Transcriptome signature analysis for treating inflammation

Abstract

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

Description

[0001] DOCKET NO.: 38120-4318 (11934W001)

[0002] - 1- Transcriptome Signature Analysis For Treating Inflammation

[0003] Field

[0004] The present disclosure provides methods of identifying a disease or condition suitable for treatment with dupilumab, methods of identifying a subject having a disease or condition suitable for treatment with dupilumab, and methods of carrying out a clinical trial for dupilumab treatment of a disease or condition.

[0005] Background

[0006] Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for 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 use in 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 polyposis.

[0007] Pharmacogenetic tests, along with other information about subjects and their disease, disorder, or condition, can play an important role in drug therapy. However, such data typically relate to the clearance of small molecules where liver or kidney metabolism is well established, or certain definitive biologic markers are deemed as an indication of the disease, condition, or disorder. A caveat of such approach has been realized by the industry recently: typically, a disease, condition, or disorder, and the treatment thereof by a therapeutic agent, may involve multiple biologic molecules' small fluctuation, sometimes in an undefined biological pathway. Individually, each of such biologies may not exert a significant effect to the subject, but the accumulative effects of these biologic molecule level's fluctuation trend found in an individual patient, before or after the therapeutic treatment, may reveal the fitness of a particular therapeutic's treatment. Therefore, there is a long-felt but unmet need for methods to identify subjects that would benefit from treatment with any particular biologic therapeutic agent.

[0008] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0009] - 2 - Summary

[0010] The present disclosure provides methods of identifying a disease or condition suitable for treatment with dupilumab, the methods comprising: a) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; b) screening the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies; and c) identifying a disease or condition in the plurality of disease studies having a differential gene expression that is in the opposite direction from the dupilumab treatment core gene signature; thereby identifying a disease or condition suitable for treatment with dupilumab.

[0011] The present disclosure also provides methods of identifying a subject having a disease or condition suitable for treatment with dupilumab, the methods comprising: a) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; 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.

[0012] The present disclosure also provides methods of carrying out a clinical trial for dupilumab treatment of a disease or condition, the methods comprising using a dupilumab core gene signature as a clinical endpoint for the clinical trial, wherein the dupilumab treatment core gene signature is generated by determining differential gene expression of a

[0013] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0014] - 3 -dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPAS, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

[0015] The present disclosure also provides methods of treating a subject having a disease or condition suitable for treatment with dupilumab, the methods comprising: a) identifying the subject as having a disease or condition suitable for treatment with dupilumab comprising: i) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; 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.

[0016] The present disclosure also provides kits comprising a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprise nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

[0017] The present disclosure also provides methods of detecting a plurality of nucleic acid molecules in a subject after treatment with dupilumab, wherein the plurality of nucleic acid molecules detected comprises the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof

[0018] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0019] - 4-comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes, the method comprising: contacting a biological sample from the subject with a plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; and detecting the presence or absence of the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

[0020] In some embodiments, the dupilumab treatment core gene signature is generated by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies and identifying a plurality of genes that are differentially expressed.

[0021] In some embodiments, the clinical trial comprises generating a normalized enrichment score (NES) for the dupilumab treatment core gene signature prior to initiation of treatment of a subject with dupilumab and at least one time point after initiation of treatment of a subject with dupilumab.

[0022] In some embodiments, when dupilumab treatment results in a decrease in the NES for the dupilumab treatment core gene signature to an acceptable value, the clinical endpoint has been achieved.

[0023] Brief Description Of The Drawings

[0024] 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.

[0025] The accompanying figures, which are incorporated in and constitute a part of this specification, illustrate several features of the present disclosure.

[0026] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0027] - 5 - Figure 1 shows a core 10-gene signature (TC10) that identified a shared type 2 disease signature; identification of a core 10-gene signature (T2C10) shared byT2ID-74 and DPL-48, with inverse effects of disease vs treatment on gene expression was observed (see, top panel); P-values (Bonferonni corrected Fisher's Exact Test) from analyses evaluating the likelyhood of gene signatures to be found in the T2IDs Pso, RA or CD, AD, asthma and CRSwNP relative to non-atopic diseases Pso, RA and CD (see botton panel); overlap was observed with T2ID-74 alone in RA and CD, but DPL-48 was only statistically significant for CD; T2C10 was not statistically associated with non-atopic inflammatory diseases Pso, RA or CD; abbreviations: EoE (esophageal biopsy tissue), AD (lesional skin biopsies), asthma (primary airway epithelial cells brushings), CRSwNP (nasal polyp tissue), AR (nasal mucosal brushings). Pso, psoriasis; RA, rheumatoid arthritis; CD, Crohn's Disease.

[0028] Figure 2 shows differential gene expression analysis for dupilumab pharmacodynamic signature (DPL-48); Phase 2 POC EoE NCT02379052 (Hirano et al., Gastroenterology, 2020, 158, 111-122); Phase 2 AD NCT01979016 (Guttman-Yassky et al., J. Allergy Clin. Immunol., 2019, 143, 155-172); Phase 1 Asthma NCT03112577; Phase 3 CRSwNP NCT02912468 (Bachert et al., Lancet, 2019, 394, 1638-1650; and Gayvert et al., J. Allergy Clin. Immunol., 2024, 154, 619-630; Phase 2b AR grass NCT03558997 (Corren et al., J. Asthma Allergy, 2021, 14, 1045-1063; and Wipperman et al., Allergy, 2024, 79, 894-907.

[0029] Figure 3 shows comorbidities between T2ID is enhanced in individuals with high peripheral blood eosinophil counts in the UKB cohort.

[0030] Figure 4 shows association between IL-4 / 13 Genetic Pathway Score and T2ID or non-atopic diseases; a Genetic Pathway Score (GPS) was generated in six cohorts (total N up to 448,079) based on common non-coding variants in / near IL4 / IL13, IL4R and STAT6; this IL-4 / IL-13 GPS was then tested for association with risk of each individual T2ID in the six cohorts (excluding the UKB), with results displayed in (A) asthma, (B) atopic dermatitis, (C) nasal polyps, (D) eosinophilic esophagitis, and (E) prurigo nodularis; as a control, the association between the IL-4 / 1 L-13 GPS and risk of three non-atopic diseases was tested in the same six cohorts, namely (F) psoriasis, (G) rheumatoid arthritis, and (H) Crohn's disease; OR, odds ratio; T2ID, type 2 inflammatory diseases; UKB, United Kingdom Biobank.

[0031] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0032] - 6 - Figure 5 shows rare burden mask association in asthma for I L5, I L21, IL21R, and IL5RA.

