Transcriptome analysis for treating inflammation

Abstract

The present disclosure provides methods of identifying a disease or condition suitable for treatment with dupilumab. The present disclosure also provides methods of identifying a subject having a disease or condition suitable for treatment with dupilumab. The present disclosure also provides methods of performing a clinical trial of dupilumab treatment of a disease or patient.

Description

Technical Field

[0001] The present disclosure provides methods for identifying a disease or condition suitable for treatment with dupilumab, methods for identifying a subject having a disease or condition suitable for treatment with dupilumab, and methods for conducting a clinical trial of treating a disease or condition with dupilumab. Technical Background

[0002] Dupilumab is a fully human monoclonal antibody that blocks the shared receptor component of interleukin 4 (IL-4) and interleukin 13 (IL-13), which are drivers of type 2 inflammation in multiple diseases, including eosinophilic esophagitis (EoE). Dupilumab is indicated in the U.S. for the treatment of subjects with uncontrolled moderate to severe atopic dermatitis, moderate to severe asthma with an eosinophilic phenotype or oral corticosteroid-dependent asthma, and inadequately controlled chronic sinusitis with nasal polyposis.

[0003] Pharmacogenomic testing, along with other information about a subject and their disease, disorder, or condition, can play an important role in drug therapy. However, these data are typically related to small molecule clearance that is well established by liver or kidney metabolism, or certain well-defined biomarkers are considered indicative of the disease, disorder, or condition. The industry has recently realized a caveat to this approach: often, a disease, disorder, or condition, and the treatment thereof by a therapeutic agent, may involve small fluctuations in multiple biomolecules, sometimes in undefined biological pathways. Individually, each biologic may not have a significant effect on a subject, but the cumulative effect of these trends in fluctuations in biomolecule levels found in an individual patient before or after therapeutic treatment may reveal suitability for a particular therapeutic treatment. Therefore, there is a long-standing but unmet need for methods to identify subjects who will benefit from treatment with any particular biotherapeutic agent.

[0004] In some embodiments, the dupilumab therapeutic core gene signature is generated by determining differential gene expression between dupilumab-treated and placebo-treated groups of multiple treatment studies and identifying a plurality of genes that are differentially expressed.

[0005] In some embodiments, the clinical trial includes generating a normalized enrichment score (NES) for the durpilumab treatment core gene signature at at least one time point before initiating treatment of the subject with durpilumab and after initiating treatment of the subject with durpilumab.

[0006] In some embodiments, the clinical endpoint is achieved when dupilumab treatment results in a decrease in the NES of the dupilumab treatment core gene signature to an acceptable value. Summary of the invention

[0007] The present disclosure provides a method for identifying a disease or patient suitable for treatment with dupilumab, the method comprising: a) generating a dupilumab treatment core gene signature; b) screening the dupilumab core gene signature against a whole transcriptome profile from multiple disease studies; and c) identifying a disease or patient with differential gene expression in the multiple disease studies that is opposite to the dupilumab treatment core gene signature; thereby identifying a disease or patient suitable for treatment with dupilumab. In some embodiments, generating a dupilumab treatment core gene signature comprises determining differential gene expression of a dupilumab treatment group and a placebo treatment group for multiple treatment studies, and identifying multiple genes that are differentially expressed. In some embodiments, multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis. In some embodiments, genes in the core gene signature identified from differential gene expression are selected as having a fold change of ≥2 and / or q<0.05 in ≥3 of the 5 treatment studies. In some embodiments, the fold change comprises subtracting the change in expression in the placebo treated group from the dupilumab treated group. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for treatment studies of asthma and grass allergy is performed by comparing gene expression with allergen challenge to gene expression without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression for treatment studies of atopic dermatitis and chronic sinusitis with nasal polyposis is analyzed by microarray.In some embodiments, the differentially expressed plurality of genes includes ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2R Y6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes, or at least 47 genes. In some embodiments, screening the dupilumab core gene signature for whole transcriptome profiles from multiple disease studies comprises: i) performing differential gene expression analysis on the whole transcriptome profiles of each disease study in the multiple disease studies; and ii) generating a normalized enrichment score (NES) for all diseases in the multiple disease studies using a plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature. In some embodiments, differential gene expression analysis of the whole transcriptome profile of each disease study in multiple disease studies is performed for the disease relative to healthy controls. In some embodiments, the multiple disease studies include the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database. In some embodiments, a gene set enrichment analysis tool that considers both positive and negative gene sets is used to generate NES. In some embodiments, the NES is generated by: a) ranking a plurality of differentially expressed genes from most positive (i.e., most upregulated) values ​​to most negative (i.e., most downregulated) values ​​to generate a ranked gene list (R+); b) independently identifying hits (i.e., the ranking of genes in the core signature) for the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the reciprocal ranking of R+ with reciprocal values; c) combining R+ and R- and re-ranking the values ​​by retaining the hits of S+ and S-; d) calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r. i / p / Σ i∈S / r / p, otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation from zero along the running score; f) repeating steps a)-step e) 1,000 times using a random gene set to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution. In some embodiments, the method further comprises calculating statistical significance by comparing the observed ES with the null distribution or the sample marker (disease / health) arrangement. In some embodiments, step a) comprises using a log2 fold change or a z-score. In some embodiments, R+ and R- are ranked by comparing the log2 fold change of the average gene expression in the disease sample with the average gene expression in the healthy sample. In some embodiments, the method comprises using a ranked list of each disease study to calculate the NES for all disease studies. In some embodiments, a disease with a significant NES is a disease suitable for treatment with dupilumab.

[0008] The present disclosure also provides a method for identifying a subject with a disease or illness suitable for treatment with dupilumab, the method comprising: a) generating a dupilumab treatment core gene signature; b) screening the dupilumab core gene signature for a full transcriptome profile from a subject; and c) determining whether the subject is suitable for dupilumab treatment. In some embodiments, generating a dupilumab treatment core gene signature includes determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies, and identifying multiple genes that are differentially expressed. In some embodiments, multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis. In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected to have a fold change of ≥2 and / or q<0.05 in ≥3 of the 5 treatment studies. In some embodiments, the fold change includes subtracting the expression change in the placebo treatment group from the dupilumab treatment group. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with durpilumab to gene expression after treatment with durpilumab. In some embodiments, differential gene expression for treatment studies of asthma and grass allergy is performed by comparing gene expression with allergen challenge to gene expression without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression for treatment studies of atopic dermatitis and chronic sinusitis with nasal polyposis is analyzed by microarray. In some embodiments, the differentially expressed plurality of genes includes ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2R Y6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.In some embodiments, screening for a dupilumab core gene signature for a whole transcriptome profile from a subject comprises: i) converting the whole transcriptome profile from the subject to a z-score; ii) ranking the z-scores; and iii) generating a standardized enrichment score (NES) for all ranked z-scores using a plurality of genes that are differentially expressed and in the dupilumab treatment core gene signature, thereby representing the dupilumab signature enrichment for the subject. In some embodiments, a gene set enrichment analysis tool that considers both positive and negative gene sets is used to generate the NES. In some embodiments, the NES is generated by: a) converting the expression of each gene within a plurality of genes to a z-score, and ranking the differentially expressed plurality of genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a value for R+; b) independently identifying hits for the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the inverse ranking of R+ with inverse values; c) combining R+ and R- and re-ranking the values ​​by retaining hits for S+ and S-; d) calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r. i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation from zero along the running score; f) repeating steps a)-step e) 1,000 times using a random gene set to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution. In some embodiments, the method further comprises calculating statistical significance by determining the 95th percentile NES from healthy control samples. In some embodiments, the method comprises calculating the NES for all disease studies using a ranked list of each disease study. In some embodiments, when the NES of the subject is higher than the NES of the healthy control, the subject is suitable for pilumab treatment.

[0009] The present disclosure also provides a method for conducting a clinical trial of dupilumab to treat a disease or patient, the method comprising using the dupilumab core gene signature as a clinical endpoint of the clinical trial. In some embodiments, the dupilumab treatment core gene signature is generated by determining the differential gene expression of the dupilumab treatment group and the placebo treatment group of multiple treatment studies and identifying multiple genes of differential expression. In some embodiments, multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis. In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected to have a fold change of ≥2 and / or q<0.05 in ≥3 of the 5 treatment studies. In some embodiments, the fold change includes subtracting the expression change in the placebo treatment group from the dupilumab treatment group. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with durpilumab to gene expression after treatment with durpilumab. In some embodiments, differential gene expression for treatment studies of asthma and grass allergy is performed by comparing gene expression with allergen challenge to gene expression without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression for treatment studies of atopic dermatitis and chronic sinusitis with nasal polyposis is analyzed by microarray. In some embodiments, the differentially expressed plurality of genes includes ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2R Y6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.In some embodiments, the clinical trial includes generating a normalized enrichment score (NES) for the dupilumab treatment core gene signature at at least one time point before starting treatment of the subject with dupilumab and after starting treatment of the subject with dupilumab. In some embodiments, the clinical endpoint is achieved when dupilumab treatment results in a decrease in the NES of the dupilumab treatment core gene signature to an acceptable value. In some embodiments, the NES is generated by: a) ranking a plurality of differentially expressed genes from most positive (i.e., most upregulated) values ​​to most negative (i.e., most downregulated) values ​​to generate a value for R+; b) independently identifying hits for a positive (i.e., most upregulated) gene set (S+) in R+ and a negative (i.e., most downregulated) gene set (S-) in R-, where R- is the inverse ranking of R+ with inverse values; c) combining R+ and R- and re-ranking the values ​​by retaining hits for S+ and S-; d) calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r. i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation from zero along the running score; f) repeating steps a)-step e) 1,000 times using a random gene set to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution. In some embodiments, the method further comprises calculating statistical significance by comparing the observed ES to the null distribution or the sample label (disease / healthy) arrangement. In some embodiments, step a) comprises comparing the gene expression after dupilumab treatment with the gene expression before starting treatment with dupilumab using the log2 fold change. In some embodiments, multiple samples are obtained from the subject and a NES is generated for each sample.

[0010] The present disclosure provides a method for treating a subject having a disease or condition suitable for treatment with dupilumab, the method comprising: a) identifying whether the subject has a disease or condition suitable for treatment with dupilumab, comprising: i) generating a dupilumab treatment core gene signature; ii) screening the dupilumab core gene signature against a full transcriptome profile from the subject; and iii) determining whether the subject is suitable for dupilumab treatment; and b) administering dupilumab to a subject having a disease or condition suitable for treatment with dupilumab. In some embodiments, the dupilumab treatment core gene signature comprises determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies, and identifying multiple genes that are differentially expressed. In some embodiments, the multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis. In some embodiments, genes in the core gene signature identified from differential gene expression are selected as having a fold change of ≥2 and / or q<0.05 in ≥3 of the 5 treatment studies. In some embodiments, the fold change comprises subtracting the expression change in the placebo treatment group from the dupilumab treatment group. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with dupilumab with gene expression after treatment with dupilumab. In some embodiments, differential gene expression for treatment studies of asthma and grass allergy is performed by comparing gene expression with allergen challenge with gene expression without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression for treatment studies of atopic dermatitis and chronic sinusitis with nasal polyposis is analyzed by microarray.In some embodiments, the differentially expressed plurality of genes includes ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2R Y6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes, or at least 47 genes. In some embodiments, screening the dupilumab core gene signature for the whole transcriptome profile from the subject comprises: i) converting the whole transcriptome profile from the subject into a z-score; ii) ranking the z-scores; and iii) generating a normalized enrichment score (NES) for all ranked z-scores using a plurality of genes that are differentially expressed and are in the dupilumab therapeutic core gene signature, thereby representing the dupilumab signature enrichment for the subject. In some embodiments, a gene set enrichment analysis tool that considers both positive and negative gene sets is used to generate an NES. In some embodiments, the NES is generated by: a) converting each gene expression within a plurality of genes to a z-score, and ranking the differentially expressed plurality of genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a value for R+; b) independently identifying hits for the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the inverse ranking of R+ with inverse values; c) combining R+ and R- and re-ranking the values ​​by retaining hits for S+ and S-; d) calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score increases by / r. i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r iis the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation from zero along the running score; f) repeating steps a)-step e) 1,000 times using a random gene set to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution. In some embodiments, the method further comprises calculating statistical significance by determining the 95th percentile NES from healthy control samples. In some embodiments, the method comprises calculating the NES for all disease studies using a ranked list of each disease study. In some embodiments, when the NES of the subject is higher than the NES of the healthy control, the subject is suitable for pilumab treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several features of the present disclosure.

[0012] The patent or application file contains at least one drawing drawn in color. Copies of this patent or patent application publication with one or more color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0013] Figure 1 The top 30 genes with the largest change in expression relative to baseline after dupilumab 300 mg qw are shown. The transcriptomes of healthy and EoE patients were reproduced with permission from Sherrill et al. (Genes Immun., 2014, 15, 361-69). All genes reached the significance threshold. EoE, eosinophilic esophagitis; qw, weekly. Pink highlights indicate positive upregulation, and blue highlights indicate negative downregulation.

[0014] Figure 2 (Panels A and B) show the effect of dupilumab 300 mg qw relative to placebo on the published EoE transcriptome in esophageal tissue at Week 12. Panel A shows the correlation between the published EoE transcriptome and the transcriptome after dupilumab treatment. Panel B shows the effect of dupilumab 300 mg qw compared to placebo on the expression of genes involved in the most significantly dysregulated pathways in EoE: Gene Ontology analysis.

[0015] Figure 3 Type 2 inflammatory gene expression signatures are shown for patients with EoE, atopic dermatitis, nasal polyps, and asthma.

[0016] Figure 4 The log2 fold change of gene expression after treatment for each indication is shown. Pink highlights indicate positive upregulation, and blue highlights indicate negative downregulation.

[0017] Figure 5The ulcerative colitis (UC) NES was significantly enriched but not in the top 10. When NES were calculated at the individual patient level using the UC study (GSE87466) for 87 patients and 21 healthy controls, 33% of the patients had a dupilumab NES that was statistically significant.

[0018] Figure 6 Results: In the CRSwNP study, a 25-gene set (identified from treatment and clinical response signatures) was shown to be more predictive of response to each CRSwNP clinical endpoint (CONG, NPS, CT-LMK, and UPSIT) than other available circulating biomarkers. Receiver operating characteristic analysis was used to assess the ability of NES scores, representing transcriptional signatures and more standard biomarkers, to distinguish responders to each of the four primary endpoints as well as responses to multiple endpoints. The predictive performance of each biomarker was summarized by calculating the area under the receiver operating characteristic curve (AUC). The dupilumab core gene signature had high overlap with the 25-gene set and could predict response to a wider range of dupilumab indications.

[0019] Figure 7 (Panels A and B) show long-term changes in gene expression profiles in the dupilumab 300 mg qw group from TREET. Panel A shows the change in EDP-NES from baseline to Week 24 and Week 52. Each lane represents a different patient. Panel D shows that the change in EDP-NES from baseline to Week 24 was significant in both adolescents and adults. EDP, EoE diagnostic panel; EoE, eosinophilic esophagitis; NES, normalized enrichment score; qw, weekly.

[0020] Figure 8(Panels A to E) show the effect of dupilumab on cell proliferation, mast cell activation, T cell and antigen presenting cell markers in patients treated with dupilumab 300 mg qw or placebo. Panel A shows the expression of cell proliferation marker MIB-1 (Ki67) (left) and the change in the proportion of cells expressing MIB-1 from baseline to week 12 (right). Panel B shows the expression of mast cell serine protease tryptase (left) and the change in the proportion of cells expressing tryptase from baseline to week 12 (right). Panel C shows the expression of cytotoxic T cell marker CD8 (left) and the change in the proportion of cells expressing CD8 from baseline to week 12 (right). Panel D shows the expression of helper T cell marker CD4 (left) and the change in the proportion of cells expressing CD4 from baseline to week 12 (right). Panel E shows the expression of antigen presenting Langerhans cell marker CD1a (left) and the change in the proportion of cells expressing CD1a from baseline to week 12 (right). Changes from baseline in the percentage of positive cells in placebo and dupilumab immunochemical images were quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-sided t-tests to compare absolute changes from baseline between placebo and treatment groups. NS, not significant; qw, weekly.

[0021] Fig. 9 The Dpx3 response signature in EoE ph3 is shown. DETAILED DESCRIPTION

[0022] Various terms related to aspects of the present disclosure are used throughout the specification and claims. Unless otherwise indicated, such terms will be given their ordinary meanings in the art. Other specifically defined terms should be interpreted in a manner consistent with the definitions provided herein.

[0023] Unless otherwise expressly stated, it is not intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a particular order. Therefore, in the claims or specification, when a method claim does not specifically state that the steps are limited to a particular order, no order is intended to be inferred in any respect. This applies to any possible non-express basis for interpretation, including matters of logic with respect to the arrangement of steps or operational flow, ordinary meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.

[0024] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0025] As used herein, the term "about" means that the numerical values ​​recited are approximate and that small variations will not significantly affect the practice of the disclosed embodiments. Where numerical values ​​are used, unless the context indicates otherwise, the term "about" means that the numerical value may vary by ±10% and still be within the scope of the disclosed embodiments.

[0026] As used herein, in certain embodiments, the term "comprising" may be replaced with "consisting of" or "consisting essentially of," as appropriate.

[0027] As used herein, the terms "nucleic acid", "nucleic acid molecule", "nucleic acid sequence", "polynucleotide" or "oligonucleotide" may include polymeric forms of nucleotides of any length, may include DNA and / or RNA, and may be single-stranded, double-stranded or multi-stranded. A strand of a nucleic acid also refers to its complementary sequence.

[0028] As used herein, the term "subject" includes any animal, including mammals. Mammals include, but are not limited to, farm animals (e.g., horses, cows, pigs), companion animals (e.g., dogs, cats), laboratory animals (e.g., mice, rats, rabbits), and non-human primates (e.g., apes and monkeys). In some embodiments, the subject is a human. In some embodiments, the subject is a patient under the care of a physician or veterinarian.

[0029] As used herein, a list comprising A, B "and / or" C provides: (i) A alone; (ii) B alone; (iii) C alone; (iv) A and B; (v) A and C; (vi) B and C; and (viii) A, B, and C. Thus, a list comprising A, B, C ... and / or N has n components, where n is a positive integer, providing all possible combinations of A, B, C ... N, up to and including all combinations of n components.

[0030] As used herein, the phrase "opposite direction" in a disease signature refers to a comparison between a disease sample and a healthy sample. If the expression of a disease gene is higher compared to a healthy gene, the gene is upregulated (red / pink). In a treatment signature, the comparison is between after treatment and before treatment. If the gene expression is lower after treatment compared to the baseline (before treatment), the gene is downregulated (blue). "Opposite direction" means that the gene changes significantly in opposite directions in the disease and treatment signatures, such as upregulated in disease (compared to healthy) and downregulated after dupilumab treatment (compared to before treatment).

[0031] During drug development, it is beneficial to identify existing approved drugs that are effective for new indications (whether diseases, patients, or conditions), or subsets of patient populations that respond better to existing drugs. In traditional drug effect studies, single-gene analysis (i.e., t-tests) of drug treatment for each individual gene is used to identify biomarkers that are suitable targets, and the level of this biomarker is changed to achieve the assumed treatment effect. However, this approach may result in the creation of a long list of statistically significant genes without linking their biological relevance. In addition, there may be little overlap in such gene lists between different clinical studies; and most importantly, they may miss the pathway effects of each gene involved in the disease, patient, or treatment effect. For example, several small gene expression changes, although not significant individually in a t-test, may have more combined effects on a given disease or patient than one gene, which has a considerable change but has a negligible effect on the disease or patient. To avoid such an outcome, the present disclosure develops a genome-wide approach in which a core set of gene signatures in drug clinical studies is first identified and then compared with the whole transcriptome profile of interest to obtain a standardized enrichment score for identifying appropriate indications or patient populations that are responsive to the drug (particularly dupilumab).

