodies A193, A194, and A195. Figure 7B shows analytical size exclusion chromatography chromatograms of antibodies A196, A197, and A198. Figure 7C shows analytical size exclusion chromatography chromatograms of antibodies A199, A200, and A201. [Figure 7C] Figure 7 shows analytical size exclusion chromatography chromatograms of anti-TL1A antibodies. Figure 7A shows analytical size exclusion chromatography chromatograms of antibodies A193, A194, and A195. Figure 7B shows analytical size exclusion chromatography chromatograms of antibodies A196, A197, and A198. Figure 7C shows analytical size exclusion chromatography chromatograms of antibodies A199, A200, and A201. [Figure 8A] Figure 8 shows the inhibition of interferon gamma in human blood by anti-TL1A antibodies: Figure 8A shows the inhibition of interferon gamma in human blood by anti-TL1A antibodies A219 and A213; and Figure 8B shows the inhibition of interferon gamma in human blood by anti-TL1A antibody A212. [Figure 8B] Figure 8 shows the inhibition of interferon gamma in human blood by anti-TL1A antibodies: Figure 8A shows the inhibition of interferon gamma in human blood by anti-TL1A antibodies A219 and A213; and Figure 8B shows the inhibition of interferon gamma in human blood by anti-TL1A antibody A212. [Figure 9A] Figure 9 shows a PLS model demonstrating the effect of pH and protein concentration on viscosity. Figure 9A shows a PLS graph. Figure 9B shows a model of predicted viscosity versus anti-TL1A antibody concentration (mg / mL). Figure 9C shows a model of estimated viscosity versus actual viscosity. Viscosity units are in mPa-s. [Figure 9B]Figure 9 shows a PLS model demonstrating the effect of pH and protein concentration on viscosity. Figure 9A shows a PLS graph. Figure 9B shows a model of predicted viscosity versus anti-TL1A antibody concentration (mg / mL). Figure 9C shows a model of estimated viscosity versus actual viscosity. Viscosity units are in mPa-s. [Figure 9C] Figure 9 shows a PLS model demonstrating the effect of pH and protein concentration on viscosity. Figure 9A shows a PLS graph. Figure 9B shows a model of predicted viscosity versus anti-TL1A antibody concentration (mg / mL). Figure 9C shows a model of estimated viscosity versus actual viscosity. Viscosity units are in mPa-s. [Figure 10] FIG. 1 shows TL1A CDx performance status for TL1A ex vivo production assays, according to some embodiments. [Figure 11A] Figure 11A shows that cell classes or subclasses in gastrointestinal tissues are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11A shows that the cell types and subcell types listed in the figure legend are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11B shows that B cells, epithelial cells, innate lymphoid cells (ILC) cells, myoid cells, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11C shows that various classes and subclasses of cells can also be identified by hierarchical clustering analysis. [Figure 11B]Figure 11A shows that cell classes or subclasses in gastrointestinal tissues are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11A shows that the cell types and subcell types listed in the figure legend are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11B shows that B cells, epithelial cells, innate lymphoid cells (ILC) cells, myoid cells, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11C shows that various classes and subclasses of cells can also be identified by hierarchical clustering analysis. [Figure 11C] Figure 11A shows that cell classes or subclasses in gastrointestinal tissues are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11A shows that the cell types and subcell types listed in the figure legend are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11B shows that B cells, epithelial cells, innate lymphoid cells (ILC) cells, myoid cells, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing. Figure 11C shows that various classes and subclasses of cells can also be identified by hierarchical clustering analysis. [Figure 11D]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11E]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11F]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11G]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11H]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11I]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11J]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 11K]Figure 11D shows that various cell types and subcell types from gastrointestinal tissues can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell-type markers. Figure 11D shows that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11D, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11E shows the same results and data as Figure 11D, except that averages across all single cells under each cell type are presented. Figure 11F is based on the same data as Figures 11D and 11E, except that an unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G shows that sub-cell types within epithelial cells, including BEST+ epithelial cells, colony cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11G, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell. Figure 11H shows the same results and data as Figure 11G, except that averages across all single cells under each cell type are presented. Figure 11I shows that sub-cell types within endothelial cells, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphatic vessels, myofibroblasts, pericytes, and smooth muscle cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11I, cell types are listed above, known cell markers are listed on the left axis, and each column in the figure represents one single cell.Figure 11J shows that sub-cell types within T cells, including cytotoxic T cells, highly activated T cells, ILC1s, ILC3s, naive / CM T cells, natural killer (NK) cells, NK T cells, TNF-like cells, gamma delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type. In Figure 11J, cell types are listed below, known cell markers are listed on the left axis, the size of the dots represents the percent of the corresponding cell type that expresses that cytokine, and the color of the dots represents the expression level based on RNA-seq, which is normalized so that all reads are the same across cells. Figure 11K shows that subcell types within myoid cells, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages, are distinguishable based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. In Figure 11K, cell types are listed on the top and known cell markers are listed on the left axis, with each column representing one single cell. In Figures 11D-11I and 11K, gene expression levels are indicated by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized so that total reads are the same across cells. [Figure 12A] FIG. 1 shows a procedure for validating the MuSiC methodology for deconvoluting bulk RNA sequencing data. [Figure 12B] The right panel shows the single cells (bottom X-axis) used to construct the pseudo-bulk RNA-sequencing data, and the left panel shows the cell types (bottom X-axis) deconvolved from the pseudo-bulk RNA-sequencing data and their respective proportions (heatmap colors). Validation in Figure 12B was performed on multiple patients, as indicated on the Y-axis as subject number. [Figure 12C]The small absolute difference between the left and right panels of Figure 12B indicates that the cell types and their respective proportions deconvolved from the pseudo-bulk RNA-seq data were in good agreement with the single-cell RNA-seq data used to construct the pseudo-bulk data. [Figure 12D] The right panel shows the single cells (bottom x-axis) used to construct the pseudo-bulk RNA-sequencing data, and the left panel shows the cell types (bottom x-axis) deconvolved from the pseudo-bulk RNA-sequencing data and their respective proportions (heatmap colors). Validation in Figure 12D was performed on multiple patients, as indicated on the y-axis as subject number. [Figure 12E] The small absolute differences between the left and right panels in Figure 12D indicate that the cell types and their respective proportions deconvolved from the pseudo-bulk RNA-seq data closely matched the single-cell RNA-seq data used to construct the pseudo-bulk data. In Figures 12B and 12D, the color of the heatmap indicates the cell proportion according to the color legend. In Figures 12C and 12E, the color of the difference map indicates the magnitude of the difference according to the color legend. [Figure 13A] FIG. 31 cell types or sub-cell types were identified in the Mount Sinai GSE83687 RNAseq dataset that were enriched or depleted in UC or CD patient tissue compared to control samples. [Figure 13B] FIG. 1 shows that 34 cell types or sub-cell types were identified in the Lloyd Price GSE111889 RNAseq dataset with enrichment or depletion in UC or CD patient tissue compared to control samples. [Figure 13C] FIG. 2 shows that 20 cell types or sub-cell types were identified in UC119 with enrichment or depletion in UC patient tissues. [Figure 13D] FIG. 2 shows that 20 cell types or sub-cell types were identified with enrichment or depletion in CD100 in CD patient tissues. [Figure 13E]FIG. 1 shows that deconvoluted cell types and cell type proportions suppress the enrichment of Paneth cells in the small intestine and ileum relative to other tissues in the gastrointestinal tract, and are consistent with the known distribution of Paneth cells. [Figure 13F] FIG. 1 shows that deconvoluted cell types and cell type proportions of Paneth cells in intestinal tissue across the rectum or colon suppressed enrichment of Paneth cells, consistent with the known distribution of Paneth cells. [Figure 13G] FIG. 1 shows that activated fibroblasts were enriched in both CD and UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Mount Sinai GSE83687 RNAseq dataset. [Figure 13H] FIG. 1 shows that activated fibroblasts were enriched in both CD and UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Lloyd Price GSE111889 RNAseq dataset. [Figure 13I] FIG. 1 shows that, based on deconvolution of UC119, activated fibroblasts were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. [Figure 13J] FIG. 1 shows that, based on CD100 deconvolution, activated fibroblasts were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. [Figure 13K] FIG. 1 shows that monocyte-derived dendritic cells (moDCs) were enriched in both CD and UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Mount Sinai GSE83687 RNAseq dataset. [Figure 13L] Lloyd Price GSE111889. Monocyte-derived dendritic cells (moDCs) were enriched in both CD and UC diseased tissue compared to non-diseased control tissue based on deconvolution of the RNAseq dataset. [Figure 13M]FIG. 1 shows that monocyte-derived dendritic cells (moDCs) were enriched in both CD and UC diseased tissues compared to non-diseased control tissues based on deconvolution of UC119. [Figure 13N] FIG. 1 shows that, based on CD100 deconvolution, monocyte-derived dendritic cells (moDCs) were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. [Figure 13O] FIG. 1 shows that CD36+ endothelial cells were enriched in both CD and UC diseased tissues compared to non-diseased control tissues based on deconvolution of the Mount Sinai GSE83687 RNAseq dataset. [Figure 13P] FIG. 1 shows that CD36+ endothelial cells were enriched in both CD and UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Lloyd Price GSE111889 RNAseq dataset. [Figure 13Q] FIG. 1 shows that based on deconvolution of UC119, CD36+ endothelial cells were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. [Figure 13R] FIG. 1 shows that based on CD100 deconvolution, CD36+ endothelial cells were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. [Figure 13S] FIG. 1 shows that BEST4+ enterocytes / epithelial cells were decreased in CD and / or UC diseased tissues compared to non-diseased control tissues based on deconvolution of the Mount Sinai GSE83687 RNAseq dataset. [Figure 13T] FIG. 1 shows that BEST4+ enterocytes / epithelial cells were decreased in CD and / or UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Lloyd Price GSE111889 RNAseq dataset. [Figure 13U] FIG. 1 shows that BEST4+ enterocytes / epithelial cells were decreased in CD and / or UC diseased tissues compared to non-diseased control tissues based on deconvolution of UC119. [Figure 13V]FIG. 1 shows that BEST4+ intestinal / epithelial cells were decreased in CD and / or UC diseased tissues compared to non-diseased control tissues based on CD100 deconvolution. [Figure 13W] FIG. 1 shows that tuft cells were reduced in CD and / or UC diseased tissue compared to non-diseased control tissue based on deconvolution of the Mount Sinai GSE83687 RNAseq dataset. [Figure 13X] Lloyd Price GSE111889. Figure 13A-13X shows that tuft cells are reduced in CD and / or UC disease tissue compared to non-diseased control tissue based on deconvolution of the RNAseq dataset. In Figures 13A-13X, the y-axis determines the percentage of identified cells among all cells. [Figure 14] FIG. 1 illustrates the software design architecture of assay interpretation software according to some embodiments herein. [Figure 15A] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15B]15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15C] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15D] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15E]15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15F] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15G] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15H]15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15I] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15J] 15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 15K]15A-15K are screen shots of an assay interpretation software application to illustrate a user experience, according to some embodiments herein. The assay interpretation software application includes a user interface including a login view (FIG. 15A), a files view (FIG. 15B), a samples view (FIG. 15C), a data analysis view (FIG. 15D), a new data analysis view (FIG. 15E), a system settings view (FIG. 15F), a clinical settings view (FIG. 15G), a lab settings view (FIG. 15H), a user management view (FIG. 15I), an activity view (FIG. 15J), or a my profile view (FIG. 15K), or any combination thereof. [Figure 16A]

[0023] Figures 16A and 16B show exemplary clinical trial studies of anti-TL1A antibodies disclosed herein. Figure 16A shows a study schema for the lead-in period of a Phase 2 clinical trial of A219 in UC, according to some embodiments herein. Figure 16B shows a study schema for the open-label extension period of a Phase 2 clinical trial of A219 in UC, according to some embodiments herein. [Figure 16B]

[0023] Figures 16A and 16B show exemplary clinical trial studies of anti-TL1A antibodies disclosed herein. Figure 16A shows a study schema for the lead-in period of a Phase 2 clinical trial of A219 in UC, according to some embodiments herein. Figure 16B shows a study schema for the open-label extension period of a Phase 2 clinical trial of A219 in UC, according to some embodiments herein. [Figure 17A] Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17B]Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17C] Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17D] Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17E] Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17H]Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17I] Figure 17 shows a comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients for various CDx models. CDx models were optimized to predict enrichment in IBD-affected tissues of ileal enterocytes (Figure 17A), colonic moDCs (Figures 17B and 17C), colonic cupules (Figures 17D and 17E), colon-resident macrophages (Figures 17F and 17G), and colonic transamplified (TA) progenitor cells (Figures 17H and 17I), as further described in Section 7.23 (Example 23). [Figure 17J] FIG. 1 shows that CD patients treated with advanced IBD therapies have a robust response after A219 treatment, as measured by both clinical remissions. [Figure 17K] FIG. 17K shows that UC patients not treated with advanced IBD therapies (“Naive” in FIG. 17K) have a higher response rate, as measured by both clinical remission (left panel of FIG. 17K) and endoscopic improvement (right panel of FIG. 17K), than both patients already treated with advanced IBD therapies (“Experienced” in FIG. 17K) and the overall patient population (both naive and experienced, shown as “Overall” in FIG. 17K). DETAILED DESCRIPTION OF THE INVENTION

[0083] Provided herein are methods, systems, and kits for treating a subject who may be suitable for treatment with an inhibitor of tumor necrosis factor (ligand) superfamily, member 15 (TL1A) activity or expression, so long as the subject is a carrier of the genotype. The subject may be a patient who may be diagnosed with an inflammatory, fibrostenosing, or fibrotic disease, such as inflammatory bowel disease (IBD) or Crohn's disease (CD). The subject may not be a patient, but may be suspected of having an inflammatory, fibrostenosing, or fibrotic disease. The genotype may, in some cases, be useful for treating inflammatory, fibrostenosing, or fibrotic diseases or conditions mediated by TL1A. In some embodiments, if the genotype is detected, the subject is treated by administering an inhibitor of TL1A activity or expression (e.g., an anti-TL1A antibody) to the subject. In some cases, it is necessary to identify the subject as suitable for treatment with an inhibitor of activity or expression in order to administer the inhibitor to the subject.

[0084] Referring to FIG. 1 , the methods, systems, and kits of the present disclosure, in some embodiments, include providing a buccal swab sample from a subject 101, optionally purifying DNA from the sample by processing sample 102, optionally assaying the processed sample to detect genotypes of at least three loci in sample 103, processing the genotypes to generate a TNFSF15 profile 104, and treating the subject with an anti-TL1A antibody or antibody fragment disclosed herein to treat a disease or disorder in the subject based on the TNFSF15 profile 105.