[0033] Figure 6 shows rare burden mask association in eosinophils for IL5, I L21, 1 L21 R, and IL5RA.

[0034] Figure 7 shows variants independently associated with asthma in the UKB cohort and selected for the IL-4 / IL-13 genetic pathway score.

[0035] Description

[0036] Various terms relating to aspects of the present disclosure are used throughout the specification and claims. Such terms are to be given their ordinary meaning in the art, unless otherwise indicated. Other specifically defined terms are to be construed in a manner consistent with the definitions provided herein.

[0037] Unless otherwise expressly stated, it is not intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a specific order.

[0038] Accordingly, where a method claim does not specifically state in the claims or descriptions that the steps are to be limited to a specific order, it is not intended that an order be inferred, in any respect. This holds for any possible non-expressed basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow, plain meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.

[0039] As used herein, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise.

[0040] As used herein, the term "about" means that the recited numerical value is approximate and small variations would not significantly affect the practice of the disclosed embodiments. Where a numerical value is used, unless indicated otherwise by the context, the term "about" means the numerical value can vary by ±10% and remain within the scope of the disclosed embodiments.

[0041] As used herein, the term "comprising" may be replaced with "consisting" or "consisting essentially of" in particular embodiments as desired.

[0042] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0043] - 7 - As used herein, the terms "nucleic acid", "nucleic acid molecule", "nucleic acid sequence", "polynucleotide", or "oligonucleotide" can comprise a polymeric form of nucleotides of any length, can comprise DNA and / or RNA, and can be single-stranded, double-stranded, or multiple stranded. One strand of a nucleic acid also refers to its complement.

[0044] As used herein, the term "subject" includes any animal, including mammals.

[0045] Mammals include, but are not limited to, farm animals (such as, for example, horse, cow, pig), companion animals (such as, for example, dog, cat), laboratory animals (such as, for example, mouse, rat, rabbits), and non-human primates (such as, for example, 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 a veterinarian.

[0046] As used herein, a list comprising A, B, "and / or" C provides: (i) A alone; (ii) B alone; (iii) C alone; (iv) A and B; (v) A and C; (vi) B and C; and (viii) A, B, and C. Thus, a list comprising A, B, C, . . . , and / or N has n constituents, where n is a positive integer provides all possible combinations of A, B, C, . . . N up to and including a combination of all n constituents.

[0047] As used herein, the phrase "opposite direction" in the disease signature, refers to a comparison between disease samples and healthy samples. A gene is up-regulated (red / pink) if the expression is higher in the disease, compared to healthy. In the treatment signature, a comparison is between post-treatment and pre-treatment. A gene is down-regulated (blue) if the expression is lower after treatment, compared to baseline (before treatment). "Opposite direction" refers to genes that significantly changed in opposite direction in disease and treatment signature, e.g. up-regulated in disease (compared to healthy) and down-regulated after dupilumab treatment (compared to before treatment).

[0048] In the process of drug discovery, it is beneficial to identify existing approved drugs effective to new indications (be it a disease, condition, or disorder), or a subset of patient population that would better respond to the existing drug. In traditional drug effect studies, single gene analysis upon drug treatment (i.e., t-test) for each individual gene is utilized to identify suitable biomarkers to a target, and to alter such biomarker's level to achieve a

[0049] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0050] - 8 -presumed treatment effect. This approach, however, may lead to the creation of a long list of statistically significant genes without connecting their biological relevance. In addition, such gene lists among different clinical studies may have little overlap; and most importantly, they may miss the pathway effects of each gene involved in the disease, condition, or treatment effects. For example, several small gene expression changes, although individually not significant in t-test, may have more collective impacts on a given disease or condition, than one gene that changes quite a bit but has negligible effects on the disease or condition. To avoid such an outcome, a genome wide approach is developed in this disclosure, wherein a core gene signature set in a drug's clinical study is first identified, compared with a whole transcriptome profile of interest, to obtain a normalized enrichment score for use to identify an appropriate indication or patient population to respond to the drug, particularly dupilumab.

[0051] The present disclosure provides methods of identifying a disease or condition suitable for treatment with dupilumab. In some embodiments, the embodiments comprise generating a dupilumab treatment core gene signature. In some embodiments, the methods comprise screening the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies. In some embodiments, the methods comprise identifying a disease or condition in the plurality of disease studies having a differential gene expression that is in the opposite direction from the dupilumab treatment core gene signature. In some embodiments, the methods identify a disease or condition suitable for treatment with dupilumab.

[0052] In some embodiments, the methods comprise generating the dupilumab treatment core gene signature comprising determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies. In some embodiments, the methods result in the identification of a plurality of genes that are differentially expressed.

[0053] In some embodiments, the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyposis. In some embodiment, the plurality of treatment studies comprises eosinophilic

[0054] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0055] - 9 -esophagitis. In some embodiments, the plurality of treatment studies comprises atopic dermatitis. In some embodiments, the plurality of treatment studies comprises asthma. In some embodiments, the plurality of treatment studies comprises grass allergy. In some embodiments, the plurality of treatment studies comprises chronic rhinosinusitis with nasal polyposis. In some embodiments, the plurality of treatment studies comprises any disease, disorder, or condition, involving or suspected to involve IL-4 and / or IL-13.

[0056] In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and a q < 0.05 in > 60% of treatment studies. Alternately, the use fold-change threshold of 1.5 or a p value (instead of q value) can be used. In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2 in > 60% of treatment studies. In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected as having a q < 0.05 in > 60% of treatment studies.

[0057] In some embodiments, the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group. For example, fold-change is the average expression in group 2 / average expression in group 1.

[0058] In some embodiments, the differential gene expression for eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyposis treatment studies are carried out 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 for eosinophilic esophagitis is carried out 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 for atopic dermatitis studies is carried out 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 for asthma treatment studies is carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab. In some embodiments,

[0059] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0060] - 10-the differential gene expression for grass allergy treatment studies is carried out 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 for chronic rhinosinusitis with nasal polyposis treatment studies is carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab.

[0061] In some embodiments, the differential gene expression for the asthma allergy treatment studies are carried out by comparing the gene expression after allergen challenge to the gene expression before allergen challenge. In some embodiments, the differential gene expression for the grass allergy treatment studies are carried out by comparing the gene expression after allergen challenge to the gene expression before allergen challenge.

[0062] In some embodiments, the differential gene expression is analyzed by a microarray or RNASeq. In some embodiments, the differential gene expression is analyzed by a microarray. In some embodiments, the differential gene expression is analyzed by a RNASeq. In some embodiments, reverse transcription polymerase chain reaction (RT-PCR) can be used to measure gene expression.

