Transcriptome analysis for treating inflammation

Abstract

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

Description

[Technical field]

[0001] The disclosure provides methods for identifying diseases or conditions suitable for treatment with dupilumab, methods for identifying subjects having diseases or conditions suitable for treatment with dupilumab, and methods for conducting clinical trials of dupilumab treatment of diseases or conditions. [Background technology]

[0002] Dupilumab, a fully human monoclonal antibody, inhibits the common 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 United States 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 rhinosinusitis with nasal polyps.

[0003] Pharmacogenetic testing, together with other information about the subject and his / her disease, disorder, or condition, can play an important role in drug therapy. However, such data is usually related to the clearance of small molecules with well-established liver or kidney metabolism, or certain definitive biological markers are considered indicators of the disease, condition, or disorder. A caveat to this approach has been recognized in recent years by the industry. That is, diseases, conditions, or disorders, and their treatment with therapeutic agents, usually involve small fluctuations of multiple biomolecules in sometimes unknown biological pathways. Individually, each of these biologics may not have a significant effect on the subject, but the cumulative effect of the trends in the levels of these biomolecules seen in individual patients before and after treatment may reveal the suitability of treatment with a particular therapeutic agent. Thus, there is a long-felt and unmet need for a method to identify subjects who may benefit from treatment with a particular biological therapeutic agent.

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

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

[0006] In some embodiments, the clinical endpoint is achieved when dupilumab treatment reduces the NES of the dupilumab therapeutic core gene signature to an acceptable value. Summary of the Invention

[0007] The present disclosure provides a method for identifying a disease or condition suitable for treatment with dupilumab, comprising: a) generating a dupilumab therapeutic core gene signature; b) screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies; and c) identifying a disease or condition having differential gene expression in the multiple disease studies in the opposite direction to the dupilumab therapeutic core gene signature, thereby identifying a disease or condition suitable for treatment with dupilumab. In some embodiments, generating the dupilumab therapeutic core gene signature comprises determining differential gene expression between a dupilumab treatment group and a placebo treatment group in multiple treatment studies to identify a plurality of differentially expressed genes. In some embodiments, the multiple treatment studies include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyps. In some embodiments, genes of 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 or more of the 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. In some embodiments, the differential gene expression in the treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyps is performed by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment studies of asthma and grass allergy is performed by comparing the gene expression with allergen challenge to the gene expression without allergen challenge. In some embodiments, the differential gene expression is analyzed by microarray or RNASeq. In some embodiments, the differential gene expression in the treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of atopic dermatitis and chronic rhinosinusitis with nasal polyps is analyzed by microarray.In some embodiments, the plurality of differentially expressed genes is selected from the group consisting 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, ATF3, P2RY6, IGFBP5, TMC5, AD 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 against full transcriptome profiles from the multiple disease studies comprises i) performing differential gene expression analysis on the full transcriptome profiles in each disease study of the multiple disease studies, and ii) generating a normalized enrichment score (NES) for all diseases in the multiple disease studies using the plurality of genes that are differentially expressed and are within the dupilumab therapeutic core gene signature. In some embodiments, performing differential gene expression analysis on the whole transcriptome profiles in each disease study of the plurality of disease studies is performed for disease and healthy subjects. In some embodiments, the plurality of disease studies includes the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database. In some embodiments, the RES is generated using a gene set enrichment analysis tool that considers both positive and negative gene sets.In some embodiments, the NES generates a ranked gene list (R+) by a) ordering a plurality of differentially expressed genes from the most positive (i.e., most upregulated) to the most negative (i.e., most downregulated) values; b) identifying hits (i.e., ranks of genes in the core signature) independently for the set of positive (i.e., most upregulated) genes in R+ (S+) and the set of negative (i.e., most downregulated) genes in R− (S−), where R− is the reverse ranking of R+ with the values ​​reversed; c) combining R+ and R− and reordering each value by keeping the hits in both S+ and S−; and d) calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the i-th gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i where p is the value of gene i and p is the weight of r), e) determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score, f) repeating steps a)-e) 1000 times on a random set of genes to calculate a null distribution of ES, and g) generating an NES as ES divided by the mean of the null distribution of ES. In some embodiments, the method further comprises calculating statistical significance by comparing the observed ES to a null distribution or a permutation of sample labels (disease / healthy). In some embodiments, step a) comprises using a log2 fold change or z-score. In some embodiments, R+ and R- are ranked by the log2 fold change of the mean gene expression in the disease samples compared to the mean gene expression in the healthy samples. In some embodiments, the method comprises calculating the NES for all disease diseases using the ranked list for each disease test. In some embodiments, diseases with significant NES are suitable for treatment with dupilumab.

[0008] The present disclosure also provides a method of identifying a subject having a disease or condition suitable for treatment with dupilumab, comprising: a) generating a dupilumab therapeutic core gene signature; b) screening the dupilumab core gene signature against a whole transcriptome profile from the subject; and c) determining whether the subject is suitable for dupilumab treatment. In some embodiments, generating the dupilumab therapeutic core gene signature comprises determining differential gene expression between a dupilumab treatment group and a placebo treatment group in multiple treatment trials, and identifying a plurality of differentially expressed genes. In some embodiments, the multiple treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyps. In some embodiments, the genes of 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 or more of 5 treatment trials. In some embodiments, the fold change comprises subtracting the change in expression in the placebo treatment group from the dupilumab treatment group. In some embodiments, the differential gene expression in the treatment trials of eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment trials of asthma and grass allergy is performed by comparing the gene expression level with allergen challenge to the gene expression level without allergen challenge. In some embodiments, the differential gene expression is analyzed by microarray or RNASeq. In some embodiments, the differential gene expression in the treatment trials of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, the differential gene expression in the treatment trials of atopic dermatitis and chronic rhinosinusitis with nasal polyps is analyzed by microarray.In some embodiments, the plurality of differentially expressed genes is selected from the group consisting 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, ATF3, P2RY6, IGFBP5, TMC5, AD ORA3, 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 against a 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) using a plurality of genes that are differentially expressed and are within the dupilumab therapeutic core gene signature to generate a normalized enrichment score (NES) for all ranked z-scores to represent the dupilumab signature enrichment in the subject. In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.In some embodiments, the NES comprises: a) converting each gene expression level in the plurality of genes into a z-score and generating a value for R+ by ordering the plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value; b) independently identifying hits for the set of positive (i.e., most upregulated) genes in R+ (S+) and the set of negative (i.e., most downregulated) genes in R− (S−), where R− is the reverse ranking of R+ with the values ​​reversed; c) combining R+ and R− and reordering each value by retaining the hits in both S+ and S−; and d) calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the i-th gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i where p is the value of gene i and p is the weight of r), e) determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score, f) repeating steps a)-e) 1000 times on a random set of genes to calculate the null distribution of ES, and g) generating an NES as ES divided by the mean of the null distribution of ES. In some embodiments, the method includes obtaining the 95th percentile of the NES from healthy control samples. NE In some embodiments, the method further comprises calculating statistical significance by determining the NES for all disease diseases using the ranked list for each disease test. In some embodiments, if the subject's NES is higher than the NES of the healthy control, the subject is suitable for dupilumab treatment.

[0009] The present disclosure also provides a method of conducting a clinical trial for dupilumab treatment of a disease or condition, comprising using a 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 differential gene expression between dupilumab and placebo treatment groups in multiple treatment trials and identifying multiple genes that are differentially expressed. In some embodiments, the multiple treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyps. In some embodiments, the genes of 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 or more of the 5 treatment trials. In some embodiments, the fold change comprises subtracting the change in expression in the placebo treatment group from the dupilumab treatment group. In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyps is performed by comparing baseline gene expression levels before treatment with dupilumab to gene expression levels after treatment with dupilumab. In some embodiments, differential gene expression in therapeutic trials of asthma and grass allergy is performed by comparing gene expression levels with allergen challenge to gene expression levels without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of atopic dermatitis and chronic rhinosinusitis with nasal polyps is analyzed by microarray.In some embodiments, the plurality of differentially expressed genes is selected from the group consisting 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, ATF3, P2RY6, IGFBP5, TMC5, AD The dupilumab therapeutic core gene signature may comprise any of the following genes: ORA3, 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 comprises generating a normalized enrichment score (NES) for the dupilumab therapeutic core gene signature at at least one time point before initiation of treatment of the subject with dupilumab and after initiation of treatment of the subject with dupilumab. In some embodiments, the clinical endpoint is achieved when dupilumab treatment reduces the NES of the dupilumab therapeutic core gene signature to an acceptable value. In some embodiments, the NES generates a value for R+ by a) ordering a plurality of differentially expressed genes from the most positive (i.e., most upregulated) to the most negative (i.e., most downregulated) values; b) independently identifying hits for the set of positive (i.e., most upregulated) genes in R+ (S+) and the set of negative (i.e., most downregulated) genes in R− (S−), where R− is the reverse ranking of R+ with the values ​​reversed; c) combining R+ and R− and reordering each value by retaining the hits in both S+ and S−; and d) calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the i-th gene is a hit.i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i where p is the value of gene i and p is the weight of r), e) determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score, f) calculating a null distribution of ES by repeating steps a)-e) 1000 times on a random set of genes, and g) generating an NES as ES divided by the mean of the null distribution of ES. In some embodiments, the method further comprises calculating statistical significance by comparing the observed ES to a null distribution or a permutation of sample labels (disease / healthy). In some embodiments, step a) comprises comparing gene expression levels after dupilumab treatment to gene expression levels before the initiation of treatment with dupilumab using log2 fold change. In some embodiments, multiple samples are obtained from the subject and an NES is generated for each sample.

[0010] The present disclosure provides a method of treating a subject having a disease or condition suitable for treatment with dupilumab, comprising: a) identifying the subject as having a disease or condition suitable for treatment with dupilumab, comprising: i) generating a dupilumab therapeutic core gene signature; ii) screening the dupilumab core gene signature against a whole transcriptome profile from the subject; and iii) determining whether the subject is suitable for dupilumab treatment; and b) administering dupilumab to the subject having a disease or condition suitable for treatment with dupilumab. In some embodiments, the dupilumab therapeutic core gene signature comprises determining differential gene expression between a dupilumab treatment group and a placebo treatment group in a multiple treatment trial, and identifying a plurality of differentially expressed genes. In some embodiments, the multiple treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyps. In some embodiments, genes of 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 or more of the 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. In some embodiments, the differential gene expression in the treatment studies of eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyps is performed by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment studies of asthma and grass allergy is performed by comparing the gene expression with allergen challenge to the gene expression without allergen challenge. In some embodiments, the differential gene expression is analyzed by microarray or RNASeq. In some embodiments, the differential gene expression in the treatment studies of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq.In some embodiments, differential gene expression in the treatment of atopic dermatitis and chronic rhinosinusitis with nasal polyps is analyzed by microarray. In some embodiments, 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, ATF3, P2RY6, IGFBP5, TMC5, AD ORA3, 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 against a 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) using a plurality of genes that are differentially expressed and are within the dupilumab therapeutic core gene signature to generate a normalized enrichment score (NES) for all ranked z-scores to represent the dupilumab signature enrichment in the subject. In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.In some embodiments, the NES comprises: a) converting each gene expression level in the plurality of genes into a z-score and generating a value for R+ by ordering the plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value; b) independently identifying hits for the set of positive (i.e., most upregulated) genes in R+ (S+) and the set of negative (i.e., most downregulated) genes in R− (S−), where R− is the reverse ranking of R+ with the values ​​reversed; c) combining R+ and R− and reordering each value by retaining the hits in both S+ and S−; and d) calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the i-th gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i is the value of gene i, and p is the weight of r), e) determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score, f) repeating steps a)-e) 1000 times on a random set of genes to calculate a null distribution of ES, and g) generating an NES as ES divided by the mean value of the null distribution of ES. In some embodiments, the method further comprises calculating statistical significance by determining the 95th percentile NES from the healthy control samples. In some embodiments, the method comprises calculating the NES for all disease conditions using the ranked list for each disease test. In some embodiments, if the subject's NES is higher than the NES of the healthy control, the subject is suitable for dupilumab treatment.

[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 executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. [Brief description of the drawings]

[0013] [Figure 1] Top 30 genes with the highest expression change vs. baseline after dupilumab 300mg weekly are shown. Healthy and EoE patient transcriptomes reproduced with permission from Sherill et al. (Genes Immun.,2014,15,361-69). All genes met the significance threshold. EoE: eosinophilic esophagitis; qw: weekly. Pink highlights indicate positive upregulation and blue highlights indicate negative downregulation. [Figure 2A-1] Figure 1 shows the effect of dupilumab 300 mg once weekly versus placebo on the published EoE transcriptome in esophageal tissue at week 12. Correlation between the published EoE transcriptome and the transcriptome following dupilumab treatment is shown. [Figure 2A-2] Same as above. [Figure 2A-3] Same as above. [Figure 2B] Effect of dupilumab 300 mg once weekly versus placebo on the published EoE transcriptome in esophageal tissue at week 12. EoE: Effect of dupilumab 300 mg once weekly versus placebo on expression of genes involved in pathways that were most significantly dysregulated in gene ontology analysis. [Figure 3-1] 1 shows type 2 inflammatory gene expression signatures in patients with EoE, atopic dermatitis, nasal polyps, and asthma. [Figure 3-2] Same as above. [Figure 4-1] The log2 fold change in gene expression following treatment in each indication is shown, with pink highlights indicating positive upregulation and blue highlights indicating negative downregulation. [Figure 4-2] Same as above. [Diagram 5] We show that ulcerative colitis (UC) NES is significantly enriched but not included in the top 10. When NES was calculated at the individual patient level using a UC study (GSE87466) including 87 patients and 21 healthy controls, dupilumab NES was statistically significant in 33% of patients. [Figure 6] We show that in the CRSwNP study, the 25-gene set (identified from the treatment and clinical response signature) was shown to have greater predictive power than other available circulating biomarkers for response to each CRSwNP clinical endpoint (CONG, NPS, CT-LMK, and UPSIT). Receiver operating characteristic analysis was used to evaluate the ability of the transcriptional signature and the NES score, which represents a more standard biomarker, to discriminate responders in each of the four primary endpoints, and responses across 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 was highly overlapping with the 25-gene set and was considered to have predictive power for response to a broader range of dupilumab indications. [Figure 7] Figure 1 shows longitudinal changes in gene expression profile in dupilumab 300mg weekly in TREET. A, Change in EDP-NES from baseline to weeks 24 and 52. Each lane represents an individual patient. D, 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. [Figure 8A]Figure 1 shows the effect of dupilumab on markers of cell proliferation in patients treated with dupilumab 300 mg weekly or placebo. Change in expression of the cell proliferation marker MIB-1 (Ki67) (left) and percentage of cells expressing MIB-1 (right) from baseline to week 12 are shown. Change in percentage of positive cells from baseline in immunochemical images for placebo and dupilumab was quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-tailed t-tests to compare absolute percent change from baseline between placebo and treatment groups. ns: not significant, qw: weekly. [Figure 8B] Figure 1 shows the effect of dupilumab on markers of mast cell activation in patients treated with dupilumab 300 mg weekly or placebo. Shown are the expression of the serine protease tryptase in mast cells (left) and the change in the percentage of cells expressing tryptase (right) from baseline to week 12. Changes in the percentage of positive cells from baseline in immunochemical images for placebo and dupilumab were quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-tailed t-tests to compare absolute percent change from baseline between placebo and treatment groups. ns: not significant, qw: weekly. [Figure 8C] Figure 1 shows the effect of dupilumab on markers of T cells in patients treated with dupilumab 300 mg weekly or placebo. Change in expression of the cytotoxic T cell marker CD8 (left) and percentage of cells expressing CD8 (right) from baseline to week 12 are shown. Change in percentage of positive cells from baseline in immunochemistry images for placebo and dupilumab was quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-tailed t-tests to compare absolute percentage change from baseline between placebo and treatment groups. ns: not significant, qw: weekly. [Figure 8D]Figure 1 shows the effect of dupilumab on markers of T cells in patients treated with dupilumab 300 mg weekly or placebo. The change from baseline to week 12 in expression of the helper T cell marker CD4 (left) and the percentage of cells expressing CD4 (right) are shown. The change from baseline in the percentage of positive cells in immunochemistry images for placebo and dupilumab was quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-tailed t-tests to compare absolute percent change from baseline between placebo and treatment groups. ns: not significant, qw: weekly. [Figure 8E] Figure 1 shows the effect of dupilumab on markers of antigen-presenting cells in patients treated with dupilumab 300 mg weekly or placebo. Expression of antigen-presenting Langerhans cell marker CD1a (left) and change in percentage of cells expressing CD1a (right) from baseline to week 12 are shown. Change in percentage of positive cells from baseline in immunochemical images for placebo and dupilumab was quantified using HALO image analysis software. Nominal P values ​​were calculated using unpaired two-tailed t-tests to compare absolute percentage change from baseline between placebo and treatment groups. ns: not significant, qw: weekly. [Figure 9] 1 shows the Dplex3 reaction signature in EoE ph3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Various terms relating to aspects of the present disclosure are used throughout the specification and claims. Unless otherwise indicated, such terms are to be given their ordinary meaning in the art. Other terms that are specifically defined are to be interpreted in a manner consistent with the definitions set forth herein.