[0032] The present disclosure provides methods for identifying a disease or patient suitable for treatment with dupilumab. In some embodiments, the embodiments include generating a dupilumab treatment core gene signature. In some embodiments, the method includes screening the dupilumab core gene signature for a whole transcriptome profile from multiple disease studies. In some embodiments, the method includes identifying a disease or patient with differential gene expression in the opposite direction to the dupilumab treatment core gene signature in multiple disease studies. In some embodiments, the method identifies a disease or patient suitable for treatment with dupilumab.

[0033] In some embodiments, the method comprises generating a dupilumab treatment core gene signature, comprising determining differential gene expression between dupilumab treatment groups and placebo treatment groups of a plurality of treatment studies. In some embodiments, the method results in identifying a plurality of differentially expressed genes.

[0034] In some embodiments, multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic sinusitis with nasal polyposis. In some embodiments, multiple treatment studies include eosinophilic esophagitis. In some embodiments, multiple treatment studies include atopic dermatitis. In some embodiments, multiple treatment studies include asthma. In some embodiments, multiple treatment studies include grass allergy. In some embodiments, multiple treatment studies include chronic sinusitis with nasal polyposis. In some embodiments, multiple treatment studies include any disease, condition, or patient involving or suspected of involving IL-4 and / or IL-13.

[0035] In some embodiments, genes in the core gene signature identified from differential gene expression are selected to have a fold change of ≥2 and q<0.05 in ≥60% of treatment studies. Alternatively, a fold change threshold of 1.5 or a p-value (rather than a q-value) can be used. In some embodiments, genes in the core gene signature identified from differential gene expression are selected to have a fold change of ≥2 in ≥60% of treatment studies. In some embodiments, genes in the core gene signature identified from differential gene expression are selected to have q<0.05 in ≥60% of treatment studies.

[0036] In some embodiments, the fold change comprises subtracting the change in expression in the placebo treated group from the dupilumab treated group. For example, the fold change is the mean expression in group 2 / the mean expression in group 1.

[0037] In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for eosinophilic esophagitis is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for atopic dermatitis is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for treatment studies of asthma is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for treatment studies of grass allergy is performed by comparing baseline gene expression before treatment with dupilumab to gene expression after treatment with dupilumab. In some embodiments, differential gene expression for CRS treatment studies is performed by comparing baseline gene expression before treatment with durpilumab to gene expression after treatment with durpilumab.

[0038] In some embodiments, differential gene expression for asthma allergy treatment studies is performed by comparing gene expression after allergen challenge with gene expression before allergen challenge. In some embodiments, differential gene expression for grass allergy treatment studies is performed by comparing gene expression after allergen challenge with gene expression before allergen challenge.

[0039] In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression is analyzed by microarray. In some embodiments, differential gene expression is analyzed by RNASeq. In some embodiments, reverse transcription polymerase chain reaction (RT-PCR) can be used to measure gene expression.

[0040] In some embodiments, differential gene expression for eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic sinusitis with nasal polyposis treatment studies is analyzed by RNASeq. In some embodiments, differential gene expression for eosinophilic esophagitis treatment studies is analyzed by RNASeq. In some embodiments, differential gene expression for atopic dermatitis treatment studies is analyzed by RNASeq. In some embodiments, differential gene expression for asthma treatment studies is analyzed by RNASeq. In some embodiments, differential gene expression for grass allergy treatment studies is analyzed by RNASeq. In some embodiments, differential gene expression for chronic sinusitis with nasal polyposis treatment studies is analyzed by RNASeq.

[0041] In some embodiments, differential gene expression of eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic sinusitis with nasal polyposis is analyzed by microarray. In some embodiments, differential gene expression of eosinophilic esophagitis is analyzed by microarray. In some embodiments, differential gene expression of atopic dermatitis is analyzed by microarray. In some embodiments, wherein differential gene expression of asthma is analyzed by microarray. In some embodiments, differential gene expression of grass allergy is analyzed by microarray. In some embodiments, differential gene expression of chronic sinusitis with nasal polyposis is analyzed by microarray.

[0042] In some embodiments, genes in the differentially expressed plurality of genes include ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P 2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.

[0043] In some embodiments, the genes in the differentially expressed plurality of genes include LRRC31, SLC45A4, BUB1, PARP12, TMEM154, AL831977, INPP4B, C2orf72, ALOX15, RASGRP1, WFDC5, CLSTN3, HBP1, LUZP6, SMCR7, KCNJ5, SLC26A4-AS1, VGLL1, PARVB, BCL6, PHACTR1, IFRD1, OR13A1, MAB21L3, TNFAIP6, IFIT2, AX747171, DNM3, DVL1, FBXO27, KIAA2022, RNF224, CLC, GATA3, AK093551, EXOG, KIAA0226, FAM86DP, CA12, AL157440, CCL26, TENM3, TSPAN15, DTX3L, REEP6, EME2, HMOX1, C9orf169, POSTN, KLK1, DERL3, APBB1IP, ATP1B1, TMPRSS11E, RPS6KL1, MAMDC2, SLC26A4, PDLIM3, TNFRSF10D, ATP8A1, ZNF382, HIST1H2AC, KCTD17, EEF1A2, NTRK1, CLDN5, PROCA1, CPT 1B, C1orf122, CNKSR3, GATSL3, S100A12, PMCH, KIF21B, GALNT6, GBP2, C7orf73, MYOM3, CAMK2N1, MYEOV, PTCHD4, SLAMF1, TLR2, CADPS2, MOSPD1, UCKL1 -AS1, AX748345, SRGAP1, NRXN1, CXCR4, IL3RA, ITGA4, CRAT, FAM46B, HBB, YOD1, NEFL, GGH, OAS2, AX748379, EFNA2, TUBA4A, LRFN2, RHBG, CXCL1, AK1243 08. LINC00672, AK092098, HMOX2, LZTS1, PHGDH, SNX9, ATP13A5, ARHGAP25, BTN2A3P, NR5A2, ARHGAP28, SPRR1A, AGFG2, RNF180, NEFM, TSPAN3, GNRH1, P SMB10, ARHGEF26-AS1, SBSPON, RAB17, ISM1, TNIP3, NFE2L3, SEMA3A, PRC1, CNNM1, CARHSP1, PLIN3, VSIG2, CTSG, C5orf56, ELOVL5, FBXO6, TOLLIP-AS1,C4orf3, CDKL5, ZNF101, DKFZp686O16217, CD36, PPAP2C, COPZ2, AK026714. AK128153, WNT9A, LPIN1, IGH2, ADAMTS14, VAMP5, LOC115110, PEBP1, DNM1. TMEM61, CAP2, abParts, STAB1, DLGAP5, OSCAR, RNF11, STAR, UCA1, KRT4, IL 1RL1, LAIR1, SH2D2A, NCKAP1L, AK289390, NUDT8, MYH11, LY6K, CPA3, CHST2. MPEG1, STARD4, AKR1C3, HEG1, PPP1R36, ALDH3B1, CXCL6, PSMB9, TIGIT, TFR C, JUN, PRSS27, KRT13, TP53TG3D, TREML2, MAPK10, GVINP1, HNRNPU-AS1, MOR N4, CECR7, TSPAN7, MTUS2, LURAP1L, HLF, MYOF, NAIP, BC063600, FAM126A, L INC00482, HIST1H2BC, HDC, LIFR, TMPRSS4, ATP12A, PCDHGA1, ZNF662, STX1B XCR1, AK128525, ADRA2A, ARMCX3, FAIM3, TPGS1, PPPIA4, SOSTDC1, HP0734 9. Igκ, BDNF, FAM69A, KLRAP1, ROMO1, MPZL3, ME1, TCN1, TMEM71, RUFY4, CCD C146, FAM13A-AS1, BSPRY, AK127124, MT1E, LOC646329, EMR1, APOL4, CLU 11orf92, AHNAK2, MRC2, PLEKHA6, PER1, CDH26, MDK, IGF2BP2, LIPA, SNX16, C.S EACAM3, PKNOX2, FOXI2, IGJ, CEP290, BC144457, CLNS1A, RNF10, KIFC3, PTP N20A, BNIP3, LOXL4, POU2AF1, DFNA5, GTSE1, GBP6, ENTPD2, TRIB3, STXBP5-A S1, SUSD2, C11orf93, AK095112, TPX2, MGST1, PLLP, SLC16A9, SYT17, GPR97 CTSK, MAP3K14, IQCK, ALDH3A1, LINC00319, GMDS, ABCG2, IL19, LINC00939.FAM111B、IFNAR2、KRT31、ANKRD36BP1、BARX2、CWH43、MS4A2、LITAF、TNFRSF10A、ERG、HSFX2、DMPK、TREH、ACADL、SIGLEC6、ADAM8、ERCC6L、GLB1L、ZNF582、EML5、AIFM2、PCDH20、KCNJ16、P2RY14、HLA-B、MCAM、SERHL2、SERHL、GPSM1、XYLT1、SLC9A3、FETUB、HCK、SERPING1、SH3GL1、CCL17、FAM83A-AS1、AKR1B15、TRPM6、FLJ00157、CDC25C、AK091624、CDKN1A、WNK4、ENC1、DQ571917、EMILIN2、FBXL2、ATP2A3、DL490846、JUND、DZIP1、BC036236、ZYG11A、C1QTNF1、BX647608、HCP5、DKFZp547K2416、EGOT、OAZ3、SH3BP5、FAM171A2、APOBEC3A、USP32P2、CSGALNACT1、BAK1、SGTA、ZNF416、TMEM38A、EDNRB、COL6A6、LOC619207、UBASH3A、CXCL16、SUCO、IL22RA1、CAPN5、ZNF92、CNTN4、LTA4H、AF268386、PRICKLE4、RFFL、RNF169、QSOX1、PLK3、BCL2L15、HS3ST1、APOBR、FES、DOPEY2、PMM1、C18orf25、KCP、BMPR1B、LOC100506714、CDC45、BC013821、ARG2、C15orf48、SLC15A2、MAP1LC3A、ANO1、PARP14、DEPDC1、CTSS、NCKAP5、UBTD1、PLEKHF1、TFAP2B、IL8、SH3RF2、SEMA6B、ARSB、NOV、NBPF16、FAM129B、HIST4H4、CDK15、PDZK1IP1、BTN3A2、AKAP2、RBM10、ACOT11、KLK5、DUSP13、DPP4、ICAM1、CHN2、ODF3B、NCK2、CD68、TRIM56、PLCXD2、TPSAB1、HEPHL1、ASB2、N4BP2L1、SALL2、AKAP6、AK093892、ZBED2、CXCR2P1、ABCC5、HDX、EVI2A、MLXIP、<h2 style=";text-align:left;direction:ltr">SYT8, SPECC1, HIST1H2AD, DKFZp667J0810, IQGAP2, GNLY, PDPR, TSPAN13, AK127120, UACA, BC020196, IL9R, SERPINE1, TSHZ3, RASA3, RAP2B, C8orf47, C PLX3、SULT1B1、C20orf197、NCF1C、MAP3K8、EBLN2、FAM149A、TMEM86A、RANB P9、ZDHHC15、RPTN、CHST7、SYCP2、ERI1、FRMD6-AS1、PPP1R15A、ANKRD20A11P 、ENPP4、UBD、IL36G、ZC3H12A、TBC1D1、FAM222A、GSTA4、HLA-DQB2、FAM43A、 GLDC、ANTXR2、TOP2A、PARP3、SCNN1D、TENM4、KIF26A、SLC6A15、MMP12、SDPR、 MYO3A、TNFRSF10B、DNAJB5、AK4、TTC40、SESN2、WBSCR17、CCDC150、GCH1、RA B23、MUC1、KIAA0232、PTK6、KRT7、IGLL5、CCR1、IL18BP、ZC3H12D、SLC2A4、CT TNBP2、TOM1、CHST6、ADAMTS15、CTAGE15、DMD、SRGAP3、DCLK1、FBXO34、TMEM 57、KRT6B、TPSB2、CTLA4、C1QC、FAM46C、ZNF117、AHDC1、AX747793、KBTBD12、 CD200R1、OLFML2B、SEMA3B、TLR8、ANKH、S100A14、LRRK2、CYP26B1、PRRX1、C YP4X1、CCL2、LINC00673、KYNU、RHCG、C2orf16、SLITRK5、ATP4A、GLCCI1、OAS L、GLIPR2、CNPPD1、RNF222、GJA1、AX746725、SAMSN1、PSTPIP2、MYL9、ZNF70 2P、PFKL、LINC00152、GPR1、MYO1H、CD209、GEMIN8P4、MMP14、SRPX、BNIPL、FB 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KBTBD11, SV2A, UBE2E2, HOPX, SDR9C7, PADI1, IGFBP3, BC040358, DQ573949.BC019880, PDZD8, BC040935, MAP1S, PANX2, SIDT1, AP4B1-AS1, AK127443, NPAS1, LRMP, SLC2A4RG, UGT1A7, KRT78, CACNB4, IL2RG, APOD, HRNR, XYLB, SL C27A6, FAM20A, GADD45B, C2, TMEM98, SLC16A2, PLAGL1, TYRO3, PDLIM2, ZNF578, HIST1H3D, IFI35, KLRB1, A4GALT, CMAS, MCOLN3, TUBB6, OTOP2, AGPAT9A NKDD1B, N4BP2L2-IT2, CEP152, BCOR, AKR1C1, PLCXD1, FAM174B, RNF182, CBRC7TM_40, AK092331, KIF20A, TPRG1L, PCLO, METRNL, LOC96610, ANKRD37, PT GES, CD9, HUNK, TEC, ZNF800, SEPT3, VSIG10L, MT2A, CD163L1, SLFN13, LAT, PLB1, PCBP1, PNMA1, NQO1, KRT40, HNF4G, CLMN, NUP62CL, SOCS6, TMEM40, ARH GAP5-AS1, ZNF667-AS1, MYRF, CH25H, PRR5L, TAGLN, BC039551, RPL23AP82, WASF3, IVL, AK097184, SLC6A14, GAB3, ANKRD36B, OXR1, FAM78Bl, CLEC5A, FUT11, TP53I11, PRG2, SLC27A2, KCND1, LGMN, ATP6V0C, PLCD3, MXD1, CDA, PGBD5, KLHL5, SFMBT2, LNX2, MXRA8, FAM122C, BC039389, SYT7, CDH3, GPR153, E IF3CL, SCRIB, SCAND1, FFAR4, BC131755, NRG2, NEK6, CTAGE4, ​​AF157115, SORBS2, TMEM254, CCL21, C9orf66, AX747517, SRGN, FCER1G, LOXL2, CHI3L1, CY P2C18, B4GALT5, AMFR, ADIRF, BC040701, MC1R, C9orf40, PCNXL2, SLPI, PIH1D2, AMACR, RNF103-CHMP3, GFI1, PLAT, TYROBP, PKDREJ, RNF24, LINC00887,PDZRN3、NPC1L1、EMR2、COL1A2、STIL、ZSCAN18、SCNN1G、TP53BP2、RAP1GAP、AK123263、BX649158、FA2H、ZNFX1、KCNK5、DLGAP1-AS1、RTN4RL1、PDE1A、FT H1、ITGAM、ZNF503-AS2、PPARGC1B、DIS3L2、FBXW4P1、ATG9B、HIST1H2AE、AFF3、SDK1、DUOX1、IQGAP3、S100A10、UNC13B、PAPL、IL1A、CPA4、LINC00707、PO C1B、IFI16、CX3CL1、NCR3LG1、LINC00933、TRIM2、RHOF、ITGA9、TNFRSF18、MTFR2、IGFBP2、IL36B、FLJ23867、FAM109A、FER1L6、VCAM1、C1orf216、IFITM3 、KCTD11、SMIM5、ADM2、CXCR2、EML1、SLC44A4、SULF1、FAM3C、BC038574、SDK2、BAI1、SPIRE1、TAS1R3、SLC45A3、BTN3A3、AX746775、AREG、S100A16、CXXC5 、CHRNB4、LRRC20、RASGRP4、ARNTL、GZMA、SPAG4、BOK、ZNF460、CDH22、GSTT2、TFPI、AL832891、AK308965、SERTAD1、PWWP2B、WWTR1、TPTEP1、BC017578、LR RC8D、NFATC2、AX747179、CDR2L、SERTAD2、AKR1B10、LOC729970、SPTBN4、SCUBE2、SERPINA1、BC034424、MVD、VMAC、AL110181、PELI1、HTR3B、NCF1B、FAM2 6F、SHCBP1、PCDHGA5、SYNGR3、WNT2B、CEACAM7、SPRR2D、NCF1、FLG2、KPNA2、MAP1LC3B2、FBXO39、CITED2、SASH1、LINC00664、KITLG、ZNF487P、TTK、HIST1 H2BK、ABHD12、ACTG1、MYZAP、CYP2B7P1、GALNT4、BTN3A1、BC069212、ANKRD6、KLB、RNF128、PKIB、CPNE4、IL17RB、F2R、ZNF827、ARMCX5-GPRASP2、USP6NL、<h2 style=";text-align:left;direction:ltr">HIST2H2BF、CECR6、HS3ST6、MICB、FBN1、PDE4D、INPP5A、AK130329、RND3、AK 096475、CRYAB、CD69、RAC2、GMFG、CAPS2、TSPYL4、VAV1、MGLL、UPK1A、GPRC5 B、C3orf52、CD37、SH3BP5L、AK094188、RP1L1、FAM180A、PPP1R3G、ARHGEF6、 BST2、CLDN1、TBKBP1、EDIL3、C15orf62、S100A8、GNA14、CFH、COL6A2、MAN1A1 、HMGCS1、ZFAND5、ZNF254、VIPR1、CYP4F35P、NPR1、GIMAP1-GIMAP5、TMEM13 3、SP6、TM4SF19、EFNA5、AK097370、SMOX、CNRIP1、TNS4、DKFZp686B07190、B C040700、ITGBL1、DBNDD1、TMEM8A、SLC38A4、PKP2、MSRB1、SKA1、LINC00265 、FER、CYP2C9、TGM1、CGNL1、TNFSF13、PRODH、BUB1B、TSPO、ZNF425、ZNF430、T NNT1, ETNK2, CSF2RB, RGS18, RGS16, SRXN1, ABHD5, PCSK5, KLRG2, HMGCS2, ADAM28, HCG26, PIK3AP1, ZDHHC2, GCNT1, ADAM11, EPHA10, SPRR2B, GPR160, O RAI1, PLEKHG2, PRSS22, TAB3, CTIF, EPS8L1, HSPA2, KLHL6, CD274, FMO4, TEF, PPM1K, SHROOM4, BCAR3, ZNF812, TMEM176B, PARP9, AK123332, SYAP1, HIST 2H2BE、ST3GAL4、ZNF667、TGM3、CECR2、IL18RAP、PCCA、XXYLT1、GLTP、ANKS1 B、ATP6V0A4、KRT77、NYAP1、RGS2、LOC283070、NAPA、CSRNP3、NELL2、CD207、 NHLH2、CMKLR1、TGM2、LIMA1、SAMD10、ARL4C、CPT1C、GCHFR、PAK7、PLAUR、MT HFD1L、KIF23、MARKS、GLTPD1、SPRY1、MT1L、OR7E91P、HTR2B、EPSTI1、ASPM、<h2 style=";text-align:left;direction:ltr">PANK1、FAM83D、KCNAB1、TTC22、PRRT4、SCIN、RAD51AP2、IRF1、TEX9、ZNF703 、GATM、USP13、ENDOU、PMP22、ISLR、FAM83E、NINJ2、PRKACB、COL21A1、RHOD、 DKK1、IFIT3、BID、C1QA、LRIG1、NUP214、SNPH、MAGED4、HCN2、ABCA12、ECT2、 WIPF1、PHACTR4、KIAA0355、ST6GALNAC1、LYNX1、CALB2、BTK、AMICA1、TMED7 -TICAM2、LFNG、CNN3、FAM3D、SLC46A2、SLURP1、RSAD2、EPHA4、BC063788、CL CF1、FAM102A、TUFT1、ICOSLG、RUSC2、EDAR、UBE2L6、BC051760、ITM2A、BC04 0684、FCER1A、FIBIN、DLGAP1、CEP72、ABCA8、AK095366、ALKBH7、ATP6V1C2、 B3GNT3、IL1RN、CTNNAL1、COL8A2、CYP2R1、CLEC2D、CCDC85B、GUCY2D、IL36RN SLC9A3R2, AR, ADORA3, GPR143, SLC18A2, PANK2, CEACAM19, MFSD7, S100A9, IGFL1, GCNT3, CD300A, KRT17, ALDH5A1, CCDC127, TMEM80, SLC16A3, NDUFA 4L2、SFRP1、CYTH4、KIF2C、SIRT7、KIAA1244、TOB1、HIST1H1C、EPHA6、DPYD、 GPR82、TYMS、C15orf52、C17orf103、FAM86FP、LOC100127983、CR936677、LOC 100129034、ICOS、LAT2、TNS1、C1orf21、TMOD3、ARHGAP27、EPB41L3、MMP25、 CTSW、UBA7、BDKRB1、LTBP2、CD1A、CYP4F22、NKPD1、C1orf110、RAB20、CCNB1 、METTL21A、CHMP4C、LOC401052、HERC2P4、KRT32、RAB44、TLR4、SCIMP、FOXO 3、ITGB5、ADCY9、UPK3B、AQP7P1、FCGR3A、GSDMB、GPR68、TEAD1、SNX8、DHRS1、<h2 style=";text-align:left;direction:ltr">NAP1L2、NGEF、MMP9、CBWD5、KIF14、MYL12A、DMTN、FSCN2、ZNF555、LGI3、OR2 A7、ADAMTS2、DDX26B、PIP5K1C、AK056780、COBL、MAL、C3orf80、LILRB2、SLC O3A1、FOLH1、SDC1、TPRN、IL34、HHIPL1、ANKRD20A5P、AADAC、FAM46A、AK123 872、GALNT14、SLC10A6、BAIAP3、SNX21、RNASE7、PPARGC1A、NCF4、SELL、GCAT 、CAMSAP3、KRT33A、CDHR1、LOC100127888、POC1B-GALNT4、HJURP、TCIRG1、G PD1、ARHGAP39、PADI3、DAPL1、AIF1L、CMPK2、PSMB8、UBE2C、MACROD1、BCAS1、 CAB39L、LNX1、C1orf168、SPP1、CHST15、CDK1、JUNB、CLEC3B、AK125699、SLC 6A11、RNF223、SLC15A1、LIMD2、KIF18A、COL7A1、FKBP1A-SDCBP2、SHROOM3、D USP4、FLG、MEOX1、IKBKAP、FJX1、FOSL2、AVPI1、CLIC3、DHRS9、BAIAP2L2、RN ASE1、ORAI2、CSF1、NMRK1、RTN1、CDKN1C、POU3F1、ST6GAL2、PXDN、ATP8B4、C LIC6、DOCK9、RAB36、PIK3C2G、SLC35F3、ALOX12、ANKRD65、MX1、GCSAM、SEMA 4D、DUSP14、LCN2、FADS6、NKX6-2、RYR3、CD226、AK123584、MAP2K1、FAM84A、P ITX1、C7orf57、C1orf177、MFHAS1、WDR52、PTPRB、CYP2U1、ARRDC1、TNFRSF1 1A、TMPRSS11B、FAM25C、SLC16A1、NFKBIE、RELB、CPNE5、SLC2A11、HOMER2、UN C79、HTR3A、ZSWIM5、CADM1、MMP19、ITPRIPL2、EREG、DYNAP、TSC22D2、SLC13 A5、CAPN14、NTN1、GALNT5、RCOR2、LPIN2、FBXL16、HNMT、RAB6B、STOM、TMTC3、<h2 style=";text-align:left;direction:ltr">CCNB2、KRT18、KPNA7、LDHD、BC033124、CHAC1、CYP7B1、IFIH1、FANCI、MYRIP 、CSRNP1、LOC100132891、TUBB2A、RGS17、SHF、SLC39A8、RALGAPA2、SRCIN1、 AMDHD2、SLCO2B1、VIT、DSG1、GAPT、PLIN5、LINC00900、PQLC1、NDRG4、CACNA 1G、RNF39、BC107108、TRIM22、LOC100288637、CHST11、DEF8、AK311374、TTC9 、TMEM74、ABTB2、OSMR、CEL、PLCB2、AK125212、ZFPM1、AX747104、NPPC、PSCA 、AKAP5、CAPN13、NTF4、FAM167A、VSIG8、LOC100996255、PCBP1-AS1、SLC6A1 、SELP、FAM118B、APOBEC3D、FHOD3、ELL2、ABHD16B、TMIE、NR1D1、NCF2、SLC9 B2、CD53、LOC285074、FABP5、VWA2、TMEM105、RHCE、TMEM253、DENND4A、FAM11 1A、DQ586822、ZNF519、SERPINB1、KDM5B-AS1、AK097161、TMEM176A、FLT3LG 、SLC38A5、KIF1C、PINK1、MAPK3、SLC7A5、MC5R、DDX60、LY75、LYZ、XK、NPR3、 SCNN1B、GRB14、DKFZp667P0924、C12orf75、RNF213、CD48、HOTAIRM1、PAX9、 SRGAP2D、SEPT5-GP1BB、CRCT1、C3AR1、LOC729603、SLC22A3、ABLIM2、H2AFJ、 NOXO1、KRT3、MUC21、VWA5A、GPR65、NKG7、FCRLB、C10orf12、DOCK3、GP6、SAM D11、IL15、IL7R、IGFBP7、HSPA6、FZD7、CYP2C19、TICAM1、CLDN10、LONRF2、AK 055623、PITRM1、ACSL6、NETO2、PKP3、BC046483、ZNF365、NRP2、ATP10A、CDC A2、NFKBIA、ZNF208、SPTBN5、SPRR2E、IRX6、TMEM173、JAK3、SMAD1、CD164L2、<h2 style=";text-align:left;direction:ltr">HIF1A、AX747832、GGTA1P、LOC284551、NAV1、THY1、SPAG5、AURKC、RIOK3、DT X2 、 c6orf132 、 Acer1 、 CD22 、 ITGAX 、 ATP11C 、 LOC100129195 、 GDPD3 、 PMAIP1 、 t CP11L2、CES1P2、C12orf54、PDE4A、PLAU、RASGEF1A、COL14A1、LINC00675、C 19orf26、LGR6、FOXE1、GIPR、SLC38A6、AK126286、LOC344887、LOC100288181 AKR1C2, BC070322, CD101, FGF1, MEIS1-AS3, MAPK7, ALDH1L1, PRKAA2, CDK14, ANKRD62, PDE3B, FASLG, ARHGAP9, DHCR24, TST, KCNMA1, BPGM, SPINK7, CO L6A3、ABCA10、CTSL1、CAST、COL18A1、AK311497、GPX3、KPRP、UPK1B、C1S、DO CK2、JARID2、DBN1、TGFB1、ZNF714、BC042588、DDX58、FPR1、IKZF1、LOC73010 1、WWC1、DMRT2、HIF1A-AS2、LRRN2、GPR34、DSE、APCDD1、DQ576756、ID1、PIM 1、LINC00346、AK054845、CISH、NFIL3、LINC00173、NDUFA6-AS1、TMEM63C、CS NK1E, CSTB, IGSf11, GCNT2, LY96, FAR2, AMER1, SLC34A3, MROH6, SYT15, C4orf6, LINC00839, EDNRA, PTP4A3, FUT3, CTNNBIP1, UBALD1, EMP1, ANKRD31, RA RRES3、AURKA、PCOLCE、SSBP3、CSK、MBLAC1、SCN2B、CRISP2、CHST9、VSIG4、L INC00511、SIPA1L2、PALMD、TIMP2、ADM、MYOZ1、IFI44L、HMMR、DKFZp686D161 30、RAB40C、MMP24-AS1、RECQL4、THRA1、CRYM、GRK5、SASH3、RRM2、KIAA0913、 SH3RF3、ERV3-1、C12orf56、REN、CBLN3、DOCK7、PBK、DIRAS3、PXDC1、RASAL1、<h2 style=";text-align:left;direction:ltr">LDLRAD1, EPGN, IFI27, C1QB, CCNA2, NFKBIZ, PRDM1, GFOD2, CGREF1, EDN2, DNAH11, IRF9, SCARA3, LGALS3, SERPINB9, CCDC69, MAFF, HSN2, MFAP3L, LOC6 46214、FPR3、NDUFB11、ADIPOR2、PACRG、SERINC2、PSORS1C2、APOL2、KLK7、L AMP3、PLA2G4A、HTR7、BC044939、RNF208、PEG10、PIK3R6、DOK3、SERTAD4、RCA N3、AK8、CABLES1、HIST1H4H、HCG22、TCERG1L、PIF1、APOL6、ARRDC3、CCL22、 ZFP36、SORBS1、EDN3、COL12A1、MDGA1、ZNF37BP、PRDM4、CCDC6、C4orf19、RBP 7、GYS2、SIGLEC1、AEBP1、TUBBP5、SAMD9、ABLIM1、DUOXA2、HIST1H2BG、GGT8 P、ID3、DNAH17、BC150585、LLGL1、BLVRA、BC068095、NBPF10、ARG1、ZEB2、ZMY ND15、ABHD4、HECA、ABHD6、BC041455、PFKFB3、DKK2、RARB、SLC15A3、SAV1、Z DHHC20、PLEKHN1、FCGBP、ZNF431、FAM25A、SHC2、KCNMB3、RASL11A、ABO、HK2 、THSD7B、PHACTR2、FNDC4、CD163、PCP4L1、CENPW、EHD1、ELF3、FAM228B、LIN C00628、BC024173、NR4A1、MIAT、BLM、PFKFB2、SLIT2、SFT2D2、CRIP2、PRLR、N UP210、HAPLN3、PRELID2、NME4、PTK2B、PFN2、ABLIM3、MUC22、RAB37、FOXQ1、 TRIM21、AK095151、ITPKC、GHR、LRRC24、SFTA2、APOBEC3B、CAMK1、AOAH、EPN1 、TMEM52、CXCL14、AK097590、DPCR1、CHI3L2、SCCPDH、ACSL4、GJA3、LINC009 20、SLC5A10、MIPEPP3、RTP1、APOL3、CD40、PDPN、SYTL5、PHC1、BBOX1、EEPD1、<h2 style=";text-align:left;direction:ltr">CIDEA、MUC4、ETV7、TMEM91、ADAMTS17、SLC37A2、MALL、NKX2-8、CES1、ASP N、BIN2、MFSD4、PRDX5、TRPS1、LRRC37A4P、LINC00568、HILPDA、SIGLEC10、 TFEC, STON1, FAM221A, GTPBP2, AIM1L, DISP2, GCKR, C21orf88, FMO1, SMPDL3B, MAF, GALE, UFSP1, AL832737, KRT2, TMC5, COL1A1, SERPINB3, HDAC5 RGCC、BC064974、C3orf67、LOC286297、AQP7、AK096066、SOX2-OT、AX7482 73、UBAP1L、EPS8L2、CLDN17、MT1H、PRKXP1、AX747264、AB209185、EPCAM、R PS6KB2、FUT6、UPP1、MT1G、AIM2、RANBP17、SGOL2、LRRC16B、DBP、SH3RF1、 TP53I3、PNLIPRP3、ST6GAL1、PLSCR4、NUF2、UNC5B、HIPK2、GPR111、H19、SL C8A1-AS1、LTBP1、FUT8、XPR1、MAP3K9、NDOR1、CNST、PPDPF、SYNPO2L、CCB L1、IGFBP6、CCDC82、CCT6B、ADSSL1、MACROD2、OSBP2、SPINK8、ANPEP、CDH1 1、EPAS1、PARD6B、CFTR、ANKRD20A3、SERPINA11、CRISP3、LUM、OLFML3、SE C14L1P1、KLC3、GYPC、BLVRB、CCNYL1、CRTAC1、SERPINE2、BC041347、SIRPG 、PDGFD、MARCH1、MRVI1-AS1、TNFAIP8L3、LAMB4、PRTFDC1、CEP55、EPHA3、 OPTN、SLC4A4、LOC100130476、ACOT4、KRTAP3-2、ACSL5、CENPE、LOC100130 557、SPRR1B、NR1D2、AK056431、KCNQ5、FSTL1、MKI67、LOC100506472、KAT NBL1、CNTNAP2、THEM5、MARCO、PRICKLE2、ACOT9、SLC9A2、ZNF770、MCF2L2、CSTA and / or IL12A. In some embodiments, any one, any two, any three, any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, any sixteen, any seventeen, any eighteen, any nineteen, or any twenty genes among the plurality of differentially expressed genes described above may be omitted from the gene expression analysis.