[0063] In some embodiments, the differential gene expression of the eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the eosinophilic esophagitis treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the atopic dermatitis treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the asthma treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the grass allergy treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by RNASeq.

[0064] In some embodiments, the differential gene expression of the eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and / or chronic rhinosinusitis with nasal polyposis is analyzed by microarray. In some embodiments, the differential gene expression

[0065] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0066] - 11 -of the eosinophilic esophagitis is analyzed by microarray. In some embodiments, the differential gene expression of the atopic dermatitis is analyzed by microarray. In some embodiments, wherein the differential gene expression of the asthma is analyzed by microarray. In some embodiments, the differential gene expression of the grass allergy is analyzed by microarray. In some embodiments, the differential gene expression of the chronic rhinosinusitis with nasal polyposis is analyzed by microarray.

[0067] In some embodiments, a gene of the plurality of genes that are differentially expressed comprises ALOX15, POSTN, CDH26, CH25H, CPAS, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

[0068] In some embodiments, any one, any two, any three, any four, any five, any six, any seven, any eight, or any nine of the genes of the plurality of genes that are differentially expressed set forth above can be omitted from the gene expression analysis.

[0069] In some embodiments, the methods comprise screening of the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies comprises performing a differential gene expression analysis on the whole transcriptome profile for each disease study in the plurality of disease studies. In some embodiments, the screening of the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies comprises generating a normalized enrichment score (N ES) for all diseases in the plurality of disease studies using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature.

[0070] In some embodiments, the NES comprises an NES for eosinophilic esophagitis (EoE-NES), a type 2 gene expression signature (type 2-NES) in EoE, and / or a DpxOme-EoE™ NES. In some embodiments, the NES comprises an EoE-NES. In some embodiments, the NES comprises a type 2-NES. In some embodiments, the NES comprises a DpxOme-EoE™ NES.

[0071] In some embodiments, the methods comprise performing a differential gene expression analysis on the whole transcriptome profile for each disease study in the plurality of disease studies is performed for disease versus healthy controls.

[0072] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0073] - 12 - In some embodiments, the plurality of disease studies comprises the Gene Expression Omnibus database or the ArrayStudio DiseaseLand database.

[0074] In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration. In some embodiments, the NES is computed separately for positive and negative gene sets.

[0075] In some embodiments, the NES is generated by ordering the plurality of genes that are differentially expressed from the most positive (i.e ., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a ranked gene list (R+). In some embodiments, the NES is generated by identifying hits (i.e., the rank for genes in the core signature) independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values. In some embodiments, the NES is generated by combining R+ and R- and reordering the values by keeping the hits for both S+ and S-. In some embodiments, the NES is generated by computing a running score by walking down the combined ranking, wherein the running score increases by / Vilpl ^s, / vlpif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r. In some embodiments, the NES is generated by determining an Enrichment Score (ES) as a maximum deviation from zero along the running score. In some embodiments, the ordering, identification, combining, computing, and determining disclosed in this paragraph is repeated with a random gene set for 1,000 times to compute the ES null distribution. In some embodiments, the random gene set is a randomly selected list of genes (same size as the original gene set) from the whole transcriptome. In some embodiments, the NES is generated as the ES divided by the arithmetic mean of ES null distribution.

[0076] In some embodiments, the methods comprise computing the statistical significance by comparing the observed ES to the null distribution or sample label (disease / healthy) permutations.

[0077] In some embodiments, the ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most

[0078] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0079] - 13 -down-regulated) values to generate a ranked gene list (R+) comprises using Iog2 foldchange or z score. In some embodiments, fold-change, statistic (e.g., Wald test, T test), signal to noise ratio (difference of means scaled by the standard deviation) can be used for 2 groups comparison. In some embodiments, gene expression value or z score can be used for single sample NES.

[0080] In some embodiments, R+ and R- are ranked by Iog2 fold-change comparing the mean gene expression in disease samples to the mean gene expression in healthy samples.

[0081] In some embodiments, the methods comprise computing the NES for all disease studies using a ranked list for each disease study.

[0082] In some embodiments, a disease with significant NES is a disease suitable for treatment with dupilumab.

[0083] The present disclosure provides methods of identifying a subject having a disease or condition suitable for treatment with dupilumab. In some embodiments, the methods comprise generating a dupilumab treatment core gene signature. In some embodiments, the methods comprise screening the dupilumab core gene signature against a whole transcriptome profile from the subject. In some embodiments, the methods comprise determining whether the subject is suitable for dupilumab treatment.

[0084] In some embodiments, the methods comprise generating the dupilumab treatment core gene signature comprising determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies and identifying a plurality of genes that are differentially expressed.

[0085] In some embodiments, the methods comprise transforming the whole transcriptome profile from the subject into z-scores. In some embodiments, the methods comprise ranking the z-scores. In some embodiments, the methods comprise generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature.

[0086] In some embodiments, the methods comprise generating the NES using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration.

[0087] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0088] - 14- In some embodiments, the NES is generated by transforming each gene expression within the plurality of genes into a z-score and ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+. In some embodiments, the methods can use the gene expression value (without any transformation) for ranking. In some embodiments, the NES is generated by identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values. In some embodiments, the NES is generated by combining R+ and R- and reordering the values by keeping the hits for both S+ and S-. In some embodiments, the NES is generated by computing a running score by walking down the combined ranking, wherein the running score increases by / r, / p / 2jes / r / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r. In some embodiments, the NES is generated by determining an Enrichment Score (ES) as a maximum deviation from zero along the running score. In some embodiments, the transforming, identification, combining, computing, and determining disclosed in this paragraph is repeated with a random gene set for 1,000 times to compute the ES null distribution. In some embodiments, the NES is generated as the NES as ES divided by the mean of ES null distribution.

[0089] In some embodiments, the NES is generated by computing the statistical significance by determining the 95thpercentile NES from healthy control samples.

[0090] In some embodiments, the NES is generated by computing the NES for all disease studies using a ranked list for each disease study. In some embodiments, the NES is generated by computing the NES for all disease studies in the plurality of disease studies using a ranked list for each disease study of the plurality of disease studies.

[0091] In some embodiments, when the NES of the subject is higher than the NES of a healthy control, the subject is suitable for dupilumab treatment.

[0092] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0093] - 15 - The present disclosure provides methods of carrying out 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 for the clinical trial.

[0094] In some embodiments, the dupilumab treatment core gene signature is generated by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies and identifying a plurality of genes that are differentially expressed.