[0085] The genotypes described herein are detected using an appropriate genotyping device (e.g., array, sequencing). In some examples, the sample is obtained indirectly or directly from a subject or patient. In some examples, the sample may be obtained by the subject. In other examples, the sample may be obtained by a healthcare professional, such as a nurse or doctor. The sample may be derived from virtually any biological fluid or tissue that contains genetic information, such as blood.

[0086] The subject disclosed herein may be a mammal, such as a mouse, rat, guinea pig, rabbit, non-human primate, or livestock. In some examples, the subject is a human. In some examples, the subject is suffering from symptoms associated with a disease or condition disclosed herein (e.g., abdominal pain, cramps, diarrhea, rectal bleeding, fever, weight loss, fatigue, loss of appetite, dehydration and malnutrition, anemia, or ulcers).

[0087] In some embodiments, the subject is susceptible to or suffers from thiopurine toxicity or a disease caused by thiopurine toxicity (e.g., pancreatitis or leukopenia). The subject may experience or is suspected of experiencing non-response or loss of response to standard therapy (e.g., anti-TNFα therapy, anti-a4-b7 therapy (vedolizumab), anti-IL12p40 therapy (ustekinumab), thalidomide, or Cytoxin).

[0088] The disease or condition disclosed herein may be an inflammatory disease, a fibrostenosing disease, or a fibrotic disease. In some instances, the disease or condition is a TL1A-mediated disease or condition. The term "TL1A-mediated disease or condition" refers to the pathology or pathogenesis of a disease or condition that is driven at least in part by TL1A signaling. In some instances, the disease or condition is an immune-mediated disease or condition, such as one mediated by TL1A.

[0089] In some embodiments, the disease or condition is an inflammatory disease or disorder mediated at least in part by TL1A signaling. Non-limiting examples of inflammatory diseases include allergies, ankylosing spondylitis, asthma, atopic dermatitis, autoimmune diseases or disorders, cancer, celiac disease, chronic obstructive pulmonary disease (COPD), chronic peptic ulcer disease, cystic fibrosis, diabetes (e.g., type 1 diabetes and type 2 diabetes), glomerulonephritis, gout, hepatitis (e.g., active hepatitis), immune-mediated diseases or disorders, inflammatory bowel diseases (IBD), such as Crohn's disease and ulcerative colitis, myositis, osteoarthritis, pelvic inflammatory disease (PID), multiple sclerosis, neurodegenerative diseases of aging, periodontal disease (e.g., periodontitis), pre-perfusion transplant rejection, psoriasis, pulmonary fibrosis, rheumatic diseases, scleroderma, sinusitis, and tuberculosis.

[0090] In some embodiments, the disease or condition is an autoimmune disease mediated at least in part by TL1A signaling. Non-limiting examples of autoimmune diseases or disorders include achalasia, Addison's disease, adult Still's disease, agammaglobulinemia, alopecia areata, amyloidosis, ankylosing spondylitis, anti-GBM / anti-TBM nephritis, antiphospholipid syndrome, autoimmune angioedema, autoimmune dysautonomia, autoimmune encephalomyelitis, autoimmune hepatitis, autoimmune inner ear disease (AIED), autoimmune myocarditis, autoimmune oophoritis, autoimmune orchitis, autoimmune pancreatitis, autoimmune retinopathy, autoimmune urticaria, axonal and neuronal neuropathy (AMAN), bovine ovarian cancer, and ovarian cancer. Disease, Behçet's disease, benign mucosal pemphigus, bullous pemphigoid, Castleman's disease (CD), celiac disease, Chagas' disease, chronic inflammatory demyelinating polyneuropathy (CIDP), chronic recurrent multifocal osteomyelitis (CRMO), Churg-Strauss syndrome (CSS) or eosinophilic granulomatosis (EGPA), cicatricial pemphigoid, Cogan's syndrome, cold agglutinin disease, congenital heart block, Coxsackie myocarditis, CREST syndrome, Crohn's disease, dermatitis herpetiformis, dermatomyositis, Devic's disease (neuromyelitis optica), discoid lupus, Dressler's syndrome, endometriosis, eosinophilic phagocytosis EoE, eosinophilic myositis, erythema nodosum, essential mixed hemoglobinemia, Evans syndrome, fibromyalgia, fibrosing alveolitis, giant cell arteritis (temporal arteritis), giant cell myocarditis, glomerulonephritis, Goodpasture's syndrome, granulomatosis with polyangiitis, Graves' disease, Guillain-Barré syndrome, Hashimoto's thyroiditis, hemolytic anemia, Henoch-Schönlein purpura (HSP), herpes gestationis or pemphigoid of gestationis (PG), hidradenitis suppurativa (HS) (reverse acne), hypogammaglobulinemia, IgA nephropathy, IgG4-related sclerosing disease, immune platelet Inferior purpura (ITP), inclusion body myositis (IBM), interstitial cystitis (IC), arthritis, juvenile diabetes mellitus (type 1 diabetes), juvenile myositis (JM), Kawasaki disease, Lambert-Eaton syndrome, leukocytoclastic vasculitis, lichen planus, lichen scleritis, liquefying conjunctivitis, linear immunoglobulin A disease (LAD), lupus, chronic Lyme disease, Meniere's disease, microscopic polyangiitis (MPA), mixed connective tissue disease (MCTD), Mooren's ulcer, Mucha-Habermann disease, multifocal motor neuropathy (MMN) or MMNCB, multiple sclerosis, myasthenia gravis, myositis, narcolepsy,Neonatal lupus, neuromyelitis optica, neutropenia, ocular cicatricial pemphigoid, optic neuritis, relapsing rheumatoid arthritis (PR), PANDAS, paraneoplastic cerebellar degeneration (PCD), paroxysmal nocturnal hemoglobinuria (PNH), Parry-Romberg syndrome, pars planitis (peripheral uveitis), psoriatic-Turner syndrome, pemphigus, peripheral neuropathy, perivenous myelitis, pernicious anemia (PA), POEMS syndrome, polyarteritis nodosa, polyglandular syndrome types I, II, and III, polymyalgia rheumatica, polymyositis, post-myocardial infarction syndrome, post-pericardiotomy syndrome, primary biliary cirrhosis, primary sclerosing cholangitis, progestational dermatitis, psoriasis, psoriatic arthritis, pure red cell aplasia (PRCA), pyoderma gangrenosum, Raynaud's phenomenon These include: elephantiasis, reactive arthritis, reflex sympathetic dystrophy, relapsing polychondritis, restless legs syndrome (RLS), retroperitoneal fibrosis, rheumatic fever, rheumatoid arthritis, sarcoidosis, Schmidt's syndrome, scleritis, scleroderma, Sjögren's syndrome, sperm & testicular autoimmunity, stiff-leg syndrome (SPS), subacute bacterial pericarditis (SBE), Susac's syndrome, sympathetic ophthalmia (SO), Takayasu's arteritis, temporal arteritis / giant cell arteritis, thrombocytopenic purpura (TTP), Tolosa-Hunt syndrome (THS), transverse myelitis, type 1 diabetes, ulcerative colitis (UC), undifferentiated connective tissue disease (UCTD), uveitis, vasculitis, Vitiligo, and Vogt-Koyanagi-Harada disease.

[0091] In some embodiments, the disease or condition is a cancer mediated at least in part by TL1A signaling. Non-limiting examples of cancers include adenoid cystic carcinoma, adrenal cancer, amyloidosis, anal cancer, ataxia telangiectasia, atypical Moll syndrome, basal cell carcinoma, cholangiocarcinoma, Barth-Hogg-Dube syndrome, bladder cancer, bone cancer, brain tumor, breast cancer, male breast cancer, carcinoid tumor, cervical cancer, colorectal cancer, ductal carcinoma, endometrial cancer, esophageal cancer, gastric cancer, gastrointestinal stromal tumor (GIST), HER2-positive breast cancer, pancreatic islet cell tumor, juvenile polyposis syndrome, renal cancer, laryngeal cancer, leukemia-acute lymphoblastic leukemia, leukemia-acute lymphocytic (ALL), leukemia-acute myeloid AML, leukemia-adult, leukemia-pediatric, leukemia-chronic lymphocytic (CLL), leukemia-chronic Myeloid leukemia (CML), liver cancer, lobular carcinoma, lung cancer, lung cancer - small cell (SCLC), lung cancer - non-small cell (NSCLC), lymphoma - Hodgkin, lymphoma - non-Hodgkin, malignant glioma, melanoma, meningioma, multiple myeloma, myelodysplastic syndrome (MDS), nasopharyngeal carcinoma, neuroendocrine tumors, oral cancer, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic neuroendocrine tumors, parathyroid cancer, penile cancer, peritoneal cancer, Peutz-Jeghers syndrome, pituitary gland tumors, polycythemia vera, prostate cancer, renal cell carcinoma, retinoblastoma, salivary gland cancer, sarcoma, sarcoma, cutaneous sarcoma, small intestine cancer, gastric cancer, testicular cancer, thymoma, thyroid cancer, uterine (endometrial) cancer, vaginal cancer, and Wilms' tumor.

[0092] In some embodiments, the disease or condition is inflammatory bowel disease, such as Crohn's disease (CD) or ulcerative colitis (UC). The subject may suffer from fibrosis, fibrostenosis, or a fibrotic disease, either alone or in combination with an inflammatory disease. In some cases, the CD is severe CD. Severe CD may be due to inflammation resulting in the formation of scar tissue (fibrostenosis) and / or swelling in the intestinal wall. In some cases, severe CD is characterized by the presence of fibrotic and / or inflammatory strictures. Strictures can be determined by computed tomography enterography (CTE) and magnetic resonance imaging enterography (MRE). The disease or condition may be characterized as refractory, which in some cases means that the disease is resistant to standard treatment (e.g., anti-TNFα therapy). Non-limiting examples of standard therapies include glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy (vedolizumab), anti-IL12p40 therapy (ustekinumab), thalidomide, and Cytoxin.

[0093] 5.1 Genotype Disclosed herein are genotypes that can be detected in a sample by analyzing genetic material in a sample obtained from a subject. In some examples, the subject may be a human. In some embodiments, the genetic material is obtained from a subject with a disease or condition disclosed herein. In some cases, the genetic material is obtained from blood, serum, plasma, sweat, hair, tears, urine, and other techniques known to those skilled in the art. In some cases, the genetic material is obtained from a biopsy, for example, from a subject's intestinal track.

[0094] The genotype of the present disclosure includes genetic material that is deoxyribonucleic acid (DNA). In some examples, the genotype includes denatured DNA molecules or fragments thereof. In some examples, the genotype includes DNA selected from genomic DNA, viral DNA, mitochondrial DNA, plasmid DNA, amplified DNA, circular DNA, circulating DNA, cell-free DNA, or exosomal DNA. In some examples, the DNA is single-stranded DNA (ssDNA), double-stranded DNA, denatured double-stranded DNA, synthetic DNA, and combinations thereof. The circular DNA can be cleaved or fragmented.

[0095] The genotypes disclosed herein include at least one polymorphism in a gene or locus described herein. In some examples, the gene or locus is a polymorphism in tumor necrosis factor (ligand) superfamily, member 15 (TNFSF15), THADA Armadillo Repeat Containing (THADA), pleckstrin homology, MyTH4 and FERM domain containing H2 (PLEKHH2), XK-related 6 (XKR6), myotubularin-related protein 9 (MTMR9), ETS proto-oncogen 1, transcription factor (ETS1), C-type lectin domain containing 16A (CLEC16A), suppressor of cytokine signaling 1 (SOCS1), protein tyrosine phosphatase non-receptor type 2 (PTPN2), inducible T-cell costimulator ligand (ICOSLG), Janus kinase 2 (JAK2), catenin delta 2 (CTNND2), regulator of G protein signaling 7 (RGS7), RNA-binding Fox-1 homolog 1 (RBFOX1), RNA-binding motif protein 17 (RBM17), 6-phosphofructose-2-kinase / fructose-2,6-biphosphatase tase 3 (PFKFB3), Ecto-NOX disulfide-thiol exchanger 1 (ENOX1), coiled-coil domain-containing 122 (CCDC122), regulator of telomere elongation helicase 1 (RTEL1), TNF receptor superfamily member 6b (TNFRSF6B), GLIS family zinc finger 3 (GLIS3), solute carrier family 1 member 1 (SLC1A1), IKAROS family zinc finger 2 (IKZF2), fatty acyl-CoA reductase 1 (FAR1), spongin 1 (SPON1), plexin A2 (PLXNA2), MIR205 host gene (MIR205HG), C-type lectin domain containing 16A (CLEC16A), PR / SET domain 14 (PRDM), autophagy-related 5 (ATG5), and prostaglandin E receptor 4 (PTGER4). In some instances, the genes or loci include those provided in Table 1. The genotypes disclosed herein are, in some instances, haplotypes. In some instances, the genotypes include a particular polymorphism, a polymorphism in linkage disequilibrium (LD) therewith, or a combination thereof.In some cases, the LD has an r of at least or about 0.70, 0.75, 0.80, 0.85, 0.90, or 1.0. 2 The genotypes disclosed herein can include at least or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more polymorphisms. In a preferred embodiment, the genotypes disclosed herein include a combination of three polymorphisms, such as those provided in Table 1.

[0096] The polymorphisms described herein may be single nucleotide polymorphisms or indels (insertion / deletion). In some examples, the polymorphism is an insertion or deletion of at least one nucleic acid base (e.g., an indel). In some examples, the genotype may include copy number variation (CNV), which is a variation in the number of nucleic acid sequences between individuals within a given population. In some examples, the CNV includes at least or about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, or 50 nucleic acid molecules. In some examples, the genotype is heterozygous. In some examples, the genotype is homozygous.

[0097] In the following embodiments, the genotypes disclosed herein are disclosed herein.

[0098] 1. A genotype that includes at least one polymorphism in a gene or locus.

[0099] 2. The genotype of embodiment 1, comprising the polymorphisms provided in Table 1.

[0100] 3. The genotype of embodiment 1-2, which is heterozygous.

[0101] 4. The genotype of embodiment 1-2, which is homozygous.

[0102] 5. The genotype according to embodiments 1 to 4, wherein the genotype comprises at least two polymorphisms.

[0103] 6. The genotype according to embodiments 1 to 4, wherein the genotype comprises at least three polymorphisms.