[0044] In some embodiments, the differentially expressed plurality of genes does not include MEOX1, ARHGEF6, GPR34, PIK3R6, STK17B, CAMK1, CNRIP1, IFIT3, AK055623, WFDC5, ADAM28, FBXL2, NFIL3, FOXQ1, CEP55, VAMP5, CCL2, MELK, SHCBP1, RRM2, PSMB10, BAK1L, KIT, SLC45A3, ZSWIM5, KRT16P2, GPR143, DDX58, RARB, LIMD2, PLIN5, C1QB, SLC16A2, CXCL16, CBRC7TM_40, P SMB9, SLC7A7, CD9, ECT2, SLC15A3, ARHGEF37, DNM3, AK098438, HLA-F, HLA-B, CDC45, FAS, SCIMP, IL18R1, MICB, NFE2L3, BDNF, SDPR, TNFRSF18, DSE, DLG AP5, KPNA2, BC069212, SKA1, MEIS1-AS3, C3AR1, APOL3, RUFY4, RGS18, TYROBP, TCIRG1, GALNT5, FPR3, ZC3HAV1L, PLK1, AX747758, CTLA4, TAP1, GPR183, CYP2R1, ORAI2, DENND4A, SCCPDH, CENPE, CDC25C, FJX1, SAV1, CHST15, SLC39A8, FLT3LG, HMMR, CENPW, CD101, SH3PXD2B, KRT23, ADA, UBE2C, FAM46A, ETV 7. FAM111B, MMP14, NDC80, AADAC, CCR1, RAC2, HCK, GABRP, KIF4A, ABHD4, PRELID2, COL6A6, BC040701, NAV1, FCER1G, POC1B, PRODH, LY96, ACOT9, BUB1, P PAP2C, FAM69A, ERCC6L, MTFR2, C1QA, PRR11, GFI1, MFAP3L, SHC2, ACSL5, PSMB8, LRP12, IFITM3, IRF1, GTSE1, AQP7, LOC100506714, CASP7, C1QC, CASC5, CCNB1, GPR68, CDCA2, LAMP3, SGOL2, NUF2, SMC2, TCERG1L, MUC4, PTPN7, NCAPH, CEP19, TAGLN, C9orf40, CCNB2, SPP1, SELP, DDX60, HCST, LOC100129550,ICOS、RANBP17、SEMA6B、KIF2C、CCNA2、COPZ2、TBC1D1、TRIM22、C5orf56、CDK1、PBK、RDH10、PARP8、SMCO2、GPR82、DEPDC1、TOP2A、MB21D1、HUNK、TTK、RYR3、FSTL1、CXCR4、GLCCI1、CLMN、IFIH1、DOK3、OASL、BC038574、GPR39、ZNF827、NEK2、ST6GAL1、GATA3、CTAGE4、DUOX1、HJURP、TMPRSS4、SEC62、EFCAB4A、ANLN、KIF20A、KIF18A、LOC100506100、CLEC4A、VWA5A、MAPK10、AMICA1、CD300A、PIF1、PARVB、LAT2、SPAG5、LTBP1、IL15RA、RHOH、IKBKAP、CHN2、BTG1、FAM83E、LINC00900、PRICKLE4、TLR8、AURKB、ABCA12、STRIP2、B2M、TMEM98、SULF1、TNFRSF10A、CENPF、CD52、PITRM1、XPR1、LIPA、CLNS1A、TPX2、ARSB、FES、IFIT2、PPM1H、ASB2、PIM2、STIL、RGS16、PIK3AP1、LOC115110、LPCAT4、COL6A5、HNF4G、ZEB2、CD274、MTHFD1L、TFEC、MKI67、KIF23、ASPM、FBXO6、IL15、HLA-C、SASH3、FMO1、CENPI、ERI1、OLFML2B、LY75、TNFRSF10D、DMD、SPTBN1、BUB1B、CHST11、LYZ、AOAH、PRC1、FAM46C、PARP14、CEL、RASL11A、OSCAR、PDE10A、LCP2、PLAT、CYTH4、APOBR、PLEKHG2、LIMA1、CLIC6、CFH、TNRC6C-AS1、TG、SFXN3、A4GALT、ODF3B、PRRX1、OR2A7、APOL4、BST2、MYL9、TYMS、STARD4、CD69、CHST9、CASS4、FLG2、NCF4、IGFBP6、HCP5、ATP2A3、LOC283070、KIF14、BLM、NUSAP1、C11orf93、C1RL-AS1、PRR5L、RAB20、ITGAX、GALNT6、MYOF、GCH1、<h2 style=";text-align:left;direction:ltr">SEMA3B、PPARGC1B、SLC38A5、PARP12、SOAT1、KIF21B、CD84、AK123771、RNF2 07、PROCA1、CLUAP1、IFI16、CSF1、MFSD4、UBE2L6、MX1、ATP8B4、IQGAP3、PCC A、ATP11C、BCL6、ZC3H12D、ABCC5、SLFN13、HDX、RASSF6、ROBO1、GMFG、SMAD1 、PLAU、PDPN、PDE3B、ANTXR2、CCDC150、LOC100507463、C1orf216、FAM101B、N UP62CL、UBA7、FANCI、CTSL1、IQCK、AKAP2、ITGAE、ZNFX1、CLDN1、USP12、C3o rf52、EPSTI1、CD40、PLSCR4、SH2D2A、LAPTM5、NBEA、APOBEC3D、PXDN、CTSW、 HDAC9, CTSS, PARP3, EVA1C, CCDC74A, IQGAP2, GBP1, EDNRA, IL3RA, ARMCX3, BC013821, IFNGR1, MC1R, ARNTL, IL7R, TLR2, BTN3A2, GNLY, LAT, ACSL4, ADAM TS9、FUT8、MPEG1、AK097957、ANKRD36B、SERTAD4、SCARA3、CAMK1D、PARP9、G PR65、DOCK7、BIN2、IFI6、TNFRSF10B、SERPINE1、LOC100288637、GIPR、IFI4 4、BTN3A3、ZNF487P、TLR4、IRF9、ASS1、TMED7-TICAM2、NTF4、RAB23、BX6476 08、TRANK1、USP54、HSD17B11、PDE4D、PTP4A3、MYO3A、CD37、SLC38A6、BTG3、S LAMF1、RNF213、CEP152、RALGAPA2、PLCB2、CD53、APCDD1、CLSTN3、SKAP1、TH Y1、PDE4A、KCNMB3、FAM3C、WIPF1、LINC00173、TRIM21、TFRC、MCAM、EPHA4、CA PN13, CLU, TMEM91, TRAF3IP3, HCG26, DTX3L, NCKAP1L, GIMAP2, MDGA1, FAR2, APBB1IP, ERG, EVI2A, CD28, XAF1, DENND3, ARHGAP15, FOLH1, GLB1L, RIC8BLINC00865、TIGIT、IGF2BP2、IL32、CADPS2、KCND1、APOL6、TMC8、CTSK、ESR1 、BTN3A1、ATP10A、ARHGAP9、SIRPG、ATP8A1、RASA3、DAB2、IL18BP、MAN1A1、AT P12A、GLRX、DDX60L、ALOX5AP、LRRC6、PRSS12、LINC00673、TRAF5、COL6A1、L AG3、DOCK2、IKZF1、EPAS1、IFITM1、OAS2、SAMD4A、IFNAR2、DOCK11、COL12A1、 CD226、NEDD9、L3MBTL1、SFMBT2、STON1、SRPX、C4orf21、PARVG、BC144457、T SHZ3、SMPDL3B、KIAA1456、GAB3、GSDMB、LOC729603、JAK3、DFNA5、NEK3、EXOG 、KLRB1, CCDC146, CCDC82, CPT1B, N4BP2L1, SRGAP3, CYTIP, CD33, KBTBD11 SIGLEC1, AK123584, SLC22A3, CELSR3, IL16, FAM111A, ZNF702P, ACAP1, IL18 RAP, GZMA, GCSAM, LRP5L, PRKXP1, MAP3K8, LOXL2, CD48, ITGA4, PDPR, P2RY1 0、PCP4L1、LINC00672、C10orf10、PCOLCE、SERPINB3、ITK、TMEM133、DLC1、GB P2、FAIM3、NAIP、GLIPR2、ASB9、COL6A2、AEBP1、OLFML3、BTN2A3P、SYCP2、AK 054970、COL6A3、NKG7、STAT4、EIF3CL、DDX26B、ZNF37BP、COL1A1、GBP4、DL49 1896、LINC00939、FN1、RAD51AP2、CBWD5、WDR52、FMO4、SP140L、NLRC3、BC06 3788、LINC00511、PPP1R12B、AX747171、AK128252、PLCE1、PTPRB、KLRAP1、PT BLP、BC040358、MIAT、EBLN2、LCK、GNRH1、GVINP1、CLEC2B、ALDH1L2、CAPN3、N R5A2、VPS13C、C1S、AF157115、N4BP2L2-IT2、F2R、AF268386、FAM24B-CUZD1、FASLG, AX747179, SOX2-OT, LOC100506123, FARP1, AK093551, UBASH3A, ABCA10, AK127443, CLEC2D, AK095366, AK092098, TIFAB, IGFBP7, MA P1B, AL831889, RNF183, AK308965, AK092331, CECR2, FBN1, ISLR, CDH11, PTGS2, DKFZp686B07190, GIMAP1-GIMAP5, C11orf92, FAM13A-AS1, B C051760, AX747833, AK095112, ITGA1, LUM, LOC646214, SLC9A2, LOC619207, DQ573949, LOC100506472, AX748379, TOP1P1, LOC100130557, AK 091624, DL490846, FGF1, AK093534, APOD, COL1A2, AK123872, C1QTNF3, AX746775, COL3A1, AK125301, ASPN, HNRNPU-AS1, SERPING1, AK12333 2, TRGC2, IFI44L, ADAMTS2, SEMA3A, SEC14L1P1, USP32P2, TUBBP5, PITRM1-AS1, DKFZp686M11215, RBMS3, EPHA3, PIEZO2, CSGALNACT1, AL832 891, BX649158, ABCA8, FCRLB, TPGS1, SCNN1D, NFKBIA, MYH11, HES4, AK126286, SYNGR3, SPNS3, SCAND1, ZNF578, JUNB, NELL2, BC041455, PRSS 22, BC068095, TGFB1, KLK5, TPTEP1, PCDHGA5, PQLC1, RASGEF1A, ZFPM1, OTOP2, LOC100129195, CECR7, CPT1C, DUOXA2, C1orf122, ROMO1, SGTA , ZNF460, EPN1, BC064974, ALKBH7, DUSP14, MVD, TEF, NDUFB11, COX7A1, TNIP1, FBXW4P1, HIST1H2BJ, CDR2L, CCDC85B and / or CYP27A1, or any other combination. ,