[0095] In some embodiments, the clinical trial comprises generating a normalized enrichment score (NES) for the dupilumab treatment core gene signature prior to initiation of treatment of a subject with dupilumab and at least one time point after initiation of treatment of a subject with dupilumab.

[0096] In some embodiments, when dupilumab treatment results in a decrease in the NES for the dupilumab treatment core gene signature to an acceptable value, the clinical endpoint has been achieved.

[0097] In some embodiments, the NES is generated by ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+. In some embodiments, the NES is generated by identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values. In some embodiments, the NES is generated by combining R+ and R- and reordering the values by keeping the hits for both S+ and S-. In some embodiments, the NES is generated by computing a running score by walking down the combined ranking, wherein the running score increases by / ’Cilp / \^l'c / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r. In some embodiments, the NES is generated by determining an Enrichment Score (ES) as a maximum deviation from zero along the running score. In some embodiments, the ordering, identification, combining, computing, and determining disclosed in this paragraph

[0098] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0099] - 16 -is repeated with a random gene set for 1,000 times to compute the ES null distribution. In some embodiments, the NES is generated as ES divided by the mean of ES null distribution.

[0100] In some embodiments, the NES is generated by computing the statistical significance by comparing the observed ES to the null distribution or sample label (disease / healthy) permutations.

[0101] In some embodiments, the ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+ comprises using Iog2 fold-change to compare gene expression after dupilumab treatment to gene expression prior to initiation of treatment with dupilumab.

[0102] In some embodiments, a plurality of samples is obtained from the subject and the NES is generated for each sample.

[0103] The differential gene expression comprises quantification (i.e., a measurement based on potentially many RNAs) of RNA / transcript expression of at least one gene in a biological sample from a subject. As used herein, "gene" is meant to also capture noncoding genes / biotypes (e.g., long non-coding RNAs). In some embodiments, the differential gene expression comprises quantification of an RNA expression level(s) of at least one gene in a biological sample from a subject. In some embodiments, the differential gene expression comprises quantification of an RNA expression level(s) of at least 10 genes.

[0104] 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

[0105] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0106] - 17 - B-cell receptor subunit constant gene. In some embodiments, the at least one gene comprises a T-cell receptor subunit constant gene.

[0107] In any of the embodiments described herein, the biological sample comprises a sample from an organ, a tissue, a cell, and / or a biological fluid from the subject. In some embodiments, the biological fluid comprises plasma, serum, lymph, semen, and / or a mucosal secretion. In some embodiments, the biological sample comprises blood, semen, saliva, urine, feces, hair, teeth, bone, tissue, or a buccal sample. In some embodiments, the biological sample is obtained from the subject by a biopsy.

[0108] In some embodiments, RNA expression can be determined in part by RNA sequencing. In some embodiments, RNA sequencing reads can be mapped to a genome. In some embodiments, the genome is the human genome. In some embodiments, the human genome is reference assembly GRCh38. In some embodiments, the RNA sequencing reads can be limited to those for 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 can 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 can be mapped using kallisto vO.45.0 with strand-specific reverse first-read mapping (Bray et al., Nat. Biotechnol., 2016, 34, 525). In some embodiments, transcript counts can be aggregated to gene counts. In some embodiments, the aggregation can be conducted using tximport (Soneson et al., FlOOOResearch, 2015, 4, 1521).

[0109] In some embodiments, the determination of a subject's NES comprises determining the RNA expression level(s) of one or more genes in a biological sample from a subject, comparing this RNA expression with the RNA expression of a corresponding gene from a placebo treatment group, determining the relative difference in RNA expression, and integrating the changes in the individual RNA expression into an NES. In some embodiments, the determination of a subject's NES comprises determining the RNA

[0110] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0111] - 18 -expression level(s) of one or more genes in multiple biological samples from a subject, determining the relative difference in RNA expression across the multiple samples, and integrating the changes in the individual RNA expression into an NES. In some embodiments, the genes whose RNA expression level(s) are measured include proteincoding genes, long non-coding RNAs, mitochondrial rRNAs, mitochondrial tRNAs, rRNAs, ribozymes, B-cell receptor subunit constant genes, and / or a T-cell receptor subunit constant genes. In some embodiments, the relative difference in RNA expression of genes in the panel are compared to the relative difference in RNA expression of genes not in the panel.

[0112] The present disclosure provides methods of treating a subject having a disease or condition suitable for treatment with dupilumab, the methods comprising: a) identifying the subject as having a disease or condition suitable for treatment with dupilumab comprising: i) generating a dupilumab treatment core gene signature; ii) screening the dupilumab core gene signature against a 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.

[0113] In some embodiments, the dupilumab treatment core gene signature comprises determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed.

[0114] In some embodiments, the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyposis.

[0115] In some embodiments, the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and / or a q < 0.05 in > 3 out of 5 treatment studies. In some embodiments, the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group.

[0116] In some embodiments, the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyposis treatment

[0117] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0118] - 19 -studies are carried out 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 for the asthma and grass allergy treatment studies are carried out 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 a microarray or RNASeq. In some embodiments, the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq. In some embodiments, the differential gene expression of the atopic dermatitis and chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by microarray.

[0119] In some embodiments, the plurality of genes that are differentially expressed comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

[0120] In some embodiments, screening the dupilumab core gene signature against a whole transcriptome profile from the subject comprises: i) transforming the whole transcriptome profile from the subject into z-scores; ii) ranking the z-scores; and iii) generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature, thereby representing the dupilumab signature enrichment for the subject.

[0121] In some embodiments, the NES is generated using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration. In some embodiments, the NES is generated by: a) transforming each gene expression within the plurality of genes into a z-score, and ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+; b) identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values; c) combining R+ and R- and reordering the values by keeping the hits for both S+ and S-; d)

[0122] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0123] - 20-computing a running score by walking down the combined ranking, wherein the running score increases by / r, / p / ^ies / r / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r; e) determining an Enrichment Score (ES) as a maximum deviation from zero along the running score; f) repeat steps a) - e) with a random gene set for 1,000 times to compute the ES null distribution; and g) generating the NES as ES divided by the mean of ES null distribution.

[0124] In some embodiments, the method further comprises computing the statistical significance by determining the 95thpercentile NES from healthy control samples. In some embodiments, the method comprises computing the NES for all disease studies using a ranked list foreach disease study. In some embodiments, when the NES of the subject is higher than the NES of a healthy control, the subject is suitable for dupilumab treatment.