[0015] Unless expressly stated otherwise, no method or embodiment set forth herein is intended to be construed as requiring that its steps be performed in a particular order. Thus, unless a method claim specifically specifies in the claim or description that the steps are to be limited to a particular order, it is not intended to dictate order in any respect. This includes any possible implicit criteria of interpretation, including logical matters regarding the arrangement of steps or workflow, general meanings derived from grammatical construction or punctuation, or the number or type of embodiments described herein.

[0016] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly indicates otherwise.

[0017] As used herein, the term "about" means that a cited numerical value is approximate and that small variations do not significantly affect the practice of the disclosed embodiments. When a numerical value is used, unless otherwise indicated by context, the term "about" means that the numerical value can vary by ±10% and remain within the range of the disclosed embodiments.

[0018] As used herein, the term "comprising" may in certain embodiments be replaced with "consisting of" or "consisting essentially of," as desired.

[0019] As used herein, the terms "nucleic acid," "nucleic acid molecule," "nucleic acid sequence," "polynucleotide," or "oligonucleotide" include polymeric forms of nucleotides of any length, including DNA and / or RNA, and can be single-stranded, double-stranded, or multistranded. A strand of a nucleic acid also refers to its complementary strand.

[0020] As used herein, the term "subject" includes any animal, including mammals. Mammals include, but are not limited to, farm animals (e.g., horses, cattle, pigs), pets (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.

[0021] As used herein, a list containing A, B, "and / or" C gives (i) A only, (ii) B only, (iii) C only, (iv) A and B, (v) A and C, (vi) B and C, and (viii) A, B, and C. Thus, a list containing A, B, C, ..., and / or N, where N is a positive integer, has N components and gives all possible combinations of A, B, C, ...N, up to N, including all combinations of the N components.

[0022] As used herein, the phrase "reverse" in disease signature refers to a comparison between disease and healthy samples. A gene is upregulated (red / pink) if its expression is higher in disease compared to healthy. In treatment signature, the comparison is made between post-treatment and pre-treatment. A gene is downregulated (blue) if its expression is lower after treatment compared to baseline (pre-treatment). "Reverse" refers to genes that are significantly changed in the opposite direction in disease and treatment signature, for example, genes that are upregulated in disease (compared to healthy) and downregulated after dupilumab treatment (compared to pre-treatment).

[0023] In the process of drug discovery, it is useful to identify existing approved drugs that are effective for a new indication (be it a disease, condition, or disorder), or a subset of a patient population that is likely to respond better to an existing drug. Traditional drug efficacy testing uses single-gene analysis (i.e., t-tests) upon drug treatment for each individual gene to identify suitable biomarkers for the target and change the levels of such biomarkers to obtain the expected therapeutic effect. However, this approach may generate long lists of statistically significant genes without correlating their biological relevance. Furthermore, such gene lists between different clinical trials may have little overlap, and most importantly, such gene lists may miss the pathway effects of each gene involved in the disease, condition, or therapeutic effect. For example, multiple small gene expression changes, while not individually significant in a t-test, may have a greater collective impact on a particular disease or condition than a single gene that changes widely but has only a small impact on the disease or condition. To avoid such an outcome, a genome-wide approach is developed in the present disclosure to first identify a set of core gene signatures in clinical trials of a drug, which are compared to whole-transcriptome profiles of interest to obtain normalized enrichment scores that are used to identify appropriate indications or patient populations that will respond to the drug, specifically dupilumab.

[0024] The present disclosure provides a method for identifying a disease or condition suitable for treatment with dupilumab. In some embodiments, the method includes generating a dupilumab therapeutic core gene signature. In some embodiments, the method includes screening the dupilumab core gene signature against whole transcriptome profiles from multiple disease studies. In some embodiments, the method includes identifying a disease or condition with differential gene expression in the multiple disease studies that is in the opposite direction to the dupilumab core gene signature. In some embodiments, the method identifies a disease or condition suitable for treatment with dupilumab.

[0025] In some embodiments, the method includes generating a dupilumab therapeutic core gene signature, which includes determining differential gene expression between dupilumab and placebo treatment groups for multiple treatment trials. In some embodiments, the method identifies multiple genes that are differentially expressed.

[0026] In some embodiments, the plurality of therapeutic trials includes eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyps. In some embodiments, the plurality of therapeutic trials includes eosinophilic esophagitis. In some embodiments, the plurality of therapeutic trials includes atopic dermatitis. In some embodiments, the plurality of therapeutic trials includes asthma. In some embodiments, the plurality of therapeutic trials includes grass allergy. In some embodiments, the plurality of therapeutic trials includes chronic rhinosinusitis with nasal polyps. In some embodiments, the plurality of therapeutic trials includes any disease, disorder, or condition in which IL-4 and / or IL-13 are implicated or suspected to be implicated.

[0027] In some embodiments, genes in a core gene signature identified from differential gene expression are selected as having a fold change > 2 in 60% or more of the treatment trials and q < 0.05. Alternatively, a fold change threshold of 1.5 or a p-value (instead of a q-value) may be used. In some embodiments, genes in a core gene signature identified from differential gene expression are selected as having a fold change > 2 in 60% or more of the treatment trials. In some embodiments, genes in a core gene signature identified from differential gene expression are selected as having a q < 0.05 in 60% or more of the treatment trials.

[0028] In some embodiments, the fold change includes subtracting the change in expression in the placebo arm from the dupilumab arm. For example, the fold change is the mean expression in group 2 / the mean expression in group 1.

[0029] In some embodiments, the differential gene expression in the treatment study of eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, the differential gene expression in the eosinophilic esophagitis is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment study of atopic dermatitis is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment study of asthma is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, the differential gene expression in the treatment study of grass allergy is performed by comparing the baseline gene expression level before treatment with dupilumab to the gene expression level after treatment with dupilumab. In some embodiments, differential gene expression in treatment trials of chronic rhinosinusitis with nasal polyps is performed by comparing baseline gene expression levels before treatment with dupilumab to gene expression levels after treatment with dupilumab.

[0030] In some embodiments, the differential gene expression in asthma allergy treatment testing is performed by comparing the gene expression level after allergen challenge with the gene expression level before allergen challenge. In some embodiments, the differential gene expression in grass allergy treatment testing is performed by comparing the gene expression level after allergen challenge with the gene expression level before allergen challenge.

[0031] 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 levels.

[0032] In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyps is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of atopic dermatitis is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of asthma is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of chronic rhinosinusitis with nasal polyps is analyzed by RNASeq.

[0033] In some embodiments, differential gene expression of eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyps 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, 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 rhinosinusitis with nasal polyps is analyzed by microarray.

[0034] In some embodiments, the genes in the plurality of differentially expressed genes are 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, S100A8, and / or 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.