[0045] In some embodiments, the differentially expressed plurality of genes does not include CDC34, ZNF703, BC019880, BC063600, VSIG8, OPTN, ADCY9, SERTAD1, MACROD1, SRCIN1, SPRR1B, BICD1, DNAJB5, AMDHD2, DEAF1, ZNF726, GJA3, SLC2A4, NME4, CCL22, EHD1, ETV5, SLC6A9, ATP6V0C, UBTD1, PITX1, JUND, MUC1, EGLN2, PPP1R15A, SNPH, TSPO, AURKC, NBPF16, METTL21A, CD164L2, PCBP1, TTC40, MARCO, SV2A, ITM2A, SYNE4, AX748015, BOK, KRT18, RAB40C, LGALS3, LLGL1, LOC646862, CCDC157, VAV1, KCTD17, DIS3L2, PIP5K 1C, LOC100130705, STXBP6, ARHGAP44, XXYLT1, DVL1, LRG1, C17orf103, RAB36, NAPRT1, GCAT, ZNF556, NETO2, ZNF208, ARID3A, GATSL2, NBPF10, KIF1C, R PL23AP82, DBP, OAZ3, CDKN1A, GLTPD1, ​​CNTNAP2, METRNL, MIF, SDC1, KLC3, HMOX2, FBXO32, NUDT8, MRC2, PFKFB2, RBM10, TMEM127, RABGGTA, MAPK8IP1, S RXN1, RAP2B, FRMD6-AS1, BAIAP2, FKBP8, RPS6KB2, FAM122C, ITPRIPL2, FAM222A, LZTS1, ENTPD2, PTPN20A, STEAP4, BPGM, SCRIB, LFNG, NFKBIZ, PCDHGA1 , MLXIP, CBX4, PWWP2B, TPRG1L, SH3BP5L, SYAP1, NAPA, RCAN3, AK095151, NCK2, DLGAP1-AS1, ARRDC1, PTK2B, LOC100996255, BC040700, TEX9, ABLIM2, RN F10, SH3GL1, HSFX2, CSRNP1, ANKS1B, KCTD11, REEP6, C1orf210, GNA15, IER3, PLCD3, BLVRB, PCNXL2, GALNT14, GPD1, BCKDHA, PLA2G4B, FKBP1A-SDCBP2,<h2 style=";text-align:left;direction:ltr">SLC34A3、UBALD1、MAPK7、LRRC16B、TPRN、C10orf12、MMP24-AS1、ITPKC、NDO R1、FAM86DP、SLC2A4RG、HIST1H2AE、SAMD10、KIAA0913、EPCAM、MEGF9、FAM86 EP、MXRA8、FBXL22、ACTG1、CX3CL1、AREG、ZDHHC2、MARCKS、MYL12A、SIRT7、M YRIP、ACSL6、JARID2、PFKL、SH2D3A、STXBP1、DHRS4L2、DZIP1、SPHK1、CHAC2、 CABLES1、ANKRD36C、S100A10、CCT6B、ANXA11、SPINT1、SERTAD2、DMTN、CHMP 4C、RTN1、EME2、KCNK5、SSBP3、MAF、SAMD4B、ARL4C、FAM83D、TUBA4A、YPEL3、U PRT、DNAJC5、DBN1、PXDC1、TMEM86A、BTBD19、SDCBP2、PLCXD1、CYP2W1、IL1A 、DKFZp667P0924、SPAG4、PANK1、FOXO3、CYP2U1、HSPA6、HECA、UNC5B、PARD6B 、PEBP1、EGOT、WWC1、NR1D2、C4orf19、HBB、DEF8、HDAC5、KIAA0226、CNNM1、B SPRY、UBE2E2、NDRG4、AK311374、MBLAC1、RPS6KL1、GPSM1、SLC9A3R2、TMEM74 、CDK14、PRDM4、SERHL2、SEC14L1、HSPB8、H2AFJ、PINK1、UFSP1、PER1、ZSWIM 4、ANKRD20A4、IGFBP2、LINC00265、CPNE5、DQ576756、MORN4、UNC13D、FAM222 B、AVPI1、TMEM63C、PMAIP1、HIST1H4E、HMGCS1、ARMCX5-GPRASP2、LRIG1、ZD HHC20、MAP3K9、RNF11、ZNF800、VMAC、CXXC5、RND3、TMEM80、LCN2、ZFP36、SIP A1L2、PDGFD、CNPPD1、FAM134B、TMED3、FZD7、DNM1、TUBB6、NINJ2、CLCF1、MAP 2K1、XK、HOTAIRM1、SALL2、C20orf24、ISG20、PNMA1、LNX2、DHCR24、PLA2G4A、<h2 style=";text-align:left;direction:ltr">PRDX5、CEACAM19、FBXO27、C15orf48、PLA2G4F、CSNK1E、SOCS6、TEAD1、PDZD 8、HIST2H2BE、FAM102A、STAR、HIST2H2BF、B3GNT7、HP07349、LGMN、TMEM79、C RYL1、SEC14L2、RNF24、CCDC127、COL18A1、FLJ11235、RUNDC3B、EFNB3、B3GN T8、AHCY、OXR1、TBKBP1、PHACTR4、ATP1B1、TSPAN13、HEBP2、S100A16、ITGB5、 SULT2B1、CTIF、ZSCAN18、SP6、PANK2、RCOR2、ARHGEF26-AS1、FAM161B、MKNK 2、KIAA1244、ARHGAP39、TST、AK8、FAM46B、AHDC1、VPS37B、IMPA2、FOSL2、ZNF 770、TMEM40、HIPK2、PRSS27、ZNF430、AX748345、KIF26A、PLEKHG4B、XYLB、A K094188、GLTP、GUCY2D、SNX8、ADIPOR2、SERPINB9、HBA1、PRSS8、PKP3、DTX2、 ZNF382、JUN、TSPYL4、CAMSAP3、CSK、ABHD6、ELF3、TMEM52、GTPBP2、LINC003 19、PAPL、BC046483、RFFL、WNT4、NUP214、MFSD7、CGREF1、C7orf73、FAM149A、 NAGK、TYRO3、ID1、PLIN3、HIST1H2BK、SNX16、ZNF582、ZFAND5、ABHD5、LDHD、 SFT2D2、LOC646999、FAM20A、NDUFA6-AS1、N4BP3、PPP1CB、CCDC6、GALE、ASCC 2、FFAR4、WWTR1、NOXO1、INPP5A、FER、FAM84A、CNKSR3、SPRR1A、PIK3C2G、MR OH6、SPECC1、LOC285074、FUT3、DIRAS3、ARHGAP28、GBP6、TMEM65、KIFC3、S10 0A14、DBNDD1、MAP1S、THRA1、BCOR、SH3D19、DMPK、AKAP6、PLEKHG6、C9orf66、 IL1RL2、NHSL1、PLEKHN1、IFRD1、CCDC110、TP53BP2、TTC9、SRGAP2D、KCNMA1、<h2 style=";text-align:left;direction:ltr">RNF208、ENPP4、RRAGD、PLB1、BDKRB1、AMER1、AK289390、NCKAP5、ANKH、FAM8 3A、PCLO、ZNF425、HEG1、SYT8、PDLIM2、CDKN1C、MRVI1-AS1、FAM78B、PLLP、A SPG、CASP10、RDH5、CAPS2、CAST、MDFIC、USP6NL、SLC10A6、BLVRA、ADSSL1、S EC24C、RTN4RL1、GFOD2、CCDC69、SPSB3、BC131755、CMAS、ANKRD6、HBP1、C8or f47, NSG1, PLEKHM1, BAI1, RNF128, TMOD3, SLC6A8, TMEM8A, RHOD, PKDREJ, NMRK1, AK130329, TAB3, CTNNBIP1, TRPS1, WNK2, RDH12, UPP1, ABHD12, KIAA0 355、UBAP1L、ARHGEF4、LINC00887、EFNA5、FAM86FP、SMCR7、KRT79、TEC、SAM D9、AKR1C3、MGST1、NOV、ITPRIP、LINC00152、S100A6、LOC401052、FAM129B、T RIM56、PDZD4、GYLTL1B、FAM109A、PKIB、HIST1H4H、FAM221A、PAX9、PYGB、AL 110181、PLEKHF1、RNF224、ARRDC3、SLC2A11、PRDM1、GYPC、PMM1、ARHGAP10、 CLIC3、RECQL4、BC039551、AX748273、TMEM154、NCOA1、CLEC3B、C15orf62、U CA1、C19orf26、SERINC2、PHACTR1、AHNAK2、ABCA3、RIOK3、MYOM3、CLEC5A、TI MP2、MT1X、LINC00568、FOXI2、DOCK9、SUCO、UCKL1-AS1、MPZL3、TAX1BP1、WN T2B、GATM、LINC00675、CXorf57、CECR6、C6orf132、ABO、MOSPD1、ARG2、ELL2、 HTR7、RAP2A、SRGAP2C、TOB1、TMIE、TICAM1、FHOD3、KATNBL1、CPPED1、UNC13 B、C1orf21、MARCH1、CITED2、TUFT1、AIFM2、KLRG2、TUBB2A、SLC7A5、COL7A1、HIST1H3F、CNN3、HIST1H2AC、CD68、AK4、SNX24、FBXL16、MACROD2、HK2、EPS8L2、DOPEY2、SMIM5、SLIT2、IL22RA1、AK127120、FAM214A、NCCRP1、ARHGAP5 -AS1、DHRS1、HCAR1、HIST1H1C、POU3F1、ADM、LINC00628、ARSG、RNF169、ADM2、BAIAP3、AIM1L、HMOX1、C15orf52、TNS1、SLPI、NCR3LG1、KLB、AK127124、 FBXO34、RPS6KA6、TMEM254、SNCG、RP1L1、CNST、C4orf3、WASF3、AX747104、RAB17、SMCO4、BC039389、BNIPL、B3GNT3、PPDPF、SNED1、FAM95C、CXCR7、CLC A3P、C7orf55-LUC7L2、LINC00551、ARHGAP20、MB、FAM13C、SLC1A3、OR7E14P、ADARB2、AK093443、SPTSSB、AK128563、ANGPT1、FMO2、RBP1、CCL14、NWD1、T RPC1、ZNF471、ZFP28、OCA2、GNE、CTH、SFRP2、FAM181B、AX747081、AGR2、PDE4C、CACNA2D3、NHS、UGT1A9、BC027846、GSTM3、OXGR1、LINC00202-1、MROH7 、TUB、PLCD4、B4GALNT4、CD177、TEX101、ABI3BP、CDKL2、TNFSF9、PLEKHG1、AK128534、RTEL1、WNT5A、AK128619、NAALADL2、PGLYRP4、FLJ41484、GLDN、CL CA4、RIMBP2、COL4A6、PMFBP1、TNNT3、CD302、AK127224、AK094577、FGF14、AQP5、SPRR2C、B3GNT6、BC010924、GSTA1、CYP3A4、LHX4、C17orf61-PLSCR3 FMO9P、PLSCR3、IL23A、NOS3、IGFBP5、FCGR1A、APOE、SLC26A9、KRT14、RRAD、SEMA3C、APOC1、GABRA4、PLA2G4E、TPPP3、NFKB2、C8orf44-SGK3、AL049415、CCDC28B and / or DKFZp586B1922, or any combination thereof.

[0046] In some embodiments, genes in the differentially expressed plurality of genes include ANKRD20A12P, SGSM1, TRIM39-RPP21, MT1F, LRRc37A, HIST2H2BA, HYDIN, CAMK2B, IFNE, TSNAX-DISC1, CXCR1, AK300387, DQ587119, LOC100499484-C9ORF174, PLA2G4D, AK316321, X69637, SLC38A3, KCNQ3, LRRC37, FLJ22184, ZNF625-ZNF20, NOG, GDF7, TMEM189-UBE2V1, LOC40 0891, ARC, LPA, TDRG1, CDH20, C18orf61, MT1M, LOC441178, LOC149086, LMAN1L, AQP7P3, MT1A, MUC5B, TGM6, CCR3, SHD, SIX2, C6orf223, LOC154092, CEB PE, LINC00092, C12orf28, NFE2, MZB1, CLDN24, ELK2AP, HK3, CLDN22, FAM110B, FW340046, GATA1, MGC45800, SLC5A8, CR1, MGC32805, IL5RA, SOCS1, FOXD 1. RGS13, TREM2, LIPG, STAP1, TTLL13, LOC100506585, GFRA2, ENPP3, CAPN8, FAIM2, SAA2, HAS2, OSM, DEFB104B, KIAA0125, LILRA6, AK124806, C9orf84, EGR2, CD1B, SIGLEC8, SNORA21, SIRPB1, SIGLEC7, MKX, ADAM12, C1orf167, CD300LB, APOC4-APOC2, ICAM4, LILRB5, IL13, BATF3, DBC1, C1orf228, BC0757 97. HRH2, COL4A3, AFF2, EXOC3L4, GCSAML, CD180, TNNC1, GFRA3, BC045578, CCDC60, DDO, GALR2, C1orf186, PITX2, APOC2, SLC2A6, CCL11, CD79A, TMEM92 , NPY4R, TNFRSF4, RPPH1, DPEP2, TPSD1, HSP90AB4P, CHST1, C2CD4C, MUC16, CDHR3, AK125558, ASRGL1, LYPD1, C4orf48, LILRB4, CRB2, SAMD14, C19orf38,, LRRC25, CD300C, FOXD2-AS1, TMOD1, CYP1B1, RENBP, CCDC19, CYP4Z1, LILRA1, CCDC170, HSD17B2, HAGHL, P2RX5, PTGDR2, BCL2A1, USP30-AS1, LYL1, CCRL2, IL2RA, CD1D, ERN2, MISP, BCL2L14, AK126744, ST8SIA6, KLHDC7B, TESC, AX747756, URGCP-MRPS24, LBX2-AS1, AATK, CTRL, CLNK and / or RET, or any combination thereof. In some embodiments, any one, any two, any three, any four, any five, any six, any seven, any eight, any nine or any ten genes of the above-mentioned differentially expressed genes can be omitted from the gene expression analysis. ,

[0047] In some embodiments, the differentially expressed plurality of genes does not include HRNR, MAP1LC3B2, TOLLIP-AS1, DCLK1, C1orf170, CCSER1, STK33, IGLON5, DHRS2, BC133670, FBXO39, ITGBL1, LINC00920, MCF2L2, AL831977, SBSPON, ANKRD36BP1, CCL20, AK 056396, FAM35DP, SEPT3, CCL21, PDXP, LOC100132891, ABHD16B, LOC100288181, DMRT2, PPP1R36, FA M83A-AS1, TRAM1L1, GRIN2A, SOWAHA, LOC729970, LOC100127983, SLC35F3, NPPC, HIF1A-AS2, DISP2 , SERPINA11, MTUS2, ACADL, PCDH20, KBTBD12, SLITRK5, AX746725, CXCL2, ISM2, AADACL2, TDRD5, AK 095621, DES, DIRAS1, LOC100289511, RNF182, CYP2B7P1, CR936677, IRX6, CES1P2, AK054845, REN, P EG10, DKK2, RTP1, IL19, KCNJ16, CTAGE15, SYT2, MMRN1, AP4B1-AS1, DNAH17, AK096066, BC041347, BC034424, DKFZp686D16130, BC150585, AB209185, DKFZp547K2416, CCDC39 and / or AJ515158, or any combination thereof.

[0048] In some embodiments, the genes in the differentially expressed plurality of genes include IFNGR1, STAT4, CTLA4, IL12A, IRF1, STAT1, GZMA, LAG3, CD28, CCL5, CD8A, IL12RB2, PRF1, CXCL9 and / or CXCL10, or any subset thereof including at least 10 genes, at least 11 genes, at least 12 genes, at least 13 genes, or at least 14 genes.

[0049] In some embodiments, the genes in the differentially expressed plurality of genes include ANO1, TMEM71, CCL26, CTSC, HRH1, ALOX15, SIDT1, POSTN, SLC26A4, HDC, LRRC31, CPA3, DPYD, TNFAIP6, NTRK1, HLF, CXCL1, CLC, B2M, COL8A2, CA2, CXCL6, DPP4, SERPINB4, DSG1, FCER1A, KRT14 and / or KRT16 or any subset thereof including at least 20 genes, at least 22 genes, at least 24 genes, at least 26 genes, or at least 27 genes.