[0104] 7. The genotype according to embodiments 1 to 4, wherein the genotype comprises at least four polymorphisms.

[0105] 8. The genotype of embodiments 1-4, wherein the genotype comprises at least five polymorphisms.

[0106] 9. The genotype of embodiments 1-4, wherein the genotype comprises at least six polymorphisms.

[0107] 10. The genotype according to embodiments 1 to 4, wherein the genotype comprises at least seven polymorphisms.

[0108] 11. The genotype according to embodiments 1 to 4, wherein the genotype comprises at least 8 polymorphisms.

[0109] 12. The genotype of embodiment 1, comprising a polymorphism in linkage disequilibrium with the polymorphisms provided in Table 1.

[0110] 13. LD has (i) a D' value of at least about 0.70, or (ii) a D' value of 0 and an r of at least about 0.70. 2 13. The genotype of embodiment 12, defined by a value:

[0111] 14. LD has (i) a D' value of at least about 0.80, or (ii) a D' value of 0 and an r of at least about 0.80. 2 13. The genotype of embodiment 12, defined by a value:

[0112] 15. LD has (i) a D' value of at least about 0.90, or (ii) a D' value of 0 and an r of at least about 0.90. 2 13. The genotype of embodiment 12, defined by a value:

[0113] 16. LD has (i) a D' value of at least about 0.95, or (ii) a D' value of 0 and an r of at least about 0.95. 213. The genotype of embodiment 12, defined by a value:

[0114] 17. The gene or locus is a member of the tumor necrosis factor (ligand) superfamily, member 15 (TNFSF15), THADA Armadillo Repeat Containing (THADA), pleckstrin homology, MyTH4 and FERM domain containing H2 (PLEKHH2), XK-related 6 (XKR6), myotubularin-related protein 9 (MTMR9), ETS proto-oncogen 1, transcription factor (ETS1), C-type lectin domain containing 16A (CLEC16A), suppressor of cytokine signaling 1 (SOCS1), protein tyrosine phosphatase non-receptor type 2 (PTPN2), inducible T-cell costimulator ligand (ICOSLG), Janus kinase 2 (JAK2), catenin delta 2 (CTNND2), regulator of G protein signaling 7 (RGS7), RNA-binding Fox-1 homolog 1 (RBFOX1), RNA-binding motif protein 17 (RBM17), 6-phosphofructose-2-kinase / fructose-2,6-biphosphatase 3 (PFKFB3), E 17. The genotype according to embodiments 1 to 16, wherein the genotype is selected from the group consisting of cto-NOX disulfide-thiol exchanger 1 (ENOX1), coiled-coil domain-containing 122 (CCDC122), regulator of telomere elongation helicase 1 (RTEL1), TNF receptor superfamily member 6b (TNFRSF6B), GLIS family zinc finger 3 (GLIS3), solute carrier family 1 member 1 (SLC1A1), IKAROS family zinc finger 2 (IKZF2), fatty acyl-CoA reductase 1 (FAR1), spongin 1 (SPON1), plexin A2 (PLXNA2), MIR205 host gene (MIR205HG), C-type lectin domain containing 16A (CLEC16A), PR / SET domain 14 (PRDM), autophagy-related 5 (ATG5), and prostaglandin E receptor 4 (PTGER4).

[0115] 18. The genotype is: (1) rs16901748, rs7759385, rs4246905; (2) rs16901748, rs7759385, rs7935393; (3) rs16901748, rs7759385, rs1892231; (4) rs16901748, rs7759385, rs12934476; (5) rs16901748, rs7759385, rs9806914; (6) rs16901748, rs7759385, rs2297437; (7) rs16901748, rs7759385, rs2070557 ;(8)rs16901748,rs7759385,rs7278257;(9)rs16901748,rs7759385,rs11221332;(10)rs16901748,rs7759385,rs41309367;(11)rs16901748,rs 7759385,rs6478109;(12)rs16901748,rs4246905,rs7935393;(13)rs16901748,rs4246905,rs1892231;(14)rs16901748,rs4246905,rs12934476; (15)rs16901748,rs4246905,rs9806914;(16)rs16901748,rs4246905,rs2297437;(17)rs16901748,rs4246905,rs2070557;(18)rs16901748,rs4 246905,rs7278257;(19)rs16901748,rs4246905,rs11221332;(20)rs16901748,rs4246905,rs41309367;(21)rs16901748,rs4246905,rs6478109; (22)rs16901748,rs7935393,rs1892231;(23)rs16901748,rs7935393,rs12934476;(24)rs16901748,rs7935393,rs9806914;(25)rs16901748,rs 7935393,rs2297437;(26)rs16901748,rs7935393,rs2070557;(27)rs16901748,rs7935393,rs7278257;(28)rs16901748,rs7935393,rs11221332;(29)rs16901748,rs7935393,rs41309367;(30)rs16901748,rs7935393,rs6478109;(31)rs16901748,rs1892231,rs12934476;(32)rs16901748,rs1892231,rs9806914;(33)rs16901748,rs1892231,rs2297437;(34)rs16901748,rs1892231,rs2070557;(35)rs16901748,rs1892231,rs7278257;(36)rs16901748,rs1892231,rs11221332;(37)rs16901748,rs1892231,rs41309367;(38)rs16901748,rs1892231,rs6478109;(39)rs16901748,rs12934476,rs9806914;(40)rs16901748,rs12934476,rs2297437;(41)rs16901748,rs12934476,rs2070557;(42)rs16901748,rs12934476,rs7278257;(43)rs16901748,rs12934476,rs11221332;(44)rs16901748,rs12934476,rs41309367;(45)rs16901748,rs12934476,rs6478109;(46)rs16901748,rs9806914,rs2297437;(47)rs16901748,rs9806914,rs2070557;(48)rs16901748,rs9806914,rs7278257;(49)rs16901748,rs9806914,rs11221332;(50)rs16901748,rs9806914,rs41309367;(51)rs16901748,rs9806914,rs6478109;(52)rs16901748,rs2297437,rs2070557;(53)rs16901748,rs2297437,rs7278257;(54)rs16901748,rs2297437,rs11221332;(55)rs16901748,rs2297437,rs41309367;(56)rs16901748,rs2297437,rs6478109;(57)rs16901748,rs2070557,rs7278257;(58)rs16901748,rs2070557,rs11221332;(59)rs16901748,rs2070557,rs41309367;(60)rs16901748, (61)rs16901748,rs7278257,rs11221332;(62)rs16901748,rs7278257,rs41309367;(63)rs16901748,rs7278257,rs647 8109;(64)rs16901748,rs11221332,rs41309367;(65)rs16901748,rs11221332,rs6478109;(66)rs16901748,rs41309367,rs6478109;(67)rs7759385,rs4246905,rs7935393;(68)rs7759385,rs4246905,rs1892231;(69)rs7759385,rs4246905,rs12934476;(70)rs7759385,rs4246905,rs9 806914;(71)rs7759385,rs4246905,rs2297437;(72)rs7759385,rs4246905,rs2070557;(73)rs7759385,rs4246905,rs7278257;(74)rs7759385,rs4246905,rs11221332;(75)rs7759385,rs4246905,rs41309367;(76)rs7759385,rs4246905,rs6478109;(77)rs7759385,rs7935393,rs18922 31;(78)rs7759385,rs7935393,rs12934476;(79)rs7759385,rs7935393,rs9806914;(80)rs7759385,rs7935393,rs2297437;(81)rs7759385,rs7935393,rs2070557;(82)rs7759385,rs7935393,rs7278257;(83)rs7759385,rs7935393,rs11221332;(84)rs7759385,rs7935393,rs41309367;(85) rs7759385, rs7935393, rs6478109; (86) rs7759385, rs1892231, rs12934476; (87) rs7759385, rs1892231, rs9806914; (88) rs7759385, rs1892231, rs2297437; (89) rs7759385, rs1892231, rs2070557; (90) rs7759385, rs1892231, rs7278257; (91) rs7759385, rs1892231, rs11221332; (92)r s7759385,rs1892231,rs41309367;(93)rs7759385,rs1892231,rs6478109;(94)rs7759385,rs12934476,rs9806914;(95)rs7759385,rs12934476,rs2297437;(96)rs7759385,rs12934476,rs2070557;(97)rs7759385,rs12934476,rs7278257;(98)rs7759385,rs12934476,rs11221332;(99)r s7759385,rs12934476,rs41309367;(100)rs7759385,rs12934476,rs6478109;(101)rs7759385,rs9806914,rs2297437;(102)rs7759385,rs9806914,rs2070557;(103)rs7759385,rs9806914,rs7278257;(104)rs7759385,rs9806914,rs11221332;(105)rs7759385,rs9806914,rs41309367;( (106) rs7759385, rs9806914, rs6478109; (107) rs7759385, rs2297437, rs2070557; (108) rs7759385, rs2297437, rs7278257; (109) rs7759385, rs2297437, rs11221332; (110) rs7759385, rs2297437, rs41309367; (111) rs7759385, rs2297437, rs6478109; (112) rs7759385, rs2070557, rs7278257;(113) rs7759385, rs2070557, rs11221332; (114) rs7759385, rs2070557, rs41309367; (115) rs7759385, rs2070557, rs6478109; (116) rs7759385, rs7278257, rs11221332; (117) rs7759385, rs7278257, rs41309367; (118) rs7759385, rs7278257, rs6478109; (119) rs7759385, rs11221332, rs4130936 7;(120)rs7759385,rs11221332,rs6478109;(121)rs7759385,rs41309367,rs6478109;(122)rs4246905,rs7935393,rs1892231;(123)rs4246905,rs7935393,rs12934476;(124)rs4246905,rs7935393,rs9806914;(125)rs4246905,rs7935393,rs2297437;(126)rs4246905,rs7935393,rs2070557 (127) rs4246905, rs7935393, rs7278257; (128) rs4246905, rs7935393, rs11221332; (129) rs4246905, rs7935393, rs41309367; (130) rs4246905, rs7935393, rs6478109; (131) rs4246905, rs1892231, rs12934476; (132) rs4246905, rs1892231, rs9806914; (133) rs4246905, rs1892231, rs2297437; (134)rs4246905,rs1892231,rs2070557;(135)rs4246905,rs1892231,rs7278257;(136)rs4246905,rs1892231,rs11221332;(137)rs4246905,rs1 (138)rs4246905,rs1892231,rs6478109;(139)rs4246905,rs12934476,rs9806914;(140)rs4246905,rs12934476,rs2297437;(141)rs4246905,rs12934476,rs2070557;(142)rs4246905,rs12934476,rs7278257;(143)rs4246905,rs12934476,rs11221332;(144)rs4246905,rs12934476,rs41309367;(145)rs4246905,rs12934476,rs6478109; (146)rs4246905,rs9806914,rs2297437;(147)rs4246905,rs9806914,rs2070557;(148)rs4246905,rs9806914,rs7278257;(149)rs4246905,rs9806914,rs11221332;(150)rs4246905,rs9806914,rs41309367;(151)rs4246905,rs9806914,rs6478109;(152)rs4246905,rs2297437,rs2070557;(153)rs4246905,rs2297437,rs7278257;(154)rs4246905,rs2297437,rs11221332;(155)rs4246905,rs2297437,rs41309367;(156)rs4246905,rs2297437,rs6478109;(157)rs4246905,rs2070557,rs7278257;(158)rs4246905,rs2070557,rs11221332;(159)rs4246905,rs2070557,rs41309367;(160)rs4246905,rs2070557,rs6478109;(161)rs4246905,rs7278257,rs11221332;(162)rs4246905,rs7278257,rs41309367;(163)rs4246905,rs7278257,rs6478109;(164)rs4246905,rs11221332,rs41309367;(165)rs4246905,rs11221332,rs6478109;(166)rs4246905,rs41309367,rs6478109;(167)rs7935393,rs1892231,rs12934476;(168)rs7935393,rs1892231,rs9806914;(169)rs7935393,rs1892231,rs2297437;(170)rs7935393,rs1892231,rs2070557;(171)rs7935393,rs1892231,rs7278257;(172)rs7935393,rs1892231,rs11221332;(173)rs7935393,rs1892231,rs41309367;(174) rs7935393, rs1892231, rs6478109; (175) rs7935393, rs12934476, rs9806914; (176) rs7935393, rs12934476, rs2297437; (177) rs7935393, rs12934476, rs2070557; (178) rs7935393, rs12934476, rs7278257; (179) rs7935393, rs12934476, rs11221332; (180) rs7935393, rs12934476, rs413093 67;(181)rs7935393,rs12934476,rs6478109;(182)rs7935393,rs9806914,rs2297437;(183)rs7935393,rs9806914,rs2070557;(184)rs7935393,rs9806914,rs7278257;(185)rs7935393,rs9806914,rs11221332;(186)rs7935393,rs9806914,rs41309367;(187)rs7935393,rs9806914,rs6478109 ;(188)rs7935393,rs2297437,rs2070557;(189)rs7935393,rs2297437,rs7278257;(190)rs7935393,rs2297437,rs11221332;(191)rs7935393,rs2297437,rs41309367;(192)rs7935393,rs2297437,rs6478109;(193)rs7935393,rs2070557,rs7278257;(194)rs7935393,rs2070557,rs11221332;( (195)rs7935393,rs2070557,rs41309367; (196)rs7935393,rs2070557,rs6478109; (197)rs7935393,rs7278257,rs11221332; (198)rs7935393,rs7278257,rs41309367; (199)rs7935393,rs7278257,rs6478109; (200)rs7935393,rs11221332,rs4130936; (201)rs7935393,rs11221332,rs6478109;(202)rs7935393,rs41309367,rs6478109;(203)rs1892231,rs12934476,rs9806914;(204)rs1892231,rs12934476,rs2297437;(205)rs1892231,rs12934476,rs2070557;(206)rs1892231,rs12934476,rs7278257;(207)rs1892231,rs12934476,rs11221332;(208)rs1892231,rs12934476,rs41309367;(209)rs1892231,rs12934476,rs6478109;(210)rs1892231,rs9806914,rs2297437;(211)rs1892231,rs9806914,rs2070557;(212)rs1892231,rs9806914,rs7278257;(213)rs1892231,rs9806914,rs11221332;(214)rs1892231,rs9806914,rs41309367;(215)rs1892231,rs9806914,rs6478109;(216)rs1892231,rs2297437,rs2070557;(217)rs1892231,rs2297437,rs7278257;(218)rs1892231,rs2297437,rs11221332;(219)rs1892231,rs2297437,rs41309367;(220)rs1892231,rs2297437,rs6478109;(221)rs1892231,rs2070557,rs7278257;(222)rs1892231,rs2070557,rs11221332;(223)rs1892231,rs2070557,rs41309367;(224)rs1892231,rs2070557,rs6478109;(225)rs1892231,rs7278257,rs11221332;(226)rs1892231,rs7278257,rs41309367;(227)rs1892231,rs7278257,rs6478109;(228)rs1892231,rs11221332,rs41309367;(229)rs1892231,rs11221332,rs6478109;(230)rs1892231,rs41309367,rs6478109;(231)rs12934476,rs9806914,rs2297437;(232)rs12934476,rs9806914,rs2070557;(233)rs12934476,rs9806914,rs7278257;(234)rs12934476,rs9806914,rs11221332;(235)rs12934476,rs9806914,rs41309367;(236)rs12934476,rs9806914,rs6478109;(237)rs12934476,rs2297437,rs2070557;(238)rs12934476,rs2297437,rs7278257;(239)rs12934476,rs2297437,rs11221332;(240)rs12934476,rs2297437,rs41309367;(241)rs12934476,rs2297437,rs6478109;(242)rs12934476,rs2070557,rs7278257;(243)rs12934476,rs2070557,rs11221332;(244)rs12934476,rs2070557,rs41309367;(245)rs12934476,rs2070557,rs6478109;(246)rs12934476,rs7278257,rs11221332;(247)rs12934476,rs7278257,rs41309367;(248)rs12934476,rs7278257,rs6478109;(249)rs12934476,rs11221332,rs41309367;(250)rs12934476,rs11221332,rs6478109;(251)rs12934476,rs41309367,rs6478109;(252)rs9806914,rs2297437,rs2070557;(253)rs9806914,rs2297437,rs7278257;(254)rs9806914,rs2297437,rs11221332;(255)rs9806914,rs2297437,rs41309367;(256)rs9806914,rs2297437,rs6478109;(257)rs9806914,rs2070557,rs7278257;(258)rs9806914,rs2070557,rs11221332;(259)rs9806914,rs2070557,rs41309367;(260)rs9806914 (262)rs9806914,rs7278257,rs41309367;(263)rs9806914,rs7278257,rs6 478109;(264)rs9806914,rs11221332,rs41309367;(265)rs9806914,rs11221332,rs6478109;(266)rs9806914,rs41309367,rs6478109;(267) rs2297437,rs2070557,rs7278257;(268)rs2297437,rs2070557,rs11221332;(269)rs2297437,rs2070557,rs41309367;(270)rs2297437,rs20 70557, rs6478109; (271) rs2297437, rs7278257, rs11221332; (272) rs2297437, rs7278257, rs41309367; (273) rs2297437, rs7278257, rs6478109; (274) rs2297437, rs11221332, rs41309367; (275) rs2297437, rs11221332, rs6478109; (276) rs2297437, rs41309367, rs6478109; (277) rs207 0557,rs7278257,rs11221332;(278)rs2070557,rs7278257,rs41309367;(279)rs2070557,rs7278257,rs6478109;(280)rs2070557,rs11221332,rs41309367;(281)rs2070557,rs11221332,rs6478109;(282)rs2070557,rs41309367,rs6478109;(283)rs7278257,rs11221332,rs41309367;The genotype according to embodiments 5 to 6, comprising at least two polymorphisms selected from (284) rs7278257, rs11221332, rs6478109; (285) rs7278257, rs41309367, rs6478109; or (286) rs11221332, rs41309367, rs6478109.