[0035] In some embodiments, the genes in the plurality of differentially expressed genes are 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, KIAA2 022, 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,CPT1B,C1orf122,CNKSR3,GATSL3,S100A12,PMCH,KIF21B,GALNT6,GBP2,C7orf73,MYOM3,CAMK2N1,MYEOV,PTCHD4,SLAMF1,TLR2,CADPS2,M OSPD1, UCKL1-AS1, AX748345, SRGAP1, NRXN1, CXCR4, IL3RA, ITGA4, CRAT, FAM46B, HBB, YOD1, NEFL, GGH, OAS2, AX748379, EFNA2, TUBA4A, LRFN2, RHBG, C XCL1, AK124308, LINC00672, AK092098, HMOX2, LZTS1, PHGDH, SNX9, ATP13A5, ARHGAP25, BTN2A3P, NR5A2, ARHGAP28, SPRR1A, AGFG2, RNF180, NEFM, TSP AN3, GNRH1, PSMB10, 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, CA P2, abParts, STAB1, DLGAP5, OSCAR, RNF11, STAR, UCA1, KRT4, IL1RL1, LAIR1, SH2D2A, N CKAP1L, AK289390, NUDT8, MYH11, LY6K, CPA3, CHST2, MPEG1, STARD4, AKR1C3, HEG1, PPP 1R36, ALDH3B1, CXCL6, PSMB9, TIGIT, TFRC, JUN, PRSS27, KRT13, TP53TG3D, TREML2, MAP K10, GVINP1, HNRNPU-AS1, MORN4, CECR7, TSPAN7, MTUS2, LURAP1L, HLF, MYOF, NAIP, BC0 63600, FAM126A, LINC00482, HIST1H2BC, HDC, LIFR, TMPRSS4, ATP12A, PCDHGA1, ZNF662 STX1B, XCR1, AK128525, ADRA2A, ARMCX3, FAIM3, TPGS1, PPPIA4, SOSTDC1, HP07349, Ig kappa, BDNF, FAM69A, KLRAP1, ROMO1, MPZL3, ME1, TCN1, TMEM71, RUFY4, CCDC146, FAM13A-AS1, BSPRY, AK127124, MT1E, LOC6463 29. 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NPAS1, LRMP, SLC2A4RG, UGT1A7, KRT78, CACNB4, IL2RG, APOD, HRNR, XYLB.SLC27A6, FAM20A, GADD45B, C2, TMEM98, SLC16A2, PLAGL1, TYRO3, PDLIM2, ZNF578, HIST1H3D, IFI35, KLRB1, A4GALT, CMAS, MCOLN3, TUBB6, OTOP2, AGPA T9ANKDD1B、N4BP2L2-IT2、CEP152、BCOR、AKR1C1、PLCXD1、FAM174B、RNF182、CBRC7TM_40、AK092331、KIF20A、TPRG1L、PCLO、METRNL、LOC96610、ANKRD37 , PTGES, CD9, HUNK, TEC, ZNF800, SEPT3, VSIG10L, MT2A, CD163L1, SLFN13, LAT, PLB1, PCBP1, PNMA1, NQO1, KRT40, HNF4G, CLMN, NUP62CL, SOCS6, TMEM40, ARHGAP5-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、GPR15 3、EIF3CL、SCRIB、SCAND1、FFAR4、BC131755、NRG2、NEK6、CTAGE4、AF157115、SORBS2、TMEM254、CCL21、C9orf66、AX747517、SRGN、FCER1G、LOXL2、CHI3L1 、CYP2C18、B4GALT5、AMFR、ADIRF、BC040701、MC1R、C9orf40、PCNXL2、SLPI、PIH1D2、AMACR、RNF103-CHMP3、GFI1、PLAT、TYROBP、PKDREJ、RNF24、LINC008 87.FTH1、ITGAM、ZNF503-AS2、PPARGC1B、DIS3L2、FBXW4P1、ATG9B、HIST1H2AE、AFF3、SDK1、DUOX1、IQGAP3、S100A10、UNC13B、PAPL、IL1A、CPA4、LINC00707 、POC1B、IFI16、CX3CL1、NCR3LG1、LINC00933、TRIM2、RHOF、ITGA9、TNFRSF18、MTFR2、IGFBP2、IL36B、FLJ23867、FAM109A、FER1L6、VCAM1、C1orf216、IFI TM3、KCTD11、SMIM5、ADM2、CXCR2、EML1、SLC44A4、SULF1、FAM3C、BC038574、SDK2、BAI1、SPIRE1、TAS1R3、SLC45A3、BTN3A3、AX746775、AREG、S100A16、C XXC5、CHRNB4、LRRC20、RASGRP4、ARNTL、GZMA、SPAG4、BOK、ZNF460、CDH22、GSTT2、TFPI、AL832891、AK308965、SERTAD1、PWWP2B、WWTR1、TPTEP1、BC01757 8、LRRC8D、NFATC2、AX747179、CDR2L、SERTAD2、AKR1B10、LOC729970、SPTBN4、SCUBE2、SERPINA1、BC034424、MVD、VMAC、AL110181、PELI1、HTR3B、NCF1B 、FAM26F、SHCBP1、PCDHGA5、SYNGR3、WNT2B、CEACAM7、SPRR2D、NCF1、FLG2、KPNA2、MAP1LC3B2、FBXO39、CITED2、SASH1、LINC00664、KITLG、ZNF487P、TTK、 HIST1H2BK、ABHD12、ACTG1、MYZAP、CYP2B7P1、GALNT4、BTN3A1、BC069212、ANKRD6、KLB、RNF128、PKIB、CPNE4、IL17RB、F2R、ZNF827、ARMCX5-GPRASP2、U SP6NL、HIST2H2BF、CECR6、HS3ST6、MICB、FBN1、PDE4D、INPP5A、AK130329、RND3、AK096475、CRYAB、CD69、RAC2、GMFG、CAPS2、TSPYL4、VAV1、MGLL、UPK1AGPRC5B、C3orf52、CD37、SH3BP5L、AK094188、RP1L1、FAM180A、PPP1R3G、ARHGEF6、BST2、CLDN1、TBKBP1、EDIL3、C15orf62、S100A8、GNA14、CFH、COL6A2、M、 <h2 style=";text-align:left;direction:ltr">AN1A1、HMGCS1、ZFAND5、ZNF254、VIPR1、CYP4F35P、NPR1、GIMAP1-GIMAP5、T MEM133、SP6、TM4SF19、EFNA5、AK097370、SMOX、CNRIP1、TNS4、DKFZp686B07 190、BC040700、ITGBL1、DBNDD1、TMEM8A、SLC38A4、PKP2、MSRB1、SKA1、LINC 00265、FER、CYP2C9、TGM1、CGNL1、TNFSF13、PRODH、BUB1B、TSPO、ZNF425、ZN F430、TNNT1、ETNK2、CSF2RB、RGS18、RGS16、SRXN1、ABHD5、PCSK5、KLRG2、HM GCS2、ADAM28、HCG26、PIK3AP1、ZDHHC2、GCNT1、ADAM11、EPHA10、SPRR2B、GP R160, ORAI1, PLEKHG2, PRSS22, TAB3, CTIF, EPS8L1, HSPA2, KLHL6, CD274, FMO4, TEF, PPM1K, SHROOM4, BCAR3, ZNF812, TMEM176B, PARP9, AK123332, SYAP 1、HIST2H2BE、ST3GAL4、ZNF667、TGM3、CECR2、IL18RAP、PCCA、XXYLT1、GLTP 、ANKS1B、ATP6V0A4、KRT77、NYAP1、RGS2、LOC283070、NAPA、CSRNP3、NELL2、 CD207、NHLH2、CMKLR1、TGM2、LIMA1、SAMD10、ARL4C、CPT1C、GCHFR、PAK7、PL AUR、MTHFD1L、KIF23、MARCKS、GLTPD1、SPRY1、MT1L、OR7E91P、HTR2B、EPSTI1 、ASPM、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<h2 style=";text-align:left;direction:ltr">TMED7-TICAM2、LFNG、CNN3、FAM3D、SLC46A2、SLURP1、RSAD2、EPHA4、BC0637 88、CLCF1、FAM102A、TUFT1、ICOSLG、RUSC2、EDAR、UBE2L6、BC051760、ITM2A 、BC040684、FCER1A、FIBIN、DLGAP1、CEP72、ABCA8、AK095366、ALKBH7、ATP6 V1C2、B3GNT3、IL1RN、CTNNAL1、COL8A2、CYP2R1、CLEC2D、CCDC85B、GUCY2D、I L36RN, SLC9A3R2, AR, ADORA3, GPR143, SLC18A2, PANK2, CEACAM19, MFSD7, S100A9, IGFL1, GCNT3, CD300A, KRT17, ALDH5A1, CCDC127, TMEM80, SLC16A3 NDUFA4L2、SFRP1、CYTH4、KIF2C、SIRT7、KIAA1244、TOB1、HIST1H1C、EPHA6、 DPYD、GPR82、TYMS、C15orf52、C17orf103、FAM86FP、LOC100127983、CR93667 7、LOC100129034、ICOS、LAT2、TNS1、C1orf21、TMOD3、ARHGAP27、EPB41L3、M MP25、CTSW、UBA7、BDKRB1、LTBP2、CD1A、CYP4F22、NKPD1、C1orf110、RAB20、 CCNB1、METTL21A、CHMP4C、LOC401052、HERC2P4、KRT32、RAB44、TLR4、SCIMP 、FOXO3、ITGB5、ADCY9、UPK3B、AQP7P1、FCGR3A、GSDMB、GPR68、TEAD1、SNX8、D HRS1、NAP1L2、NGEF、MMP9、CBWD5、KIF14、MYL12A、DMTN、FSCN2、ZNF555、LGI 3、OR2A7、ADAMTS2、DDX26B、PIP5K1C、AK056780、COBL、MAL、C3orf80、LILRB2 、SLCO3A1、FOLH1、SDC1、TPRN、IL34、HHIPL1、ANKRD20A5P、AADAC、FAM46A、A K123872、GALNT14、SLC10A6、BAIAP3、SNX21、RNASE7、PPARGC1A、NCF4、SELL、<h2 style=";text-align:left;direction:ltr">GCAT、CAMSAP3、KRT33A、CDHR1、LOC100127888、POC1B-GALNT4、HJURP、TCIR G1、GPD1、ARHGAP39、PADI3、DAPL1、AIF1L、CMPK2、PSMB8、UBE2C、MACROD1、B CAS1、CAB39L、LNX1、C1orf168、SPP1、CHST15、CDK1、JUNB、CLEC3B、AK12569 9、SLC6A11、RNF223、SLC15A1、LIMD2、KIF18A、COL7A1、FKBP1A-SDCBP2、SHRO OM3、DUSP4、FLG、MEOX1、IKBKAP、FJX1、FOSL2、AVPI1、CLIC3、DHRS9、BAIAP2 L2、RNASE1、ORAI2、CSF1、NMRK1、RTN1、CDKN1C、POU3F1、ST6GAL2、PXDN、ATP8 B4、CLIC6、DOCK9、RAB36、PIK3C2G、SLC35F3、ALOX12、ANKRD65、MX1、GCSAM、 SEMA4D、DUSP14、LCN2、FADS6、NKX6-2、RYR3、CD226、AK123584、MAP2K1、FAM8 4A、PITX1、C7orf57、C1orf177、MFHAS1、WDR52、PTPRB、CYP2U1、ARRDC1、TNF RSF11A、TMPRSS11B、FAM25C、SLC16A1、NFKBIE、RELB、CPNE5、SLC2A11、HOME R2、UNC79、HTR3A、ZSWIM5、CADM1、MMP19、ITPRIPL2、EREG、DYNAP、TSC22D2、 SLC13A5、CAPN14、NTN1、GALNT5、RCOR2、LPIN2、FBXL16、HNMT、RAB6B、STOM、T MTC3、CCNB2、KRT18、KPNA7、LDHD、BC033124、CHAC1、CYP7B1、IFIH1、FANCI、 MYRIP、CSRNP1、LOC100132891、TUBB2A、RGS17、SHF、SLC39A8、RALGAPA2、SRC IN1、AMHDD2、SLCO2B1、VIT、DSG1、GAPT、PLIN5、LINC00900、PQLC1、NDRG4、C ACNA1G、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、S LC6A1, SELP, FAM118B, APOBEC3D, FHOD3, ELL2, ABHD16B, TMIE, NR1D1, NCF2, SLC9B2, CD53, LOC285074, FABP5, VWA2, TMEM105, RHCE, TMEM253, DENND4A , FAM111A, 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、SAMD11、IL15、IL7R、IGFBP7、HSPA6、FZD7、CYP2C19、TICAM1、CLDN10、 LONRF2, AK055623, PITRM1, ACSL6, NETO2, PKP3, BC046483, ZNF365, NRP2, ATP10A, CDCA2, NFKBIA, ZNF208, SPTBN5, SPRR2E, IRX6, TMEM173, JAK3, SMAD 1, CD164L2, HIF1A, AX747832, GGTA1P, LOC284551, NAV1, THY1, SPAG5, AURKC, RIOK3, DTX2, C6orf132, ACER1, CD22, ITGAX, ATP11C, LOC100129195, GDP D3、PMAIP1、TCP11L2、CES1P2、C12orf54、PDE4A、PLAU、RASGEF1A、COL14A1、LINC00675、C19orf26、LGR6、FOXE1、GIPR、SLC38A6、AK126286、LOC344887、<h2 style=";text-align:left;direction:ltr">LOC100288181, AKR1C2, BC070322, CD101, FGF1, MEIS1-AS3, MAPK7, ALDH1L1, PRKAA2, CDK14, ANKRD62, PDE3B, FASLG, ARHGAP9, DHCR24, TST, KCNMA1, BP GM、SPINK7、COL6A3、ABCA10、CTSL1、CAST、COL18A1、AK311497、GPX3、KPRP、 UPK1B、C1S、DOCK2、JARID2、DBN1、TGFB1、ZNF714、BC042588、DDX58、FPR1、IK ZF1、LOC730101、WWC1、DMRT2、HIF1A-AS2、LRRN2、GPR34、DSE、APCDD1、DQ57 6756、ID1、PIM1、LINC00346、AK054845、CISH、NFIL3、LINC00173、NDUFA6-AS 1, TMEM63C, CSNK1E, CSTB, IGSf11, GCNT2, LY96, FAR2, AMER1, SLC34A3, MROH6, SYT15, C4orf6, LINC00839, EDNRA, PTP4A3, FUT3, CTNNBIP1, UBALD1, EMP 1、ANKRD31、RARRES3、AURKA、PCOLCE、SSBP3、CSK、MBLAC1、SCN2B、CRISP2、C HST9、VSIG4、LINC00511、SIPA1L2、PALMD、TIMP2、ADM、MYOZ1、IFI44L、HMMR、 DKFZp686D16130、RAB40C、MMP24-AS1、RECQL4、THRA1、CRYM、GRK5、SASH3、R RM2、KIAA0913、SH3RF3、ERV3-1、C12orf56、REN、CBLN3、DOCK7、PBK、DIRAS3、 PXDC1, RASAL1, LDLRAD1, EPGN, IFI27, C1QB, CCNA2, NFKBIZ, PRDM1, GFOD2, CGREF1, EDN2, DNAH11, IRF9, SCARA3, LGALS3, SERPINB9, CCDC69, MAFF, HSN2 、MFAP3L、LOC646214、FPR3、NDUFB11、ADIPOR2、PACRG、SERINC2、PSORS1C2、A POL2、KLK7、LAMP3、PLA2G4A、HTR7、BC044939、RNF208、PEG10、PIK3R6、DOK3、SERTAD4、RCAN3、AK8、CABLES1、HIST1H4H、HCG22、TCERG1L、PIF1、APOL6、ARRDC3、CCL22、ZFP36、SORBS1、EDN3、COL12A1、MDGA1、ZNF37BP、PRDM4、CCDC6、C4orf19、RBP7、GY、 <h2 style=";text-align:left;direction:ltr">S2, SIGLEC1, AEBP1, TUBBP5, SAMD9, ABLIM1, DUOXA2, HIST1H2BG, GGT8P, ID3, DNAH17, BC150585, LLGL1, BLVRA, BC068095, NBPF10, ARG1, ZEB2, ZMYND15 、ABHD4、HECA、ABHD6、BC041455、PFKFB3、DKK2、RARB、SLC15A3、SAV1、ZDHHC 20、PLEKHN1、FCGBP、ZNF431、FAM25A、SHC2、KCNMB3、RASL11A、ABO、HK2、THSD 7B, PHACTR2, FNDC4, CD163, PCP4L1, CENPW, EHD1, ELF3, FAM228B, LINC00628, BC024173, NR4A1, MIAT, BLM, PFKFB2, SLIT2, SFT2D2, CRIP2, PRLR, NUP210 HAPLN3、PRELID2、NME4、PTK2B、PFN2、ABLIM3、MUC22、RAB37、FOXQ1、TRIM2 1、AK095151、ITPKC、GHR、LRRC24、SFTA2、APOBEC3B、CAMK1、AOAH、EPN1、TMEM 52、CXCL14、AK097590、DPCR1、CHI3L2、SCCPDH、ACSL4、GJA3、LINC00920、SL C5A10、MIPEPP3、RTP1、APOL3、CD40、PDPN、SYTL5、PHC1、BBOX1、EEPD1、CIDEA 、MUC4、ETV7、TMEM91、ADAMTS17、SLC37A2、MALL、NKX2-8、CES1、ASPN、BIN2、 MFSD4、PRDX5、TRPS1、LRRC37A4P、LINC00568、HILPDA、SIGLEC10、TFEC、STON 1, 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、AX748273、UBAP1L、EPS8L 2、CLDN17、MT1H、PRKXP1、AX747264、AB209185、EPCAM、RPS6KB2、FUT6、UPP1、MT1G, AIM2, RANBP17, SGOL2, LRRC16B, DBP, SH3RF1, TP53I3, PNLIPRP3, ST6GAL1, PLSCR4, NUF2, UNC5B, HIPK2, GPR111, H19, SLC8A1-AS1, LTBP1, FUT8, XPR1, MAP3K9, NDOR1, CNST, PPDPF , SYNPO2L, CCBL1, IGFBP6, CCDC82, CCT6B, ADSSL1, MACROD2, OSBP2, SPINK8, ANPEP, CDH11, EPAS1, PARD6B, CFTR, ANKRD20A3, SERPINA11, CRISP3, LUM, OLFML3, SEC14L1P1, KLC3, GYPC, B LVRB, CCNYL1, CRTAC1, SERPINE2, BC041347, SIRPG, PDGFD, MARCH1, MRVI1-AS1, TNFAIP8L3, LAMB4, PRTFDC1, CEP55, EPHA3, OPTN, SLC4A4, LOC100130476, ACOT4, KRTAP3-2, ACSL5, CENPE, LOC100130557, SPRR1B, NR1D2, AK056431, KCNQ5, FSTL1, MKI67, LOC100506472, KATNBL1, 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 of the genes of the plurality of differentially expressed genes described above can be excluded from the gene expression analysis.

[0036] In some embodiments, the plurality of differentially expressed genes includes 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, CB RC7TM_40, PSMB9, SLC7A7, CD9, ECT2, SLC15A3, ARHGEF37, DNM3, AK098438, HLA-F, HLA-B, CDC45, FAS, SCIMP, IL18R1, MICB, NFE2L3, BDNF, SDPR, TNFRS F18, DSE, DLGAP5, KPNA2, BC069212, SKA1, MEIS1-AS3, C3AR1, APOL3, RUFY4, RGS18, TYROBP, TCIRG1, GALNT5, FPR3, ZC3HAV1L, PLK1, AX747758, CTLA4, T AP1, GPR183, CYP2R1, ORAI2, DENND4A, SCCPDH, CENPE, CDC25C, FJX1, SAV1, CHST15, SLC39A8, FLT3LG, HMMR, CENPW, CD101, SH3PXD2B, KRT23, ADA, UBE2 C, FAM46A, ETV7, FAM111B, MMP14, NDC80, AADAC, CCR1, RAC2, HCK, GABRP, KIF4A, ABHD4, PRELID2, COL6A6, BC040701, NAV1, FCER1G, POC1B, PRODH, LY96, ACOT9, BUB1, PPAP2C, FAM69A, ERCC6L, MTFR2, C1QA, PRR11, GFI1, MFAP3L, SHC2, ACSL5, PSMB8, LRP12, IFITM3, IRF1, GTSE1, AQP7, LOC100506714, CASP 7, 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、<h2 style=";text-align:left;direction:ltr">MYOF、GCH1、SEMA3B、PPARGC1B、SLC38A5、PARP12、SOAT1、KIF21B、CD84、AK1 23771、RNF207、PROCA1、CLUAP1、IFI16、CSF1、MFSD4、UBE2L6、MX1、ATP8B4、I QGAP3、PCCA、ATP11C、BCL6、ZC3H12D、ABCC5、SLFN13、HDX、RASSF6、ROBO1、G MFG、SMAD1、PLAU、PDPN、PDE3B、ANTXR2、CCDC150、LOC100507463、C1orf216、 FAM101B、NUP62CL、UBA7、FANCI、CTSL1、IQCK、AKAP2、ITGAE、ZNFX1、CLDN1、 USP12、C3orf52、EPSTI1、CD40、PLSCR4、SH2D2A、LAPTM5、NBEA、APOBEC3D、PX DN, CTSW, HDAC9, CTSS, PARP3, EVA1C, CCDC74A, IQGAP2, GBP1, EDNRA, IL3RA, ARMCX3, BC013821, IFNGR1, MC1R, ARNTL, IL7R, TLR2, BTN3A2, GNLY, LAT, AC SL4、ADAMTS9、FUT8、MPEG1、AK097957、ANKRD36B、SERTAD4、SCARA3、CAMK1D 、PARP9、GPR65、DOCK7、BIN2、IFI6、TNFRSF10B、SERPINE1、LOC100288637、GI PR, IFI44, BTN3A3, ZNF487P, TLR4, IRF9, ASS1, TMED7-TICAM2, NTF4, RAB23, BX647608, TRANK1, USP54, HSD17B11, PDE4D, PTP4A3, MYO3A, CD37, SLC38A6 、BTG3、SLAMF1、RNF213、CEP152、RALGAPA2、PLCB2、CD53、APCDD1、CLSTN3、S KAP1、THY1、PDE4A、KCNMB3、FAM3C、WIPF1、LINC00173、TRIM21、TFRC、MCAM、E PHA4, CAPN13, CLU, TMEM91, TRAF3IP3, HCG26, DTX3L, NCKAP1L, GIMAP2, MDGA1, FAR2, APBB1IP, ERG, EVI2A, CD28, XAF1, DENND3, ARHGAP15, FOLH1, GLB1LRIC8B, LINC00865, TIGIT, IGF2BP2, IL32, CADPS2, KCND1, APOL6, TMC8, CTS K、ESR1、BTN3A1、ATP10A、ARHGAP9、SIRPG、ATP8A1、RASA3、DAB2、IL18BP、MA N1A1、ATP12A、GLRX、DDX60L、ALOX5AP、LRRC6、PRSS12、LINC00673、TRAF5、C OL6A1、LAG3、DOCK2、IKZF1、EPAS1、IFITM1、OAS2、SAMD4A、IFNAR2、DOCK11、 COL12A1, CD226, NEDD9, L3MBTL1, SFMBT2, STON1, SRPX, C4orf21, PARVG, BC 144457、TSHZ3、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、A CAP1、IL18RAP、GZMA、GCSAM、LRP5L、PRKXP1、MAP3K8、LOXL2、CD48、ITGA4、P DPR、P2RY10、PCP4L1、LINC00672、C10orf10、PCOLCE、SERPINB3、ITK、TMEM1 33、DLC1、GBP2、FAIM3、NAIP、GLIPR2、ASB9、COL6A2、AEBP1、OLFML3、BTN2A3 P、SYCP2、AK054970、COL6A3、NKG7、STAT4、EIF3CL、DDX26B、ZNF37BP、COL1A 1、GBP4、DL491896、LINC00939、FN1、RAD51AP2、CBWD5、WDR52、FMO4、SP140L 、NLRC3、BC063788、LINC00511、PPP1R12B、AX747171、AK128252、PLCE1、PTP RB、KLRAP1、PTBLP、BC040358、MIAT、EBLN2、LCK、GNRH1、GVINP1、CLEC2B、AL DH1L2, CAPN3, NR5A2, VPS13C, C1S, AF157115, N4BP2L2-IT2, F2R, AF268386.FAM24B-CUZD1, FASLG, AX747179, SOX2-OT, LOC100506123, FARP1, AK093551, UBASH3A, ABCA10, AK127443, CLEC2D, AK095366, AK092098, TIFAB, IGFBP7, MAP1B, AL831889, RNF183, AK308965, AK092331, CECR2, FBN1, ISLR, CDH11, PTGS2, DKFZp686B07190, GIMAP1-GIMAP5, C11orf92, FAM13A- AS1, BC051760, AX747833, AK095112, ITGA1, LUM, LOC646214, SLC9A2, LOC619207, DQ573949, LOC100506472, AX748379, TOP1P1, LOC100130557, AK091624, DL490846, FGF1, AK093534, APOD, COL1A2, AK123872, C1QTNF3, AX746775, COL3A1, AK125301, ASPN, HNRNPU-AS1, SERPING1, AK123332, TRGC2, IFI44L, ADAMTS2, SEMA3A, SEC14L1P1, USP32P2, TUBBP5, PITRM1-AS1, DKFZp686M11215, RBMS3, EPHA3, PIEZO2, CSGALNACT1, AL832891, B X649158, ABCA8, FCRLB, TPGS1, SCNN1D, NFKBIA, MYH11, HES4, AK126286, SYNGR3, SPNS3, SCAND1, ZNF578, JUNB, NELL2, BC041455, PRSS22, BC0680 95, 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 combination thereof.