[0050] In some embodiments, the genes in the differentially expressed plurality of genes include CDH26, LOXL4, CFI, CCL24, CH25H, KRTAP3-2, IVL, SPRR3, TPSAB1, MMP12, UBC, RASGRP1, MKI67, TGFB1, VCAM1, FLG, TFRC, MMP9, GATA3, CTSS, EML5, IL13RA1, EDNRA, UPK1B, DHRS9, VIM , SPRR2D, COL3A1, COL1A1, TUBB, CEACAM7, SERPINE1, COL1A2, TNFSF13B, SPP1, FZD10, CCL5, KIAA1199, MMP2, TNC, SPRR2C, COL5A2, PPIA, CLTC, STAT6, SH2D1B, HPRT1, CDH1, GUSB, FMO2, CLEC7A, CLDN7 and / or ALAS1. In some embodiments, any one, any two, any three, any four, any five, any six, any seven, any eight, any nine, or any ten genes of the above-mentioned differentially expressed genes can be omitted from the gene expression analysis.

[0051] In some embodiments, the genes in the differentially expressed plurality of genes include CTSC, ANO1, ORAI1, ATIP1345, C1orf74, GCNT2, TRPM6, AP2M1, NTRK1, TRAPPC3, PITRMI1, MFHAS1, CLNS1A, NDUFA4, PHLDB2, TNIP2, CA2, ID3, COX6C and / or GLDC, or any subset thereof including at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes. In some embodiments, the genes in the differentially expressed plurality of genes include CRISP3, C1orf177, SPINK8, NUCB2, ZNF416, AMACR, TP5313, AMFR, BC107108, MUC21, CRISP2, GYS2, SFTA2, PAQR8, A1F1L, SPRR3, NDUFA4L2, CSTB, KRT4 and / or AK097161, or any subset thereof including at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes.

[0052] In some embodiments, the genes in the differentially expressed plurality of genes include GPR97, LRRC31, GCNT2, VSTM1, HK3, CCL26, CEBPE, ATP13A5, CCR3, TRPM6, CTSC, ANO1, SUSD2, SLC26A4-AS1, BCL2L15, MT-CO2, LITAF, SYNPO, AP2M1, and / or CLC, or any subset thereof including at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes. In some embodiments, the genes in the differentially expressed plurality of genes include ZNF416, ENDOU, SEPT5-GP1BB, EPGN, CRISP3, C2orf16,

[0053] HSPA2, C1orf177, UACA, SPINK8, EPB41L3, CCNYL1, NDUFA4L2, SFTA2, NCOA1, AMFR, TGM3, KRT13, DPCR1 and / or NUCB2, or any subset thereof comprising at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes or at least 19 genes.

[0054] In some embodiments, the genes in the differentially expressed plurality of genes include BC043620, ARHGEF16, ATP13A5, MT-ATP6, RBM38, ARHGEF35, RHOT2, PPARGC1B, MRPL43, GIPC2, PP7080, GAD1, SCRN2, UQCC, FADD, LINC00116, ZNF48, NDUFB10, CLNS1A and / or PHLDA3, or any subset thereof including at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes. In some embodiments, genes in the differentially expressed plurality of genes include EPB41L3, NUCB2, A2ML1, CRISP3, TMED3, ZNF426, KRT32, MAPK3, CRYAB, C1orf177, LAMB4, AIF1L, BC107108, TMEM57, TNFRSF11A, CSTB, DPCR1, VSIG10L, CRISP2 and / or ACPP, or any subset thereof including at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes.

[0055] In some embodiments, the genes differentially expressed in the downward direction in the plurality of genes include ALOX15, CCL26, POSTN, NRXN1 and / or CCR3. In some embodiments, the genes differentially expressed in the upward direction in the plurality of genes include SPINK8 and / or DSG1.

[0056] In some embodiments, the genes in the differentially expressed plurality of genes include MUC5AC, CCL18, CCL13, FCER2, CCL11, IL33, PTGDS, TSLP, DPP4, IL25, IL4, IL13, IL5, GATA3, CCR4, CLC, CCR3, PTGDR2, TPSAB1, HDC, IL1RL1, GATA1, SIGLEC8, CMA1, POSTN, ALOX15, CCL24, HRH1, STAT6, IL4R, MUC5B, ARG1, FCER1A, CCL17, and / or IL13RA, or any subset thereof including at least 38 genes, at least 30 genes, at least 32 genes, at least 33 genes, or at least 34 genes. In some embodiments, genes in the differentially expressed plurality of genes include CDA, EMR4P, CPR97, SIGLEC10, CD500LB, IL5RA, SIGLEC8, RAB37, IL1RL1, TESC, TREML2, ADAM5, MMP25, DAPK2, TRPM6, TPSAB1, CPA3, TPSB2, AIM2, SFRP1, CADM1, SCIN, CFI, CCNT3, CDH25, NTN1, EDAR, CAPN14, CALNT4, SIDT1, PLA2G3, IFFO2, HAS3, CDH3, ID3, MFHAS1, SLC10A1, SERPINB4, IGFBP3, SUSD2, TNFSF13, LHFPL2, CTSC, CCNT2, SH3RF2, LITAF, KCNJ2, MAP3K14, TMTC3, KITLG, SCK1, TMEM173, PDZK1P1, IGFL1, EML1, SPINK7, CNFN, ZNF365, BNIP3, ME1, PPP2R2C, ANKRD37, CCNYL1, HSPA2, SAMO5, RFK, ZNF101, ZNF555, ZNF662, KIF21A, KRTAP3-2, YOD1, ZNF02, PHACTR2, PALMD, CYP2C15, TFAP2B, BOC, PFN2 and / or FAM125A, or any subset thereof comprising at least 75 genes, at least 76 genes, at least 77 genes, at least 78 genes or at least 79 genes. In some embodiments, the genes in the plurality of genes that are differentially expressed include CCL26, MUC5B, CLC, IL1RL1, HDC, IL13, FCER1G, GATA2, and / or KIT.

[0057] In some embodiments, the genes in the differentially expressed multiple genes include FLG, DSG1, SPINK7, SPINK8, SPINK5, KLK7, HAS3, THBS1, MMP9, LOX, POSTN, TIMP1, HAS2, IL13, PDGFRA and / or LTBP2, or any subset thereof including at least 10 genes, at least 11 genes, at least 12 genes, at least 13 genes or at least 14 genes. In some embodiments, the genes in the differentially expressed multiple genes include DSG1, SPINK5, SPINK7 and / or SPINK8.

[0058] In some embodiments, the genes in the plurality of genes that are differentially expressed include CCL26, CCR3, ANO1, and / or SPINK8.

[0059] In some embodiments, genes in the differentially expressed plurality of genes include UPK1B, SH2D1B, CDH26, POSTN, and / or DSG1. In some embodiments, genes in the differentially expressed plurality of genes include ALOX15. In some embodiments, genes in the differentially expressed plurality of genes include CCL26 and / or CCR3. In some embodiments, genes in the differentially expressed plurality of genes include POSTN. In some embodiments, genes in the differentially expressed plurality of genes include MUC5B. In some embodiments, genes in the differentially expressed plurality of genes include mKi67, several collagen genes, DSG1, and / or SPINK family members. In some embodiments, genes in the differentially expressed plurality of genes include SPINK5, SPINK7, and / or SPINK8. In some embodiments, genes in the differentially expressed plurality of genes include ANO1. In some embodiments, genes in the differentially expressed plurality of genes include NRXN1 and / or NTRK1. In some embodiments, genes in the differentially expressed plurality of genes include IL13, CCL17, CCL18, and / or CCL26. In some embodiments, the genes in the plurality of differentially expressed genes include K16 and / or MKI67.

[0060] In some embodiments, the method includes screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies, including performing differential gene expression analysis on the whole transcriptome profiles of each disease study in the multiple disease studies. In some embodiments, screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies includes generating a normalized enrichment score (NES) for all diseases in the multiple disease studies using multiple genes that are differentially expressed and located in the dupilumab treatment core gene signature.

[0061] In some embodiments, the NES includes NES for eosinophilic esophagitis (EoE-NES), type 2 gene expression signature in EoE (type 2-NES), and / or DpxOme-EoE TM NES. In some embodiments, the NES comprises EoE-NES. In some embodiments, the NES comprises type 2-NES. In some embodiments, the NES comprises DpxOme-EoE TM NES.

[0062] In some embodiments, the method comprises performing differential gene expression analysis on the whole transcriptome profile of each disease study in a plurality of disease studies for the disease relative to healthy controls.

[0063] In some embodiments, the plurality of disease studies includes the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database.

[0064] In some embodiments, a gene set enrichment analysis tool that considers both positive gene sets and negative gene sets is used to generate an NES. In some embodiments, the NES is calculated separately for the positive gene set and the negative gene set.

[0065] In some embodiments, the NES is generated by ranking a plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a ranked gene list (R+). In some embodiments, the NES is generated by independently identifying hits (i.e., the ranking of genes in the core signature) of the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the reciprocal ranking of R+ with reciprocal values. In some embodiments, the NES is generated by combining R+ and R- and re-ranking the values ​​by retaining the hits of S+ and S-. In some embodiments, the NES is generated by calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r iis the value of gene i, and p is the weight of r. In some embodiments, NES is generated by determining the enrichment score (ES) as the maximum deviation from zero along the running score. In some embodiments, the ranking, identification, combination, calculation and determination disclosed in this paragraph are repeated 1,000 times using a random gene set to calculate the ES null distribution. In some embodiments, the random gene set is a list of genes randomly selected from the whole transcriptome (same size as the original gene set). In some embodiments, NES is generated by dividing the ES by the arithmetic mean of the ES null distribution.

[0066] In some embodiments, the method comprises calculating statistical significance by comparing the observed ES to a null distribution or a sample marker (disease / healthy) permutation.

[0067] In some embodiments, ranking the differentially expressed plurality of genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a ranked gene list (R+) includes using a log2 fold change or a z-score. In some embodiments, fold changes, statistics (e.g., Wald test, T test), signal-to-noise ratio (mean difference divided by standard deviation) can be used for comparison of the two groups. In some embodiments, gene expression values ​​or z-scores can be used for a single sample NES.

[0068] In some embodiments, R+ and R- are ranked by comparing the log2 fold change of mean gene expression in disease samples to mean gene expression in healthy samples.

[0069] In some embodiments, the method includes calculating the NES for all disease studies using the ranked list for each disease study.

[0070] In some embodiments, the disease with a significant NES is a disease suitable for treatment with durpilumab.

[0071] The present disclosure provides methods for identifying a subject with a disease or illness suitable for treatment with dupilumab. In some embodiments, the method includes generating a dupilumab treatment core gene signature. In some embodiments, the method includes screening the dupilumab core gene signature for a full transcriptome profile from a subject. In some embodiments, the method includes determining whether a subject is suitable for dupilumab treatment.

[0072] In some embodiments, the method includes generating a dupilumab treatment core gene signature, comprising determining differential gene expression between dupilumab treatment groups and placebo treatment groups for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed.

[0073] In some embodiments, the method includes converting the whole transcriptome profile from the subject into a z-score. In some embodiments, the method includes ranking the z-scores. In some embodiments, the method includes generating a standardized enrichment score (NES) for all ranked z-scores using a plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature.

[0074] In some embodiments, the method includes generating the NES using a gene set enrichment analysis tool that considers both positive gene sets and negative gene sets.

[0075] In some embodiments, the NES is generated by converting each gene expression within a plurality of genes into a z-score and ranking the differentially expressed plurality of genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a value of R+. In some embodiments, the method can use the gene expression values ​​(without any conversion) for sorting. In some embodiments, the NES is generated by independently identifying the hits of the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the reciprocal ranking of R+ with reciprocal values. In some embodiments, the NES is generated by combining R+ and R- and re-ranking the values ​​by retaining the hits of S+ and S-.

[0076] In some embodiments, the NES is generated by walking down the combinatorial ranking to calculate a run score, where if the i-th gene is a hit, the run score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r. In some embodiments, the NES is generated by determining the enrichment score (ES) as the maximum deviation from zero along the running score. In some embodiments, the transformation, identification, combination, calculation and determination disclosed in this paragraph are repeated 1,000 times using a random gene set to calculate the ES null distribution. In some embodiments, the NES is generated as the NES by dividing the ES by the mean of the ES null distribution.

[0077] In some embodiments, the NES is generated by determining the 95th percentile NES from healthy control samples to calculate statistical significance.

[0078] In some embodiments, the NES is generated by calculating the NES for all disease studies using the ranked list of each disease study. In some embodiments, the NES is generated by calculating the NES for all disease studies in a plurality of disease studies using the ranked list of each disease study in a plurality of disease studies.

[0079] In some embodiments, a subject is suitable for piloumab treatment when the subject's NES is higher than the NES of a healthy control.

[0080] The present disclosure provides a method for conducting a clinical trial of dupilumab for treating a disease, disorder or condition, the method comprising using the dupilumab core gene signature as a clinical endpoint of the clinical trial.

[0081] In some embodiments, the dupilumab therapeutic core gene signature is generated by determining differential gene expression between dupilumab-treated and placebo-treated groups of multiple treatment studies and identifying a plurality of genes that are differentially expressed.

[0082] In some embodiments, the clinical trial includes generating a normalized enrichment score (NES) for the durpilumab treatment core gene signature at at least one time point before initiating treatment of the subject with durpilumab and after initiating treatment of the subject with durpilumab.

[0083] In some embodiments, the clinical endpoint is achieved when dupilumab treatment results in a decrease in the NES of the dupilumab treatment core gene signature to an acceptable value.

[0084] In some embodiments, the NES is generated by ranking a plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a value for R+. In some embodiments, the NES is generated by independently identifying hits for a positive (i.e., most upregulated) gene set (S+) in R+ and a negative (i.e., most downregulated) gene set (S-) in R-, where R- is a reciprocal ranking of R+ with reciprocal values. In some embodiments, the NES is generated by combining R+ and R- and re-ranking the values ​​by retaining hits for S+ and S-. In some embodiments, the NES is generated by calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r iis the value of gene i, and p is the weight of r. In some embodiments, the NES is generated by determining the enrichment score (ES) as the maximum deviation from zero along the running score. In some embodiments, the ranking, identification, combination, calculation and determination disclosed in this paragraph are repeated 1,000 times using a random gene set to calculate the ES null distribution. In some embodiments, the NES is generated by dividing the ES by the mean of the ES null distribution.

[0085] In some embodiments, the NES is generated by comparing the observed ES to a null distribution or a sample marker (disease / healthy) permutation to calculate statistical significance.

[0086] In some embodiments, ranking the differentially expressed plurality of genes from most positive (i.e., most upregulated) value to most negative (i.e., most downregulated) value to generate a value for R+ comprises comparing gene expression after dupilumab treatment with gene expression before initiation of treatment with dupilumab using a log2 fold change.

[0087] In some embodiments, multiple samples are obtained from a subject, and a NES is generated for each sample.

[0088] Differential gene expression includes quantification of RNA / transcript expression of at least one gene in a biological sample from a subject (i.e., based on the measurement of potentially many RNAs). As used herein, "gene" also means capturing non-coding genes / biotypes (e.g., long non-coding RNAs). In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least one gene in a biological sample from a subject. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 10 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 20 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 30 gene levels. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 40 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 50 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 60 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 70 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 80 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 90 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 100 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 125 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 150 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 175 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 200 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 300 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 400 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 500 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 600 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 700 genes. In some embodiments, differential gene expression comprises quantification of one or more RNA expression levels of at least 800 genes.In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 900 genes. In some embodiments, differential gene expression includes quantification of one or more RNA expression levels of at least 1,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 5,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 10,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 15,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 20,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 25,000 genes. In some embodiments, differential gene expression includes quantification of RNA expression levels of at least 30,000 genes.

[0089] In some embodiments, at least one gene includes a protein coding gene, a non-coding gene, a long non-coding RNA, a mitochondrial rRNA, a mitochondrial tRNA, an rRNA, a ribozyme, a B cell receptor subunit constant gene and / or a T cell receptor subunit constant gene or any combination thereof. In some embodiments, at least one gene includes a protein coding gene. In some embodiments, at least one gene includes a non-coding gene. In some embodiments, at least one gene includes a long non-coding RNA. In some embodiments, at least one gene includes a mitochondrial rRNA. In some embodiments, at least one gene includes a mitochondrial tRNA. In some embodiments, at least one gene includes rRNA. In some embodiments, at least one gene includes a ribozyme. In some embodiments, at least one gene includes a B cell receptor subunit constant gene. In some embodiments, at least one gene includes a T cell receptor subunit constant gene.

[0090] In any of the embodiments described herein, the biological sample includes a sample of an organ, tissue, cell and / or biological fluid from a subject. In some embodiments, the biological fluid includes plasma, serum, lymph, semen and / or mucosal secretions. In some embodiments, the biological sample includes blood, semen, saliva, urine, feces, hair, teeth, bones, tissue or oral samples. In some embodiments, the biological sample is obtained from a subject by biopsy.

[0091] In some embodiments, RNA expression can be determined in part by RNA sequencing.

[0092] In some embodiments, RNA sequencing reads can be mapped to a genome. In some embodiments, the genome is a human genome. In some embodiments, the human genome is a reference assembly GRCh38. In some embodiments, RNA sequencing reads can be limited to at least one protein-coding gene, at least one long non-coding RNA, at least one mitochondrial rRNA, at least one mitochondrial tRNA, at least one rRNA, at least one ribozyme, at least one B cell receptor subunit constant gene and / or at least one T cell receptor subunit constant gene. Those reads. In some embodiments, RNA sequencing reads are not limited thereto. In some embodiments, the sequence can be mapped with a chain-specific reverse first read mapping or with a chain-specific forward first read mapping without chain specificity. In some embodiments, kallisto v0.45.0 can be used to map sequences with chain-specific reverse first read mapping (Bray et al., Nat. Biotechnol., 2016, 34, 525). In some embodiments, transcript counts can be aggregated into gene counts. In some embodiments, tximport (Soneson et al., F1000 Research, 2015, 4, 1521) can be used for aggregation.

[0093] In some embodiments, the determination of the NES of the subject includes determining one or more RNA expression levels of one or more genes in a biological sample from the subject, comparing the RNA expression with the RNA expression of the corresponding gene from the placebo treatment group, determining the relative difference in RNA expression, and integrating the changes in individual RNA expression into the NES. In some embodiments, the determination of the NES of the subject includes determining one or more RNA expression levels of one or more genes in multiple biological samples from the subject, determining the relative difference in RNA expression in multiple samples, and integrating the changes in individual RNA expression into the NES. In some embodiments, the genes whose one or more RNA expression levels are measured include protein coding genes, long non-coding RNAs, mitochondrial rRNAs, mitochondrial tRNAs, rRNAs, ribozymes, B cell receptor subunit constant genes, and / or T cell receptor subunit constant genes. In some embodiments, the relative differences in RNA expression of genes in the comparison panel are compared with the relative differences in RNA expression of genes not in the panel.

[0094] The present disclosure provides a method for treating a subject having a disease or condition suitable for treatment with dulpilumab, the method comprising: a) identifying whether the subject has a disease or condition suitable for treatment with dulpilumab, comprising: i) generating a dulpilumab treatment core gene signature; ii) screening the dulpilumab core gene signature against a whole transcriptome profile from the subject; and iii) determining whether the subject is suitable for dulpilumab treatment; and b) administering dulpilumab to the subject having a disease or condition suitable for treatment with dulpilumab.

[0095] In some embodiments, the dupilumab therapeutic core gene signature comprises determining differential gene expression between dupilumab treated groups and placebo treated groups for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed.

[0096] In some embodiments, multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis.

[0097] In some embodiments, genes in the core gene signature identified from differential gene expression are selected as having a fold change ≥ 2 and / or q < 0.05 in ≥ 3 of 5 treatment studies. In some embodiments, the fold change comprises subtracting the change in expression in the placebo treatment group from the dupilumab treatment group.