[0116] 19. The genotype of embodiment 18, wherein rs7278257 is replaced with rs56124762.

[0117] 20. The genotype of embodiment 18, wherein rs7278257 is replaced by rs2070558.

[0118] 21. The genotype of embodiment 18, wherein rs7278257 is replaced by rs2070561.

[0119] 22. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, imm_11_127948309 and rs1892231.

[0120] 23. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, imm_11_127948309 and rs9806914.

[0121] 24. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, imm_11_127948309, and imm_21_44478192.

[0122] 25. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, imm_11_127948309, and imm_21_44479552.

[0123] 26. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, rs1892231 and rs9806914.

[0124] 27. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, rs1892231 and imm_21_44478192.

[0125] 28. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, rs1892231 and imm_21_44479552.

[0126] 29. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, rs9806914, and imm_21_44478192.

[0127] 30. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, rs9806914, and imm_21_44479552.

[0128] 31. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_9_116608587, imm_21_44478192, and imm_21_44479552.

[0129] 32. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, rs1892231 and rs9806914.

[0130] 33. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, rs1892231 and imm_21_44478192.

[0131] 34. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, rs1892231 and imm_21_44479552.

[0132] 35. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, rs9806914, and imm_21_44478192.

[0133] 36. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, rs9806914, and imm_21_44479552.

[0134] 37. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from imm_11_127948309, imm_21_44478192, and imm_21_44479552.

[0135] 38. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, rs9806914, and imm_21_44478192.

[0136] 39. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, rs9806914, and imm_21_44479552.

[0137] 40. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, imm_21_44478192, and imm_21_44479552.

[0138] 41. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs9806914, imm_21_44478192, and imm_21_44479552.

[0139] 42. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs56124762 and rs1892231.

[0140] 43. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs56124762 and rs16901748.

[0141] 44. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231 and rs16901748.

[0142] 45. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs56124762, rs1892231 and rs16901748.

[0143] 46. ​​The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070558, and rs1892231.

[0144] 47. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070558, and rs16901748.

[0145] 48. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231 and rs16901748.

[0146] 49. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs2070558, rs1892231 and rs16901748.

[0147] 50. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070561 and rs1892231.

[0148] 51. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070561 and rs16901748.

[0149] 52. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231 and rs16901748.

[0150] 53. The genotype according to embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs2070561, rs1892231 and rs16901748.

[0151] 54. The genotype of embodiment 11, wherein the genotype comprises eight polymorphisms selected from any 8-SNP combination of Model_1 to Model_495 listed in Table 25.

[0152] 55. The genotype of embodiments 1-53, wherein the genotype comprises a minor allele provided in Table 1 for at least one polymorphism.

[0153] 56. The genotype of embodiments 1-53, wherein the genotype comprises the major alleles provided in Table 1 for at least one polymorphism.

[0154] 57. The genotype of embodiments 1-56, wherein the presence of the genotype predicts a positive therapeutic response in an IBD patient to treatment with an inhibitor of TL1A expression activity with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%.

[0155] 58. The genotype of any one of embodiments 1 to 57, wherein the presence of the genotype predicts a positive therapeutic response in an IBD patient to treatment with an inhibitor of TL1A expression activity with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0156] 59. The genotype of any one of embodiments 1 to 58, wherein the presence of the genotype predicts a positive therapeutic response in an IBD patient to treatment with an inhibitor of TL1A expression activity with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0157] 60. The genotype of embodiments 1-59, wherein the presence of the genotype predicts a positive treatment response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%.

[0158] 61. The genotype of any one of embodiments 1 to 60, wherein the presence of the genotype predicts a positive therapeutic response in an IBD patient to treatment with an inhibitor of TL1A expression activity with at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% accuracy.

[0159] 62. The genotype of any one of embodiments 1 to 61, wherein the presence of the genotype predicts a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a positive rate of at least about 10%, 15%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 55%, 60%, 65%, or 70%.

[0160] Embodiments disclosed herein provide genotypes that are associated with and therefore indicative of a subject having or susceptible to a particular disease or condition, or a subclinical phenotype thereof. Furthermore, the genotypes disclosed herein are associated with increased expression or activity of TNFSF15 (TL1A). Thus, the genotypes indicate that the subject will have a positive therapeutic response to an inhibitor of TL1A activity or expression. Table 1 provides exemplary polymorphisms that are associated with and therefore predictive of a positive therapeutic response to an inhibitor of TNFSF15 (TL1A) expression or activity. The term "positive therapeutic response" refers to the reduction or elimination of at least one symptom of a disease or condition (e.g., Crohn's disease) following the induction of treatment (e.g., an anti-TL1A antibody). [Table 1] TIFF2026500100000008.tif163168The present disclosure provides a model including three polymorphisms (e.g., a "3-SNP model") that, when detected in a sample obtained from a subject, indicates a positive therapeutic response of the subject to treatment with, for example, an inhibitor of TL1A activity or expression. Non-limiting examples of models described herein include Model A (rs6478109, rs7278257, and rs1892231); Model B (rs6478109, rs2070557, and rs9806914); Model C (rs6478109, rs7935393, and rs1892231); Model D (rs6478109, rs7935393, and rs9806914); Model E (rs6478109, rs9806914, and rs16901748); Model F (rs6478109, rs16901748, and rs2297437); Model G (rs6478109, rs1892231, and rs16901748); These include Model H (rs6478109, rs2070557, and rs7935393); Model I (rs6478109, rs7278257, and rs7935393); Model J (rs6478109, rs9806914, and rs1892231); and Model K (rs6478109, rs7278257, and rs16901748).

[0161] The present disclosure provides models including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more polymorphisms (e.g., "1-SNP Model," "2-SNP Models," or "3-SNP Models") that, when detected in a sample obtained from a subject, indicate a positive therapeutic response of the subject to treatment, such as with an inhibitor of TL1A activity or expression. Non-limiting examples of the models described herein include the 10-SNP, 9-SNP, 8-SNP, 7-SNP, 6-SNP, 5-SNP, 4-SNP, 3-SNP, 2-SNP, or 1-SNP models listed in Table 31.

[0162] The genotypes and / or polymorphisms provided in Table 1 herein have been validated as genotypes and / or polymorphisms that correlate with increased TL1A expression in inflammatory cells, increased inflammation, IBD phenotypes, increased IBD-enriched cell types, decreased IBD-depleted cell types, and / or increased positive therapeutic response in IBD patients to treatment with inhibitors of TL1A activity or expression, as further described in Section 7 (EXAMPLES). Accordingly, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for identifying subjects or patients for various methods provided herein, including Sections 2, 5.2, and 7 (EXAMPLES). Similarly, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for identifying subjects or patients for various kits and compositions provided herein, including Sections 2, 5.5, 5.7, and 7 (EXAMPLES). Additionally, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for various methods of patient selection provided herein, including those described in Sections 2, 5.2, and 7.

[0163] The present disclosure further provides simple methods for verifying the suitability of genotypes and / or polymorphisms, genotype combinations, and / or polymorphism combinations for various treatment methods, patient selection methods, and / or kits and compositions provided herein, including in Sections 2, 5.2, 5.5, 5.7, and 7.

[0164] The polymorphisms identified in the analyses provided in the Examples above, either alone or in combination (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more), can be used to predict a positive therapeutic response to an inhibitor of TL1A activity or expression (e.g., an anti-TL1A antibody) in a subject or patient. The polymorphisms described herein can be used in diagnostic or prognostic tests to identify subjects suitable for treatment with an inhibitor of TL1A activity or expression to treat a disease or condition described herein in the subject. In some cases, the diagnostic is a companion diagnostic test, such as, for example, a TL1A companion diagnostic test ("TL1A CDx").

[0165] To validate the rules and polymorphisms, an external cohort of IBD (e.g., UC or CD) patients can be identified and genotyped. The positive predictive value, negative predictive value, specificity, sensitivity, and positivity rate for a patient population can be calculated from the patient's genotype and the IBD patient's clinical response or clinical remission after TL1A inhibitor therapy, e.g., as described in Section 7 (Examples). The present disclosure provides that because the polymorphisms provided herein have already been selected via the machine learning algorithms provided herein (e.g., the previous few paragraphs and Section 7 (Examples)) to be associated with IBD readouts (e.g., TL1A expression, TL1A activity, IBD phenotype, increase in IBD-enriched cell types, decrease in IBD-depleted cell types, patient clinical response, and patient clinical remission, as described in Section 7 (Examples)), rules and combinations of polymorphisms for a given positive predictive value, negative predictive value, specificity, sensitivity, and / or positivity rate can be identified by the machine learning algorithm and validated as provided herein without undue experimentation.

[0166] In some embodiments of the methods provided herein, the genotype comprises a polymorphism. In certain embodiments of the methods provided herein, the polymorphism comprises a SNP. In some embodiments of the methods provided herein, the combination of genotypes comprises a combination of polymorphisms. In certain embodiments of the methods provided herein, the combination of polymorphisms comprises a combination of SNPs.

[0167] Tables 1 and 27 provide exemplary SNPs that predict clinical response or remission in patients treated with TL1A inhibitor therapy. The present disclosure provides that various combinations of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict elevated TL1A expression, combinations of polymorphisms that predict elevated TL1A transcriptome, or combinations of both polymorphisms that predict elevated TL1A expression and elevated TL1A transcriptome. The present disclosure also provides that various combinations of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict clinical response or remission in patients treated with TL1A inhibitor therapy, as listed in Tables 1 and 27. The present disclosure further provides that various combinations of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict clinical response or remission in patients treated with TL1A inhibitor therapy, as listed in Tables 1 and 27.

[0168] Thus, in one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of two polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of three polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of four polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of five polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of six polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of seven polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of eight polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression includes a combination of nine polymorphisms that predict elevated TL1A expression. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression includes a combination of ten polymorphisms that predict elevated TL1A expression. In one embodiment, the polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression include one polymorphism that predicts elevated TL1A expression. In some embodiments, the polymorphisms or combinations of polymorphisms that predict elevated TL1A expression provided herein for the various methods (including this paragraph) are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the polymorphism or combination of polymorphisms that predict elevated TL1A expression for the various methods provided herein (including this paragraph) are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0169] Alternatively, in one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of two polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of three polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of four polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of five polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of six polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of seven polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression includes a combination of eight polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression includes a combination of nine polymorphisms that predict elevated TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression includes a combination of ten polymorphisms that predict elevated TL1A activity. In one embodiment, the polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression include one polymorphism that predicts elevated TL1A activity. In some embodiments, the polymorphisms or combinations of polymorphisms that predict elevated TL1A activity provided herein for the various methods (including this paragraph) are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the polymorphism or combination of polymorphisms that predict elevated TL1A activity for the various methods provided herein (including this paragraph) are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0170] Furthermore, in one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of two polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of three polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of four polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of five polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of six polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of seven polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of eight polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of nine polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of ten polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises one polymorphism that predicts an increase in IBD-enriched cell types. In some embodiments, a polymorphism or combination of polymorphisms that predicts an increase in IBD-enriched cell types provided herein for the various methods (including this paragraph) is selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, a polymorphism or combination of polymorphisms that predicts an increase in IBD-enriched cell types provided herein for the various methods (including this paragraph) is selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0171] Furthermore, in one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of two polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of three polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of four polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of five polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of six polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of seven polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of eight polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of nine polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises a combination of ten polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive therapeutic response to an inhibitor of TL1A activity or expression comprises one polymorphism that predicts a reduction in IBD-depleted cell types. In some embodiments, the polymorphism or combination of polymorphisms predictive of reduction in IBD-depleted cell types provided herein for the various methods (including this paragraph) are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057).In some embodiments, the polymorphism or combination of polymorphisms predictive of reduction in IBD-depleted cell types provided herein for the various methods (including this paragraph) are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0172] In some embodiments, the elevated TL1A expression, elevated TL1A activity, increases in IBD-enriched cell types, and decreases in IBD-depleted cell types in this section (including the previous paragraph) are relative to that in tissues or subjects not affected by IBD (e.g., UC or CD).