[0125] The present disclosure also provides kits, and method of using the kits. The kits can be used, for example, to detect the presence or absence of genes encoding any one or more of ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes, and / or mRNA molecules and / or cDNA molecules derived therefrom. In some embodiments, any one or more of the aforementioned genes (and / or mRNA molecules 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 aforementioned genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) can be detected in a subject after treatment with dupilumab. In some embodiments, any one or more of the aforementioned genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) can be detected in a subject prior to treatment with dupilumab and after treatment with dupilumab.

[0126] In some embodiments, the kit comprises a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprise nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at

[0127] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0128] - 21 -least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes. In some embodiments, the kit comprises a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprises nucleotide sequences that are complementary to at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, or at least 45 of the nucleic acid molecules encoding the aforementioned genes (and / or mRNA molecules and / or cDNA molecules derived therefrom).

[0129] In some embodiments, the nucleic acid molecules comprising nucleotide sequences that are complementary to at least ten of the nucleic acid molecules encoding the aforementioned genes (and / or mRNA molecules and / or cDNA molecules derived therefrom) are probes. In some embodiments, the nucleotide sequences of the probes comprise 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 to 100, 10 to 75, 12 to 50, 15 to 25, or 18 to 23 nucleotides in length.

[0130] In any of the embodiments described throughout the present disclosure, the probes can comprise a label. In some embodiments, the label is a fluorescent label, a radiolabel, or biotin.

[0131] In any of the embodiments described throughout the present disclosure, each of the plurality of nucleic acid molecules is linked to a solid support. Solid supports are solid-state substrates or supports with which molecules, such as any of the probes disclosed herein, can be associated. A form of solid support is an array. Another form of solid support is an array detector. An array detector is a solid support to which multiple different probes have been coupled in an array, grid, or other organized pattern. A form for a solid-state substrate is a multiwell plate, such as a standard 96-well type. In some embodiments, a multiwell glass slide can be employed that normally contains one array per well. 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.

[0132] The present disclosure also provides methods of detecting a plurality of nucleic acid molecules in a subject, wherein the plurality of nucleic acid molecules detected comprises nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3,

[0133] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0134] - 22 - TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes. The methods comprise: contacting a biological sample from the subject with a plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; and detecting the presence or absence of the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes. Any of the kits described herein can be used in these methods. In addition, any of the probes, or combinations thereof, described herein can be used in these methods. Any of the biological samples described herein can be used in these methods.

[0135] In some embodiments, the amount of the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes, in the biological sample is determined.

[0136] In some embodiments, each of the plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes, are labeled, and the detection step comprises detecting the label.

[0137] In some embodiments, each of the plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6

[0138] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0139] - 23 -genes, at least 7 genes, at least 8 genes, or at least 9 genes, is linked to a solid support. Any of the solid support described herein can be used in these methods.

[0140] The results herein showing significant comorbidity as well as shared molecular signatures among T2IDs that share responsiveness to dual IL14 / IL13 blockade support the hypothesis that these T2IDs share a common underlying molecular causation and perhaps a common underlying genetic predisposition, which likely involves the I L4 / IL13 pathway. To test this possibility, a genetic pathway score (GPS) for the IL4 / IL13 pathway that aggregates information across the genetic loci specifically encoding this pathway was developed. Six common non-coding variants (minor allele frequency (MAF) >1%) associated with the IL4 / 13 pathway were selected based on each being independently associated with risk of asthma (P < 5 x 10“8) in a GWAS performed in the UKB cohort (comprising 55,830 cases with and 261,925 controls without asthma or other common atopic diseases): 1) two variants in / near IL4 / IL13, the genes for IL4 and I L13; 2) two variants in / near IL4R, the gene for the IL4Ra receptor component shared by the two receptor complexes for IL4 and I L13; and 3) two variants in / near STAT6, the gene for the transcription factor that specifically binds and is activated by IL4Ra. The association between this IL4 / 13 GPS comprised by these six gene variants was examined and the risk of each individual T2ID using data from six additional validation cohorts (total N up to 214,395), excluding the UKB.

[0141] A statistically significant association was discovered between the IL4 / IL13 GPS and all five diseases in the validation cohorts. Individuals with a high GPS also had higher disease risk, and vice versa, when compared to the rest of the population (Figure 4). Individuals in the top 20% of the GPS had 1.28-fold greater risk of PN (95% Cl 1.11-1.48, P = 5.5 x ICT4), followed by 1.21-fold for EoE (95% Cl 1.05-1.39, P = 0.009), 1.16-fold for NP (95% Cl 1.06-1.26, P = 0.001), 1.11-fold for AD (95% Cl 1.03-1.20, P = 0.006) and 1.10-fold for asthma (95% Cl 1.07-1.13, P = 1.0 x 10-11). The three non-atopic diseases tested were either not associated (RA and CD) or inversely associated (psoriasis) with the IL4 / IL13 GPS (Figure 4). These data support prior observations that T2IDs likely share a genetic predisposition, and thus a common underlying molecular causation.

[0142] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0143] - 24- The following examples are provided to describe the embodiments in greater detail. They are intended to illustrate, not to limit, the claimed embodiments. The following examples provide those of ordinary skill in the art with a disclosure and description of how the compounds, compositions, articles, devices and / or methods described herein are made and evaluated, and are intended to be purely exemplary and are not intended to limit the scope of any claims. Efforts have been made to ensure accuracy with respect to numbers (such as, for example, amounts, temperature, etc.), but some errors and deviations may be accounted for.

[0144] Examples

[0145] Example 1: Materials and Methods

[0146] Data sources

[0147] United Kingdom Biobank (UKB) database: UKB is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants, which is regularly augmented with additional data and is globally accessible. Case-control status for ICD-10 codes were defined using Electronic Medical Records (EMR).

[0148] In the UKB cohort, disease status was defined based on data from hospital records (inpatient and outpatient), General Practitioner clinics and death registries, collected between 2006 and 2022. For EMR-based diagnoses, the cases were defined as individuals having 2 outpatient encounters on different dates or 1 inpatient encounter. Registry-based diagnoses required only one occurrence of the ICD-10. Individuals having only 1 outpatient encounter were assigned as missing. All other individuals are assigned to controls.

[0149] Geisinger Health System (GHS) database: GHS is a comprehensive healthcare network that serves residents throughout Pennsylvania and New Jersey. The system includes several hospitals, a medical school, a research institute, and a health plan division. Geisinger's database is a vast repository of patient health information, including EMR, genomic data, and clinical data, which is used for patient care, research, and improving healthcare delivery.

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[0151] - 25 - The GHS cohort comprised of 2-million individuals from the U.S. with 27 years (1996-2023) of inpatient and outpatient hospital follow-up data on average. Case-control status for ICD-10 codes was determined as outlined above for the UKB cohort.