[0098] In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with durpilumab to gene expression after treatment with durpilumab. In some embodiments, differential gene expression for treatment studies of asthma and grass allergy is performed by comparing gene expression with allergen challenge to gene expression without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression for treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression for treatment studies of atopic dermatitis and chronic sinusitis with nasal polyposis is analyzed by microarray.

[0099] In some embodiments, the differentially expressed plurality of genes includes ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2,

[0100] S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes, or at least 47 genes.

[0101] In some embodiments, screening a whole transcriptome profile from a subject for a dupilumab core gene signature comprises: i) converting the whole transcriptome profile from the subject into z-scores; ii) ranking the z-scores; and iii) generating a normalized enrichment score (NES) for all ranked z-scores using a plurality of genes that are differentially expressed and in the dupilumab therapeutic core gene signature, thereby representing the enrichment of the dupilumab signature for the subject.

[0102] In some embodiments, a gene set enrichment analysis tool that considers both positive and negative gene sets is used to generate an NES. In some embodiments, the NES is generated by: a) converting each gene expression within a plurality of genes to a z-score, and ranking the differentially expressed plurality of genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a value for R+; b) independently identifying hits for the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, where R- is the inverse ranking of R+ with inverse values; c) combining R+ and R- and re-ranking the values ​​by retaining hits for S+ and S-; d) calculating a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r iis the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation of the score from zero along the run; f) repeating steps a)-e) 1,000 times using a random gene set to calculate the ES null distribution; and g) generating a NES as the ES divided by the mean of the ES null distribution.

[0103] In some embodiments, the method further comprises calculating statistical significance by determining the 95th percentile NES from healthy control samples. In some embodiments, the method comprises calculating the NES for all disease studies using a ranked list of each disease study. In some embodiments, when the NES of the subject is higher than the NES of the healthy controls, the subject is suitable for pilumab treatment.

[0104] The present disclosure also provides kits and methods of using the kits. The kits can be used, for example, to detect the presence or absence of genes encoding any one or more of the following: ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, T PSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, and / or mRNA molecules and / or cDNA molecules derived therefrom. In some embodiments, any one or more of the above genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) can be detected in a subject before treatment with dupilumab. In some embodiments, any one or more of the above genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) can be detected in a subject after treatment with dupilumab. In some embodiments, any one or more of the above genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) can be detected in a subject before treatment with durpilumab and after treatment with durpilumab.

[0105] In some embodiments, the kit comprises a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprises nucleic acid molecules encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HA In some embodiments, the kit comprises a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprises a nucleotide sequence complementary to at least 15, at least 20, at least 25, at least 30, at least 35, at least 40 or at least 45 of the nucleic acid molecules encoding the above-mentioned genes (and / or the mRNA molecules and / or cDNA molecules derived therefrom).

[0106] In some embodiments, a nucleic acid molecule comprising at least ten complementary nucleotide sequences in a nucleic acid molecule encoding the above-mentioned gene (and / or an mRNA molecule and / or a cDNA molecule derived therefrom) is a probe. In some embodiments, the nucleotide sequence of the probe comprises at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or 100% sequence complementarity with the target gene. In some embodiments, the probe length is 8 to 100, 10 to 75, 12 to 50, 15 to 25 or 18 to 23 nucleotides.

[0107] In any of the embodiments described throughout this disclosure, the probe can include a label. In some embodiments, the label is a fluorescent label, a radioactive label, or biotin.

[0108] In any embodiment described throughout the disclosure, each of a plurality of nucleic acid molecules is connected to a solid support. A solid support is a solid substrate or support to which a molecule (such as any of the probes disclosed herein) can be associated. One form of a solid support is an array. Another form of a solid support is an array detector. An array detector is a solid support to which a variety of different probes are coupled in an array, grid or other organized pattern. One form of a solid substrate is a multiwell plate, such as a standard 96-well type. In some embodiments, a multiwell slide containing an array per well can be used. In some embodiments, the solid support is a microarray. In some embodiments, the solid support is a chip. In some embodiments, the solid support is a bead.

[0109] The present disclosure also provides a method for detecting multiple nucleic acid molecules in a subject, wherein the multiple nucleic acid molecules detected include encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD At least ten of the nucleic acid molecules of 1B, HAS3, TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3.The method comprises: contacting a biological sample from a subject with a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprises nucleic acid molecules encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, C MYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2 RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3 at least ten complementary nucleotide sequences in a nucleic acid molecule; and detecting ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TP The presence or absence of at least ten nucleic acid molecules of SB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3. Any kit described herein can be used in these methods. In addition, any probe described herein or a combination thereof can be used in these methods. Any biological sample described herein can be used in these methods.

[0110] In some embodiments, the presence of a gene encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPS The amount of at least ten nucleic acid molecules of B2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3.

[0111] In some embodiments, the markers include those encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P Each of the plurality of nucleic acid molecules having a nucleotide sequence complementary to at least ten nucleic acid molecules of 2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, and the detecting step comprises detecting the label.

[0112] In some embodiments, a polypeptide comprising a polypeptide encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P Each of the plurality of nucleic acid molecules having a complementary nucleotide sequence in at least ten nucleic acid molecules of 2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3 is attached to a solid support. Any solid support described herein can be used in these methods.

[0113] The following examples are provided to describe the embodiments in more detail. They are intended to illustrate but not limit the claimed embodiments. The following examples provide disclosure and description of how the compounds, compositions, articles, devices and / or methods described herein are prepared and evaluated for those of ordinary skill in the art, and are intended to be exemplary only and are not intended to limit the scope of any claim. Efforts have been made to ensure accuracy with respect to numbers (such as, for example, amounts, temperatures, etc.), but some errors and deviations may be considered.

[0114] Example

[0115] Example 1: Dupilumab normalizes the disease transcriptome in eosinophilic esophagitis

[0116] Study Design

[0117] The published data were collected as part of a multicenter, randomized, double-blind, parallel-group, placebo-controlled, Phase 2 study of dupilumab in adults with active eosinophilic esophagitis (EoE). For example, in Hirano et al., Gastroenterology, 2020,

[0118] A full description of the study design is provided in 158, 111-22.

[0119] In the Phase 2 study, subjects completed a 35-day screening period and were subsequently randomized 1:1 to receive weekly 300 mg dupilumab (600 mg loading dose on day 1) subcutaneously or matching placebo for 12 weeks and a 16-week follow-up period. An independent data and safety monitoring committee monitored the subjects' safety data in a blinded manner. The primary endpoint of the study was the Straumann Dysphagia Instrument (SDI) patient-reported outcome (PRO) score from baseline to week 10.

[0120] Phase 3 TREET consists of three parts. In Part A, patients were randomized (1:1) to subcutaneous dupilumab 300 mg qw or matching placebo for 24 weeks. All patients from Part A who continued in Part C (Part A-Part C) subsequently received dupilumab 300 mg qw for an additional 28 weeks. In Part B, patients were randomized (1:1:1) to dupilumab 300 mg qw, dupilumab 300 mg q2w, or placebo qw. Patients from Part B continued to Part B-Part C (ongoing); the dupilumab group continued on the same dosing regimen, and patients from the placebo group were re-randomized (1:1) to dupilumab 300 mg qw or q2w. Patients were followed for 12 weeks after discontinuation of study drug. The co-primary endpoints were histologic remission (defined as ≤6 eosinophils per high-power field [eos / hpf]) and change from baseline in Dysphagia Symptom Questionnaire [DSQ] scores at week 24 (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330).

[0121] Subjects

[0122] The Phase 2 study enrolled adult subjects (18-65 years) with documented EoE who were unresponsive to protein pump inhibitors (PPIs) and had active esophageal inflammation at screening. 2Such patients were identified by a peak cell count of ≥15 eosinophils (400× magnification of the field of view), as indicated by esophageal pinch biopsy samples from at least 2 of 3 esophageal sites. These samples were obtained by endoscopy no more than 2 weeks after at least 8 weeks of treatment with high-dose (or twice-daily dosing) PPIs. Subjects were also required to have a self-reported history of an average of ≥2 dysphagia episodes per week in the 4 weeks prior to screening, an SDIPRO score of ≥5 at screening and baseline, and a documented history or presence of at least one type 2 comorbid atopic disease. Full inclusion and exclusion criteria have been published previously (see Hirano et al., Gastroenterology, 2020, 158, 111-22).

[0123] Phase 3 TREET enrolled patients with EoE aged ≥12 years who had not responded to high-dose PPIs for ≥8 weeks, had a peak intraepithelial eosinophil count of ≥15eos / hpf in at least two of three biopsied esophageal regions, and had a patient-reported history of dysphagia averaging two or more times per week in the 4 weeks prior to screening (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330). Eligible patients also had at least four episodes of dysphagia in the 2 weeks prior to baseline, recorded by DSQ eDiary (at least two of which required fluids, coughing, gagging, vomiting, or medical care for relief), completed at least 11 of the 14 days in the 2 weeks prior to baseline, and had a baseline DSQ score of ≥10 (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330).

[0124] RNA sequencing and analysis

[0125] Biopsy samples for RNA analysis were collected from the proximal, mid, and distal esophagus during screening and week 12 endoscopy and frozen in RNALater TM After RNA extraction, strand-specific RNA-seq libraries were prepared using the KAPA Stranded mRNA-Seq Kit (KAPABiosystems, Roche Sequencing and Life Sciences, MA, USA). Sequencing was performed by multiplexing single read runs (80 bp, 40M reads) on an Illumina 2000 device (Illumina Inc., CA, USA). Reads were mapped to the human genome (National Center for Biotechnology Information GRCh37) using Array Studio software (OmicSoft, NC, USA). Differentially expressed genes were identified using the DESeq2 package.

[0126] Using a gene set enrichment analysis tool that considers both positive and negative gene sets (see the World Wide Web "mathworks.com / matlabcentral / fileexchange / 33599-gsea2"; Lim et al., Pac. Symp. Biocomput., 2009, 504-15), the top 50 most upregulated genes and the top 50 most downregulated genes in EoE were used to generate a standardized enrichment score (NES). For a single-sample NES, the gene expression profiles were first converted to z scores, and the single-sample NES was calculated using the ranked z scores in each sample to represent the overall disease signature score of the sample, denoted as EoE-NES.

[0127] Gene Ontology term enrichment analysis

[0128] Unbiased global transcriptome analysis was performed using gene set enrichment analysis (Wapenaar et al., Immunogenetics, 2007, 59, 349-57) with gene ontology (GO) biological process gene sets from the molecular signature database (MSigDB, c5.bp.v7.0; The Gene Ontology Consortium, Nucleic Acids Res., 2019, 47, D330-D338; Ashburner et al., Nat. Genet., 2000, 25, 25-29). Gene sets of size > 100 were pre-filtered to ensure biological process specificity, and top GO terms were selected if the false discovery rate in both EoE relative to health and after dupilumab treatment relative to baseline comparisons was < 0.05. Eosinophil-related genes and mast cell-related genes were derived from the literature (Esnault et al., PLoS One, 2013, 8, e67560; and Abonia et al., J. Allergy Clin. Immunol., 2010, 126, 140-149).

[0129] Immunohistochemistry (IHC)

[0130] Esophageal biopsies were stained with the following IHC markers: proliferation marker (MIB-1), lymphocyte markers (CD4, CD8), mast cell markers (chymotrypsin, tryptase), Fc receptor IgE marker (FcεRI), and Langerhans cell marker (CD1a). Scanned IHC images were annotated individually by a board-certified veterinary pathologist and analyzed using HALO (version 2.2) image analysis software. Quantification of CD4 expression was performed using an area quantification algorithm, and for the remaining markers (CD8, MIB-1, CD1a, chymotrypsin, tryptase, FcεRI), a nuclear algorithm was used.

[0131] Statistical analysis

[0132] Differential expression analysis was performed using DESeq2 version 1.26.0. For the Phase 2 study, data at Week 12 were compared to baseline in both groups (placebo and dupilumab 300 mg qw). For the Phase 3 TREET study, data at Weeks 24 and 52 were compared to baseline values ​​within the three groups (dupirocumab 300 mg qw, dupilumab 300 mg q2w, and placebo). The dupilumab 300 mg qw group included patients pooled from Parts A and B for assessment at baseline and Week 24, and all patients assigned to weekly dosing in Part C for assessment at Week 52. The dupilumab 300 mg q2w group included patients from Part B for assessment at baseline and Week 24, and all patients assigned to alternate-week dosing in Part C for assessment at Week 52. The placebo group included patients pooled from Parts A and B for assessment at baseline and Week 24. The placebo / dupirocumab 300 mg qw group at week 52 included patients assigned to placebo in both Parts A and B and to durpivoumab 300 mg qw only in Part C.

[0133] Genes were considered to be significantly modulated by treatment (dupirocumab or placebo) if a threshold of ≥2-fold relative log change from baseline and q value ≤0.05 was achieved, reflecting adjustment for multiple testing. Pearson correlations were calculated between published gene changes in EoE (disease versus healthy) and gene changes after durpilumab treatment (after versus before treatment). For IHC analysis, nominal P values ​​were calculated using unpaired two-sided t tests to compare absolute changes from baseline between placebo and treatment groups.

[0134] Transcriptome analysis

[0135] Transcriptome results were available for 16 of 24 placebo patients (67%) and 22 of 23 dupilumab patients (96%) enrolled in a phase 2 clinical trial (NCT02379052). However, biopsy samples could not be obtained from 6 patients, who were excluded from the analysis (see Hirano et al., Gastroenterology, 2020, 158, 111-22).

[0136] Compared with baseline expression levels, no differentially expressed genes were found in the placebo group at week 12 (relative log change from baseline ≥ 2, q ≤ 0.05). Treatment with 300 mg dexmecambilin weekly (qw) upregulated 1,302 genes at week 12 relative to baseline (DpxOme-EoE TM ), of which 513 genes were downregulated and 789 genes were upregulated (heat map data not shown). Since the post-treatment results were very similar in all 3 esophageal regions sampled (data not shown), the average values ​​are presented across all samples (data not shown). The top 50 most upregulated genes and the top 50 most downregulated genes in EoE were used to generate a standardized enrichment score (EoE-NES). Among the upregulated or downregulated genes, dupilumab treatment was associated with a significantly lower EoE-NES (Wilcoxon rank sum test, P < 5.0 × 10 -8 ). In contrast, the placebo group showed no significant changes. The 30 genes showing the highest expression changes due to dupilumab included genes related to type 2 inflammation, tissue remodeling / fibrosis, barrier function, and proliferation / differentiation (see Figure 1 ). Genes upregulated in the EoE transcriptome that were downregulated by dupilumab included ALOX15, CCL26, POSTN, NRXN1, and CCR3; genes downregulated in placebo subjects and upregulated by dupilumab included SPINK8 and DSG1.

[0137] Normalization of the EoE transcriptome

[0138] Treatment with durpilumab (week 12 relative to baseline) normalized the transcriptome at week 12, as can be seen by comparison with published EoE transcriptomes and healthy transcriptomes. Figure 2 (Panel A) shows that the published EoE transcriptome relative to the healthy transcriptome and DpxOme-EoE TM A strong negative correlation was observed between the 12th week and the baseline (Pearson correlation coefficient: ρ = –0.872; P < 1 × 10 -6For genes that did not reach the significance threshold, there was a normalized trend (heat map data not shown). Dupilumab also significantly regulated many genes that were not included in the published EoE transcriptome (heat map data not shown). In addition, many genes that did not overlap between the EoE and dupilumab signatures were not measurable in one or the other dataset. Figure 2 (Panel B) shows that the main genes altered in EoE and modified by durpivolumab treatment included the following GO groups: (i) immune function / inflammation (e.g., interleukin-12 production, B cell-mediated immunity, response to type I interferon); (ii) eosinophil migration and remodeling (e.g., extracellular matrix disorganization); (iii) mast cell activation; and (iv) epithelial differentiation (e.g., keratinization and cornification).

[0139] Inflammation-related genes

[0140] At week 12, dupilumab modulated type 2 inflammatory genes including IL4, IL13, IL13RA1, IL4R, IL5, IL33, TSLP, IL25, CCL11, CCL13, CCL17, CCL18, CCL24, CCL26, IL1RL1, FCER1A, FCER2, CCR3, CCR4, SIGLEC8, HDC, PTGDS, PTGDR2, CLC, ALOX15, MUC5B, MUC5AC, POSTN, DPP4, CMA1, TPSAB1, HRH1, GATA1, GATA3, ARG1, and STAT6 (heat map data not shown). At week 12, dupilumab also upregulated eosinophil-related genes, including ADAM8, CD300LB, DAPK2, EMR4P, GPR97, IL1RL1, IL5RA, MMP25, RAB37, SIGLEC10, SIGLEC8, TESC, TREML2, TRPM6, and CDA, as well as genes associated with mast cell activation, including AIM2, CADM1, CAPN14, CDH26, CDH3, CFI, CPA3, CTSC, EDAR, GALNT4, GCNT2, GCNT3, HAS3, ID3, IFFO2, IGFBP3, KCNJ2, KITLG, LHFPL2, LITAF, MAP3K14, MFHAS1, NTN1, PDZK 1IP1, PLA2G3, SCIN, SERPINB4, SFRP1, SGK1, SH3RF2, SIDT1, SLC16A1, SUSD2, TMEM173, TMTC3, TNFSF13, TPSAB1, TPSB2, ANKRD37, BNIP3, BOC, CCNYL1, CNFN, CYP2C18, EML1, FAM126A, HSPA2, IGFL1, KIF21A, KRTAP3-2, ME1, PALMD, PFN2, PHACTR2, PPP2R2C, RFK, SAMD5, SPINK7, TFAP2B, YOD1, ZNF101, ZNF365, ZNF555, ZNF662, and ZNF92 (data not shown). Specifically, they included CCL26, MUC5B, CLC, IL1RL1, HDC, IL13, FCER1G, GATA2, and KIT. In addition, dupilumab treatment reduced eosinophil tissue infiltration at week 12, with similar effects in all 3 regions sampled (Hirano et al., Gastroenterology, 2020, 158, 111-22). Changes in eosinophil-related gene expression were consistent with the reduction in eosinophil density observed in esophageal biopsies after dupilumab treatment.

[0141] Fibrosis, remodeling and barrier function related genes

[0142] At week 12, dupilumab treatment also modulated genes associated with fibrosis, matrix remodeling, TGFβ, and integrin signaling, such as collagen family genes and barrier-related genes, including FLG, DSG1, SPINK7, SPINK8, SPINK5, KLK7, HAS3, THBS1, MMP9, LOX, POSTN, TIMP1, HAS2, IL13, PDGFRA, and LTBP2 (heat map data not shown). Notably, these genes included DSG1, SPINK5, SPINK7, and SPINK8.

[0143] Transcriptome analysis across type 2 inflammatory diseases

[0144] Since IL-4 and IL-13 are central mediators of type 2 inflammation, a type 2 gene expression signature (type 2-NES) was assessed in EoE compared with relevant control tissues from published trials, other trials, and published studies of other atopic indications (heat map data not shown). Although heterogeneity was evident across all indications, type 2-NES was significantly higher in the following disease groups relative to controls: EoE (P = 0.026; Mishra et al., IL-5 promotes eosinophil trafficking to the esophagus, J. Immunol., 2002, 168, 2464-69), atopic dermatitis (GSE121212; P = 6.9 × 10 -7 ; Tsoi et al., J. Invest. Dermatol. 2019, 139, 1480-89), nasal polyps (GSE136825; P = 0.00073; Peng et al., Eur. Respir. J., 2019, 54, 1900732) and asthma (GSE85567; P = 0.0047; Nicodemus-Johnson et al., 2016, JCI Insight 1, e90151) (see Figure 3 ).