[0173] 5.2 Treatment method Disclosed herein are methods of treating a disease or condition, or symptoms of a disease or condition, in a subject, comprising administering a therapeutically effective amount of one or more therapeutic agents to the subject. In some embodiments, the one or more therapeutic agents are administered to the subject alone (e.g., monotherapy). In some embodiments, the one or more therapeutic agents are administered in combination with an additional agent. In some embodiments, the therapeutic agent is a first-line therapy for the disease or condition. In some embodiments, the therapeutic agent is a second-line, third-line, or fourth-line treatment for the disease or condition. In some embodiments, the therapeutic agent comprises an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression.

[0174] Various embodiments provide methods of treating inflammatory bowel disease (IBD), comprising administering an anti-TL1A antibody described herein to a subject in need thereof. In some embodiments, the subject comprises one or more at-risk genotypes. In some embodiments, the IBD is a severe form of IBD.

[0175] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) greater than a cutoff, the PRI being calculated from a combination of polymorphisms determined from a sample from the subject, wherein a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0176] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0177] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating the PRI from the combination of polymorphisms; wherein a PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression. Provided herein are methods for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition.

[0178] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0179] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predicted response index (PRI) greater than a cutoff, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and wherein a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a positive correlation coefficient with a response probability score (RPS).

[0180] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with RPS. A method is provided herein.

[0181] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from the combination of polymorphisms, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the RPS.

[0182] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a positive correlation coefficient with RPS.

[0183]

[0010] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) above a cutoff, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, wherein a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a positive correlation coefficient with a model risk score (MRS).

[0184] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with MRS. A method is provided herein.

[0185] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from the combination of polymorphisms, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the MRS.

[0186] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a positive correlation coefficient with the MRS.

[0187] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0188] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating the PRI from the combination of polymorphisms; wherein a PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression. Provided herein are methods for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition.

[0189] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; A method is provided herein, comprising:

[0190] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with RPS. A method is provided herein.

[0191] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from the combination of polymorphisms, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the RPS.

[0192] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a positive correlation coefficient with RPS.

[0193] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with MRS. A method is provided herein.

[0194] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from the combination of polymorphisms, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the MRS.

[0195] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a positive correlation coefficient with the MRS.

[0196] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) below a cutoff, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and wherein a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a negative correlation coefficient with a response probability score (RPS).

[0197] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with RPS. A method is provided herein.

[0198] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from the combination of polymorphisms, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the RPS.

[0199] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a negative correlation coefficient with RPS.

[0200]

[0010] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) below a cutoff, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and wherein a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a negative correlation coefficient with a model risk score (MRS).

[0201] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with MRS. A method is provided herein.

[0202] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from the combination of polymorphisms, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the MRS.

[0203] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a negative correlation coefficient with the MRS.

[0204] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with RPS. A method is provided herein.

[0205] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from the combination of polymorphisms, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the RPS.

[0206] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a negative correlation coefficient with RPS.

[0207] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with MRS. A method is provided herein.

[0208] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from the combination of polymorphisms, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the MRS.

[0209] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a negative correlation coefficient with the MRS.

[0210] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) greater than a cutoff, the PRI being calculated from a combination of genotypes determined from a sample from the subject, wherein a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0211] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0212] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating the PRI from the genotype combination; wherein a PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression. Provided herein are methods for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition.

[0213] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0214] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predicted response index (PRI) greater than a cutoff, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and wherein a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a positive correlation coefficient with a response probability score (RPS).

[0215] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with RPS. A method is provided herein.

[0216] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a PRI from the genotype combination, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the RPS.

[0217] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a positive correlation coefficient with RPS.

[0218]

[0010] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) above a cutoff, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, wherein a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a positive correlation coefficient with a model risk score (MRS).

[0219] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with MRS. A method is provided herein.

[0220] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a PRI from the genotype combination, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the MRS.

[0221] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a positive correlation coefficient with the MRS.

[0222] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0223] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating the PRI from the genotype combination; wherein a PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression. Provided herein are methods for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition.

[0224] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; A method is provided herein, comprising:

[0225] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with RPS. A method is provided herein.

[0226] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from the genotype combination, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the RPS.

[0227] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a positive correlation coefficient with RPS.

[0228] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a positive correlation coefficient with MRS. A method is provided herein.

[0229] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from the genotype combination, where a PRI above a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a positive correlation coefficient with the MRS.

[0230] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) if the PRI is above a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a positive correlation coefficient with the MRS.

[0231] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predicted response index (PRI) below a cutoff, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, wherein a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a negative correlation coefficient with a response probability score (RPS).

[0232] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with RPS. A method is provided herein.

[0233] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating the PRI from the genotype combination; wherein a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression, and the PRI has a negative correlation coefficient with the RPS.

[0234] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a negative correlation coefficient with RPS.

[0235]

[0010] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression to the subject, wherein the subject is selected based on a predictive response index (PRI) below a cutoff, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, wherein a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and wherein the PRI has a negative correlation coefficient with a model risk score (MRS).

[0236] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with MRS. A method is provided herein.

[0237] In a further aspect, there is provided a method for determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating the PRI from the genotype combination; wherein a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression, and the PRI has a negative correlation coefficient with the MRS.

[0238] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a negative correlation coefficient with the MRS.

[0239] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with RPS. A method is provided herein.

[0240] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from the genotype combination, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the RPS.

[0241] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which PRI has a negative correlation coefficient with RPS.

[0242] In another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; and (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is below a cutoff; (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Including, PRI has a negative correlation coefficient with MRS. A method is provided herein.

[0243] In a further aspect, there is provided a method of determining a predictive response index (PRI) for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from the genotype combination, where a PRI below a cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression; wherein the PRI has a negative correlation coefficient with the MRS.

[0244] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) if the PRI is below a cutoff, selecting the subject for treatment with an inhibitor of TL1A activity or expression; Including, A method is provided herein in which the PRI has a negative correlation coefficient with the MRS.

[0245] In one aspect, a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predicted response index (PRI) as follows: (1) or (2): (1) If the PRI is positively correlated with the Response Probability Score (RPS), subjects are selected if the PRI is above the cutoff, or (2) If PRI is negatively correlated with RPS, subjects will be selected if PRI is below the cutoff; are selected based on comparison with the cutoffs a PRI is calculated from a combination of genotypes determined from a sample from the subject and a comparison of the predicted response index (PRI) according to (1) or (2) to a cutoff, and predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject.

[0246] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is: (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0247] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a PRI from a combination of genotypes, the comparison being (1) or (2) (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0248] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) PRI and (1) or (2): (1) If PRI is positively correlated with RPS, select subjects if PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0249] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is: (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0250] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from a combination of genotypes, wherein the comparison is (1) or (2) (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0251] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) PRI and (1) or (2): (1) If PRI is positively correlated with RPS, select subjects if PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0252] In one aspect, a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predicted response index (PRI) as follows: (1) or (2): (1) If the PRI is positively correlated with the Model Risk Score (MRS), select subjects if the PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, subjects will be selected if PRI is below the cutoff. are selected based on comparison with the cutoffs a PRI is calculated from a combination of genotypes determined from a sample from the subject and a comparison of the predicted response index (PRI) according to (1) or (2) to a cutoff, and predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject.

[0253] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the genotype combination; (iii) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is: (1) If the PRI is positively correlated with the MRS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0254] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a PRI from a combination of genotypes, the comparison being (1) or (2) (1) If PRI is positively correlated with MRS, if PRI is above the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; or (2) If PRI is negatively correlated with MRS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0255] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the genotype combination; (c) calculating a predicted response index (PRI) from the genotype combination; (d) PRI and (1) or (2): (1) If PRI is positively correlated with MRS, select subjects if PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0256] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect the genotype combination; (iv) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is: (1) If the PRI is positively correlated with the MRS, the subject is determined to be suitable if the MRS is above the cutoff; or (2) If PRI has a negative correlation with MRS, the subject is determined to be suitable if MRS is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0257] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a PRI from a combination of genotypes, wherein the comparison is (1) or (2) (1) If PRI is positively correlated with MRS, if PRI is above the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; or (2) If PRI is negatively correlated with MRS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0258] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect the genotype combination; (d) calculating a predicted response index (PRI) from the genotype combination; (e) PRI and (1) or (2): (1) If PRI is positively correlated with MRS, select subjects if PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0259] In one aspect, a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predicted response index (PRI) as follows: (1) or (2): (1) If the PRI is positively correlated with the Response Probability Score (RPS), subjects are selected if the PRI is above the cutoff, or (2) If PRI is negatively correlated with RPS, subjects will be selected if PRI is below the cutoff; are selected based on comparison with the cutoffs a PRI is calculated from a combination of polymorphisms determined from a sample from the subject and a comparison of the predicted response index (PRI) according to (1) or (2) to a cutoff, and predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject.

[0260] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from a combination of polymorphisms, wherein the subject is selected from (1) or (2) as follows: (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0261] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from a combination of polymorphisms, the comparison being (1) or (2) (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0262] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) PRI and (1) or (2): (1) If PRI is positively correlated with RPS, select subjects if PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0263] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from a combination of polymorphisms, wherein the subject is: (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0264] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from a combination of polymorphisms, the comparison being (1) or (2) (1) If the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression if the PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0265] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) PRI and (1) or (2): (1) If PRI is positively correlated with RPS, select subjects if PRI is above the cutoff; or (2) If PRI is negatively correlated with RPS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0266] In one aspect, a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predicted response index (PRI) as follows: (1) or (2): (1) If the PRI is positively correlated with the Model Risk Score (MRS), select subjects if the PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, subjects will be selected if PRI is below the cutoff. are selected based on comparison with the cutoffs a PRI is calculated from a combination of polymorphisms determined from a sample from the subject and a comparison of the predicted response index (PRI) according to (1) or (2) to a cutoff, and predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject.

[0267] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from a combination of polymorphisms, wherein the subject is selected from (1) or (2) as follows: (1) If the PRI is positively correlated with the MRS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with MRS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0268] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a PRI from a combination of polymorphisms, the comparison being (1) or (2) (1) If PRI is positively correlated with MRS, if PRI is above the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; or (2) If PRI is negatively correlated with MRS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0269] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (c) calculating a predicted response index (PRI) from the polymorphism combination; (d) PRI and (1) or (2): (1) If PRI is positively correlated with MRS, select subjects if PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0270] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) preparing DNA from the sample; (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (iv) calculating a predicted response index (PRI) from a combination of polymorphisms, wherein the subject is: (1) If the PRI is positively correlated with the MRS, the subject is determined to be suitable if the PRI is above the cutoff; or (2) If PRI is negatively correlated with MRS, subjects are determined to be suitable if PRI is below the cutoff; and determining that the subject is suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI with a cutoff according to the present invention; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0271] In a further aspect, there is provided a method for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a PRI from a combination of polymorphisms, the comparison being (1) or (2) (1) If PRI is positively correlated with MRS, if PRI is above the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; or (2) If PRI is negatively correlated with MRS, if PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; Provided herein are methods for determining a predicted response index (PRI) compared to a cutoff for a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, as determined according to:

[0272] In yet another aspect, there is provided a method of selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising: (a) obtaining or having obtained a sample from a subject; and (b) preparing DNA from the sample; (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; (d) calculating a predicted response index (PRI) from the polymorphism combination; (e) PRI and (1) or (2): (1) If PRI is positively correlated with MRS, select subjects if PRI is above a cutoff; or (2) If PRI is negatively correlated with MRS, select subjects if PRI is below the cutoff; selecting the subject for treatment with an inhibitor of TL1A activity or expression based on a comparison with a cutoff according to Provided herein is a method for selecting a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition for treatment with an inhibitor of TL1A activity or expression, comprising:

[0273] In one aspect, provided herein is a computer-implemented method comprising: (a) administering a genotype combination to a subject having an inflammatory, fibrotic or fibrostenosing disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a predicted response index (PRI) greater than a cutoff, wherein the PRI is calculated from the genotype combination, and a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0274] In one aspect, provided herein is a computer-implemented method comprising: (a) administering a combination of polymorphisms to a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject will be responsive to treatment with an inhibitor of TL1A activity or expression based on a predicted response index (PRI) above a cutoff, wherein the PRI is calculated from the combination of polymorphisms, and a PRI above the cutoff predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0275] In one aspect, provided herein is a computer-implemented method, comprising: (a) administering a genotype combination to a subject with an inflammatory, fibrotic or fibrostenosing disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a predicted response index (PRI) greater than a cutoff, wherein the PRI is calculated from the genotype combination, and the PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and the PRI has a positive correlation coefficient with RPS, MRS, or both RPS and MRS.

[0276] In one aspect, provided herein is a computer-implemented method comprising: (a) administering a combination of polymorphisms to a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject will be responsive to treatment with an inhibitor of TL1A activity or expression based on a predicted response index (PRI) greater than a cutoff, wherein the PRI is calculated from the combination of polymorphisms, and a PRI greater than the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and the PRI has a positive correlation coefficient with RPS, MRS, or both RPS and MRS.

[0277] In one aspect, provided herein is a computer-implemented method, comprising: (a) administering a genotype combination to a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a predicted response index (PRI) below a cutoff, wherein the PRI is calculated from the genotype combination, and a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and the PRI has a negative correlation coefficient with RPS, MRS, or both RPS and MRS.