[0152] Optum claims database: Optum® Ciinformatics® Data Mart is a large administrative health insurance claims database that is geographically diverse and includes patients from all 50 US states. The database comprises longitudinal data for patients with a health insurance plan provided by United Healthcare, a large, national provider of health insurance including Medicare Advantage with data on enrollment, patient demographics, inpatient and outpatient diagnoses and procedures, outpatient medication dispensations, and mortality.

[0153] In the Optum cohort, a disease case and a control (disease-free case) of patients continuously enrolled in their healthcare plan between 10 / 01 / 2017-09 / 30 / 2022 were identified for each T2IMD (AD, asthma, RA, PN, EoE, NP). The description of the case-control status is detailed in Supplementary Materials.

[0154] Differential gene expression analysis

[0155] Throughout, statistical significance for differential gene expression for RNAseq studies was based on estimates from DESeq2 with | fold change|>2 and q<0.05, reflecting adjustment for multiple hypothesis testing. For microarray studies, the limma package from Bioconductor was used to identify differentially expressed genes, similarly defined as those with | fold change | >2 and q<0.05.

[0156] Differential expression analysis for genes altered in disease was performed by comparing disease tissue to the respective anatomical control tissue from individual without the disease. A permutation test was implemented to assess the differentially expressed genes that are commonly observed in multiple disease-vs-healthy studies (Supplementary Materials). Transcriptomic changes mediated by treatment were assessed by comparing the post-treatment time point to the baseline time point with multi-factor design = "patient + timepoint (data not shown).

[0157] Fisher's exact test (FET) was utilized to assess the statistical significance of the association between the gene signatures and the disease signatures. Prior to analysis,

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[0159] - 26 -disease signatures were identified through differential gene expression analysis, comparing disease vs healthy samples. For each disease signature, FET was applied to compare the frequency of the gene signatures of interest against those genes not differentially expressed in disease. This was done separately for T2 (EoE, AD, asthma, CRSwNP) and non-T2 (psoriasis, rheumatoid arthritis, Crohn's disease) disease groups. The test provided a p-value for each comparison, indicating the likelihood that the observed association between gene and disease signatures occurred by chance. Given the multiple hypotheses being tested (associations across several diseases and gene signatures), Bonferroni correction was utilized to account for the multiplicity of tests and control the false discovery rate.

[0160] Disease gene expression datasets

[0161] The EoE dataset (GSE58640) comprises data from 10 patients with active EoE (positive EoE diagnosis with >15 eosinophils per high-power field (HPF) in a concurrent esophageal biopsy) and 6 healthy individuals (no EoE diagnosis history with 0 eosinophils per HPF in a concurrent esophageal biopsy) (Sherrill et al., Genes Immun., 2014, 15, 361-369). The AD dataset (GSE121212) contains data from 5-mm skin punch biopsies from the extremities of 27 patients with AD and 38 healthy individuals (Tsoi et a., J. Invest.

[0162] Dermatol., 2019, 139, 1480-1489). The asthma dataset (GSE85567) includes data from primary airway epithelial cells of 74 adults with asthma and 41 adults without asthma (Nicodemus-Johnson et al., JCI Insight 1, 2016, e90151). The CRSwNP dataset (GSE136825) consists of polyp samples from 42 patients with CRSwNP and 33 inferior turbinate samples from non-chronic rhinosinusitis controls (Peng et al., Eur. Respir. J., 2019, 54, 1900732. Differential gene expression analysis

[0163] A permutation test was implemented to assess the likelihood of observing >74 common differentially expressed genes in multiple disease-vs-healthy studies (Sherrill et al., Genes Immun., 2014, 15, 361-369; Tsoi et al., J. Invest. Dermatol., 2019, 139, 1480-1489; Nicodemus-Johnson et al., JCI Insight 1, 2016, e90151; Peng et al., Eur. Respir. J., 2019, 54, 1900732). In each test, four disease studies were randomly selected from QIAGEN OmicSoft DiseaseLand database to identify the number of commonly regulated genes in at least three studies. The process was repeated 1 x 106times to extract a background distribution of the

[0164] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0165] -27-number of common gene. A P-value for the observation was computed from the resulting background distribution, comparing against the number of genes to be more extreme than or equal to 74 genes.

[0166] Example 2: Co-prevalence Between T2IDs is Enhanced in Individuals with Blood Eosinophilia

[0167] The possibility that the degree of systemic T2-inflammation in an individual, as assessed by blood eosinophilia, might relate to the risk of co-occurrence of multiple atopic diseases in the same individual was explored. Consistent with this hypothesis, it was found that the co-prevalence between T2IDs in the UKB cohort strongly increased in the presence of high blood eosinophil counts (Figure 3). Asthma with low eosinophils (<150 cells / pL) was 1.25-fold (95% Cl 1.08-1.44) more prevalent among NP cases than matched controls, but the co-prevalence increased to 8.06-fold (95% Cl 7.36-8.82) in patients with asthma and high eosinophils (>300 cells / pL). For AD, the difference in co-prevalence was drastically enhanced: AD with low eosinophils did not co-occur with asthma (PR = 1.04, 95% Cl 0.89-1.23) or NP (PR = 0.47, 95% Cl 0.22-1.01), whereas a significant association was observed between AD with high eosinophils and both diseases (PR of 3.35 and 3.85, respectively; Figure 3).

[0168] Example 3: T2IDs Share a Common Transcriptomic Signature Normalized by IL4 / 13 Blockade

[0169] T2IDs share pathological features of T2-inflammation in their affected tissues (including infiltrates of eosinophils, mast cells and TH2 cells, elevated T2 cytokines, tissue hyperplasia, and IgE production) (Gandhi et al., Nat. Rev. Drug Discov., 2016, 15, 35-50); emerging transcriptomic analyses in individual T2IDs is revealing that affected tissues have clear molecular signatures relative to controls, which normalize in response to IL4 / IL13 blockade (Gandhi et al., Nat. Rev. Drug Discov., 2016, 15, 35-50; Nhu et al., Front. Med., 2017, 4, 128; Bentley et al., Am. Rev. Respir. Dis., 1992, 146, 500-506; Ying et al., J.