[0037] In some embodiments, the plurality of differentially expressed genes is selected from the group consisting of 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, DI S3L2, PIP5K1C, LOC100130705, STXBP6, ARHGAP44, XXYLT1, DVL1, LRG1, C17orf103, RAB36, NAPRT1, GCAT, ZNF556, NETO2, ZNF208, ARID3A, GATSL2, NBP F10, KIF1C, RPL23AP82, DBP, OAZ3, CDKN1A, GLTPD1, ​​CNTNAP2, METRNL, MIF, SDC1, KLC3, HMOX2, FBXO32, NUDT8, MRC2, PFKFB2, RBM10, TMEM127, RABGGTA , MAPK8IP1, SRXN1, RAP2B, FRMD6-AS1, BAIAP2, FKBP8, RPS6KB2, FAM122C, ITPRIPL2, FAM222A, LZTS1, ENTPD2, PTPN20A, STEAP4, BPGM, SCRIB, LFNG, NF KBIZ, PCDHGA1, MLXIP, CBX4, PWWP2B, TPRG1L, SH3BP5L, SYAP1, NAPA, RCAN3, AK095151, NCK2, DLGAP1-AS1, ARRDC1, PTK2B, LOC100996255, BC040700, T EX9, ABLIM2, RNF10, SH3GL1, HSFX2, CSRNP1, ANKS1B, KCTD11, REEP6, C1orf210, GNA15, IER3, PLCD3, BLVRB, PCNXL2, GALNT14, GPD1, BCKDHA, PLA2G4B,<h2 style=";text-align:left;direction:ltr">FKBP1A-SDCBP2、SLC34A3、UBALD1、MAPK7、LRRC16B、TPRN、C10orf12、MMP24 -AS1、ITPKC、NDOR1、FAM86DP、SLC2A4RG、HIST1H2AE、SAMD10、KIAA0913、EPC AM、MEGF9、FAM86EP、MXRA8、FBXL22、ACTG1、CX3CL1、AREG、ZDHHC2、MARKKS、 MYL12A、SIRT7、MYRIP、ACSL6、JARID2、PFKL、SH2D3A、STXBP1、DHRS4L2、DZIP 1、SPHK1、CHAC2、CABLES1、ANKRD36C、S100A10、CCT6B、ANXA11、SPINT1、SER TAD2、DMTN、CHMP4C、RTN1、EME2、KCNK5、SSBP3、MAF、SAMD4B、ARL4C、FAM83D、 TUBA4A、YPEL3、UPRT、DNAJC5、DBN1、PXDC1、TMEM86A、BTBD19、SDCBP2、PLCX D1、CYP2W1、IL1A、DKFZp667P0924、SPAG4、PANK1、FOXO3、CYP2U1、HSPA6、HEC A、UNC5B、PARD6B、PEBP1、EGOT、WWC1、NR1D2、C4orf19、HBB、DEF8、HDAC5、KI AA0226、CNNM1、BSPRY、UBE2E2、NDRG4、AK311374、MBLAC1、RPS6KL1、GPSM1、S LC9A3R2、TMEM74、CDK14、PRDM4、SERHL2、SEC14L1、HSPB8、H2AFJ、PINK1、UF SP1、PER1、ZSWIM4、ANKRD20A4、IGFBP2、LINC00265、CPNE5、DQ576756、MORN4 、UNC13D、FAM222B、AVPI1、TMEM63C、PMAIP1、HIST1H4E、HMGCS1、ARMCX5-GP RASP2、LRIG1、ZDHHC20、MAP3K9、RNF11、ZNF800、VMAC、CXXC5、RND3、TMEM80、 LCN2、ZFP36、SIPA1L2、PDGFD、CNPPD1、FAM134B、TMED3、FZD7、DNM1、TUBB6、 NINJ2、CLCF1、MAP2K1、XK、HOTAIRM1、SALL2、C20orf24、ISG20、PNMA1、LNX2、<h2 style=";text-align:left;direction:ltr">DHCR24、PLA2G4A、PRDX5、CEACAM19、FBXO27、C15orf48、PLA2G4F、CSNK1E、S OCS6、TEAD1、PDZD8、HIST2H2BE、FAM102A、STAR、HIST2H2BF、B3GNT7、HP0734 9、LGMN、TMEM79、CRYL1、SEC14L2、RNF24、CCDC127、COL18A1、FLJ11235、RUN DC3B、EFNB3、B3GNT8、AHCY、OXR1、TBKBP1、PHACTR4、ATP1B1、TSPAN13、HEBP2 、S100A16、ITGB5、SULT2B1、CTIF、ZSCAN18、SP6、PANK2、RCOR2、ARHGEF26-A S1、FAM161B、MKNK2、KIAA1244、ARHGAP39、TST、AK8、FAM46B、AHDC1、VPS37B、 IMPA2, FOSL2, ZNF770, TMEM40, HIPK2, PRSS27, ZNF430, AX748345, KIF26A, PLEKHG4B, XYLB, AK094188, GLTP, GUCY2D, SNX8, ADIPOR2, SERPINB9, HBA1, P RSS8、PKP3、DTX2、ZNF382、JUN、TSPYL4、CAMSAP3、CSK、ABHD6、ELF3、TMEM52 、GTPBP2、LINC00319、PAPL、BC046483、RFFL、WNT4、NUP214、MFSD7、CGREF1、C 7orf73、FAM149A、NAGK、TYRO3、ID1、PLIN3、HIST1H2BK、SNX16、ZNF582、ZFA ND5、ABHD5、LDHD、SFT2D2、LOC646999、FAM20A、NDUFA6-AS1、N4BP3、PPP1CB、 CCDC6、GALE、ASCC2、FFAR4、WWTR1、NOXO1、INPP5A、FER、FAM84A、CNKSR3、SP RR1A、PIK3C2G、MROH6、SPECC1、LOC285074、FUT3、DIRAS3、ARHGAP28、GBP6、T MEM65、KIFC3、S100A14、DBNDD1、MAP1S、THRA1、BCOR、SH3D19、DMPK、AKAP6、P LEKHG6、C9orf66、IL1RL2、NHSL1、PLEKHN1、IFRD1、CCDC110、TP53BP2、TTC9、<h2 style=";text-align:left;direction:ltr">SRGAP2D、KCNMA1、RNF208、ENPP4、RRAGD、PLB1、BDKRB1、AMER1、AK289390、N CKAP5、ANKH、FAM83A、PCLO、ZNF425、HEG1、SYT8、PDLIM2、CDKN1C、MRVI1-AS 1、FAM78B、PLLP、ASPG、CASP10、RDH5、CAPS2、CAST、MDFIC、USP6NL、SLC10A6 、BLVRA、ADSSL1、SEC24C、RTN4RL1、GFOD2、CCDC69、SPSB3、BC131755、CMAS、A NKRD6, HBP1, C8orf47, NSG1, PLEKHM1, BAI1, RNF128, TMOD3, SLC6A8, TMEM8A, RHOD, PKDREJ, NMRK1, AK130329, TAB3, CTNNBIP1, TRPS1, WNK2, RDH12, UPP 1、ABHD12、KIAA0355、UBAP1L、ARHGEF4、LINC00887、EFNA5、FAM86FP、SMCR7 、KRT79、TEC、SAMD9、AKR1C3、MGST1、NOV、ITPRIP、LINC00152、S100A6、LOC40 1052、FAM129B、TRIM56、PDZD4、GYLTL1B、FAM109A、PKIB、HIST1H4H、FAM221 A、PAX9、PYGB、AL110181、PLEKHF1、RNF224、ARRDC3、SLC2A11、PRDM1、GYPC、 PMM1、ARHGAP10、CLIC3、RECQL4、BC039551、AX748273、TMEM154、NCOA1、CLE C3B、C15orf62、UCA1、C19orf26、SERINC2、PHACTR1、AHNAK2、ABCA3、RIOK3、M YOM3、CLEC5A、TIMP2、MT1X、LINC00568、FOXI2、DOCK9、SUCO、UCKL1-AS1、MP ZL3、TAX1BP1、WNT2B、GATM、LINC00675、CXorf57、CECR6、C6orf132、ABO、MOS PD1、ARG2、ELL2、HTR7、RAP2A、SRGAP2C、TOB1、TMIE、TICAM1、FHOD3、KATNBL 1、CPPED1、UNC13B、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、NC CRP1、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、C XCR7、CLCA3P、C7orf55-LUC7L2、LINC00551、ARHGAP20、MB、FAM13C、SLC1A3、OR7E14P、ADARB2、AK093443、SPTSSB、AK128563、ANGPT1、FMO2、RBP1、CCL14 、NWD1、TRPC1、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、GLD N、CLCA4、RIMBP2、COL4A6、PMFBP1、TNNT3、CD302、AK127224、AK094577、FGF14、AQP5、SPRR2C、B3GNT6、BC010924、GSTA1、CYP3A4、LHX4、C17orf61-PLSCR 3、FMO9P、PLSCR3、IL23A、NOS3、IGFBP5、FCGR1A、APOE、SLC26A9、KRT14、RRAD、SEMA3C、APOC1、GABRA4、PLA2G4E、TPPP3、NFKB2、C8orf44-SGK3、AL049415、Does not contain CCDC28B, and / or DKFZp586B1922, or any combination thereof.

[0038] In some embodiments, the genes of the plurality of differentially expressed genes are 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, LOC400891, ARC, LPA, TDRG1, CDH20, C18orf61, MT1M, LOC441178, LOC149086, LMAN1L, AQP7P3, MT1A, MUC5B, TGM6, CCR3, SHD, SIX2, C6orf223 , LOC154092, CEBPE, LINC00092, C12orf28, NFE2, MZB1, CLDN24, ELK2AP, HK3, CLDN22, FAM110B, FW340046, GATA1, MGC45800, SLC5A8, CR1, MGC32805, IL 5RA, SOCS1, FOXD1, RGS13, TREM2, LIPG, STAP1, TTLL13, LOC100506585, GFRA2, ENPP3, CAPN8, FAIM2, SAA2, HAS2, OSM, DEFB104B, KIAA0125, LILRA6, AK 124806, C9orf84, EGR2, CD1B, SIGLEC8, SNORA21, SIRPB1, SIGLEC7, MKX, ADAM12, C1orf167, CD300LB, APOC4-APOC2, ICAM4, LILRB5, IL13, BATF3, DBC1, C1orf228, BC075797, HRH2, COL4A3, AFF2, EXOC3L4, GCSAML, CD180, TNNC1, GFRA3, BC045578, CCDC60, DDO, GALR2, C1orf186, PITX2, APOC2, SLC2A6, CCL 11, CD79A, TMEM92, NPY4R, TNFRSF4, RPPH1, DPEP2, TPSD1, HSP90AB4P, CHST1, C2CD4C, MUC16, CDHR3, AK125558, ASRGL1, LYPD1, C4orf48, LILRB4, CRB2,SAMD14, C19orf38, DQ658414, 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 of the genes of the plurality of differentially expressed genes described above can be excluded from the gene expression analysis.

[0039] In some embodiments, the plurality of differentially expressed genes includes HRNR, MAP1LC3B2, TOLLIP-AS1, DCLK1, C1orf170, CCSER1, STK33, IGLON5, DHRS2, BC133670, FBXO39, ITGBL1, LINC00920, MCF2L2, AL831977, SBSPON, ANKRD36BP1, CCL20, AK056396, FAM35DP, SEPT3, CCL21, PDXP, LOC100132891, ABHD16B, LOC100288181, DMRT2, PPP1R36, FAM 83A-AS1, TRAM1L1, GRIN2A, SOWAHA, LOC729970, LOC100127983, SLC35F3, NPPC, HIF1A-AS2, DISP2, SER PINA11, MTUS2, ACADL, PCDH20, KBTBD12, SLITRK5, AX746725, CXCL2, ISM2, AADACL2, TDRD5, AK095621, DES, DIRAS1, LOC100289511, RNF182, CYP2B7P1, CR936677, IRX6, CES1P2, AK054845, REN, PEG10, DKK2, does not include RTP1, IL19, KCNJ16, CTAGE15, SYT2, MMRN1, AP4B1-AS1, DNAH17, AK096066, BC041347, BC034424, DKFZp686D16130, BC150585, AB209185, DKFZp547K2416, CCDC39, and / or AJ515158, or any combination thereof.

[0040] In some embodiments, the genes in the plurality of differentially expressed genes include IFNGR1, STAT4, CTLA4, IL12A, IRF1, STAT1, GZMA, LAG3, CD28, CCL5, CD8A, IL12RB2, PRF1, CXCL9, and / or CXCL10, or any subset thereof comprising at least 10 genes, at least 11 genes, at least 12 genes, at least 13 genes, or at least 14 genes.

[0041] In some embodiments, the genes in the plurality of differentially expressed 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 comprising at least 20 genes, at least 22 genes, at least 24 genes, at least 26 genes, or at least 27 genes.

[0042] In some embodiments, the genes in the plurality of differentially expressed genes are 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 1, any 2, any 3, any 4, any 5, any 6, any 7, any 8, any 9, or any 10 genes of the plurality of differentially expressed genes described above can be excluded from the gene expression analysis.

[0043] In some embodiments, the genes in the plurality of differentially expressed 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 comprising 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 of the plurality of differentially expressed 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 comprising at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes.

[0044] In some embodiments, the genes in the plurality of differentially expressed 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 comprising 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 of the plurality of differentially expressed genes include ZNF416, ENDOU, SEPT5-GP1BB, EPGN, CRISP3, C2orf16, 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.

[0045] In some embodiments, the genes of the plurality of differentially expressed 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 comprising 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 of the plurality of differentially expressed 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 comprising at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, or at least 19 genes.

[0046] In some embodiments, the genes in the plurality of genes differentially expressed in the downward direction comprise ALOX15, CCL26, POSTN, NRXN1, and / or CCR3. In some embodiments, the genes in the plurality of genes differentially expressed in the upward direction comprise SPINK8 and / or DSG1.

[0047] In some embodiments, the genes in the plurality of differentially expressed genes comprise 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 comprising at least 38 genes, at least 30 genes, at least 32 genes, at least 33 genes, or at least 34 genes. In some embodiments, the genes of the plurality of differentially expressed genes are selected from the group consisting of 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, LIT AF, 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 differentially expressed genes comprise CCL26, MUC5B, CLC, IL1RL1, HDC, IL13, FCER1G, GATA2, and / or KIT.

[0048] In some embodiments, the genes in the plurality of differentially expressed genes comprise FLG, DSG1, SPINK7, SPINK8, SPINK5, KLK7, HAS3, THBS1, MMP9, LOX, POSTN, TIMP1, HAS2, IL13, PDGFRA, and / or LTBP2, or any subset thereof comprising 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 plurality of differentially expressed genes comprise DSG1, SPINK5, SPINK7, and / or SPINK8.

[0049] In some embodiments, the genes in the plurality of differentially expressed genes include CCL26, CCR3, ANO1, and / or SPINK8.

[0050] In some embodiments, the gene of the plurality of differentially expressed genes comprises UPK1B, SH2D1B, CDH26, POSTN, and / or DSG1. In some embodiments, the gene of the plurality of differentially expressed genes comprises ALOX15. In some embodiments, the gene of the plurality of differentially expressed genes comprises CCL26 and / or CCR3. In some embodiments, the gene of the plurality of differentially expressed genes comprises POSTN. In some embodiments, the gene of the plurality of differentially expressed genes comprises MUC5B. In some embodiments, the gene of the plurality of differentially expressed genes comprises mKi67, some collagen genes, DSG1, and / or SPINK8 family members. In some embodiments, the gene of the plurality of differentially expressed genes comprises SPINK5, SPINK7, and / or SPINK8. In some embodiments, the gene of the plurality of differentially expressed genes comprises ANO1. In some embodiments, the gene of the plurality of differentially expressed genes comprises NRXN1 and / or NTRK1. In some embodiments, the genes in the plurality of differentially expressed genes comprise IL13, CCL17, CCL18, and / or CCL26.In some embodiments, the genes in the plurality of differentially expressed genes comprise K16 and / or MKI67.

[0051] In some embodiments, the method includes screening the dupilumab core gene signature against full transcriptome profiles from the multiple disease studies, and performing differential gene expression analysis on the full transcriptome profiles in each disease study of the multiple disease studies. In some embodiments, screening the dupilumab core gene signature against the full transcriptome profiles from the multiple disease studies includes 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 included in the dupilumab therapeutic core gene signature.

[0052] In some embodiments, the NES comprises an Eosinophilic Esophagitis NES (EoE-NES), a Type 2 Gene Expression Signature in EoE (Type 2-NES), and / or a DpxOme-EoE™ NES. In some embodiments, the NES comprises an EoE-NES. In some embodiments, the NES comprises Type 2-NES. In some embodiments, the NES comprises a DpxOme-EoE™ NES.