[0278] In one aspect, provided herein is a computer-implemented method comprising: (a) administering a combination of polymorphisms to a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject will be responsive to treatment with an inhibitor of TL1A activity or expression based on a predictive response index (PRI) below a cutoff, wherein the PRI is calculated from the combination of polymorphisms, and a PRI below the cutoff predicts a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, and the PRI has a negative correlation coefficient with RPS, MRS, or both RPS and MRS.

[0279] In one aspect, there is provided a computer-implemented method for determining a response probability score (RPS) for a subject, comprising: (a) receiving genotype data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition, the genotype data including a combination of polymorphisms; (b) Genotype data, (i) assigning a weighted numerical value to each polymorphism in the combination of polymorphisms to generate a plurality of weighted values; and (ii) summing multiple weighted values; analyzing the subject with a first statistical algorithm configured to create a model risk score (MRS) for the subject by performing operations including: (c) providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); (d) applying a cutoff to the RPS, wherein the RPS for the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition; Provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, comprising:

[0280] In another aspect, there is provided a computer-implemented method for determining a response probability score (RPS) for a subject, comprising: (a) obtaining a plurality of multi-single nucleotide polymorphism (multi-SNP) models, each multi-SNP model predicting a positive therapeutic response to an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject; (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from the subject; and (c) calculating a model risk score (MRS) utilizing one or more statistical algorithms configured to perform operations including: (i) assigning a weighted value to each polymorphism of the plurality of polymorphisms to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; (d) applying a logarithmic scale and cutoff to the MRS to generate a response probability score (RPS); Provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, comprising:

[0281] In a further aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression, based at least in part on a predicted response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, and determining a comparison of the PRI with a cutoff to predict a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression.

[0282] In yet another aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) detecting the presence of a combination of polymorphisms in a sample from a subject; (b) applying a statistical algorithm to the combination of polymorphisms detected in step (a) to generate PRIs; (c) determining a comparison of the PRI with the cutoff; and Provided herein is a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising administering an inhibitor of TL1A activity or expression to a subject who is predicted to have a positive therapeutic response to the inhibitor of TL1A activity or expression, as determined by a predicted response index (PRI) calculated by:

[0283] In one aspect, there is provided a method of treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenosing disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression; (i) obtaining or having obtained a sample from a subject; (ii) subjecting the sample to an assay adapted to detect the combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, where the PRI is further determined in comparison to a cutoff; and (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression; Provided herein are methods for treating an inflammatory, fibrotic, or fibrostenosing disease or condition in a subject, comprising:

[0284] In various embodiments of the methods provided herein, including in Sections 2, 5.2 (e.g., the previous paragraph), and 7, the method further includes preparing DNA from the sample.

[0285] In various embodiments of the methods provided herein, including Sections 2, 5.2 (e.g., the previous paragraph), and Section 7, the comparison of PRI to the cutoff is determined according to either (1) or (2): (1) if PRI is positively correlated with RPS, then determine the subject's PRI if PRI is above the cutoff, or (2) if PRI is negatively correlated with RPS, then determine the subject's PRI if PRI is below the cutoff. The term "Model Risk Score" or "MRS" refers to a SNP combination model or

number

[0286] The term "response probability score" or "RPS" refers to a score calculated via a mathematical function that uses a patient's genotype or combination of genotypes as variable inputs, where the genotype input for any SNP in the model can be a mathematical expression selected from the mathematical expressions set forth in Table 28. Such an RPS can be used to indicate the probability that a patient will have a therapeutic response to TL1A inhibitor treatment, with a higher RPS indicating a higher probability that the patient will have a therapeutic response and a lower RPS indicating a lower probability that the patient will have a therapeutic response. In some examples of the methods provided herein, the RPS can be used as a basis for binary classification of patients as responders (CDx positive) or non-responders (CDx negative). The RPS cutoff for determining such binary classification of patients can be determined by training a model using treatment response data from a patient cohort, as described elsewhere in this disclosure, e.g., in Section 7.20.4, such that the RPS cutoff provides the highest accuracy for classifying responders, non-responders, or both responders and non-responders. Such RPS cutoffs may also be determined by other machine learning or computerized clustering methods, such as those known and practiced in the art and described elsewhere in this disclosure, e.g., in Section 7.20.4. In another example, the mathematical function (e.g., the coefficients of the function) used to calculate the RPS from a patient's genotype or genotype combination can be trained and normalized so that the RPS has a range of 0 to 1, with 0.5 being the cutoff for optimally classifying patients as responders (CDx positive) or non-responders (CDx negative) to TL1A inhibitor treatment. In one specific example, the RPS is calculated as described in this section and in Section 7.20.4. In another specific example, the RPS is calculated as RPS=1 / (1+e (-MRS)) where (1) if the RPS is ≥ 0.5, the prediction is "yes, responder" and CDx positive, and (2) if the RPS is < 0.5, the prediction is "no, non-responder" and CDx negative. In general, if the RPS is ≥ cutoff, the prediction is "yes, responder" and CDx positive, and if the RPS is < cutoff, the prediction is "no, non-responder" and CDx negative.

[0287] The term "predictive response index" or "PRI" refers to a value calculated via a mathematical function using a patient's genotype or combination of genotypes as variable inputs that predicts (i) an increase in the level of TNFSF15 (TL1A) protein expression in a sample obtained from a subject or patient compared to a reference level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual), (ii) an increase in IBD-enriched cell types compared to reference levels in tissue not affected by IBD, (iii) a decrease in IBD-depleted cell types compared to reference levels in tissue not affected by IBD, and / or (iv) an increase in a positive treatment response in an IBD patient to treatment with a TL1A inhibitor compared to a reference response level in patients not selected by the genotype or model, wherein the genotype input for any SNP in the model can be a mathematical expression selected from the mathematical expressions set forth in Table 28. Such a PRI can be an MRS, as described in this section and in Section 7. Such a PRI can also be an RPS, as described in this section and in Section 7. The PRI can also be a mathematical function that generates a score that correlates positively or negatively with the MRS or RPS calculated from a subject's genotype. In some examples, the PRI correlates with the RPS with a correlation coefficient of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or at least about 0.99. In other examples, the PRI correlates with the RPS with a correlation coefficient of at most about -0.7, at most about -0.75, at most about -0.8, at most about -0.85, at most about -0.9, at most about -0.95, or at most about -0.99.

[0288] In some embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, the MRS is calculated for a combination of a β coefficient and a corresponding SNP, and the combination of a β coefficient and a corresponding SNP is selected from the combinations listed in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, the MRS is calculated for a combination of a β coefficient and a corresponding SNP.

number

number

[0289] In some embodiments of the various methods provided herein, including Sections 2, 5.2, and 7, the RPS is calculated for a combination of a β coefficient and a corresponding SNP, where the combination of a β coefficient and a corresponding SNP is selected from the combinations listed in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein, including Sections 2, 5.2, and 7, the RPS is calculated using the combination of a β coefficient and a corresponding SNP as follows: RPS=1 / (1+e (-MRS) ) and

number

number

[0290] In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.6. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.65. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.7. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.75. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.8. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.85. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.9. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of at least about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.99.

[0291] In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.6. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.65. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.7. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.75. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.8. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.85. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS by a correlation coefficient of at most about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.99.

[0292] In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.6. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.65. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.7. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.75. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.8. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.85. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.9. In some embodiments of the various methods provided herein, PRI is positively correlated with RPS with a correlation coefficient of about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.99.

[0293] In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.6. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.65. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.7. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.75. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.8. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.85. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.9. In some embodiments of the various methods provided herein, PRI is negatively correlated with RPS with a correlation coefficient of about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about −0.99.

[0294] In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.6. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.65. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.7. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.75. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.8. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.85. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.9. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of at least about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the MRS with a correlation coefficient of at least about 0.99.

[0295] In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.6. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.65. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.7. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.75. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.8. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.85. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS by a correlation coefficient of at most about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the MRS with a correlation coefficient of at most about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the MRS with a correlation coefficient of at most about -0.99.

[0296] In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.6. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.65. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.7. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.75. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.8. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.85. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.9. In some embodiments of the various methods provided herein, PRI is positively correlated with MRS with a correlation coefficient of about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the MRS with a correlation coefficient of about 0.99.

[0297] In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.6. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.65. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.7. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.75. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.8. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.85. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.9. In some embodiments of the various methods provided herein, PRI is negatively correlated with MRS with a correlation coefficient of about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the MRS with a correlation coefficient of about −0.99.

[0298] In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is MRS. In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is RPS. In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is equal to MRS. In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is equal to RPS. In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is equal to -1 x MRS (a correlation coefficient of -1 with MRS). In certain embodiments of the various methods provided herein, including in Sections 2, 5.2, and 7, PRI is equal to -1 x RPS (a correl...

Claims

1. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition of a subject, the method comprising administering a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor to the subject, wherein the subject is selected based on a predictive response index (PRI) above a cutoff, the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and the PRI above the cutoff predicts a positive therapeutic response of the subject to treatment with the TL1A activity or expression inhibitor with a positive predictive value of at least about 29%.

2. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition, wherein the method is (a) Whether a subject with an inflammatory, fibrous, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression, (i) Obtaining or having obtained a sample from the subject, (ii) to provide the sample to an assay adapted to detect combinations of polymorphisms, (iii) Calculating a predictive response index (PRI) from the combination of the polymorphisms, wherein if the PRI exceeds a cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression. The decision will be made by, (b) Treating the subject by administering a therapeutically effective amount of the inhibitor of TL1A activity or expression to the subject, Methods that include...

3. A method for determining the predictive response index (PRI) for subjects with inflammatory, fibrous, or fibrostenotic diseases or conditions, (a) Obtaining or having obtained a sample from the subject, (b) providing the sample to an assay adapted to detect combinations of polymorphisms, (c) Calculating the PRI from the combination of the polymorphisms, A PRI above the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression, Methods that include...

4. A method for selecting subjects having inflammatory, fibrous, or fibrostenotic diseases or conditions for treatment with inhibitors of TL1A activity or expression, (a) Obtaining or having obtained a sample from the subject, (b) providing the sample to an assay adapted to detect combinations of polymorphisms, (c) Calculating the predictive response index (PRI) from the combination of the polymorphisms, (d) If the PRI exceeds the cutoff, the subject is selected for treatment with an inhibitor of TL1A activity or expression, Methods that include...

5. The method according to any one of claims 2 to 4, further comprising preparing DNA from the sample.

6. The method according to claim 1, wherein the PRI is the response probability score (RPS).

7. The method according to claim 1, wherein the PRI has a positive correlation coefficient with RPS.

8. The method according to claim 7, wherein the correlation coefficient is the Pearson correlation coefficient or the Spearman correlation coefficient.

9. The method according to claim 7 or 8, wherein the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1.

10. A computer implementation method for determining the response probability score (RPS) for a target, (a) Receiving genotype data obtained from a sample from the subject having an inflammatory, fibrous, or fibrostenotic disease or condition, wherein the genotype data includes polymorphic combinations, (b) The genotype data (i) Assigning weighted numerical values ​​to each polymorphism in the combination of the polymorphisms to generate multiple weighted values, and (ii) Summing the multiple weighted values, The analysis is performed using a first statistical algorithm configured to create a Model Risk Score (MRS) for the subject by performing operations including the following: (c) Providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS), (d) Applying the cutoff to the RPS, wherein the RPS with respect to the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression for the treatment of the inflammatory, fibrous, or fibrostenotic disease or condition, Computer implementation methods, including those mentioned above.

11. A computer implementation method for determining the response probability score (RPS) for a target, (a) Obtaining multiple multi-SNP models, each multi-SNP model predicting a positive therapeutic response to an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrous, or fibrostenotic disease or condition in the subject, (b) Receiving genotype data for multiple polymorphisms obtained from the subject, (c) Calculating a Model Risk Score (MRS) using one or more statistical algorithms configured to perform operations including (i) assigning weighted values ​​to each of the multiple polymorphisms in order to generate multiple weighted values, and (ii) summing the multiple weighted values, (d) Applying a logarithmic scale and cutoff to the MRS to generate a response probability score (RPS), Computer implementation methods, including those mentioned above.

12. The method according to claim 6 or 7, wherein the RPS is in the range of 0 to 1.

13. The method according to claim 6 or 7, wherein the cutoff is 0.

5.

14. The RPS is calculated as 1 / (1+e(-MRS)), and the MRS is [Math 1] The method according to claim 6 or 7, wherein χi is calculated as the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and βi is the weight of the i-th SNP in the model.

15. The method according to claim 1, wherein PRI is a Model Risk Score (MRS).

16. The method according to claim 1, wherein the PRI has a positive correlation coefficient with MRS.

17. The method according to claims 15 and 16, wherein the correlation coefficient is the Pearson correlation coefficient or the Spearman correlation coefficient.

18. The method according to claim 16, wherein the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1.

19. The aforementioned MRS, [Math 2] The method according to claim 10, wherein χi is calculated as the mathematical representation of the i-th SNP in the model.

20. The SNP in the aforementioned model is (i) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 2 for homozygous surrogate alleles; (ii) For homozygous reference alleles, 1; for heterozygous reference alleles and substitute alleles, 1; for homozygous substitute alleles, 0; (iii) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (iv) 0 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (v) 1 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles; and / or (vi) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles. The method according to claim 10, which is mathematically represented by xi as shown above.

21. The method according to claim 1, wherein the combination of polymorphisms includes one or more polymorphisms selected from Table 27, or surrogate polymorphisms in a chain disequilibrium determined by an R2 of at least 0.85, or a combination thereof.

22. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

23. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

24. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of negative predictive values.

25. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

26. The method according to claim 1, wherein the cutoff is such that the PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression at a positive rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%.

27. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

28. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types at positive predictive values ​​of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

29. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types at a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

30. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

31. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

32. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with negative predictive values ​​of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

33. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with negative predictive values ​​of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

34. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

35. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

36. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with a positive rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%.

37. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with a positive rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%.

38. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts an increase in one or more IBD-enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

39. The method according to claim 1, wherein the cutoff is such that a PRI above the cutoff predicts a decrease in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

40. The method according to any one of claims 28 to 39, wherein the one or more IBD-enriched cell types include one, two, three, fourteen cell types selected from the group consisting of activated fibroblasts, monocyte-derived dendritic cells (moDCs), and CD36+ endothelial cells, intestinal and colon cells, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

41. The method according to any one of claims 29 to 39, wherein the one or more IBD-depleted cell types include one or two cell types selected from the group consisting of tuft cells and BEST4+ epithelial cells.