[0170] Immunol., 1997, 158, 3539-3544; Doherty et al., Curr. Opin. Immunol., 2007, 19, 676-680;

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[0172] - 28 - Kiehl et al., Br. J. Dermatol., 2001, 145, 720-729; and Salimi et al., J. Exp. Med., 2013, 210, 2939-2950) (see, Table 1). To determine whether T2IDs share overlapping molecular signatures (which would further support a common underlying immune deviation and possible causation), transcriptional profiles across the various T2IDs were compared using publicly available RNA sequencing data generated across several atopic diseases and related tissues relative to controls: esophageal biopsy tissue from patients with EoE (Sherrill et al., Genes Immun., 2014, 15, 361-369), lesional skin biopsies from patients with AD (Tsoi et al., J. Invest. Dermatol., 2019, 139, 1480-1489), primary airway epithelial cells brushings from patients with asthma (Nicodemus-Johnson et al., JCI Insight 1, 2016, e90151), and NP tissue from patients with CRSwNP. Differential gene expression analysis within each study was performed to identify genes significantly altered in each disease vs control tissue ( | fold-changes| >2 and q < 0.05), then compared expression profiles across T2IDs, resulting in identification of 74 genes that were differentially expressed in at least three out of the four T2IDs evaluated; 61 were up-regulated and 13 were down-regulated (P = 3.4 x 10“4) (data not shown). These 74 genes collectively comprise a shared T2ID transcriptomic signature (termed T2ID-74GENESIG), and gene ontology analysis of these 74 genes identified associations with expected immune and inflammatory mechanisms.

[0173] The molecular effects of dual IL4 / IL13 blockade on these transcriptomic signatures was assessed across each T2ID. Transcriptional profiling data from tissue samples obtained from 5 dupilumab clinical studies evaluating I L4 / IL13 blockade in T2IDs was analyzed: 1) EOE: esophageal mucosal pinch biopsies at week 12 after treatment (vs baseline) in patients with EoE (Hirano et al., Gastroenterology, 2020, 158, 111-122 ellO); 2) AD: full-thickness punch biopsies (from areas that were lesional at baseline) at baseline and week 16 after treatment in patients with AD (56); 3) Asthma: sputum at week 4 after treatment in a bronchial allergen challenge in patients with asthma; 4) CRSwNP and AR: nasal brushings at week 24 after treatment in patients with CRSwNP (Gayvert et al., J. Allergy Clin. Immunol., 2024, 154, 619-630), and nasal brushings at week 16 after treatment and nasal allergen challenge in patients with AR (Wipperman et al., Allergy, 2024, 79, 894-907). In each of

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[0175] - 29 -these diseases (EoE, AD, asthma, and CRSwNP, IL4Ra-blockade with dupilumab reversed expression of the T2ID-74GENESIG (data not shown).

[0176] To independently examine the expression of genes modulated by dupilumab across indications, 48 genes were identified (termed T2ID-48DUPI RESPONSE GENES) with shared response to dupilumab in clinical trials in at least three of the five diseases studied (data not shown). This gene set represented genes responding to IL4 / IL13 inhibition in T2IDs. While previous analyses have studied individual disease signatures, this is the first report of a comprehensive molecular response to IL4 / IL13 inhibition across multiple diseases, tissue types, and expression profiling platforms. Furthermore, a core signature of 10 genes overlapping between T2ID-74GENESIG and T2ID-48DUPI_RESPONSE_GENES (termed T2ID-10GENE_CORE) (P = 6.9 x 1019) was elucidated (see, Figure 1, top panel; Figure 2, and Table 1).

[0177] Table 1: Description of the ten genes in T2C10

[0178]

[0179] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0180] - 30-

[0181]

[0182] POSTN (Murota et al., Cell. Mol. Life Sci., 2017, 74, 4321-4328; Woodruff et al., Am. J. Respir. Crit. Care Med., 2009, 180, 388-395; Parulekar et al., Curr. Opin. Pulm. Med., 2014, 20, 60-65; and Takayama et al., J. Allergy Clin. Immunol., 2006, 118, 98-104); ADORA3 (Della Latta et al., Pharmacol. Res., 2013, 76, 182-189); ALOX15 (Matoso et al., Mod. Pathol., 2013,

[0183] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0184] - 31 - 26, 665-676; Kuperman et al., J. Allergy Clin. Immunol., 2005, 116, 305-311; Kristjansson et al., Nat. Genet., 2019, 51, 267-276; and Andersson et al., Am. J. Respir. Cell. Mol. Biol., 2008, 39, 648-656); IGFBP3 (Veraldi et al., Am. J. Respir. Crit. Care Med., 2009, 180, 611-617; and Lee et al., J. Biol. Chem., 2011, 286, 17898-17909); CDH26 (Lachowicz- Scroggins et al., Cell Discov., 2018, 4, 7); CDH3 (Naydenov et al., Cells, 2022, 11); TPSAB1 (Morgenstern et al., J. Allergy Clin. Immunol., 2022, 149, 2062-2077); CPA3 (Dunn et al., J. Allergy Clin. Immunol., 2020, 145, 1629-1640 el624; and Higham et al., Clin. Transl. Immunology, 2022, 11, el417); SERPINB4 (de Koning et al., PLoS One, 2011, 6, e22645); and CH25H (Ding et al., Inflamm. Res., 2023, 72, 1099-1119).

[0185] To determine the specificity of the transcriptome signatures in differentiating T2IDs and non-atopic diseases, the likelihood these three gene signatures would be found in T2IDs relative to the non-atopic inflammatory diseases Psoriasis, RA and CD was evaluated using a Bonferroni corrected Fisher's Exact Test (see, Figure 1, bottom panel). As expected, T2ID-74GENESIG was significantly associated with the T2IDs with minor association in other non-atopic inflammatory diseases, likely reflecting shared non-specific inflammatory pathways. Notably, the T2ID-10GENE_CORE signature was the most significant differentiator between T2IDs and non-atopic inflammatory diseases (see, Figure 1, bottom panel). T2ID-10GENE_CORE, resulting from the intersection of T2ID disease signatures with those genes responding to dupilumab blockade, was the most precise molecular phenotype characterizing IL4 / IL13-mediated T2ID or patient subpopulations.

[0186] Example 4: Association with Genes in the IL-4 / IL-13 Pathway

[0187] A genetic score was created that includes variants in 4 genes that play a central role in the IL-4 / IL-13 signaling pathway (specifically IL4, I L13, IL4R and STAT6), which is referred to as a GPS for IL-4 / IL-13 signaling. The six variants (two near I L4 / IL13, two near IL4R, and two near STAT6) were selected for the IL-4 / IL-13 GPS, with weights corresponding to the effect on asthma risk in the UKB cohort (total N=488,557). To independently test the association between this I L-4 / IL-13 GPS and risk of being diagnosed with a T2IMD, the GPS for each individual was calculated in six additional cohorts (Figure 5), totaling 448,079

[0188] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0189] - 32 -individua Is. Individuals were then grouped into quintiles based on their GPS and then risk of disease calculated in each quintile relative to everyone else in the population, with analyses performed separately for asthma, AD, NP, EoE, and PN.