[0053] In some embodiments, the method comprises performing differential gene expression analysis on whole transcriptome profiles in each disease test of multiple disease tests performed on disease and healthy subjects.

[0054] In some embodiments, the plurality of disease tests comprises the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database.

[0055] In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets, hi some embodiments, the NES is calculated separately for the positive and negative gene sets.

[0056] In some embodiments, the NES is generated by generating a ranked gene list (R+) by ordering a plurality of differentially expressed genes from the most positive (i.e., most upregulated) to the most negative (i.e., most downregulated) values. In some embodiments, the NES is generated by identifying hits (i.e., ranks of genes in the core signature) independently 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 reverse ranking of R+ with the values ​​reversed). In some embodiments, the NES is generated by combining R+ and R- and reordering the values ​​by keeping the hits from both S+ and S-. In some embodiments, the NES is generated by calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the ith gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and 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 cumulative score. In some embodiments, the ordering, identifying, combining, calculating, and determining disclosed in this paragraph are repeated 1,000 times with a random gene set to calculate the null distribution of ES. In some embodiments, the random gene set is a list of genes (same size as the original gene set) randomly selected from the entire transcriptome. In some embodiments, ES is generated as ES divided by the arithmetic mean of the null distribution of ES.

[0057] In some embodiments, the method further comprises calculating statistical significance by comparing the observed ES to a null distribution or a permutation of the sample labels (disease / healthy).

[0058] In some embodiments, ordering the multiple differentially expressed genes from most positive (i.e., most upregulated) to most negative (i.e., most downregulated) values ​​to generate a ranked gene list (R+) comprises using log2 fold changes or z-scores. In some embodiments, fold changes, statistics (e.g., Wald test, T-test), signal-to-noise ratios (difference in mean values ​​scaled by standard deviation) can be used to compare two groups. In some embodiments, gene expression values ​​or z-scores can be used for single sample NES.

[0059] In some embodiments, R+ and R- are ranked by the log2 fold change in the mean gene expression in disease samples compared to the mean gene expression in healthy samples.

[0060] In some embodiments, the method includes calculating the NES for all disease tests using the ranked list for each disease test.

[0061] In some embodiments, diseases with significant NES are amenable to treatment with dupilumab.

[0062] The present disclosure provides a method for identifying a subject having a disease or condition suitable for treatment with dupilumab. In some embodiments, the method includes generating a dupilumab therapeutic core gene signature. In some embodiments, the method includes screening the dupilumab core gene signature against a whole transcriptome profile from the subject. In some embodiments, the method includes determining whether the subject is suitable for dupilumab treatment.

[0063] In some embodiments, the methods include generating a dupilumab therapeutic core gene signature, which comprises determining differential gene expression between dupilumab and placebo treatment groups in multiple treatment trials and identifying a plurality of differentially expressed genes.

[0064] In some embodiments, the method includes converting a whole transcriptome profile from a subject into a z-score. In some embodiments, the method includes ranking the z-scores. In some embodiments, the method includes generating a normalized enrichment score (NES) for all ranked z-scores using a plurality of genes that are differentially expressed and are within a dupilumab therapeutic core gene signature.

[0065] In some embodiments, the methods include generating the NES using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.

[0066] In some embodiments, the NES is generated by converting each gene expression level in the plurality of genes to a z-score and generating a value for R+ by ordering the plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value. In some embodiments, the method can use the gene expression level values ​​(without any transformation) for ranking. In some embodiments, the NES is generated by independently identifying hits for the positive (i.e., most upregulated) gene set in R+ (S+) and the negative (i.e., most downregulated) gene set in R- (S-), where R- is the reverse ranking of R+ with the values ​​reversed. In some embodiments, the NES is generated by combining R+ and R- and reordering the values ​​by keeping the hits from both S+ and S-. In some embodiments, the NES is generated by calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the ith gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i is the value of gene i, and p is the weight of r. In some embodiments, the NES is generated by determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score. In some embodiments, the ordering, identifying, combining, calculating, and determining disclosed in this paragraph are repeated 1,000 times on a random set of genes to calculate a null distribution of ES. In some embodiments, the NES is generated as ES divided by the mean of the null distribution of ES.

[0067] In some embodiments, the method is generated by calculating statistical significance by determining the 95th percentile NES from healthy control samples.

[0068] In some embodiments, the NES is generated by calculating an NES for all disease tests using the ranked list for each disease test in the plurality of disease tests. In some embodiments, the NES is generated by calculating an NES for all disease tests in the plurality of disease tests using the ranked list for each disease test in the plurality of disease tests.

[0069] In some embodiments, if the subject's NES is higher than the NES of a healthy control, the subject is suitable for dupilumab treatment.

[0070] The present disclosure provides methods of conducting a clinical trial for dupilumab treatment of a disease, disorder, or condition, the method comprising using a dupilumab core gene signature as a clinical endpoint of the clinical trial.

[0071] In some embodiments, the dupilumab therapeutic core gene signature is generated by determining differential gene expression between dupilumab and placebo treatment groups in multiple treatment trials and identifying multiple genes that are differentially expressed.

[0072] In some embodiments, the clinical trial involves generating a normalized enrichment score (NES) for the dupilumab therapeutic core gene signature before initiation of treatment of the subject with dupilumab and at least one time point after initiation of treatment of the subject with dupilumab.

[0073] In some embodiments, the clinical endpoint is achieved when dupilumab treatment reduces the NES of the dupilumab therapeutic core gene signature to an acceptable value.

[0074] In some embodiments, the NES is generated by ordering a plurality of differentially expressed genes from the most positive (i.e., most upregulated) to the most negative (i.e., most downregulated) values ​​to generate a value for R+. In some embodiments, the NES is generated by independently identifying hits for the positive (i.e., most upregulated) gene set in R+ (S+) and the negative (i.e., most downregulated) gene set in R- (S-), where R- is the reverse ranking of R+ with the values ​​reversed. In some embodiments, the NES is generated by combining R+ and R- and reordering the values ​​by keeping hits from both S+ and S-. In some embodiments, the NES is generated by calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the ith gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i is the value of gene i, and p is the weight of r. In some embodiments, the NES is generated by determining an enrichment score (ES) as the maximum deviation from zero along the cumulative score. In some embodiments, the ordering, identifying, combining, calculating, and determining disclosed in this paragraph are repeated 1,000 times on a random set of genes to calculate a null distribution of ES. In some embodiments, the NES is generated as ES divided by the mean of the null distribution of ES.

[0075] In some embodiments, the NES is generated by calculating statistical significance by comparing the observed ES to a null distribution or a permutation of the sample labels (disease / healthy).

[0076] In some embodiments, ordering the plurality of differentially expressed genes from the most positive (i.e., most upregulated) to the most negative (i.e., most downregulated) value to generate the value of R+ comprises comparing gene expression levels after dupilumab treatment to gene expression levels before initiation of treatment with dupilumab using log2 fold change.

[0077] In some embodiments, multiple samples are obtained from a subject and a NES is generated for each sample.

[0078] Differential gene expression involves quantification of RNA / transcript expression of at least one gene in a biological sample from a subject (i.e., a potentially many RNA-based measurement). As used herein, "gene" is meant to include non-coding genes / biotypes (e.g., long non-coding RNA). In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least one gene in a biological sample from a subject. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 10 genes. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 20 genes. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 30 genes. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 40 genes. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 50 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 60 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 70 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 80 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 90 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 100 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 125 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 150 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 175 genes. In some embodiments, differential gene expression involves quantification of RNA expression level(s) of at least 200 genes.In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 300 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 400 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 500 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 600 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 700 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 800 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 900 genes. In some embodiments, the differential gene expression comprises quantification of the RNA expression level(s) of at least 1,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 5,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 10,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 15,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 20,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 25,000 genes. In some embodiments, the differential gene expression comprises quantification of RNA expression levels of at least 30,000 genes.

[0079] In some embodiments, the at least one gene comprises 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, the at least one gene comprises a protein coding gene. In some embodiments, the at least one gene comprises a non-coding gene. In some embodiments, the at least one gene comprises a long non-coding RNA. In some embodiments, the at least one gene comprises a mitochondrial rRNA. In some embodiments, the at least one gene comprises a mitochondrial tRNA. In some embodiments, the at least one gene comprises an rRNA. In some embodiments, the at least one gene comprises a ribozyme. In some embodiments, the at least one gene comprises a B cell receptor subunit constant gene. In some embodiments, the at least one gene comprises a T cell receptor subunit constant gene.

[0080] In any of the embodiments described herein, the biological sample includes a sample derived from 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 a blood, semen, saliva, urine, feces, hair, tooth, bone, tissue, or buccal sample. In some embodiments, the biological sample is obtained from the subject by biopsy.

[0081] In some embodiments, RNA expression may be determined in part by RNA sequencing. In some embodiments, the RNA sequencing reads may be mapped to a genome. In some embodiments, the genome is the human genome. In some embodiments, the human genome is the reference assembly GRCh38. In some embodiments, the RNA sequencing reads may 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. In some embodiments, the RNA sequencing reads are not so limited. In some embodiments, the sequences may be mapped without strand specificity, with strand-specific reverse-first read mapping, or with strand-specific forward-first read mapping. In some embodiments, the sequences may be mapped using kallisto v0.45.0 with strand-specific reverse-first read mapping (Bray et al., Nat. Biotechnol., 2016, 34, 525). In some embodiments, transcript counts can be merged into gene counts. In some embodiments, the merger can be performed using tximport (Soneson et al., F1000Research, 2015, 4, 1521).

[0082] In some embodiments, determining the subject's NES comprises determining the RNA expression level(s) of one or more genes in a biological sample from the subject, comparing the RNA expression levels to the RNA expression levels of the corresponding genes from a placebo, determining the relative difference in RNA expression levels, and integrating the changes in the individual RNA expression levels into the NES. In some embodiments, determining the subject's NES comprises determining the RNA expression level(s) of one or more genes in multiple biological samples from the subject, determining the relative difference in RNA expression levels across the multiple samples, and integrating the changes in the individual RNA expression levels into the NES. In some embodiments, the genes whose RNA expression level(s) are measured include protein-coding genes, long non-coding RNA, mitochondrial rRNA, mitochondrial tRNA, rRNA, ribozymes, B cell receptor subunit constant genes, and / or T cell receptor subunit constant genes. In some embodiments, the relative difference in RNA expression levels of genes in the panel is compared to the relative difference in RNA expression levels of genes not in the panel.

[0083] The present disclosure provides methods of treating a subject having a disease or condition suitable for treatment with dupilumab, the method comprising: a) identifying the subject as having a disease or condition suitable for treatment with dupilumab, the identification comprising: i) generating a dupilumab therapeutic core gene signature; ii) screening the dupilumab core gene signature against a whole transcriptome profile from the subject; and iii) determining whether the subject is suitable for dupilumab treatment; and b) administering dupilumab to the subject having the disease or condition suitable for treatment with dupilumab.

[0084] In some embodiments, the dupilumab therapeutic core gene signature comprises determining differential gene expression between dupilumab and placebo treatment groups in multiple treatment trials and identifying multiple genes that are differentially expressed.

[0085] In some embodiments, the multiple treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergies, and chronic sinusitis with nasal polyps.

[0086] In some embodiments, genes of the core gene signature identified from differential gene expression are selected as having a fold change > 2 and / or q < 0.05 in 3 or more 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.

[0087] In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyps is performed by comparing baseline gene expression levels before treatment with dupilumab to gene expression levels after treatment with dupilumab. In some embodiments, differential gene expression in therapeutic trials of asthma and grass allergy is performed by comparing gene expression levels with allergen challenge to gene expression levels without allergen challenge. In some embodiments, differential gene expression is analyzed by microarray or RNASeq. In some embodiments, differential gene expression in therapeutic trials of eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq. In some embodiments, differential gene expression in therapeutic trials of atopic dermatitis and chronic rhinosinusitis with nasal polyps is analyzed by microarray.

[0088] In some embodiments, the plurality of differentially expressed genes is selected from the group consisting 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, ATF3, P2RY6, IGFBP5, TMC5, AD ORA3, 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.

[0089] In some embodiments, screening the dupilumab core gene signature against a whole transcriptome profile from the subject includes: i) converting the whole transcriptome profile from the subject into a z-score; ii) ranking the z-scores; and iii) using a plurality of genes that are differentially expressed and are within the dupilumab therapeutic core gene signature to generate a normalized enrichment score (NES) for all ranked z-scores to represent the dupilumab signature enrichment in the subject.

[0090] In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets. In some embodiments, the NES is generated by: a) converting each gene expression level in the plurality of genes into a z-score and generating a value for R+ by ordering the plurality of differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value; b) independently identifying hits for the positive (i.e., most upregulated) gene set in R+ (S+) and the negative (i.e., most downregulated) gene set in R− (S−), where R− is the reverse ranking of R+ with the values ​​reversed; c) combining R+ and R− and reordering each value by keeping the hits in both S+ and S−; and d) calculating a cumulative score by walking down the combined rankings, where the cumulative score is calculated as / r if the ith gene is a hit. i / p / Σ iεS / r / p or decrease by 1 / (2N-S), where S is the total number of genes in S+ and S-, and r i where p 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 cumulative score, f) calculating the null distribution of ES by repeating steps a)-e) 1000 times on a random set of genes, and g) generating the NES as ES divided by the mean of the null distribution of ES.

[0091] 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 diseases using the ranking list for each disease test. In some embodiments, if the subject's NES is higher than the NES of the healthy control, the subject is suitable for dupilumab treatment.

[0092] The disclosure also provides kits and methods of using the kits, which may include, for example, 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, ADOR A3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3, and / or mRNA and / or cDNA molecules derived therefrom. In some embodiments, any one or more of the above genes (and / or mRNA and / or cDNA molecules derived therefrom) can be detected in a subject prior to treatment with dupilumab. In some embodiments, any one or more of the above genes (and / or mRNA 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 and / or cDNA molecules derived therefrom) can be detected in a subject before treatment with dupilumab and after treatment with dupilumab.

[0093] In some embodiments, the kit comprises a plurality of nucleic acid molecules, the plurality of nucleic acid molecules being selected from the group consisting 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, IGF In some embodiments, the kit comprises a nucleotide sequence complementary to at least 10 of the nucleic acid molecules encoding the genes (and / or mRNA and / or cDNA molecules derived therefrom) of BP3, ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3. In some embodiments, the kit comprises a plurality of nucleic acid molecules, the plurality of nucleic acid molecules comprising a nucleotide sequence complementary to at least 15, at least 20, at least 25, at least 30, at least 35, 40, or at least 45 of the nucleic acid molecules encoding the genes (and / or mRNA and / or cDNA molecules derived therefrom).

[0094] In some embodiments, a nucleic acid molecule comprising a nucleotide sequence complementary to at least 10 of the nucleic acid molecules encoding the above genes (and / or mRNA and / or cDNA molecules derived therefrom) is a probe. In some embodiments, the nucleotide sequence of the probe has at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% sequence complementarity to the target gene. In some embodiments, the probe is 8-100, 10-75, 12-50, 15-25, or 18-23 nucleotides in length.

[0095] In any of the embodiments described throughout this disclosure, the probe can include a label, hi some embodiments, the label is a fluorescent label, a radioactive label, or biotin.

[0096] In any of the embodiments described throughout this disclosure, each of the plurality of nucleic acid molecules is bound to a solid support. A solid support is a solid-state substrate or support to which a molecule, such as any of the probes disclosed herein, can be bound. The form of the solid support is an array. Another form of solid support is an array detector. An array detector is a solid support to which multiple types of probes are bound in an array, grid, or other organized pattern. The form of the solid-state substrate is a multi-well plate, such as a standard 96-well type. In some embodiments, a multi-well glass slide, usually containing one 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.

[0097] The present disclosure relates to a method for detecting a plurality of nucleic acid molecules in a subject, the plurality of nucleic acid molecules being detected being selected from the group consisting 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, TP Also provided are methods comprising the step of determining whether a nucleic acid molecule encoding 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 is present.The method comprises: subjecting a biological sample to the subject for 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, TPSB2, IGFBP3, ATF3, The nucleic acid molecule includes a nucleotide sequence complementary to at least 10 of the nucleic acid molecules encoding P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3. and contacting the nucleic acid molecule with a plurality of nucleic acid molecules including 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 and detecting the presence or absence of at least 10 nucleic acid molecules encoding F3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3. Any of the kits described herein can be used in these methods. Additionally, any of the probes described herein, or combinations thereof, can be used in these methods. Any of the biological samples described herein can be used in these methods.