42. The method according to claim 1, wherein the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

43. The method according to claim 1, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

44. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition of a subject, comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject has a predictive response index (PRI) of (1) or (2): (1) If the PRI has a positive correlation with the response probability score (RPS), and the PRI is above the cutoff, the target is selected, (2) If the PRI has a negative correlation with the RPS, the target is selected if the PRI falls below the cutoff. Selected based on comparison with the cutoff, The PRI is calculated from the combination of polymorphisms determined from the sample from the subject and the comparison of the predicted response index (PRI) according to (1) or (2) with the cutoff, and the positive therapeutic response of the subject to treatment with the inhibitor of TL1A activity or expression is predicted with a positive prediction value of at least about 29%. method.

45. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition, (a) Whether a subject with an inflammatory, fibrous, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression, (i) Obtaining or having obtained a sample from the subject, (ii) to provide the sample to an assay adapted to detect combinations of polymorphisms, (iii) Calculating a predictive response index (PRI) from the combination of the polymorphisms, wherein the subject is (1) or (2) (1) If the PRI has a positive correlation with the RPS, if the PRI is above the cutoff, the subject is determined to be suitable, (2) If the PRI has a negative correlation with the RPS, and the PRI is below the cutoff, the subject is determined to be suitable. Based on a comparison of the PRI with the cutoff, it is determined that the treatment with an inhibitor of TL1A activity or expression is suitable. The decision will be made by, (b) Treating the subject by administering a therapeutically effective amount of the inhibitor of TL1A activity or expression to the subject, Methods that include...

46. A method for determining a comparison between the predictive response index (PRI) and a cutoff for subjects with inflammatory, fibrous, or fibrostenotic diseases or conditions, (a) Obtaining or having obtained a sample from the subject, (b) providing the sample to an assay adapted to detect combinations of polymorphisms, (c) Calculating the PRI from the combination of the polymorphisms, wherein the comparison is (1) or (2) (1) If the PRI has a positive correlation with RPS, or if the PRI is above the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression, (2) If the PRI has a negative correlation with RPS, or if the PRI is below the cutoff, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression. A method determined according to the following.

47. A method for selecting subjects having inflammatory, fibrous, or fibrostenotic diseases or conditions for treatment with inhibitors of TL1A activity or expression, (a) Obtaining or having obtained a sample from the subject, (b) providing the sample to an assay adapted to detect combinations of polymorphisms, (c) Calculating the predictive response index (PRI) from the combination of the polymorphisms, (d) The PRI and (1) or (2): (1) If the PRI has a positive correlation with RPS, or if the PRI exceeds the cutoff, select the target, (2) If the PRI has a negative correlation with the RPS, select the target if the PRI is below the cutoff. Based on a comparison with the cutoff, the subject is selected for treatment with the TL1A activity or expression inhibitor, Methods that include...

48. The method according to any one of claims 45 to 47, further comprising preparing DNA from the sample.

49. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition of a subject, the method comprising administering a therapeutically effective dose of a TL1A activity or expression inhibitor to the subject, at least in part on a predictive response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, determining a comparison between the PRI and a cutoff, and predicting a positive therapeutic response in the subject to treatment with the TL1A activity or expression inhibitor.

50. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition, (a) To detect the presence of polymorphic combinations in the sample derived from the subject, (b) Applying a statistical algorithm to the combination of polymorphisms detected in step (a) in order to generate the PRI, (c) Determine the comparison between the PRI and the cutoff, A method comprising administering a TL1A activity or expression inhibitor to a subject who is predicted to show a positive therapeutic response to a TL1A activity or expression inhibitor, as determined by the predictive response index (PRI) calculated by [a specific method].

51. A method for treating an inflammatory, fibrous, or fibrostenotic disease or condition, (a) Whether a subject with an inflammatory, fibrous, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression, (i) Obtaining or having obtained a sample from the subject, (ii) to provide the sample to an assay adapted to detect combinations of polymorphisms, (iii) Calculating a predictive response index (PRI) from the combination of the polymorphisms, wherein the PRI is further determined by comparison with a cutoff. The decision will be made by, (b) Treating the subject by administering a therapeutically effective amount of the inhibitor of TL1A activity or expression to the subject, Methods that include...

52. The method according to any one of claims 50 to 51, further comprising preparing DNA from the sample.

53. The comparison between PRI and cutoff is (1) or (2): (1) If the PRI has a positive correlation with RPS, if the PRI exceeds the cutoff, determine the PRI of the target, or (2) If the PRI has a negative correlation with RPS, or if the PRI is below the cutoff, determine the PRI of the target. The method according to any one of claims 49 to 50, as determined in accordance with the following:

54. The method according to any one of claims 44 to 47, wherein the correlation coefficient is the Pearson correlation coefficient or the Spearman correlation coefficient.

55. The method according to any one of claims 44 to 47, wherein, if the PRI has a positive correlation with the RPS, the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1, and if the PRI has a negative correlation with the RPS, the negative correlation coefficient is at most about -0.6, at most about -0.65, at most about -0.7, at most about -0.75, at most about -0.8, at most about -0.85, at most about -0.95, at most about -0.99, or 1.

56. The method according to any one of claims 44 to 47, wherein the RPS is in the range of 0 to 1.

57. The method according to any one of claims 44 to 47, wherein the cutoff is 0.5 when the PRI has a positive correlation with the RPS, or the cutoff is -0.5 when the PRI has a negative correlation with the RPS.

58. The RPS is calculated as 1 / (1+e(-MRS)), and the MRS is [Math 3] The method according to any one of claims 44 to 47, wherein χi is calculated as the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and βi is the weight of the i-th SNP in the model.

59. The SNP in the aforementioned model is (i) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 2 for homozygous surrogate alleles; (ii) For homozygous reference alleles, 1; for heterozygous reference alleles and substitute alleles, 1; for homozygous substitute alleles, 0; (iii) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (iv) 0 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (v) 1 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles; and / or (vi) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles. The method according to claim 58, which is mathematically represented by xi as shown above.

60. The method according to any one of claims 44 to 47, wherein the combination of polymorphisms includes one or more polymorphisms selected from Table 27, or surrogate polymorphisms in a chain disequilibrium such that they are determined by an R2 of at least 0.85, or a combination thereof.

61. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a positive therapeutic response to the subject to treatment with an inhibitor of TL1A activity or expression of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

62. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a positive therapeutic response in the subject to treatment with an inhibitor of TL1A activity or expression with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

63. The method according to any one of claims 44 to 47, wherein the cutoff predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression, with a comparison of the PRI with the cutoff according to (1) or (2) being at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% negative predictive value.

64. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

65. The method according to any one of claims 44 to 47, wherein the cutoff predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject, where the comparison of the PRI with the cutoff according to (1) or (2) is at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%.

66. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

67. The method according to any one of claims 44 to 47, wherein the cutoff is such that the comparison of the PRI with the cutoff according to (1) or (2) predicts an increase in one or more IBD enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

68. The method according to any one of claims 44 to 47, wherein the cutoff is such that the comparison of the PRI with the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types by a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

69. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

70. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

71. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts an increase in one or more IBD-enriched cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

72. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types by a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

73. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts an increase in one or more IBD enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

74. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

75. The method according to any one of claims 44 to 47, wherein the cutoff predicts an increase in one or more IBD enriched cell types with a positive rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% when comparing the PRI with the cutoff according to (1) or (2).

76. The method according to any one of claims 44 to 47, wherein the cutoff predicts a decrease in one or more IBD-depleted cell types with a positive rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% when comparing the PRI with the cutoff according to (1) or (2).

77. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts an increase in one or more IBD enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

78. The method according to any one of claims 44 to 47, wherein the cutoff is such that a comparison of the PRI with the cutoff according to (1) or (2) predicts a decrease in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

79. The method according to claim 67, wherein the one or more IBD-enriched cell types include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from the group consisting of activated fibroblasts, monocyte-derived dendritic cells (moDCs), and CD36+ endothelial cells, intestinal and colon cells, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

80. The method according to claim 68, wherein the one or more IBD-depleted cell types include one or two cell types selected from the group consisting of tuft cells and BEST4+ epithelial cells.

81. The method according to any one of claims 44 to 47, wherein the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

82. The method according to any one of claims 44 to 47, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

83. (i) The PRI is calculated using the combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31. (ii) The MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31, and / or (iii) The RPS is calculated using the combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31. The method according to claim 1.

84. The method according to claim 1, wherein the combination of polymorphisms is detected in the sample by subjecting the sample to an assay configured to detect the presence of at least three nucleotides corresponding to nucleic acid position 501 among at least three of sequence numbers 2001 to 2048 and 2057 to 2059.

85. A computer implementation system comprising at least one processor, (a) Receiving genotype data obtained from a sample from the subject having an inflammatory, fibrous, or fibrostenotic disease or condition, wherein the genotype data includes polymorphic combinations, (b) Applying a first statistical algorithm to the genotype data, wherein the first statistical algorithm is (i) Assigning weighted numerical values ​​to each polymorphism in the combination of the polymorphisms to generate multiple weighted values, and (ii) Summing the multiple weighted values, The system is configured to generate the aforementioned Model Risk Score (MRS) by performing operations that include the following: (c) Applying a second statistical algorithm to the MRS, wherein the second statistical algorithm is configured to perform a logarithmic function on the MRS to generate a response probability score (RPS), (d) Applying the cutoff to the RPS, wherein the RPS with respect to the cutoff indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression for the treatment of the inflammatory, fibrous, or fibrostenotic disease or condition, To provide an application configured to determine a target response probability score (RPS) by performing an operation including the following, instructions executable by at least one processor, A computer-implemented system including this.

86. A computer implementation system comprising at least one processor, (a) Receiving multiple multi-SNP models, each multi-SNP model predicting a positive therapeutic response to an inhibitor of TL1A activity or expression for the treatment of an inflammatory, fibrous, or fibrostenotic disease or condition in the subject, (b) Receiving genotype data for multiple polymorphisms obtained from the subject, (c) Calculating a Model Risk Score (MRS) using one or more statistical algorithms configured to perform operations including (i) assigning weighted values ​​to each of the multiple polymorphisms in order to generate multiple weighted values, and (ii) summing the multiple weighted values, (d) Applying a logarithmic scale and cutoff to the MRS to generate a response probability score (RPS), To provide an application configured to determine a target response probability score (RPS) by performing an operation including the following, instructions executable by at least one processor, A computer-implemented system including this.

87. The computer implementation system according to any one of claims 85 to 86, wherein the RPS is in the range of 0 to 1.

88. The computer implementation system according to any one of claims 85 to 86, wherein the cutoff is 0.

5.

89. The computer implementation system according to claim 85, wherein the genotype data is a combination of single nucleotide polymorphisms (SNPs).

90. The RPS is calculated as 1 / (1+e(-MRS)), and the MRS is [Math 4] The computer implementation system according to any one of claims 85 to 86, wherein χi is calculated as the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and βi is the weight of the i-th SNP in the model.

91. The aforementioned MRS is, [Math 5] The computer implementation system according to any one of claims 85 to 86, wherein χi is calculated as and is the mathematical representation of the i-th SNP in the model.

92. The SNP in the aforementioned model is (i) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 2 for homozygous surrogate alleles; (ii) For homozygous reference alleles, 1; for heterozygous reference alleles and substitute alleles, 1; for homozygous substitute alleles, 0; (iii) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (iv) 0 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 1 for homozygous surrogate alleles; (v) 1 for homozygous reference alleles, 0 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles; and / or (vi) 0 for homozygous reference alleles, 1 for heterozygous reference and surrogate alleles, and 0 for homozygous surrogate alleles. The computer implementation system according to claim 86, which is mathematically represented by χi as shown above.

93. The computer implementation system according to claim 86, wherein the combination of polymorphisms includes one or more polymorphisms selected from Table 27, or surrogate polymorphisms in a chain disequilibrium determined by at least 0.85 R2, or combinations thereof.

94. The computer implementation system according to claim 86, wherein the combination of polymorphisms includes at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, or at least 16 polymorphisms.

95. The computer implementation system according to claim 86, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP models in Table 5, a 2-SNP combination selected from the 2-SNP models in Table 5, a 3-SNP combination selected from the 3-SNP models in Table 5, a 4-SNP combination selected from the 4-SNP models in Table 5, a 5-SNP combination selected from the 5-SNP models in Table 5, a 6-SNP combination selected from the 6-SNP models in Table 5, a 7-SNP combination selected from the 7-SNP models in Table 5, or an 8-SNP combination selected from the 8-SNP models in Table 5.

96. (i) The MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 21 of Table 31, and / or (ii) The RPS is calculated using the combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding β coefficients listed in column 1 of Table 31. The computer implementation system according to claim 86.

97. The computer-implemented system according to claim 86, wherein the combination of polymorphisms is detected in the sample by subjecting the sample to an assay configured to detect the presence of at least three nucleotides corresponding to nucleic acid position 501 among at least three of sequence numbers 2001 to 2048 and 2057 to 2059.

98. The method according to claim 1 or the computer implementation system according to claim 85, wherein the subject has been treated with advanced IBD therapy prior to treatment with the TL1A activity or expression inhibitor.

99. The method according to claim 1 or the computer-implemented system according to claim 85, wherein the subject has not been treated with advanced IBD therapy prior to treatment with the inhibitor of TL1A activity or expression.

100. The method or system according to claim 98, wherein the advanced IBD therapy comprises one or more selected from the group consisting of a biological agent for IBD, an S1P1 modulator, or a JAK inhibitor.

101. The method or system according to claim 100, wherein the biological therapeutic agent for IBD comprises an anti-TNFα antibody, an anti-IL23 antibody, or an anti-integrin α4β7 antibody.

102. The method according to claim 1 or the computer implementation system according to claim 85, wherein the inhibitor of TL1A activity or expression is an antibody that binds to TL1A or an antigen-binding fragment thereof (anti-TL1A antibody or antigen-binding fragment), and the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable region including (a) HCDR1 containing the amino acid sequence shown by SEQ ID NO: 1, (b) HCDR2 containing the amino acid sequence shown by any one of SEQ ID NOs: 2 to 5, and (c) HCDR3 containing the amino acid sequence shown by any one of SEQ ID NOs: 6 to 9, and a light chain variable region including (d) LCDR1 containing the amino acid sequence shown by SEQ ID NO: 10, (e) LCDR2 containing the amino acid sequence shown by SEQ ID NO: 11, and (f) LCDR3 containing the amino acid sequence shown by any one of SEQ ID NOs: 12 to 15.