[0145] discuss

[0146] In the published study, dupilumab significantly modulated the expression of 1,302 genes, reversing the disease transcriptional signature in EoE. Treatment with dupilumab resulted in significantly lower EoE-NES (P < 5.0 × 10 -8), while no significant changes were observed in the placebo group. Dupilumab normalized the expression of genes dysregulated in EoE in the esophagus, including those involved in type 1 and type 2 inflammation, fibrosis / remodeling, barrier function, mast cell activation, cell proliferation / differentiation, and eosinophilic inflammation, as well as those that could not be normalized by corticosteroid treatment (UPK1B, SH2D1B, CDH26, POSTN, and DSG1; Nhu and Moawad, Curr. Treat. Options. Gastroenterol., 2019, 17, 48-62).

[0147] The most typical feature of EoE is the infiltration of eosinophils into the esophageal mucosa (Collins, Dig. Dis., 2014, 32, 68-73). Other pathological changes associated with EoE include basal cell hyperplasia and expansion of intercellular spaces, fibrosis of the lamina propria, and muscle hypertrophy (Guarino et al., World J. Gastrointest. Pharmacol. Ther., 2016, 7, 66-77). GO pathway analysis identified immune response and inflammation, eosinophil migration, and epithelial differentiation (keratinization and keratinization) as the main pathways that are dysregulated in EoE and regulated by dupilumab, indicating that dupilumab treatment has a potential effect on histological changes and potential inflammation associated with EoE. Previous studies of other biologic treatments targeting type 2 inflammatory responses in EoE have had mixed results, with some (e.g., anti-IL-13 and anti-IL-5 agents; Ko and Chehade, Clin. Rev. Allergy Immunol., 2018, 55, 205-16; Rothenberg et al., J. Allergy Clin. Immunol., 135, 500-07; Hirano et al., Gastroenterology, 2019, 156, 592-603) leading to histologic improvement without symptom resolution, while others (e.g., anti-IgE) had no effect (Clayton et al., Gastroenterology, 2014, 147, 602-09). No other biologics have demonstrated improvement in dysphagia symptoms in EoE trials other than dupilumab, suggesting that the IL-4 / IL-13 pathway effects of dupilumab are important for improving EoE disease severity. Furthermore, although IL-13 has been shown to induce EoE in murine models, mice deficient in IL-5, eotaxin-1, or STAT-6 demonstrated significant protection from the development of EoE following IL-13 challenge (Mishra and Rothenberg, Gastroenterology, 2003, 125, 1419-27), indicating that multiple type 2 inflammatory components interact in disease pathogenesis.

[0148] Gene expression dysregulation in esophageal biopsies of patients with EoE not only demonstrates abnormalities in type 2 inflammation, but also demonstrates abnormalities in fibrosis / remodeling, impaired barrier function, and epithelial proliferation. Treatment with dupilumab reverses type 2 inflammation observed in patients with active EoE. In a Phase 2 study, dupilumab reduced mean eosinophil counts by 107% and significantly reduced peak intraepithelial eosinophil counts by week 12 (Hirano et al., Gastroenterology, 2020, 158, 111-22). The current analysis identified many EoE dysregulated genes associated with type 2 inflammatory responses regulated by dupilumab. ALOX15 (15-lipoxygenase) is one of the most highly regulated genes by dupilumab, which is a proinflammatory mediator upregulated in asthma (Clayton et al., Gastroenterology, 147, 602-09 (2014)). ALOX15 is regulated by both IL-4 and IL-13, which explains the significant effect of dupilumab treatment on the expression of the gene (Snodgrass and Brüne, Front. Pharmacol. 2019, 10, 719). The most severely dysregulated genes in the published EoE transcriptome (Blanchard et al., J. Clin. Invest., 2006, 116, 536-47), eotaxin CCL26 (eotaxin-3) and its receptor CCR3 (Pease and Williams, J. Allergy Clin. Immunol., 2019, 143, 552-53) were both downregulated by dupilumab in this study. The key role of eotaxin in eosinophil infiltration in EoE has previously been demonstrated in vivo in CCR3-deficient mice (Blanchard et al., J. Clin. Invest.,

[0149] 2006, 116, 536-47). POSTN (periostin), an extracellular matrix protein that is upregulated in EoE and may play a role in fibrosis and type 2 inflammatory responses, is significantly downregulated under dupilumab treatment, whereas it has been previously shown that POSTN remains dysregulated under corticosteroid treatment (Blanchard et al., J. Allergy Clin. Immunol., 2007, 120, 1292-1300; O'Dwyer and Moore, Cell. Mol. Life. Sci., 2017, 74, 4305-14). Studies in mice indicate a role for POSTN in the recruitment of eosinophils to the esophagus in EoE (Blanchard et al., Mucosal Immunol., 2008, 1, 289-96). Other regulated genes associated with type 2 inflammation include MUC5B, which is downregulated in EoE but overexpressed in chronic sinusitis with nasal polyps (Zhang et al., Allergy, 2019, 74, 131-40) and underexpressed in the airways of asthmatics (Bonser and Erle, J. Clin. Med., 2017, 6, 112). Treatment with dupilumab resulted in upregulation of MUC5B, resulting in an expression pattern similar to that of healthy controls.

[0150] Similar to asthma, remodeling and fibrosis are key clinical features of chronic EoE that contribute to symptoms and disease severity. Mouse models have demonstrated the role of IL-5-mediated eosinophilia in fibrosis and tissue remodeling in EoE (Mishra et al., Gastroenterology, 2018, 134, 204-14). In the study disclosed herein, dupilumab reduced the expression of collagen genes and POSTN, demonstrating a direct effect on genes associated with fibrosis. Continuous eosinophilic tissue infiltration contributes to the development of basal hyperplasia and lamina propria fibrosis. This leads to physical changes in the esophagus, such as stiffness and changes in smooth muscle function. These changes, in turn, cause different degrees of severity of clinical symptoms (Aceves, Dig. Dis., 2014, 32, 15-21). The molecular and histological improvements in fibrosis and remodeling markers are consistent with the improvements in esophageal distensibility also observed in the disclosed trials (Hirano et al., Gastroenterology, 2020, 158, 111-22). Treatment with dupilumab resulted in symptom relief and visual improvement of endoscopic features in patients with active EoE (Hirano et al., Gastroenterology, 2020, 158, 111-22). Significant improvements in EoE-EREFS, a measure of endoscopically identified EoE esophageal mucosal inflammation and remodeling features, were observed in patients receiving dupilumab compared with the placebo group (P = 0.0015). Significant improvements in histological severity and extent (EoE-HSS), a measure of eosinophil density, basal hyperplasia, eosinophilic abscesses, eosinophil surface stratification, surface epithelial changes, dyskeratotic epithelial cells, and dilated intercellular spaces, were also noted (P < 0.0001; Hirano et al., Gastroenterology, 2020, 158, 111-22). DpxOme TM-EoE NES is highly correlated with the total EoE-HSS grade, demonstrating the biological association between molecular signatures and clinical measures. The expression of CTSC is the single gene most highly correlated with the total EoE-HSS, demonstrating the importance of proinflammatory proteolytic activity in the pathogenesis of the disease. The key signature of EoE is the downregulation of most genes involved in esophageal differentiation (Rochman et al., J.Allergy Clin.Immunol., 2007, 140, 738-49). Consistent with clinical results, the analysis showed that dupilumab standardized many genes involved in proliferation (hyperplasia), fibrosis and epithelial barrier function. These genes include mKi67, several collagen genes, DSG1 and SPINK family members. The SPINK gene family is believed to be involved in preventing proteolytic degradation of epithelium and mucosa (Wapenaar et al., Immunogenetics, 2007, 59, 349-57). Although SPINK5 and SPINK7 have a role in the regulation of barrier function, SPINK7 also regulates proinflammatory cytokine production (Azouz et al., Sci. Transl. Med., 2018, 10, eaap9736). SPINK8 is the third most highly expressed SPINK in the esophagus and is downregulated in EoE, but was upregulated by dupilumab in the current study, indicating that dupilumab also helps restore epithelial barrier function in patients with active EoE.

[0151] In addition to improved epithelial barrier integrity and reduced fibrosis, patients receiving dupilumab also showed significant improvement in dysphagia symptoms relative to placebo (Hirano et al., Gastroenterology, 2020, 158, 111-22). ANO1 is a calcium-activated chloride channel associated with gastric smooth muscle contraction and pruritus, upregulated in esophageal biopsies in EoE mouse models and patient samples, and is also regulated by dupilumab (Subramanian et al., Proc. Natl. Acad. Sci. USA, 2005, 102, 15545-50; Vanoni et al., 2020, J. Allergy Clin. Immunol., 145, 239-54). The expression of ANO1 is associated with eosinophil counts in EoE and may also be associated with IL-13-induced basal cell proliferation (Vanoni et al., J. Allergy Clin. Immunol., 2020, 145, 239-54) and mucus hypersecretion (Lin et al., Exp. Cell Res., 2015, 334, 260-69). The expression of ANO1 is induced by IL-4 in vitro and is elevated in allergic rhinitis (Kang et al., Am. J. Physiol. Lung Cell. Mol. Physiol., 2017 313, L466-L476). Other neural-related genes identified in the published EoE transcriptome were also normalized by dupilumab, including NRXN1 and NTRK1, indicating that neuroinflammation regulated by IL-4 and / or IL-13 in EoE may contribute to dysphagia and other symptoms.

[0152] Dupilumab inhibits type 2 inflammatory cytokines IL-4 and IL-13 and blocks their proinflammatory signaling, which is associated with a variety of allergic diseases from asthma to atopic dermatitis (Gandhi et al., Expert Rev. Clin. Immunol., 2007, 13, 425-37). In atopic dermatitis, treatment with dupilumab modifies genes associated with the following diseases: type 2 inflammation (e.g., IL13, CCL17, CCL18, and CCL26), proliferation (K16 and MKI67), epidermal differentiation and barrier, and lipid metabolism (Hamilton et al., J. Allergy Clin. Immunol., 2014, 134, 1293-1300; Guttman-Yassky E. et al., J. Allergy Clin. Immunol. 2019, 143, 155-72), highlighting some similarities in the potential pathogenesis of EoE and atopic dermatitis. Hyperplasia is also observed in other type 2 inflammatory conditions (e.g., epidermal thickening in AD), and treatment with durpilumab has been shown to reduce epidermal thickness and proliferation markers (e.g., K16 and mKi67) in atopic dermatitis (Kang et al., Am. J. Physiol. Lung Cell. Mol. Physiol., 2017, 313, L466-L476). TM Additional analysis of NESs for -EoE and type 2 inflammatory signatures revealed that both signatures were upregulated in disease relative to control samples across a variety of indications (Sherrill et al., Genes Immun., 2014, 15, 361-69; Tsoi et al., J. Invest. Dermatol. 2019, 139, 1480-89; Peng et al., Eur. Respir. J., 2019, 54, 1900732; Nicodemus-Johnson et al., 2016, JCI Insight 1, e90151), further demonstrating that despite the diversity of tissue types and RNA profiling platforms used for these analyses, there are shared molecular pathogenesis.

[0153] Dupilumab, which inhibits IL-4 and IL-13, normalized the transcriptome of esophageal pinch biopsies from patients with EoE, including genes associated with inflammation, eosinophils, barrier function, and fibrosis, consistent with findings showing a reduction in histologic disease features and symptoms. Results suggest that dupilumab suppresses type 2 inflammatory responses in EoE and contributes to the restoration of the esophageal mucosa. The combined effects of dupilumab on molecular signatures, symptoms, and histopathologic features in patients with active EoE suggest a critical role for IL-4 and IL-13 in the pathogenesis of EoE.

[0154] Example 2: Generating a core gene signature for dupilumab treatment

[0155] Core genes were selected based on a fold change ≥2 and q < 0.05 in ≥3 of 5 treatment studies: i) eosinophilic esophagitis (EoE); gene expression in esophageal biopsies by RNAseq; ii) atopic dermatitis (AD); gene expression in skin biopsies by microarray; iii) asthma; bronchial allergen challenge with house dust mite (HDM); gene expression in sputum by RNAseq; iv) grass; nasal challenge with timothy grass; gene expression in nasal brushes by RNAseq; v) chronic rhinosinusitis with nasal polyposis (CRSwNP); gene expression in nasal brushes by microarray.

[0156] Differential gene expression was analyzed using the limma package for microarrays (Ritchie et al., Nuc.) Acids Res., 2015, 43, e47) and DESeq2 for RNAseq (Love et al., Genome Biol., 2014, 15, 550). In the EoE / AD / CRSwNP study, baseline (before treatment) and post-treatment analyses were performed. In the asthma / grass study, treatment comparisons were made with and without allergen attack. The treatment effect of the placebo control can be analyzed using differential fold change analysis by subtracting the change in the placebo group from the change in the treatment group. Statistical significance can be determined by a permutation test, in which patient treatment assignments are randomly arranged and p-values ​​are calculated by comparing the actual differential fold changes with the background distribution of values ​​from the arrangement. The results are shown in Figure 4 middle.

[0157] Use case 1: New indication screening

[0158] Technologies for whole transcriptome profiling are becoming increasingly popular and affordable. The amount of data generated and shared on the public domain (e.g., Gene Expression Omnibus) is growing exponentially. Screening the dupilumab core signature across a large number of disease studies could reveal previously unrecognized links between pathways perturbed by dupilumab and disease mechanisms. Diseases with differential gene expression (in opposite directions) of the dupilumab signature could represent potential new indications for the drug.

[0159] Using the gene set enrichment analysis tool (Lim et al., Pac. Symp. Biocomput., 2009, 504-515), which considers both positive and negative gene sets, a standardized enrichment score (NES) was generated for all disease studies from ArrayStudio DiseaseLand. Differential expression analysis was first performed in each study (disease versus healthy control). The NES was then calculated using genes in the full transcriptome list sorted by fold change to represent the enrichment of the dupilumab signature in the study. Hot hits from the screening included rhinitis, asthmatic rhinitis, allergic asthma, asthma, eosinophilic esophagitis, and atopic dermatitis. Some of these diseases are known indications for dupilumab, where the disease signature is reversed after treatment.

[0160] Use Case 2: Patient Stratification

[0161] In diseases where not all patients will benefit from dupilumab, the overall dupilumab NES will be diluted in the screen. In these diseases, the NES can be computed at the individual patient level. Instead of generating the NES on a whole transcriptome list ranked by disease relative to healthy fold change, the gene expression profiles are first converted to z-scores and the NES is calculated using the ranked z-scores in each sample to represent the patient's dupilumab signature enrichment. Patients with a higher NES may benefit more from dupilumab.

[0162] For example, in the disease screen described earlier, the ulcerative colitis (UC) NES was significantly enriched but did not make it into the top 10. When NES were calculated at the individual patient level using the UC study (GSE87466) for 87 patients and 21 healthy controls, 33% of patients had a dupilumab NES that was statistically significant. Figure 5 Based on the analysis, in some embodiments, only the subset of patients with significantly high NES scores can be included in the dupilumab-treated patient population.

[0163] Use Case 3: Molecular Pathology Endpoints

[0164] Compared with healthy controls, genes differentially expressed in esophageal biopsies of EoE patients are called EoE disease signatures (Sherrill et al., Genes Immun., 2014, 15, 361-69). The disease signature is further refined into smaller gene sets for use as the EoE diagnostic panel (EDP) (Dellon et al., Clin. Transl. Gastroenterol., 2017, 8, e74). In the EoE Phase 3 study, the NES of the EDP gene was used as a secondary endpoint as an indicator of disease activity. The dupilumab treatment but not the placebo group showed a significant reduction in NES. Comparable results were also observed using a gene signature representing type 2 inflammation (the gene signature had previously been collated from the literature and preclinical experiments). Similar methods can generally be applied to all indications using the dupilumab core gene signature to better capture potential molecular pathological activity and changes after dupilumab treatment.

[0165] In the CRSwNP study, a 25-gene set (identified from treatment and clinical response signatures) was shown to predict response to each CRSwNP clinical endpoint (nasal congestion / obstruction (CONG), nasal polyp score (NPS), Lund-Mackay computed tomography score (CT-LMK), and University of Pennsylvania Smell Identification Test (UPSIT)) better than other available circulating biomarkers. Receiver operating characteristic analysis was used to assess the ability of the NES scores, which represent transcriptional signatures and more standard biomarkers, to distinguish responders to each of the four primary endpoints as well as responses to multiple endpoints. The predictive performance of each biomarker was summarized by calculating the area under the receiver operating characteristic curve (AUC). The results are plotted on Figure 6 Middle. The dupilumab core gene signature has high overlap with the 25-gene set and is predictive of response to a wider range of dupilumab indications.

[0166] Example 3: Gene signature enrichment score

[0167] Gene set enrichment analysis (GSEA) uses the Kolmogorov-Smirnov statistical test to evaluate whether a predefined gene set (in this case, the dupilumab core signature) is statistically enriched for genes at two extremes in a list ranked by differential expression between two biological states (Subramanian et al., Proc. Natl. Acad. Sci. USA 2005, 102, 15545-50). The algorithm is very useful for jointly detecting the differential expression of gene sets, even if the fold change of each individual gene may be small. Since the dupilumab core signature includes up-regulated and down-regulated genes, GSEA is extended to evaluate the enrichment of two complementary gene sets for N-ranked genes, as follows:

[0168] (a) Rank N genes from most positive to most negative (e.g., log2 fold change, z score) using R + express.

[0169] (b) Independent identification of R + The positive gene set S + and R - Negative gene set S - The hits, among which R - is R with the inverse value + The inverse order of .

[0170] (c) Combination R + and R - And by retaining S + and S - The hits re-rank the values.

[0171] (d) Calculate the running score by walking down the combinatorial ranking. If the i-th gene is a hit, the score will be increased by |r i | p / Σ i∈S |r| p , otherwise it will be reduced by 1 / (2N-S), where S is S + and S - The total number of combinations of genes in r i is the value of gene i, and p is the weight of r.

[0172] (e) The enrichment score (ES) was determined as the maximum deviation from zero along the run.

[0173] (f) Steps (ae) were repeated 1,000 times using random gene sets to calculate the ES null distribution. The normalized enrichment score (NES) was determined as the ES divided by the mean of the ES null distribution.

[0174] (g) Statistical significance can be calculated by comparing the observed ES with the null distribution.

[0175] In use case number 1, R is ranked by comparing the log2 fold change of mean gene expression in disease samples to that in healthy samples. There is a ranked list for each disease study, and NES can be calculated for all studies. Diseases with significant NES will be considered as potential indications for dupilumab. Statistical significance can be calculated based on the null distribution of ES from a random gene set (step f above) or on sample marker (disease / healthy) permutations.

[0176] In use case number 2, gene expression is converted to z-scores gene by gene. In each sample, all genes are sorted by z-score and used as a reference list R to calculate NES. Patients with significant NES may benefit from dupilumab treatment. The statistical significance threshold can be determined as the 95th percentile NES of healthy control samples.

[0177] In use case number 3, R can be ranked by comparing the log2 fold change of gene expression after treatment to the baseline (pre-treatment) gene expression. NES is calculated for each patient and represents how the gene changes with treatment. NES can also be calculated for each sample, representing the overall expression level of the core signature in the sample.

[0178] Example 4: The transcriptomic effects of dupilumab are persistent

[0179] In Parts A and B of the Phase 3 TREET study (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330), transcriptome results were available for 95 of 122 patients (78%) treated with dupilumab 300 mg qw, 60 of 81 patients (74%) treated with dupilumab 300 mg q2w, and 84 of 118 patients (71%) treated with placebo. In part C of the extended active treatment phase, transcriptome results were available for 60 of 114 patients (53%) who continued to receive dupilumab 300 mg qw (dupirocumab / dupirocumab 300 mg qw) and 32 of 79 patients (41%) who received dupilumab 300 mg q2w (dupirocumab / dupirocumab q2w), both through week 52, and for 52 of 74 patients (70%) who switched from placebo to dupilumab 300 mg qw (placebo / dupirocumab 300 mg qw) and 22 of 37 patients (59%) who switched to dupilumab 300 mg q2w at the start of part C (placebo / dupirocumab 300 mg q2w) and received dupilumab for 24 weeks. For the Phase 3 TREET study, results for the same dose regimen and time points were pooled across study parts.