[0098] In some embodiments, the biological sample is treated with 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 amounts of at least ten nucleic acid molecules encoding ATF3, P2RY6, IGFBP5, TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3 are determined.

[0099] In some embodiments, 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, A Each of the plurality of nucleic acid molecules comprising a nucleotide sequence complementary to at least ten nucleic acid molecules encoding DORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3 is labeled, and the detecting step includes detecting the label.

[0100] In some embodiments, 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, Each of the plurality of nucleic acid molecules comprising a nucleotide sequence complementary to at least 10 nucleic acid molecules encoding TMC5, ADORA3, RAB44, EMR4P, SERPINB10, P2RY1, P2RY14, AURKA, CLEC10A, CD1C, CD1E, CST1, NOS2, FAM19A2, ALDH5A1, CEACAM3, DGAT2, S100A8, and RNF103-CHMP3 is bound to a solid support. Any of the solid supports described herein can be used in these methods.

[0101] The following examples are presented to further describe the embodiments. They are intended to illustrate, not limit, the claimed embodiments. The following examples provide those skilled in the art with disclosures and explanations of how the compounds, compositions, articles, devices and / or methods described herein are made and evaluated, and are intended to be merely illustrative and are not intended to limit the scope of any claims. Efforts are made to ensure accuracy with respect to numerical values ​​(e.g., amounts, temperatures, etc.), but some error and variation can be accounted for. EXAMPLES

[0102] Example 1 Dupilumab normalizes the eosinophilic esophagitis disease transcriptome Study design The data disclosed 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). A complete description of the study design is presented, for example, in Hirano et al., Gastroenterology, 2020, 158, 111-22.

[0103] In this phase 2 study, subjects completed a 35-day screening period and were then randomized 1:1 to receive either dupilumab 300 mg (loading dose 600 mg on day 1) weekly by subcutaneous injection or matching placebo for 12 weeks with a 16-week follow-up period. Blinded monitoring of subject safety data was performed by an independent Data and Safety Monitoring Committee. The primary endpoint of the study was patient-reported outcome (PRO) scores from baseline to week 10 using the Straumann Dysphagia Instrument (SDI).

[0104] Phase 3 TREET consisted of three parts. In part A, patients were randomized (1:1) to receive dupilumab 300 mg subcutaneously once weekly or matching placebo for 24 weeks. All patients in part A who continued in part C (parts A-C) then received dupilumab 300 mg weekly for an additional 28 weeks. In part B, patients were randomized (1:1:1) to receive dupilumab 300 mg weekly, dupilumab 300 mg twice weekly, or placebo weekly. Patients from part B continued in parts B-C (ongoing). Dupilumab arms continued on the same dosing regimen, and patients in the placebo arms were re-randomized (1:1) to dupilumab 300 mg weekly or twice weekly. Patients were followed for 12 weeks after discontinuing study drug. Co-primary endpoints were histological response (defined by eosinophil counts per high-power field [eos / hpf] ≤6) and change from baseline in Dysphagia Symptom Questionnaire [DSQ] score at week 24 (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330).

[0105] subject The phase 2 study enrolled adult subjects (ages 18-65) with confirmed EoE who had not responded to protein pump inhibitors (PPIs) and had active esophageal inflammation at screening. These patients had esophageal inflammation of at least 0.3 mm in a high-power field (400x magnification) as demonstrated by esophageal pinch biopsy specimens from at least 2 of 3 esophageal sites. 2 Subjects were identified by determining a peak eosinophil cell count of ≥15 cells per field of view. These samples were obtained by endoscopy performed within 2 weeks after at least 8 weeks of treatment with high-dose (or twice-daily) PPIs. Subjects were also required to have a self-reported history of ≥2 episodes of dysphagia on average per week in the 4 weeks prior to screening, an SDI PRO score of ≥5 at screening and baseline, and at least one confirmed history of or current type 2 comorbid atopic disease. Complete inclusion and exclusion criteria have been previously published (see Hirano et al., Gastroenterology, 2020, 158, 111-22).

[0106] Phase 3 TREET enrolled patients aged 12 years or older with EoE who had failed to respond to high-dose PPIs for ≥8 weeks, had peak intraepithelial eosinophil counts ≥15 eos / hpf in at least 2 of 3 biopsied esophageal regions, and had a self-reported history of ≥2 dysphagia episodes per week on average 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 recorded in the DSQ eDiary in the 2 weeks prior to baseline (at least two of which had to involve fluids, coughing, nausea, or vomiting or required medical treatment for relief), completed at least 11 of the 14-day DSQ eDiary 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).

[0107] RNA sequencing and analysis Pinch biopsies for RNA analysis were taken from the proximal, mid, and distal esophagus at screening and 12-week endoscopy and frozen in RNALater™. After RNA extraction, strand-specific RNA-seq libraries were prepared using the KAPA Stranded mRNA-Seq kit (KAPA Biosystems, Roche Sequencing and Life Sciences, MA, USA). After amplification, sequencing was performed by multiplexed single-read runs (80bp, 40M reads) on an Illumina HiSeq® 2000 device (Illumina Inc., CA, USA). Each read was 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.

[0108] Using a gene set enrichment analysis tool (see world wide web "mathworks.com / matlabcentral / eleplantication / 33599-gsea2"; Lim et al., Pac. Symp. Biocomput., 2009, 504-15), which considers both positive and negative gene sets, the top 50 most upregulated and top 50 most downregulated genes in EoE were used to generate a normalized enrichment score (NES). For single-sample NES, gene expression profiles were first converted to z-scores, and 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-NE.

[0109] Gene ontology term enrichment analysis We performed an unbiased global transcriptome analysis using 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) and gene set enrichment analysis (Wapenaar et al., Immunogenetics, 2007, 59, 349-57). We prefiltered gene sets of size >100 to ensure biological process specificity, and selected top GO terms if the false discovery rate was <0.05 for both EoE vs. healthy and post-dupilumab vs. baseline comparisons. Eosinophil- 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).

[0110] Immunohistochemistry (IHC) Esophageal biopsies were stained for the following IHC markers: proliferation marker (MIB-1), lymphocyte markers (CD4, CD8), mast cell markers (chymase, tryptase), Fc receptor for IgE marker (FcεRI), and Langerhans cell marker (CD1a). Scanned IHC images were annotated independently by a 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, chymase, tryptase, FcεRI), a cell nucleus algorithm was used.

[0111] statistical analysis Differential expression analysis was performed using DESeq2 version 1.26.0. In the phase 2 study, data at week 12 were compared to baseline within two groups (placebo and dupilumab 300 mg weekly). In the phase 3 TREET study, data at weeks 24 and 52 were compared to baseline values ​​within three groups (dupilumab 300 mg weekly, dupilumab 300 mg twice weekly, and placebo). The dupilumab 300 mg weekly group included patients pooled from parts A and B for baseline and week 24 assessments, and all patients assigned to weekly dosing in part C for week 52 assessments. The dupilumab 300 mg twice weekly group included patients from part B for baseline and week 24 assessments, and all patients assigned to biweekly dosing in part C for week 52 assessments. The placebo group included patients pooled from Parts A and B for assessments at baseline and Week 24. The placebo / dupilumab 300 mg once weekly group at Week 52 included patients assigned to placebo in Parts A and B and who received dupilumab 300 mg once weekly in Part C only.

[0112] Genes were considered significantly modulated by treatment (dupilumab or placebo) if they reached a threshold of ≥2-fold relative log fold change from baseline and q-value ≤0.05, thereby reflecting adjustment for multiple testing. Pearson correlations were calculated between published genetic alterations in EoE (disease vs. healthy) and genetic alterations after dupilumab treatment (post vs. pre). To perform IHC analysis, nominal p-values ​​were calculated using unpaired two-tailed t-tests to compare absolute percent changes from baseline between placebo and treatment groups.

[0113] Transcriptome analysis Transcriptome results were available for 16 of 24 placebo patients (67%) and 22 of 23 dupilumab patients (96%) enrolled in the Phase 2 clinical trial (NCT02379052). However, biopsy samples were not available for six patients, and the patients were excluded from the analysis (see Hirano et al., Gastroenterology, 2020, 158, 111-22).

[0114] No genes were found to be differentially expressed within the placebo group at week 12 compared to baseline expression levels (relative log fold change from baseline ≥ 2, q ≤ 0.05). Treatment with dupilumab 300 mg once weekly (qw) modulated expression of 1,302 genes (DprxOme-EoE™) at week 12 versus baseline, of which 513 were downregulated and 789 were upregulated (heatmap data not shown). Post-treatment results were highly similar in all three esophageal regions sampled (data not shown), and therefore average values ​​across all samples are presented (data not shown). The top 50 most upregulated and top 50 most downregulated genes in EoE were used to generate normalized enrichment scores (NES). Across upregulated or downregulated genes, dupilumab treatment was associated with significantly lower EoE-NES (Wilcoxon rank sum test, P < 5.0 × 10 -8 ). In contrast, there were no significant changes associated with the placebo group. The 30 genes that showed the greatest changes in expression with dupilumab included those associated with type 2 inflammation, tissue remodeling / fibrosis, barrier function, and proliferation / differentiation (see Figure 1). Genes upregulated in the EoE transcriptome and downregulated by dupilumab included ALOX15, CCL26, POSTN, NRXN1, and CCR3, while genes downregulated in placebo subjects and upregulated by dupilumab included SPINK8 and DSG1.

[0115] Normalization of the EoE transcriptome Treatment with dupilumab (week 12 vs. baseline) normalized the transcriptome at week 12, as seen in comparison with published EoE and healthy transcriptomes. As shown in Figure 2 (Panel A), a strong negative correlation was observed between published EoE and healthy transcriptomes and DplexOme-EoE™ (week 12 vs. baseline) (Pearson correlation coefficient: ρ = -0.872, P < 1 × 10 -6 ). Genes that did not meet the significance threshold also tended to normalize (heatmap data not shown). Dupilumab also significantly regulated many genes not included in the published EoE transcriptome (heatmap data not shown). Furthermore, many of the genes with no 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 dupilumab 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 remodeling (e.g., extracellular matrix degradation), (iii) mast cell activation, and (iv) epithelial differentiation (e.g., keratinization and keratinization).

[0116] Inflammation-related genes 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 (heatmap data not shown). At week 12, dupilumab inhibited eosinophil-related genes, including ADAM8, CD300LB, DAPK2, EMR4P, GPR97, IL1RL1, IL5RA, MMP25, RAB37, SIGLEC10, SIGLEC8, TESC, TREML2, TRPM6, and CDA, as well as SCIN, SE, including AIM2, CADM1, CAPN14, CDH26, CDH3, CFI, CPA3, CTSC, EDAR, GALNT4, GCNT2, GCNT3, HAS3, ID3, IFFO2, IGFBP3, KCNJ2, KITLG, LHFPL2, LITAF, MAP3K14, MFHAS1, NTN1, PDZK1IP1, PLA2G3, and EGFR-associated genes. It regulated genes related to mast cell activation, including RPINB4, SFRP1, SGK1, SH3RF2, SIDT1, SLC16A1, SGD2, TMEM173, TMTC3, TNFSF13, TPSAB1, TPSB2, ANKRD37, BNIP3, BOC, CCNYL1, CNFN, CYP2C18, EML1, FAM126A, HSPA2, IGFL1, KIF21A, KRT3-2, ME1, PALMD, PFN2, PHACTR2, PPP2R2C, RFK, SAMD5, SPINK7, TFAP2B, HOD1, ZNF101, ZNF365, ZNF555, ZNF662, and ZNF92 (data not shown). In detail, CCL26, MUC5B, CLC, IL1RL1, HDC, IL13, FCER1G, GATA2, and KIT were involved. Furthermore, dupilumab treatment reduced eosinophil tissue infiltration at week 12, with similar effects in all three sampled regions (Hirano et al., Gastroenterology, 2020, 158, 111-22).The observed changes in eosinophil-associated gene expression were consistent with the decreased density of eosinophils observed in esophageal biopsies after dupilumab treatment.

[0117] Fibrosis, remodeling, and barrier function-related genes At week 12, dupilumab treatment also modulated genes related to fibrosis, interstitial 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 (heatmap data not shown). Notably, these genes included DSG1, SPINK5, SPINK7, and SPINK8.

[0118] Transcriptome analysis across type 2 inflammatory diseases As IL-4 and IL-13 are central mediators of type 2 inflammation, we assessed the type 2 gene expression signature (type 2-NES) in EoE compared to relevant control tissues in disclosed studies, other studies, and published studies of other atopic indications (heatmap data not shown). Although heterogeneity was evident across all indications, type 2-NES was significantly higher in disease groups compared 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).

[0119] Consideration In the disclosed study, dupilumab significantly modulated the expression of 1,302 genes and reversed the disease transcriptional signature of EoE. Treatment with dupilumab significantly reduced EoE-NES (P<5.0×10 -8 ), whereas no significant changes were observed in the placebo group. Dupilumab normalized esophageal expression of genes dysregulated in EoE, including genes involved in type 1 and type 2 inflammation, fibrosis / remodeling, barrier function, mast cell activation, cell proliferation / differentiation, and eosinophilic inflammation, as well as genes not normalized by corticosteroid treatment (UPK1B, SH2D1B, CDH26, POSTN, and DSG1; Nhu & Moawad, Curr. Treat. Options. Gastroenterol., 2019, 17, 48-62).

[0120] The most characteristic feature of EoE is the infiltration of the esophageal mucosa by eosinophils (Collins,Dig. Dis., 2014, 32, 68-73). Other pathological changes associated with EoE include basal cell thickening and expansion of intercellular spaces, fibrosis of the lamina propria, and muscular 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 major pathways dysregulated in EoE and modulated by dupilumab, indicating the potential impact of dupilumab treatment on the associated histological changes and underlying inflammation associated with EoE. Previous trials of other biological therapeutics targeting type 2 inflammatory responses in EoE have had mixed results, with some agents (e.g., anti-IL-13 and anti-IL-5 agents; Ko & 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) producing histological improvement but not symptom resolution, and others (e.g., anti-IgE agents) having no effect (Clayton et al., Gastroenterology,2014,147,602-09). No other biologic agent has been shown to improve dysphagia symptoms in EoE trials, suggesting that dupilumab's effects on the IL-4 / IL-13 pathway are important in ameliorating EoE disease severity. Furthermore, IL-13 has been shown to induce EoE in mouse models, whereas mice lacking IL-5, eotaxin-1, or STAT-6 showed substantial protection against EoE development after IL-13 challenge (Mishra & Rothenberg, Gastroenterology, 2003, 125, 1419-27), indicating the interplay of many of the bimodal inflammatory components in the pathogenesis of the disease.

[0121] Dysregulated gene expression in esophageal biopsies from EoE patients indicates not only type 2 inflammation but also fibrosis / remodeling, impaired barrier function, and abnormalities in epithelial proliferation. Treatment with dupilumab reversed type 2 inflammation observed in patients with active EoE. In a phase 2 study, dupilumab reduced mean eosinophil counts by 107% by week 12 and significantly reduced peak intraepithelial eosinophil counts (Hirano et al., Gastroenterology, 2020, 158, 111-22). This analysis identified many EoE dysregulated genes associated with type 2 inflammatory responses that were modulated by dupilumab. One of the genes most significantly modulated by dupilumab, ALOX15 (15-lipoxygenase), is a proinflammatory mediator that is upregulated in asthma (Clayton et al., Gastroenterology, 147, 602-09 (2014)). ALOX15 is regulated by both IL-4 and IL-13, which explains the large effect of dupilumab treatment on the expression of this gene (Snodgrass & Brune, Front. Pharmacol. 2019, 10, 719). The eosinophil chemotactic CCL26 (eotaxin-3), the most dysregulated gene in the published EoE transcriptome (Blanchard et al., J. Clin. Invest., 2006,116,536-47), and its receptor CCR3 (Pease & Williams, J. Allergy Clin. Immunol., 2019, 143, 552-53), were both downregulated by dupilumab in this study. The critical role of eotaxin in eosinophil infiltration in EoE has previously been demonstrated in vivo in CCR3-deficient mice (Blanchard et al., J. Clin. Invest., 2006, 116, 536-47).POSTN (periostin), an extracellular matrix protein upregulated in EoE that is likely involved in fibrosis and type 2 inflammatory responses, has previously been shown to be significantly downregulated by dupilumab treatment but remains dysregulated by corticosteroid treatment (Blanchard et al., J. Allergy Clin. Immunol., 2007, 120, 1292-1300; O'Dwyer & Moore, Cell. Mol. Life. Sci., 2017, 74, 4305-14). Studies in mice have demonstrated a role for POSTN in eosinophil recruitment to the esophagus in EoE (Blanchard et al.,Mucosal Immunol.,2008,1,289-96). Other regulatory genes related to type 2 inflammation include MUC5B, which is downregulated in EoE but overexpressed in chronic rhinosinusitis with nasal polyps (Zhang et al., Allergy, 2019, 74, 131-40) and underexpressed in the airways of asthmatics (Bonser & Erle, J. Clin. Med., 2017, 6, 112). Treatment with dupilumab upregulated MUC5B, resulting in an expression pattern similar to that of healthy controls.