103. The method according to claim 1 or the computer implementation system according to claim 85, wherein the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, and the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable domain having an amino acid sequence identical by at least about 90% to any one of SEQ ID NOs. 101-135 or 310-302, and a light chain variable domain having an amino acid sequence identical by at least about 90% to any one of SEQ ID NOs. 201-206 or 303.

104. The method or system according to claim 102, wherein the heavy chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 101-135 or 310-302.

105. The method or system according to claim 102, wherein the light chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs. 201-206 or 303.

106. The method according to claim 1 or the computer implementation system according to claim 85, wherein the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, and the anti-TL1A antibody or antigen-binding fragment comprises (a) a heavy chain variable framework region comprising a human IGHV1-46*02 framework or a modified human IGHV1-46*02 framework, and (b) a light chain variable framework region comprising a human IGKV3-20 framework or a modified human IGKV3-20 framework, wherein the heavy chain variable framework region and the light chain variable framework region collectively comprise less than 14 amino acid modifications from the human IGHV1-46*02 framework and the human IGKV3-20 framework.

107. The amino acid modifications of fewer than 14 amino acids are: (a) the amino acid modification is located at position 47 of the heavy chain variable region, and the amino acid at position 47 is R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V; or (b) the amino acid modification is located at position 45 of the heavy chain variable region, and the amino acid at position 45 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V. (c) The amino acid modification is located at position 55 of the heavy chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; (d) The amino acid modification is located at position 78 of the heavy chain variable region, and the amino acid at position 78 is A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, or Y; (e) The amino acid modification is located at position 55 of the heavy chain variable region. (f) The amino acid modification is located at position 80 of the region, and the amino acid at position 80 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; (g) The amino acid modification is located at position 89 of the heavy chain variable region, and the amino acid at position 89 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V; (g) The amino acid modification is located at position 89 of the heavy chain variable region, and the amino acid at position 89 is A, R F, P, S, T, W, Y, or V; (g) The method or system according to claim 106, comprising: (h) the amino acid modification is at position 91 of the heavy chain variable region, and the amino acid at position 91 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; or a combination of two or more modifications selected from (a) to (h).

108. The method or system according to claim 107, wherein the amino acid modifications of the less than 14 amino acid modifications include A47R, R45K, M55I, V78A, M80I, R82T, V89A, and M91L in the heavy chain variable region according to the numbering of Aho or Kabat.

109. The method or system according to claim 107, wherein the amino acid modifications of the less than 14 amino acid modifications include (a) modifications at amino acid position 54 of the light chain variable region, and / or (b) modifications at amino acid position 55 of the light chain variable region, according to the numbering Aho or Kabat.

110. The method or system according to claim 106, wherein the amino acid modification of fewer than 14 amino acids comprises (a) the amino acid modification being located at position 54 in the light chain variable region, and the amino acid at position 54 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V, and / or (b) the amino acid modification being located at position 55 in the light chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V.

111. The method or system according to claim 110, wherein the amino acid modifications of the less than 14 amino acid modifications include L54P and / or L55W in the light chain variable region according to the Aho or Kabat numbering.

112. The inhibitor of TL1A activity or expression is an antibody that binds to TL1A or an antigen-binding fragment thereof. A heavy chain variable region including Sequence ID 301 X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYCAR[HCDR3]WGQGTTVTVSS, and The method according to claim 1 or the computer implementation system according to claim 85, comprising a light chain variable region including Sequence ID No. 303 EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRREPEDFAVYYC[LCDR3]FGGGTKLEIK, where each of X1 to X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V.

113. The inhibitor of TL1A activity or expression is an antibody that binds to TL1A or an antigen-binding fragment thereof. A heavy chain variable region including sequence number 302 X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYC[HCDR3]WGQGTTVTVSS, and The method according to claim 1 or the computer implementation system according to claim 85, comprising a light chain variable region including Sequence ID No. 303 EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRREPEDFAVYYC[LCDR3]FGGGTKLEIK, where each of X1 to X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, or V.

114. (A) X1 is Q or E; (B) X2 is R or K (C) X3 is A or R; (D) X4 is M or I; (E) X5 is V or A; (F) X6 is M or I; (G) X7 is R or T; (H)X8 is V or A; (I) X9 is M or L (J) X10 is L or P; (K)X11 is L or W; or (L)X1-X11 is any combination of (A) to (K). The method or system according to claim 112.

115. The method or system according to claim 112, wherein the antibody or antigen-binding fragment comprises a heavy chain CDR1 represented by SEQ ID NO: 1, a heavy chain CDR2 represented by any one of SEQ ID NOs: 2 to 5, a heavy chain CDR3 represented by any one of SEQ ID NOs: 6 to 9, a light chain CDR1 represented by SEQ ID NO: 10, a light chain CDR2 represented by SEQ ID NO: 11, and a light chain CDR3 represented by any one of SEQ ID NOs: 12 to 15.

116. The method or system according to claim 112, wherein the antibody or antigen-binding fragment comprises a heavy chain framework (FR) 1 represented by SEQ ID NO: 304, a heavy chain FR2 represented by SEQ ID NO: 305 or SEQ ID NO: 313, a heavy chain FR3 represented by any one of SEQ ID NOs: 306, 307, 314 or 315, a heavy chain FR4 represented by SEQ ID NO: 308, a light chain FR1 represented by SEQ ID NO: 309, a light chain FR2 represented by SEQ ID NO: 310, a light chain FR3 represented by SEQ ID NO: 311, or a light chain FR4 represented by SEQ ID NO: 312, or a combination thereof.

117. The antibody or antigen-binding fragment is, according to Kabat numbering, (a) 297A, 297Q, 297G, or 297D, (b) 279F, 279K, or 279L, (c) 228P, (d) 235A, 235E, 235G, 235Q, 235R, or 235S, (e) 237A, 237E, 237K, 237N, or 237R, (f) 234A, 234V, or 234F, (g) 233P, (h) 328A, (i) 327Q or 327T, (j) 329A, 329G, 329Y, or 329R, (k) 331S, (l) 236F or 236R, (m) 238A, 238E, 238G, 238H, 238I, 238V, 238W, or 238Y, (n) 248A, (o) 254D, 254E, 254G, 254H, 254I, 254N, 254P, 254Q, 254T, or 254V, (p) 255N, (q) 256H, 256K, 256R, or 256V, (r) 264S, (s) 265H, 265K, 265S, 265Y, or 265A, (t) 267G, 267H, 267I, or 267K, (u) 268K, (v) 269N or 269Q, (w) 270A, 270G, 270M, or 270N, (x ) 271T, (y) 272N, (z) 292E, 292F, 292G, or 292I, (aa) 293S, (bb) 301W, (cc) 304E, (dd) 311E, 311G, or 311S, (ee) 316F, (ff) 328V, (gg) 330R, (hh) 339E or 339L, (ii) 343I or 343V, (jj) 373A, 373G, or 373S, (kk) 376E, 376W, or 376Y, (ll) 380D, (mm) 382D or 382P, (nn) 385P, (oo) 424H, 424M, or 424V, (pp) 434I, ( qq) 438G, (rr) 439E, 439H, or 439Q, (ss) 440A, 440D, 440E, 440F, 440M, 440T, or 440V, (tt) E233P, (uu) L235E, (vv) L234A and L235A, (ww) L234A, L235A, and G237A, (xx) L234A, L235A, and P329G, (yy) L234F, L235E, and P331S, (zz) L234A, L235E, and G237A, (aaa), L234A, L235E, G237A, and P331S (bbb) L234A, L235A,The method according to claim 1, or the computer implementation system according to claim 85, comprising a human IgG1 Fc region including G237A, P238S, H268A, A330S, and P331S(IgG1σ, (ccc)L234A, L235A, and P329A, (ddd)G236R and L328R, (eee)G237A, (fff)F241A, (ggg)V264A, (hhh)D265A, (iii)D265A and N297A, (jjj)D265A and N297G, (kkk)D270A, (lll)A330L, (mmm)P331A or P331S, or any two or more combinations selected from (nnn)(a) to (uu).

118. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment comprises a human IgG4 Fc region.

119. The method according to claim 1 or the computer-implemented system according to claim 85, wherein the antibody or antigen-binding fragment includes an Fc region having a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 320 to 362.

120. The method according to claim 1 or the computer-implemented system according to claim 85, wherein the antibody of the antigen-binding fragment includes a fragment crystallizable (Fc) region that exhibits reduced antibody-dependent cell-mediated cytotoxicity (ADCC) function and / or reduced complement-dependent cytotoxicity (CDC) compared to human IgG1.

121. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment comprises an Fc region, and the Fc comprises the human IgG1 comprising SEQ ID NO:

320.

122. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment includes an Fc region, and the ADCC function of the Fc region, which includes a reduced ADCC, is reduced by at least about 50% compared to human IgG1.

123. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment includes an Fc region, and the CDC function of the Fc region, which includes a reduced CDC, is reduced by at least about 50% compared to human IgG1.

124. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment comprises an Fc region, and the Fc comprises, according to Kabat numbering, (i) a human IgG4 Fc region, or (ii) (a) S228P, (b) S228P and L235E, or (c) a human IgG4 Fc region comprising S228P, F234A and L235A.

125. The method according to claim 1 or the computer implementation system according to claim 85, wherein the antibody or antigen-binding fragment comprises an Fc region, and the Fc comprises a human IgG2 Fc region; an IgG2-IgG4 cross-subclass Fc region; an IgG2-IgG3 cross-subclass Fc region; an IgG2 (IgG2m4) comprising H268Q, V309L, A330S, P331S; or an IgG2 comprising V234A, G237A, P238S, H268A, V309L, A330S, P331S (IgG2σ).

126. The antibody or antigen-binding fragment includes an Fc region, and the Fc is numbered according to Kabat numbering as 329A, 329G, 329Y, 331S, 236F, 236R, 238A, 238E, 238G, 238H, 238I, 238V, 238W, 238Y, 248A, 254D, 254E, 254G, 254H, 254I, 254N, 254P, 254Q, 254T, 254V, The method according to claim 1 or the computer implementation system according to claim 85, comprising human IgG1 having substitutions selected from 264S, 265H, 265K, 265S, 265Y, 265A, 267G, 267H, 267I, 267K, 434I, 438G, 439E, 439H, 439Q, 440A, 440D, 440E, 440F, 440M, 440T, and 440V.

127. The method according to claim 1, or the computer-implemented system according to claim 85, wherein the antibody or antigen-binding fragment includes an Fc region, and the Fc comprises a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 320 to 362.

128. The method according to claim 1, or the computer-implemented system according to claim 85, wherein the antibody or antigen-binding fragment includes an Fc region, and the Fc contains a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of sequence numbers 401 to 413.

129. The method according to claim 1, or the computer-implemented system according to claim 85, wherein the antibody or antigen-binding fragment comprises a heavy chain containing any one of SEQ ID NOs. 501 to 513, or a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs. 501 to 513.

130. The method according to claim 1, or the computer-implemented system according to claim 85, wherein the antibody or antigen-binding fragment comprises a light chain containing any one of SEQ ID NOs. 514, or a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs.

514.

131. The method according to claim 1 or the computer-implemented system according to claim 85, wherein the combination of polymorphisms is detected in the sample by subjecting the sample to an assay configured to detect the presence of a combination of nucleotides corresponding to nucleic acid position 501 in a combination of sequences selected from sequence numbers 2001 to 2041 and 2057 to 2059.

132. The method according to claim 1 or the computer-implemented system according to claim 85, wherein the inflammatory, fibrous, or fibrostenotic disease or condition includes inflammatory bowel disease, Crohn's disease, obstructive Crohn's disease, ulcerative colitis, intestinal fibrosis, intestinal fibrostenosis, rheumatoid arthritis, or primary sclerosing cholangitis.

133. The method or system according to claim 132, wherein the Crohn's disease is Crohn's disease of the ileum, ileocolon, or colon.

134. The method according to claim 1 or the computer implementation system according to claim 85, wherein the subject has or is at risk of developing non-response or loss of response to standard therapy including glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy, anti-IL12p40 therapy, or a combination thereof.

135. The method according to claim 1 or the computer-implemented system according to claim 85, further comprising determining whether the subject having an inflammatory, fibrous, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression, based at least partially on the at least three polymorphisms detected in the sample.

136. The method or system according to claim 135, wherein the at least three polymorphisms are detected by utilizing an assay comprising a quantitative polymerase chain reaction (qPCR), a nucleic acid sequencing reaction, or a genotyping array.

137. The method according to claim 1 or the computer implementation system according to claim 85, wherein the combination of polymorphisms includes or consists of any combination of polymorphisms listed in row x of column 2 of Table 31, where x is any number between 2 and 1374.

138. The method according to claim 1 or the computer implementation system according to claim 85, wherein the combination of polymorphisms includes or consists of any combination of polymorphisms listed in row x of column 2 of Table 31, where x is any number from 2 to 1374, the polymorphism of the combination of polymorphisms has a β coefficient listed in row x of column 1 of Table 31, and the polymorphism of the combination of polymorphisms is numerically coded as listed in row x of column 2 of Table 1.

139. The method according to any one of claims 1 to 4, 6, or 10 to 11, further comprising providing a sample to determine PRI for claims 1 to 4, MRS for claims 10 to 11, or RPS for claim 6.

140. The method according to any one of claims 1 to 3, further comprising selecting a subject according to PRI for claims 1 to 3.

141. The method according to claim 140, wherein the PRI is RPS or MRS.

142. The genetic material in the sample is contacted with one or more nucleic acid primer pairs having a forward primer and a reverse primer capable of hybridizing to one or more target nucleic acid sequences, wherein the one or more target nucleic acid sequences collectively include the polymorphic chromosomal positions in row x of column 2 of Table 31, and x is any number from 2 to 1374. The target nucleic acid sequence is amplified by a polymerase chain reaction with the nucleic acid primer pair in the contact step, The results of the amplification process are input into a computer system, The computer system analyzes the results to determine the PRI for claims 1 to 4, the MRS for claims 10 to 11, or the RPS for claim 6, wherein the computer system comprises a storage unit configured to store the parameters in row y of column 1 of table 31, and y is the same as x in the contact process. The method according to any one of claims 1 to 4, 6, or 10 to 11, including the method described in any one of claims 1 to 4, 6, or 10 to 11.

143. (i) the β0 is about 0.0077127943934849, or (ii) the β0 is about 0.008, the method according to claim 14.

144. (i) the MRS cutoff is about 0.0322446725024791, or (ii) the MRS cutoff is about 0.03, the method according to claim 14.

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