[0180] Using data from the phase 3 TREET study, we examined changes over time in genes included in the EoE Diagnostic Panel (EDP; a further refined, smaller gene set compared with the EoE disease transcriptome) (Wen et al., Gastroenterology, 2013, 145, 1289-1299) in patients treated with durpilumab 300 mg qw (see Figure 7, Panel A). EDP-NES calculated based on the relative change before and after treatment was included as a secondary endpoint in the trial (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330). Compared with baseline, EDP was significantly reduced at week 24 in patients treated with dupilumab 300 mg qw (P = 5.0 × 10 -18 ), and continued to week 52 in the dupilumab / dupirocumab 300 mg qw group (P = 8.0 × 10 -18 ), with no significant difference between the two time points (P=0.53). In general, the same patients with reduced EDP at week 24 maintained suppression until week 52.

[0181] Transcriptome effects observed in adolescents and adults. In the Phase 3 TREET study, the EDP transcriptome was assessed at baseline and Week 24 in subgroups of adolescent and adult patients treated with durpivolumab 300 mg qw to determine whether the effects of durpivolumab treatment on the transcriptome were observed across age groups (see Figure 7 , Panel B). A significant decrease in EDP-NES from baseline to Week 24 was observed in both age groups (adolescents: P = 2.6 × 10 -7 ; Adults: P = 1.8 × 10 -13 ). EDP-NES did not differ significantly between adolescents and adults at baseline (P = 0.14) and week 24 (P = 0.44).

[0182] Example 5: Dupilumab inhibits MIB-1 (Ki67), tryptase and CD4 + T cell infiltration and moderate increase in CD1a + cell

[0183] Long-term eosinophil-dominated inflammation promotes epithelial hyperplasia in patients with EoE (Cheng et al., Am. J. Physiol. Gastrointest. Liver Physiol., 2012, 303, G1175-G1187), and dupilumab significantly reduced the expression of MIB-1 (Ki67, a marker of cell proliferation) at 12 weeks after treatment compared with placebo treatment (P = 0.011; see Figure 8, panel A), consistent with observations of improved epithelial differentiation (by transcriptome analysis) and reduced basal zone hyperplasia in patients treated with dupilumab (Hirano et al., Gastroenterology, 2020, 158, 111-122). This is consistent with findings in patients with AD in whom dupilumab gradually reversed characteristic epidermal hyperplasia (Guttman-Yassky et al., J. Allergy Clin. Immunol., 2019, 143, 155-172). Although overall staining for tryptase (a marker of mast cell activation released during mast cell degranulation) was lower, the number of cells expressing tryptase was reduced at week 12 in patients treated with dupilumab 300 mg qw compared with patients receiving placebo (P=0.003, see Figure 8 , Panel B). Although no CD8 + Significant differences in cytotoxic T cells (see Figure 8 , Panel C), but dupilumab significantly reduced CD4 + Cell (T helper cell) population (P = 0.027, see Figure 8 , Panel D). In contrast, CD1a + cells (a marker of antigen-presenting Langerhans cells) increased (P = 0.008; see Figure 8 , Panel E). No significant changes in the number of FcεRI- (Fc receptor for IgE) or chymotrypsin- (mast cell mediator; serine protease with chymotrypsin-like activity) positive cells were observed as a result of dupilumab treatment (data not shown).

[0184] Example 6: Dpx3 response signature in EoE ph3

[0185] Gene Set Enrichment Analysis (GSEA) Normalized Enrichment Score (NES) reflects the extent to which the activity level of a transcript set is overrepresented at the extremes (top or bottom) of the entire ranked transcript list. Gene expression was converted to z-scores gene by gene, and in each sample, all genes were ranked according to the z-scores and used as a reference list to calculate the single-sample NES. The NES calculated for the dupilumab response signature reflects the expression of the gene set in each esophageal biopsy from an EoE patient to assess molecular response. An NES close to 0 indicates no response, while a negative score significantly deviating from 0 reflects a response.

[0186] Both dupilumab 300 mg QW and Q2W showed significant dupilumab molecular response (strong negative NES) in EoE (see Fig. 9The mean NES changes from baseline to Week 24 and Week 52 were: -0.32 in the placebo / pilumab 300 mg group (p = 4.0 × 10 -2 ) and -2.59(5.6×10 -21 ), the dupirocumab 300 mg Q2W / dupirocumab 300 mg Q2W group was -2.09 (6.6×10 -20 ) and -2.00(9.3×10 -13 ), and in the dupilumab 300 mg QW / dupilumab 300 mg QW group, it was -2.18 (9.3×10 -31 ) and -2.22(6.1×10 -23 ). EoE: eosinophilic esophagitis; NES: normalized enrichment score; Q2W: every 2 weeks; QW: once a week.

[0187] All patent documents, websites, other publications, accession numbers, etc. cited above or below are incorporated by reference as a whole for all purposes, to the extent that each individual project is specifically and individually indicated as being so incorporated by reference. If different versions of a sequence are associated with accession numbers at different times, it means the version associated with the accession number of the effective submission date of the present application. The effective submission date means the earlier date in the actual submission date or the submission date of the priority application with reference to the accession number (if applicable). Similarly, if different versions of publications, websites, etc. are published at different times, it means the version published most recently on the effective submission date of the present application unless otherwise stated. Unless otherwise specifically stated, any feature, step, element, embodiment or aspect of the present disclosure may be used in combination with any other feature, step, element, embodiment or aspect. Although the present disclosure has been described in detail by explanation and example for the purpose of clarity and understanding, it will be apparent that certain changes and modifications may be implemented within the scope of the appended claims.

Claims

1. A method for identifying a disease or patient suitable for treatment with dupilumab, the method comprising: a) Generate a core gene signature for treatment with dupilumab; b) screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies; as well as c) identifying diseases or patients in the multiple disease studies that have differential gene expression in a direction opposite to the dupilumab therapeutic core gene signature; This allows identification of diseases or patients suitable for treatment with dupilumab.

2. The method of claim 1, wherein generating the dupilumab treatment core gene signature comprises determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies, and identifying a plurality of differentially expressed genes.

3. The method of claim 2, wherein the plurality of treatment studies includes eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis.

4. The method of claim 2, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold change ≥ 2 and / or q < 0.05 in ≥ 3 out of 5 treatment studies.

5. The method of claim 4, wherein the fold change comprises subtracting the expression change in the placebo-treated group from the dupilumab-treated group.

6. The method of claim 3, wherein the treatment of eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis is performed by comparing baseline gene expression before treatment with durpilumab to the gene expression after treatment with durpilumab {ARMTEA-24027-CNPT / 02078963v1}1 The differential gene expression studied.

7. The method of claim 3, wherein said differential gene expression for said asthma and grass allergy treatment studies is performed by comparing said gene expression with allergen challenge to said gene expression without allergen challenge.

8. The method of claim 2, wherein the differential gene expression is analyzed by microarray or RNASeq.

9. The method of claim 8, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

10. The method of claim 8, wherein the differential gene expression of the atopic dermatitis and CRS with nasal polyposis treatment study is analyzed by microarray.

11. The method of claim 2, wherein the differentially expressed genes include ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF 3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.

12. The method of claim 1, wherein screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies comprises: i) performing differential gene expression analysis on the whole transcriptome profile of each of the plurality of disease studies; as well as ii) using {ARMTEA-24027-CNPT / 02078963v1}2 that are differentially expressed and in the dupilumab treatment core gene signature The plurality of genes generates a normalized enrichment score (NES) for all diseases in the plurality of disease studies.

13. The method of claim 12, wherein performing differential gene expression analysis on the whole transcriptome profile of each of the plurality of disease studies is performed for the disease relative to healthy controls.

14. The method of claim 12, wherein the plurality of disease studies comprises the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database.

15. The method of claim 12, wherein the NES is generated using a gene set enrichment analysis tool that considers both positive gene sets and negative gene sets.

16. The method of claim 15, wherein the NES is generated by: a) ranking the plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate a ranked gene list (R+); b) independently identifying the hits (i.e., the ranking of genes in the core signature) of the positive (i.e., most up-regulated) gene set (S+) in R+ and the negative (i.e., most down-regulated) gene set (S-) in R-, where R- is the reciprocal ranking of R+ with the reciprocal value; c) combining R+ and R- and re-ranking the values ​​by retaining the hits of S+ and S-; d) Calculate a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation of the score from zero along the run; f) repeating step a)-step e) 1,000 times using random gene sets to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution.

17. The method of claim 16, further comprising calculating statistical significance by comparing the observed ES to the null distribution or sample marker (disease / healthy) permutation.

18. The method of claim 16, wherein step a) comprises using log2 fold change or z-score.

19. The method of claim 16, wherein R+ and R- are ranked by comparing the log2 fold change of the mean gene expression in the disease sample to the mean gene expression in the healthy sample.

20. The method of claim 16, comprising calculating the NES for all disease studies using the ranked list for each disease study.

21. The method of any one of claims 1 to 20, wherein the disease with a significant NES is a disease suitable for treatment with durpilumab.

22. A method of identifying a subject having a disease or condition suitable for treatment with durpilumab, the method comprising: a) Generate a core gene signature for treatment with dupilumab; b) screening the dupilumab core gene signature based on the whole transcriptome profile of the subject; as well as c) determining whether the subject is suitable for treatment with pilivumab.

23. The method of claim 22, wherein generating the dupilumab treatment core gene signature comprises determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies, and identifying a plurality of differentially expressed genes. {ARMTEA-24027-CNPT / 02078963v1}4 24. The method of claim 23, wherein the plurality of treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis.

25. The method of claim 23, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold change ≥ 2 and / or q < 0.05 in ≥ 3 out of 5 treatment studies.

26. The method of claim 25, wherein the fold change comprises subtracting the change in expression in the placebo-treated group from the dupilumab-treated group.

27. The method of claim 24, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis treatment study is performed by comparing baseline gene expression before treatment with durpilumab to the gene expression after treatment with durpilumab.

28. The method of claim 24, wherein said differential gene expression for said asthma and grass allergy treatment studies is performed by comparing said gene expression with allergen challenge to said gene expression without allergen challenge.

29. The method of claim 23, wherein the differential gene expression is analyzed by microarray or RNASeq.

30. The method of claim 29, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

31. The method of claim 29, wherein the differential gene expression of the atopic dermatitis and CRS with nasal polyposis treatment study is analyzed by microarray.

32. The method of claim 23, wherein the differentially expressed genes include ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, AT F3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.

33. The method of claim 22, wherein screening the dupilumab core gene signature against a whole transcriptome profile from the subject comprises: i) converting the whole transcriptome profile from the subject into a z-score; ii) sorting the z scores; as well as iii) generating a standard enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and in the dupilumab therapeutic core gene signature, thereby representing the enrichment of the dupilumab signature for the subject.

34. The method of claim 33, wherein the NES is generated using a gene set enrichment analysis tool that considers both positive gene sets and negative gene sets.

35. The method of claim 34, wherein the NES is generated by: a) converting the expression of each gene within the plurality of genes into a z-score, and ranking the differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate an R+ value; b) independently identifying hits of the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, wherein R- is the inverse ranking of R+ with inverse values; c) Combine R+ and R- and recalculate the values ​​by retaining the hits of S+ and S-{ARMTEA-24027-CNPT / 02078963v1}6 New rank; d) Calculate a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation of the score from zero along the run; f) repeating step a)-step e) 1,000 times using random gene sets to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution.

36. The method of claim 35, further comprising calculating the statistical significance by determining the 95th percentile NES of healthy control samples.

37. The method of claim 35, comprising calculating the NES for all disease studies using the ranked list for each disease study.

38. The method of claim 22, wherein the subject is suitable for piloumab treatment when the NES of the subject is higher than the NES of a healthy control.

39. A method for conducting a clinical trial of dupilumab for treating a disease or patient, the method comprising using the dupilumab core gene signature as a clinical endpoint of the clinical trial.

40. The method of claim 39, wherein the dupilumab treatment core gene signature is generated by determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies and identifying a plurality of differentially expressed genes.

41. The method of claim 40, wherein the plurality of treatment studies includes eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis.

42. The method of claim 40, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold change ≥ 2 and / or q < 0.05 in ≥ 3 out of 5 treatment studies.

43. The method of claim 42, wherein the fold change comprises subtracting the change in expression in the placebo treated group from the dupilumab treated group.

44. The method of claim 41, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis treatment study is performed by comparing baseline gene expression before treatment with durpilumab to the gene expression after treatment with durpilumab.

45. The method of claim 41, wherein said differential gene expression for said asthma and grass allergy treatment studies is performed by comparing said gene expression with allergen challenge to said gene expression without allergen challenge.

46. ​​The method of claim 40, wherein the differential gene expression is analyzed by microarray or RNASeq.

47. The method of claim 46, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

48. The method of claim 46, wherein the differential gene expression of the atopic dermatitis and CRS with nasal polyposis treatment study is analyzed by microarray.

49. The method of claim 40, wherein the differentially expressed genes include ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFB P3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any of them contained in {ARMTEA-24027-CNPT / 02078963v1}8 The invention also relates to a subset of at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes, or at least 47 genes.

50. The method of claim 39, wherein the clinical trial comprises generating a normalized enrichment score (NES) for the durpilumab treatment core gene signature at at least one time point before initiating treatment of the subject with durpilumab and after initiating treatment of the subject with durpilumab.

51. The method of claim 50, wherein the clinical endpoint has been achieved when dupilumab treatment results in a decrease in the NES of the dupilumab treatment core gene signature to an acceptable value.

52. The method of claim 50, wherein the NES is generated by: a) ranking the differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate an R+ value; b) independently identifying hits of the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, wherein R- is the inverse ranking of R+ with inverse values; c) combining R+ and R- and re-ranking the values ​​by retaining the hits of S+ and S-; d) Calculate a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation of the score from zero along the run; f) repeating step a)-step e) 1,000 times using random gene sets to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution.

53. The method of claim 52, further comprising calculating statistical significance by comparing the observed ES to the null distribution or sample marker (disease / healthy) permutation.

54. The method of claim 52, wherein step a) comprises comparing gene expression after durpilumab treatment with gene expression before initiation of treatment with durpilumab using log2 fold change.

55. The method of claim 50, wherein a plurality of samples are obtained from the subject and the NES is generated for each sample.

56. A method of treating a subject having a disease or condition suitable for treatment with durpilumab, the method comprising: a) identifying whether the subject has a disease or condition suitable for treatment with dupilumab, comprising: i) Generate a core gene signature for treatment with dupilumab; ii) screening the dupilumab core gene signature based on the whole transcriptome profile of the subject; and iii) determining whether the subject is suitable for treatment with pilumab; and b) administering durpilumab to said subject having a disease or condition suitable for treatment with durpilumab.

57. The method of claim 56, wherein generating the dupilumab treatment core gene signature comprises determining differential gene expression between dupilumab treatment groups and placebo treatment groups for multiple treatment studies, and identifying a plurality of differentially expressed genes. {ARMTEA-24027-CNPT / 02078963v1}10 58. The method of claim 57, wherein the plurality of treatment studies includes eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyposis.

59. The method of claim 57, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold change ≥ 2 and / or q < 0.05 in ≥ 3 out of 5 treatment studies.

60. The method of claim 59, wherein the fold change comprises subtracting the change in expression in the placebo treated group from the dupilumab treated group.

61. The method of claim 58, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyposis treatment study is performed by comparing baseline gene expression prior to treatment with durpilumab to the gene expression after treatment with durpilumab.

62. The method of claim 58, wherein said differential gene expression for said asthma and grass allergy treatment studies is performed by comparing said gene expression with allergen challenge to said gene expression without allergen challenge.

63. The method of claim 57, wherein the differential gene expression is analyzed by microarray or RNASeq.

64. The method of claim 63, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

65. The method of claim 63, wherein the differential gene expression of the atopic dermatitis and CRSwNP treatment study is analyzed by microarray.

66. The method of claim 57, wherein the differentially expressed genes include ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, AT F3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, or any subset thereof comprising at least 40 genes, at least 42 genes, at least 44 genes, at least 46 genes or at least 47 genes.

67. The method of claim 56, wherein screening the dupilumab core gene signature against a whole transcriptome profile from the subject comprises: i) converting the whole transcriptome profile from the subject into a z-score; ii) sorting the z scores; as well as iii) generating a standard enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and in the dupilumab therapeutic core gene signature, thereby representing the enrichment of the dupilumab signature for the subject.

68. The method of claim 67, wherein the NES is generated using a gene set enrichment analysis tool that considers both positive gene sets and negative gene sets.

69. The method of claim 68, wherein the NES is generated by: a) converting the expression of each gene within the plurality of genes into a z-score, and ranking the differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value to generate an R+ value; b) independently identifying hits of the positive (i.e., most upregulated) gene set (S+) in R+ and the negative (i.e., most downregulated) gene set (S-) in R-, wherein R- is the inverse ranking of R+ with inverse values; c) Combine R+ and R- and recalculate the values ​​by retaining the hits of S+ and S-{ARMTEA-24027-CNPT / 02078963v1}12 New rank; d) Calculate a running score by walking down the combined ranking, where if the i-th gene is a hit, the running score is increased by / r i / p / Σ i∈S / r / p , otherwise it is reduced by 1 / (2N-S), where S is the total number of combinations of genes in S+ and S-; r i is the value of gene i, and p is the weight of r; e) determining the enrichment score (ES) as the maximum deviation of the score from zero along the run; f) repeating step a)-step e) 1,000 times using random gene sets to calculate the ES null distribution; and g) generating the NES as the ES divided by the mean of the ES null distribution.

70. The method of claim 69, further comprising calculating the statistical significance by determining the 95th percentile NES of healthy control samples.

71. The method of claim 69, comprising calculating the NES for all disease studies using a ranked list for each disease study.

72. The method of claim 56, wherein the subject is suitable for piloumab treatment when the NES of the subject is higher than the NES of a healthy control.

73. A kit comprising a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprises nucleic acid molecules encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, At least ten complementary nucleotide sequences in the nucleic acid molecules of TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3.

74. The kit of claim 73, wherein each of the plurality of nucleic acid molecules is attached to a solid support.

75. The kit of claim 74, wherein the solid support comprises a chip, a multiwell plate, or beads.

76. A method for detecting a plurality of nucleic acid molecules in a subject after treatment with dupilumab, wherein the plurality of nucleic acid molecules detected comprises nucleic acid molecules encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, C At least ten of the nucleic acid molecules of D1B, HAS3, TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3, the method comprising: The biological sample from the subject is contacted with a plurality of nucleic acid molecules comprising nucleic acid molecules encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, at least ten complementary nucleotide sequences of the nucleic acid molecules of TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3; and Detection of ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, The presence or absence of at least ten nucleic acid molecules of ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3.

77. The method of claim 76, wherein the genes encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TP The amount of at least ten nucleic acid molecules of SB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3.

78. The method of claim 76 or claim 77, wherein the markers include those encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, A Each of the plurality of nucleic acid molecules having a nucleotide sequence complementary to the at least ten nucleic acid molecules of TF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8 and RNF103-CHMP3, and the detecting step comprises detecting the marker.

79. The method of any one of claims 76 to 78, wherein the polypeptide comprising the polypeptide encoding ALOX15, CCL26, SLC26A4, POSTN, SLC9A3, CLC, DPP4, MMP12, CDH26, CD209, NTRK2, SOCS1, CH25H, TREM2, CPA3, SERPINB4, IL1RL1, PDCD1LG2, F13A1, CDH3, TPSAB1, CMYA5, CD1B, HAS3, TPSB2, IGFBP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, {ARMTEA-24027-CNPT / 02078963v1}15 Each of the plurality of nucleic acid molecules of a complementary nucleotide sequence of the at least ten nucleic acid molecules of CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3 is attached to a solid support.

80. The method of claim 79, wherein the solid support comprises a chip, a multiwell plate, or beads.

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