[0122] Similar to asthma, remodeling and fibrosis are key clinical features of chronic EoE that contribute to symptoms and disease severity. In mouse models, a role for IL-5-mediated eosinophilia in fibrosis and tissue remodeling in EoE has been shown (Mishra et al., Gastroenterology, 2018, 134, 204-14). In studies disclosed herein, dupilumab reduced collagen gene and POSTN expression, demonstrating a direct effect on genes associated with fibrosis. Persistent tissue infiltration of eosinophils contributes to the development of basal cell layer thickening and lamina propria fibrosis. This results in physical changes in the esophagus, such as stiffness and altered smooth muscle function. These changes then lead to clinical symptoms of varying severity (Aceves, Dig. Dis., 2014, 32, 15-21). The molecular and histological improvements in fibrosis and remodeling features are consistent with the improvement in esophageal distensibility also observed in the disclosed study (Hirano et al., Gastroenterology, 2020, 158, 111-22). Treatment with dupilumab resulted in both symptomatic relief and visual improvement in endoscopic findings in patients with active EoE (Hirano et al., Gastroenterology, 2020, 158, 111-22). Significant improvements have been observed in EoE-EREFS, a measure of endoscopically identified inflammation and remodeling characteristics of the EoE esophageal mucosa, in patients receiving dupilumab compared to placebo (P=0.0015). Significant improvements were also observed in histological severity and extent (EoE-HSS), a measure of eosinophil density, basal cell layer thickening, eosinophil abscesses, surface layering of eosinophils, alterations in the surface epithelium, dyskeratinized epithelial cells, and expansion of intercellular spaces (P<0.0001; Hirano et al., Gastroenterology, 2020, 158, 111-22). DplexOme™-EoE NES showed high correlation with total EoE-HSS grade, demonstrating the biological relevance of the molecular signature to clinical measurements. Expression of CTSC was the single gene that showed the highest correlation with total EoE-HSS, indicating the importance of proinflammatory proteolytic activity in disease pathogenesis.A 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 findings, this analysis showed that dupilumab normalized many genes involved in proliferation (thickening), fibrosis, and epithelial barrier function. These genes included mKi67, several collagen genes, DSG1, and SPINK family members. The SPINK gene family is thought to be involved in protecting epithelia and mucosa against proteolysis (Wapenaar et al., Immunogenetics, 2007, 59, 349-57). SPINK5 and SPINK7 play a role in regulating barrier function, while SPINK7 also regulates the production of proinflammatory cytokines (Azouz et al., Sci. Transl. Med., 2018, 10, eaap9736). SPINK8, the third most highly expressed SPINK in the esophagus, is downregulated in EoE but was upregulated by dupilumab in this study, indicating that dupilumab also helps restore epithelial barrier function in patients with active EoE.

[0123] In addition to improved epithelial barrier integrity and reduced fibrosis, patients receiving dupilumab also showed significant improvement in dysphagia symptoms compared to placebo (Hirano et al., Gastroenterology, 2020, 158, 111-22). ANO1, a calcium-activated chloride channel associated with gastric smooth muscle contraction and itch, was upregulated in esophageal biopsies and patient samples in mouse models of EoE and was also modulated by dupilumab (Subramanian et al., Proc. Natl. Acad. Sci . USA, 2005, 102, 15545-50; Vanani et al., 2020, J. Allergy Clin. Immunol., 145, 239-54). ANO1 expression, which has been shown to correlate with eosinophil counts in EoE and is thought to be related to IL-13-induced basal cell proliferation (Vanani et al., J. Allergy Clin. Immunol., 2020, 145, 239-54) and mucus hypersecretion (Lin et al., Exp. Cell Res., 2015, 334, 260-69), 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 neuro-related genes identified in the public EoE transcriptome, including NRXN1 and NTRK1, were also normalized by dupilumab, suggesting that neuroinflammation regulated by IL-4 and / or IL-13 in EoE may contribute to dysphagia and other symptoms.

[0124] Dupilumab inhibits the type 2 inflammatory cytokines IL-4 and IL-13, blocking their pro-inflammatory signaling that is involved in many allergic diseases ranging from asthma to atopic dermatitis (Gandhi et al., Expert Rev. Clin. Immunol., 2007, 13, 425-37). In atopic dermatitis, treatment with dupilumab alters genes related to type 2 inflammation (e.g., IL13, CCL17, CCL18, and CCL26), hyperplasia (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 of the similarities in the underlying pathogenesis of EoE and atopic dermatitis. Although thickening is also observed in other type 2 inflammatory conditions (e.g., acanthosis in AD), treatment with dupilumab has been shown to reduce epidermal thickness and markers of proliferation (e.g., K16 and mKi67) in atopic dermatitis (Kang et al., Am. J. Physiol. Lung Cell. Mol. Physiol., 2017, 313, L466-L476). Further analyses using NES derived from the DplxOme™-EoE and Type 2 inflammation signatures revealed upregulation of both signatures in disease compared to control samples across indications (Sherrill et al., Genes Immun., 2014, 15, 361-69; Tsii 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 indicating a common molecular etiology despite the diverse tissue types and RNA profiling platforms used in these analyses.

[0125] Dupilumab inhibits IL-4 and IL-13 and normalizes the transcriptome of esophageal pinch biopsies from EoE patients, including genes related to inflammation, eosinophils, barrier function, and fibrosis, consistent with findings from studies showing a reduction in histological disease characteristics and symptoms. These results suggest that dupilumab suppresses type 2 inflammatory responses in EoE and aids in esophageal mucosal recovery. The effects of dupilumab on a combination of molecular features, symptoms, and histopathological features in patients with active EoE suggest an important role for IL-4 and IL-13 in the pathogenesis of EoE.

[0126] Example 2 Generation of Dupilumab Therapeutic Core Gene Signature The criteria for core gene selection were fold change ≥2 and q<0.05 in three of five treatment studies: i) eosinophilic esophagitis (EoE): esophageal biopsy gene expression profiled by RNAseq, ii) atopic dermatitis (AD): skin biopsy gene expression profiled by microarray, iii) asthma: bronchial allergen challenge with house dust mite (HDM): sputum gene expression profiled by RNAseq, and iv) grass: intranasal challenge with timothy grass: nasal brushing gene expression profiled by RNAseq, and v) chronic rhinosinusitis with nasal polyps (CRSwNP): nasal brushing gene expression profiled by microarray. Differential gene expression was analyzed using the limma package (Ritzie et al., Nuc. Acids Res., 2015, 43, e47) for microarrays and DESeq2 (Lab et al., Genome Biol., 2014, 15, 550) for RNAseq. In the EoE / AD / CRSwNP study, analysis was performed comparing baseline (pre-treatment) to post-treatment. In the asthma / grass study, treatment comparisons were performed with or without allergen challenge. Placebo-controlled treatment effects can be analyzed using differential fold-change analysis by subtracting the percent change in the placebo group from the percent change in the treatment group. Statistical significance can be determined by permutation tests, where p-values ​​are calculated by randomly permuting patients' treatment assignments and comparing the actual differential fold-change to the background distribution of values ​​obtained from permutations. Results are shown in Figure 4.

[0127] Use Case 1: Screening for new indications Whole-transcriptome profiling technologies are becoming increasingly widespread and affordable. The amount of data being generated and shared in the public domain (e.g., Gene Expression Omnibus) is growing exponentially. Screening the dupilumab core signature in a vast number of disease studies will likely reveal previously unrecognized links between pathways disrupted by dupilumab and disease mechanisms. Diseases with differential gene expression (in the reverse direction) of the dupilumab signature may represent potential new indications for the drug.

[0128] Using the gene set enrichment analysis tool that considers both positive and negative gene sets (Lim et al., Pac. Symp. Biocomput., 2009, 504-515), normalized enrichment scores (NES) were generated for all disease studies from ArrayStudio DiseaseLand. First, differential expression analysis was performed in each study (disease vs. healthy controls). NES was then calculated using genes in the whole transcriptome list ranked by fold change to represent the enrichment of dupilumab signature in the study. Top hits in the screen included rhinitis, asthmatic rhinitis, allergic asthma, asthma, eosinophilic esophagitis, and atopic dermatitis. Some of these diseases were known indications for dupilumab where the disease signature was reversed after treatment.

[0129] Use Case 2: Patient Stratification In diseases where not all patients benefit from dupilumab, the overall NES of dupilumab is diluted in screening. In these diseases, the NES can be calculated at the individual patient level. Instead of generating the NES with a whole transcriptome list ranked by fold change between disease and healthy subjects, we first converted the gene expression profile to a z-score and calculated the NES using the ranked z-score in each sample to represent the enrichment of the dupilumab signature in the patient. Patients with a higher NES are more likely to benefit from dupilumab.

[0130] For example, in the disease screen described above, the NES for ulcerative colitis (UC) is significantly enriched but is not included in the top 10. When the NES is calculated at the individual patient level using a UC study (GSE87466) including 87 patients and 21 healthy controls, the dupilumab NES was statistically significant in 33% of patients. The results are shown in Figure 5. Based on this analysis, in some embodiments, only this subset of patients with significantly higher NES scores can be included in the population of patients treated with dupilumab.

[0131] Use Case 3 Molecular Pathology Endpoints Genes that are differentially expressed in esophageal biopsies of EoE patients compared to healthy controls are referred to as the EoE disease signature (Sherrill et al., Genes Immun., 2014,15,361-69). This disease signature was further subdivided into a finer set of genes for use as an EoE diagnostic panel (EDP) (Dellon et al.,Clin.Transl. Gastroenterol.,2017,8,e74). EoE phase 3 trials used the NES of EDP genes as a secondary endpoint as an indicator of disease activity. Comparable results were also observed when using a gene signature representing type 2 inflammation previously collected from the literature and preclinical experiments that showed that NES was significantly reduced with dupilumab treatment but not in the placebo group. A similar approach can be broadly applied across all indications using the dupilumab core gene signature to more effectively capture the underlying molecular pathology activity and changes following dupilumab treatment.

[0132] In the CRSwNP study, a set of 25 genes (identified from the treatment and clinical response signature) was shown to be more predictive of response to each CRSwNP clinical endpoint (nasal congestion / obstruction (CONG), nasal polyp score (NPS), Lund-Mackay Mackay computed tomography score (CT-LMK), and University of Pennsylvania Smell Identification Test (UPSIT)) compared with other available circulating biomarkers. Receiver operating characteristic analysis was used to evaluate the ability of the transcriptional signature and the NES score, which represents a more standard biomarker, to discriminate responders in each of the four primary endpoints, and across multiple endpoints. The predictive performance of each biomarker was summarized by calculating the area under the receiver operating characteristic curve (AUC). The results are shown in Figure 6. The dupilumab core gene signature was highly overlapping with the 25 gene set and was considered to be predictive of response to a broader range of dupilumab indications.

[0133] Example 3 Gene Signature Enrichment Score Gene set enrichment analysis (GSEA) uses the Kolmogorov-Smirnov statistical test to assess whether a predefined gene set (in this case, the dupilumab core signature) is statistically enriched in the two extreme genes of a list ranked by differential expression between two biological states (Subramanian et al., Proc. Natl. Acad. Sci. USA 2005, 102, 15545-50). This algorithm is extremely useful in detecting differential expression of a set of genes collectively, even when the fold change is small for each individual gene. Since the dupilumab core signature includes both up- and down-regulated genes, the enrichment of two complementary gene sets for the N-ranked genes is assessed by extending GSEA as follows: (a)R + Order the N genes, denoted by, from most positive to most negative values ​​(e.g., log2 fold change, z score).

[0134] (b)R + Positive gene set in S + , and R - Negative gene set in S - Identify hits independently for - has the reverse value of R + (The ranking is the reverse of that of

[0135] (c)R + and R - Combining these, S + and S - Reorder each value by keeping hits in both

[0136] (d) Calculate the cumulative score by walking down the combined rankings. The score is calculated as |r i |p / Σ iεS |r| p otherwise, it is lowered by 1 / (2N-S) (where S is + and S - is the total number of genes in i is the value of gene i and p is the weight of r.

[0137] e) The enrichment score (ES) is determined as the maximum deviation from zero along the cumulative score.

[0138] (f) Repeat steps (a–e) 1000 times on a random set of genes to calculate the null distribution of ES. Calculate the normalized enrichment score (NES) as ES divided by the mean of the null distribution of ES.

[0139] (g) Statistical significance can be calculated by comparing the observed ES to the null distribution.

[0140] In use case 1, R is ranked by the log2 fold change of the mean gene expression in disease samples compared to the mean gene expression in healthy samples. There is a ranked list for each disease test, and NES can be calculated for every test. Diseases with significant NE are considered 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 permutation of sample labels (disease / healthy).

[0141] In use case 2, gene expression levels are converted to z-scores for each 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 from healthy control samples.

[0142] In use case 3, R can rank gene expression levels after treatment by the log2 fold change compared to baseline (pre-treatment) gene expression levels. NES is calculated for each patient and indicates how genes change with treatment. NES can also be calculated per sample and represents the overall expression level of the core signature in that sample.

[0143] Example 4 The transcriptomic effects of dupilumab are durable In parts A and B of the phase 3 TREET trial (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330), transcriptomic results were available for 95 of 122 (78%) patients receiving dupilumab 300 mg once weekly, 60 of 81 (74%) patients receiving dupilumab 300 mg twice weekly, and 84 of 118 (71%) patients receiving placebo. In Part C of the extended active treatment period, transcriptomic results were available for 60 of 114 (53%) patients who continued dupilumab 300 mg weekly (dupilumab / dupilumab 300 mg weekly) and 32 of 79 (41%) patients who continued dupilumab 300 mg twice weekly (dupilumab / dupilumab twice weekly) both through week 52, as well as for 52 of 74 (70%) patients who switched from placebo to dupilumab 300 mg weekly at the start of Part C and received dupilumab for 24 weeks (placebo / dupilumab 300 mg weekly), and 22 of 37 (59%) patients who switched to dupilumab 300 mg twice weekly at the start of Part C and received dupilumab for 24 weeks (placebo / dupilumab 300 mg twice weekly). In the Phase 3 TREET trial, results from the same dosing regimens and time points will be pooled across each part of the study.

[0144] Using data from the Phase 3 TREET study, changes in genes included in the EoE Diagnostic Panel (EDP: a smaller set of genes further subdivided compared to the EoE disease transcriptome) (Wen et al., Gastroenterology, 2013, 145, 1289-1299) were examined over time in patients treated with dupilumab 300 mg weekly (see Figure 7, Panel A). EDP-NES, calculated from the relative percent change pre- and post-treatment, was included as a secondary endpoint in the study (Dellon et al., N. Engl. J. Med., 2022, 387, 2317-2330). Compared to baseline, patients receiving dupilumab 300 mg weekly showed a significant reduction in EDP at week 24 (P=5.0×10 -18 ), and this reduction was sustained through week 52 in the dupilumab / dupilumab 300 mg once weekly arm (P = 5.0 × 10 -18 ), with no significant difference between the two time points (P = 0.53). In general, the same patients who experienced a reduction in EDP at week 24 maintained the suppression through week 52.

[0145] Transcriptomic effects were seen in adolescents and adults. The EDP transcriptome was assessed at baseline and week 24 in a subgroup of adolescent and adult patients treated with dupilumab 300 mg weekly in the Phase 3 TREET study to determine whether the impact of dupilumab treatment on the transcriptome was seen across age groups (see Figure 7, Panel B). Significant reductions in EDP-NES were observed from baseline to week 24 in both age groups (Adolescents: P = 2.6 x 10 -7 , Adult: P=1.8×10 -13 There were no significant differences in EDP-NES between adolescents and adults at baseline (P = .14) or at week 24 (P = .44).

[0146] Example 5 Dupilumab inhibits MIB-1 (Ki67), tryptase, and CD4 + Suppresses T cell infiltration and CD1a + Increased the number of cells to some extent Prolonged eosinophil-dominant inflammation promotes epithelial thickening in EoE patients (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) 12 weeks after treatment compared with placebo treatment (P=0.011, see Figure 8, Panel A), which is consistent with the observation of improved epithelial differentiation (by transcriptome analysis) and reduced basal cell layer thickening in dupilumab-treated patients (Hirano et al., Gastroenterology, 2020, 158, 111-122). This is consistent with findings in AD patients in which dupilumab progressively reversed characteristic acanthosis (Guttman-Yassky et al., J. Allergy Clin. Immunol., 2019, 143, 155-172). Although overall staining was low for tryptase, a marker of mast cell activation released upon mast cell degranulation, the number of cells expressing tryptase was reduced at week 12 in patients treated with dupilumab 300 mg once weekly compared with those receiving placebo (P=0.003; see Figure 8, Panel B). CD8 + No significant differences were observed in cytotoxic T cells (see Figure 8, Panel C), but dupilumab significantly increased patients' CD4 + The CD1a T helper cell population was significantly decreased (P=0.027, see FIG. 8, panel D). In contrast, + cells (a marker of antigen-presenting Langerhans cells) were increased with dupilumab treatment compared with placebo (P = 0.008, see Figure 8, Panel E). There was no significant change in the number of FcεRI (Fc receptor for IgE) or chymase (mast cell mediator, a serine proteinase with chymotrypsin-like activity) positive cells with dupilumab treatment (data not shown).

[0147] Example 6 Dplex3 response signature in EoE ph3 The normalized enrichment score (NES) of gene set enrichment analysis (GSEA) reflects the degree to which the activity level of a set of transcripts is over-represented at either extreme (top or bottom) across the ranked list of transcripts. Gene expression is converted to a z-score for each gene, and in each sample, all genes are sorted by z-score and used as a reference list to calculate the NES of a single sample. The NES calculated for the dupilumab response signature reflects the expression level of a set of genes 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 outside 0 reflects a response.

[0148] Dupilumab 300 mg once weekly and twice weekly both demonstrate significant dupilumab molecular responses (strong negative NES) in EoE (see Figure 9). The mean percent change in NES from baseline to weeks 24 and 52 was -0.32 (p=4.0×10) in the placebo / dupilumab 300 mg group. -2 ) and -2.59(5.6×10 -21 ), and −2.09 (6.6 × 10 -20 ) and -2.00(9.3×10 -13 ), and −2.18 (9.3 × 10 -31 ) and -2.22(6.1×10 -23 ) EoE: eosinophilic esophagitis, NES: normalized enrichment score, Q2W: once every 2 weeks, QW: once a week.

[0149] All patent documents, websites, other publications, accession numbers, and the like cited above or below are incorporated by reference in their entirety for all purposes to the same extent as if each was specifically and individually indicated to be incorporated by reference. Where the version of a sequence associated with an accession number varies over time, the version associated with that accession number as of the effective filing date of this application is meant. Effective filing date means the earlier of the actual filing date for which the accession number is given or the filing date of the priority application, if applicable. Similarly, where different versions of publications, websites, etc. have been published at different times, the version last published as of the effective filing date of the application is meant unless otherwise indicated. 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, unless otherwise indicated. Although the present disclosure has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent that certain changes and modifications may be made within the scope of the appended claims.

Claims

1. A method for identifying a disease or condition suitable for treatment with dupilumab, a) To generate a dupilumab therapeutic core gene signature, b) Screening the dupilumab core gene signature against the entire transcriptome profile from multiple disease trials, c) In the aforementioned disease trials, by identifying diseases or conditions that have differential gene expression in the opposite direction to the dupilumab therapeutic core gene signature, The method comprising identifying a disease or condition suitable for treatment with dupilumab.

2. The method according to claim 1, wherein generating the dupilumab treatment core gene signature includes determining differential gene expression between the dupilumab treatment group and the placebo treatment group in multiple treatment trials, and identifying multiple genes that are differentially expressed.

3. The method according to claim 2, wherein the plurality of treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyps.

4. The method according to claim 2, wherein the gene of the core gene signature identified from differential gene expression is selected such that in three or more of the five treatment trials, the magnification change is ≥ 2 and / or q < 0.

05.

5. The method according to claim 4, wherein the magnification change includes subtracting the change in expression levels in the placebo treatment group from the dupilumab treatment group.

6. The method according to claim 3, wherein differential gene expression in the treatment trials for eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab with the gene expression level after treatment with dupilumab.

7. The method according to claim 3, wherein the differential gene expression in the asthma and grass allergy treatment trial is performed by comparing the gene expression level when an allergen challenge is performed with the gene expression level when no allergen challenge is performed.

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

9. The method according to claim 8, wherein the differential gene expression in the therapeutic trials for eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq.

10. The method according to claim 8, wherein the differential gene expression in the treatment trials for atopic dermatitis and chronic sinusitis with nasal polyps is analyzed by microarray.

11. The differentially expressed genes are 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 The method according to claim 2, comprising 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. Screening the dupilumab core gene signature against the entire transcriptome profile from multiple disease trials is i) Perform differential gene expression analysis on the entire transcriptome profile in each of the multiple disease trials, ii) The method according to claim 1, comprising generating normalized enrichment scores (NES) for all diseases in the plurality of disease trials using the plurality of genes which are differentially expressed and which are located within the dupilumab therapeutic core gene signature.

13. The method according to claim 12, wherein differential gene expression analysis is performed on the entire transcriptome profile in each of the multiple disease tests for both diseased and healthy subjects.

14. The method according to claim 12, wherein the plurality of disease tests include the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database.

15. The method according to claim 12, wherein the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.

16. The aforementioned NES, a) To generate a ranked gene list (R+) by ordering multiple genes that are differentially expressed from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value, b) Identifying hits (i.e., the rank of genes in the core signature) independently for the positive (i.e., most upregulated) gene set (S+) within R+ and the negative (i.e., most downregulated) gene set (S-) within R- (where R- is the inverse ranking of R+ with reversed values), c) Reordering each value by combining R+ and R- and retaining hits in both S+ and S-, d) Calculate the cumulative score by walking down the combined rankings (however, the cumulative score is calculated if the i-th gene is a hit, / r ｉ / ｐ / Σ ｉεＳ / r / ｐ It increases by 1 / (2N-S) or decreases by 1 / (2N-S) (where S is the total number of genes in S+ and S-, and r ｉ (where 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 cumulative score, f) Repeat steps a) to e) 1000 times with a random gene set to calculate the null distribution of ES, g) The method according to claim 15, wherein NES is generated as the value obtained by dividing ES by the mean of the null distribution of ES.

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

18. The method according to claim 16, wherein step a) includes using a log2 multiplier change or a z score.

19. The method according to claim 16, wherein R+ and R- are ranked by a log2 ratio change obtained by comparing the average gene expression level in diseased samples with the average gene expression level in healthy samples.

20. The method according to claim 16, comprising calculating the NES for all disease trials using a ranking list for each disease trial.

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

22. A method for identifying subjects with diseases or conditions suitable for treatment with dupilumab, a) To generate a dupilumab therapeutic core gene signature, b) Screening the dupilumab core gene signature against the entire transcriptome profile from the subject, c) The method comprising determining whether the subject is suitable for dupilumab treatment.

23. The method according to claim 22, wherein generating the dupilumab treatment core gene signature includes determining differential gene expression between the dupilumab treatment group and the placebo treatment group in multiple treatment trials, and identifying multiple genes that are differentially expressed.

24. The method according to claim 23, wherein the plurality of treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyps.

25. The method according to claim 23, wherein the gene of the core gene signature identified from differential gene expression is selected such that in three or more of the five treatment trials, the magnification change is ≥ 2 and / or q < 0.

05.

26. The method according to claim 25, wherein the magnification change includes subtracting the change in expression levels in the placebo treatment group from the dupilumab treatment group.

27. The method according to claim 24, wherein differential gene expression in the treatment trials for eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab with the gene expression level after treatment with dupilumab.

28. The method according to claim 24, wherein the differential gene expression in the asthma and grass allergy treatment trial is performed by comparing the gene expression level when an allergen challenge is performed with the gene expression level when no allergen challenge is performed.

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

30. The method according to claim 29, wherein the differential gene expression in the therapeutic trials for eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq.

31. The method according to claim 29, wherein the differential gene expression in the treatment trials for atopic dermatitis and chronic sinusitis with nasal polyps is analyzed by microarray.

32. The differentially expressed multiple genes are 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, The method according to claim 23, comprising 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. Screening the dupilumabu core gene signature against the entire transcriptome profile from the aforementioned subjects is i) Converting the entire transcriptome profile from the subject into a z-score, ii) Ranking the aforementioned z scores, iii) The method according to claim 22, comprising generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes differentially expressed and located within the dupilumab therapeutic core gene signature, thereby representing the degree of enrichment of the dupilumab core gene signature in the subject.

34. The method according to claim 33, wherein the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.

35. The aforementioned NES, a) Convert the expression levels of each gene within the plurality of genes into z-scores, and generate an R+ value by ordering the plurality of genes that are differentially expressed from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value, b) Independently identify hits for the positive (i.e., most upregulated) gene set (S+) within R+ and the negative (i.e., most downregulated) gene set (S-) within R- (where R- is the inverse ranking of R+ with reversed values), c) Reordering each value by combining R+ and R- and retaining hits in both S+ and S-, d) calculating the cumulative score by walking down the combined ranking (however, the cumulative score increases by only / r ｉ / ｐ / Σ ｉεＳ / r / ｐ only when the i-th gene is a hit, or decreases by 1 / (2N - S) (where S is the total number of genes in S+ and S-, r ｉ is the value of gene i, and p is the weight of r)) and, e) Determining the enrichment score (ES) as the maximum deviation from zero along the cumulative score, f) Repeat steps a) to e) 1000 times with a random gene set to calculate the null distribution of ES, g) The method according to claim 34, wherein NES is generated as the value obtained by dividing ES by the mean of the null distribution of ES.

36. The method according to claim 35, further comprising calculating statistical significance by determining the 95th percentile NES from a healthy control sample.

37. The method of claim 35, comprising calculating the NES for all disease trials using a ranking list for each disease trial.

38. The method according to claim 22, wherein if the NES of the subject is higher than that of a healthy control, the subject is suitable for dupilumab treatment.

39. A method for assisting the conduct of a clinical trial for the treatment of a disease or condition with dupilumab, comprising using a dupilumab core gene signature as the clinical endpoint of the clinical trial.

40. The method according to claim 39, wherein the dupilumab treatment core gene signature is generated by determining differential gene expression between the dupilumab treatment group and the placebo treatment group in multiple treatment trials, and by identifying multiple genes that are differentially expressed.

41. The method according to claim 40, wherein the plurality of treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyps.

42. The method according to claim 40, wherein the gene of the core gene signature identified from differential gene expression is selected such that in three or more of the five treatment trials, the magnification change is ≥ 2 and / or q < 0.

05.

43. The method according to claim 42, wherein the magnification change includes subtracting the change in expression levels in the placebo treatment group from the dupilumab treatment group.

44. The method according to claim 41, wherein differential gene expression in the treatment trials for eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab with the gene expression level after treatment with dupilumab.

45. The method according to claim 41, wherein the differential gene expression in the asthma and grass allergy treatment trial is performed by comparing the gene expression level when an allergen challenge is performed with the gene expression level when no allergen challenge is performed.

46. The method according to claim 40, wherein the differential gene expression is analyzed by a microarray or RNASeq.

47. The method according to claim 46, wherein the differential gene expression in the therapeutic trials for eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq.

48. The method according to claim 46, wherein the differential gene expression in the treatment trials for atopic dermatitis and chronic sinusitis with nasal polyps is analyzed by microarray.

49. The differentially expressed multiple genes are 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, The method according to claim 40, comprising 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.

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

51. The method according to claim 50, wherein the clinical endpoint is achieved if dupilumab treatment reduces the NES of the dupilumab treatment core gene signature to an acceptable level.

52. The aforementioned NES, a) To generate ranked R+ values ​​by ordering the differentially expressed genes from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value, b) Independently identify hits for the positive (i.e., most upregulated) gene set (S+) within R+ and the negative (i.e., most downregulated) gene set (S-) within R- (where R- is the inverse ranking of R+ with reversed values), c) Reordering each value by combining R+ and R- and retaining hits in both S+ and S-, d) Calculate the cumulative score by walking down the combined rankings (however, the cumulative score is calculated if the i-th gene is a hit, / r ｉ / ｐ / Σ ｉεＳ / r / ｐ It increases by 1 / (2N-S) or decreases by 1 / (2N-S) (where S is the total number of genes in S+ and S-, and r ｉ (where 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 cumulative score, f) Repeat steps a) to e) 1000 times with a random gene set to calculate the null distribution of ES, g) The method according to claim 50, wherein NES is generated as the value obtained by dividing ES by the mean of the null distribution of ES.

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

54. The method according to claim 52, wherein step a) includes comparing the gene expression level after dupilumab treatment with the gene expression level before the initiation of dupilumab treatment using log2 multiplier changes.

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

56. A method for supporting the treatment of subjects with diseases or conditions suitable for treatment with dupilumab, a) Identifying the subjects who have a disease or condition suitable for treatment with dupilumab, i) To generate a dupilumab therapeutic core gene signature, ii) Screening the dupilumab core gene signature against the entire transcriptome profile from the subject, iii) The identification includes determining whether the subject is suitable for dupilumab treatment, The method comprising administering dupilumab to a subject having a disease or condition suitable for treatment with dupilumab.

57. The method according to claim 56, wherein generating the dupilumab therapeutic core gene signature includes determining differential gene expression between the dupilumab treatment group and the placebo treatment group in multiple therapeutic trials, and identifying multiple genes that are differentially expressed.

58. The method according to claim 57, wherein the plurality of treatment trials include eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic sinusitis with nasal polyps.

59. The method according to claim 57, wherein the gene of the core gene signature identified from differential gene expression is selected such that in three or more of the five treatment trials, the magnification change is ≥ 2 and / or q < 0.

05.

60. The method according to claim 59, wherein the magnification change includes subtracting the change in expression levels in the placebo treatment group from the dupilumab treatment group.

61. The method according to claim 58, wherein differential gene expression in the treatment trials for eosinophilic esophagitis, atopic dermatitis, and chronic sinusitis with nasal polyps is performed by comparing the baseline gene expression level before treatment with dupilumab with the gene expression level after treatment with dupilumab.

62. The method according to claim 58, wherein the differential gene expression in the asthma and grass allergy treatment trial is performed by comparing the gene expression level when an allergen challenge is performed with the gene expression level when no allergen challenge is performed.

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

64. The method according to claim 63, wherein the differential gene expression in the therapeutic trials for eosinophilic esophagitis, asthma, and grass allergy is analyzed by RNASeq.

65. The method according to claim 63, wherein the differential gene expression in the treatment trials for atopic dermatitis and chronic sinusitis with nasal polyps is analyzed by microarray.

66. The differentially expressed multiple genes are 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, The method according to claim 57, comprising 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. Screening the dupilumabu core gene signature against the entire transcriptome profile from the aforementioned subjects is i) Converting the entire transcriptome profile from the subject into a z-score, ii) Ranking the aforementioned z scores, iii) The method according to claim 56, comprising generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes differentially expressed and located within the dupilumab therapeutic core gene signature, thereby representing the degree of enrichment of the dupilumab core gene signature in the subject.

68. The method according to claim 67, wherein the NES is generated using a gene set enrichment analysis tool that takes into account both positive and negative gene sets.

69. The aforementioned NES, a) Convert the expression levels of each gene within the plurality of genes into z-scores, and generate an R+ value by ordering the plurality of genes that are differentially expressed from the most positive (i.e., most upregulated) value to the most negative (i.e., most downregulated) value, b) Independently identify hits for the positive (i.e., most upregulated) gene set (S+) within R+ and the negative (i.e., most downregulated) gene set (S-) within R- (where R- is the inverse ranking of R+ with reversed values), c) Reordering each value by combining R+ and R- and retaining hits in both S+ and S-, d) Calculate the cumulative score by walking down the combined rankings (however, the cumulative score is calculated if the i-th gene is a hit, / r ｉ / ｐ / Σ ｉεＳ / r / ｐ It increases by 1 / (2N-S) or decreases by 1 / (2N-S) (where S is the total number of genes in S+ and S-, and r ｉ (where 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 cumulative score, f) Repeat steps a) to e) 1000 times with a random gene set to calculate the null distribution of ES, g) The method according to claim 68, wherein NES is generated as the value obtained by dividing ES by the mean of the null distribution of ES.

70. The method according to claim 69, further comprising calculating statistical significance by determining the 95th percentile NES from a healthy control sample.

71. The method according to claim 69, comprising calculating the NES for all disease trials using a ranking list for each disease trial.

72. The method according to claim 56, wherein if the NES of the subject is higher than that of a healthy control, the subject is suitable for dupilumab treatment.

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