[0190] Asthma, a T2ID, was selected for the selection of SNPs for the I L-4 / 1 L-13 GPS because: (i) it is one of 5 diseases for which I L-4 / 1 L-13 blockade has been shown to be effective in phase 3 clinical trials, providing definitive evidence for a causal role of this pathway in disease pathophysiology; and (ii) it is the most prevalent of those 5 diseases and therefore the one expected to provide greatest power for human genetic studies. To identify SNPs near the four IL4 / IL-13 genes that were independently associated with risk of asthma, a GWAS in the UKB cohort was performed using Regenie (Mbatchou et el., Nat. Genet., 2021, 53, 1097-1103), comprising 55,830 cases with asthma and 261,925 controls without asthma or other common allergic diseases (specifically AD, AR, and food allergies). All ancestries were analyzed in a single analysis (301088, 7587, 6562, 1347, 409, 762 individuals respectively of European, South Asian, African, East Asian, Admixed American and other ancestry), and included as covariates age, sex, age2 and age-by-sex and 10 ancestry-informative principal components. 10,382,224 variants were examined with a MAE >0.5% imputed using the TOPMed reference panel. Formal conditional analysis was applied fiteratively to identify independent associations with asthma in this GWAS. Briefly, in the first iteration, peak common variant associations (i.e., variants located more than 1 Mb apart and with the most significant association among those with a P < 5 x 10-8 and MAF >1%), 129 variants in total were identified. Genotype data (dosages) for these 129 variants were then added as covariates and the GWAS repeated with Regenie (step 2 only), using the same additional covariates and step 1 predictors included in the original analysis. In the second iteration, peak associations in this conditional GWAS as above (32 new variants) were identified and added them as covariates to the model, which now contained dosages for 161 (129 + 32) variants. This process was continued until there were no remaining common variant associations with asthma at a P < 5 10-8; in total 9 rounds of conditional analyses were performed and identified 194 independent associations in total.

[0191] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0192] - 33 - Of the 194 variants independently associated with asthma risk in the UKB cohort, 8 were located within 250 Kb of IL4 / IL13 (both genes are located in the same locus, 13 Kb apart), IL4R or STAT6 (Figure 6). Of these, 2 variants (near IL4 and IL4R) were excluded because there was evidence from transcriptomics studies that these variants might regulate the expression of genes other than IL4 / IL4R, C5orf56 (Vosa et al., Nat. Genet, 2021, 53, 1300-1310) and IL21R (Paia et al., Nat. Genet., 2017, 49, 700-707 (2017), respectively. The remaining 6 variants (two near IL4 / IL13, two near IL4R and two near STAT6) were selected for the I L-4 / IL-13 GPS, with weights corresponding to the effect on asthma risk in the UKB cohort.

[0193] An important caveat of this GPS is that for most variants (the exception being a variant in STAT6) there was no evidence that they directly regulate the expression of IL4 / IL13, IL4R or STAT6, as they are not in high linkage disequilibrium with coding variants or published sentinel eQTLs (in any tissue) or plasma pQTLs for these genes. It is possible that these variants regulate the expression of a different gene nearby, of which IL5 (which is 113 Kb away from I L13) and IL21R (37 Kb away from IL4R) are potential candidates based on known biology. However, this is unlikely, for the following reasons. First, for 5 of the 6 variants, the nearest gene is one of these four IL-4 / IL-13 pathway genes. This is noteworthy because it was previously shown through analysis of rare coding variants from exome sequencing that the nearest gene to a GWAS lead variant has a very high probability of being the causal gene underlying the observed common variant association (Backman et al., Nature, 2021, 599, 628-634). The probability of being causal decreases rapidly with increasing distance between gene and GWAS lead variant. Second, to specifically test if IL5 (fifth nearest gene) or IL21R (second nearest gene) were likely to underlie the observed association between variants in the GPS and asthma risk, rare coding variation from exome sequencing was tested but found no association with IL5 (despite a very strong association with eosinophil counts), IL21R or the corresponding receptor (IL5RA) or ligand (IL21) pair (Figure 5). Third, common variants near IL5RA strongly associated with eosinophil counts were not associated with asthma risk (Figure 7). Fourth, we note that human clinical trials have shown that blockade of the IL-5 signaling pathway has not consistently improved lung

[0194] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0195] - 34-function in asthma (Flood-Page et al., Am. J. Respir. Crit. Care Med., 2007, 176, 1062-1071), suggesting genes in this pathway may have a low prior probability of being associated with asthma risk. Altogether, these observations suggest it is unlikely that the association between the 6 variants included in the GPS and asthma risk is not mediated through modulation of the expression / function of IL4 / IL13, IL4R or STAT6.

[0196] Discussion

[0197] Herein is presented transcriptomic evidence supporting the hypothesis that many if not all T2IDs are highly interrelated and share a common pathological mechanism driven by IL4 and IL13, allowing effective treatment with the same therapeutic modality - i.e., IL4 / IL13 blockade. The data demonstrate that these conditions have related disease transcriptomic signatures that can be reversed with dual IL4 / IL13-blockade. Results from these analyses provide the most comprehensive empirical evidence to date supporting the notion that most (if not all) T2IDs are highly co-prevalent, but not increased in frequency in non-atopic immune diseases such as RA, CD and psoriasis.

[0198] Provided herein is a comprehensive overview and comparison across multiple T2IDs, showing that they all share a very related transcriptomic signature in their affected tissues, which is normalized by dual I L4 / I L13-blockade. Despite the distinct anatomical locations, tissue types, diseases, and profiling platforms, the demonstration of a common transcriptomic signature may help provide for disease-agnostic molecular assessment of T2-inflammation-d riven pathology, and could potentially be utilized as a tool for identifying diseases not typically considered to be mediated by T2-inflammation and / or subpopulations of patients most likely to benefit from IL4 / IL13 inhibition.

[0199] Taken together, the data strongly supports the unifying hypothesis that most (if not all) T2IDs share transcriptomic signatures and genetic predispositions that suggest they all share over-activation of the IL4 / IL13 pathway as a key causative driver, consistent with and explaining their heretofore consistent clinical responsiveness to dual IL / 4IL13blockade. Another major advantage of dual IL4 / 13 blockade involves the safety of this approach, as opposed to general immunosuppressants or targeting other immune pathways. The safety

[0200] 65561822DOCKET NO.: 38120-4318 (11934W001)

[0201] - 35 -of IL4 / 13 blockade relates to the primordial role of this pathway. That is, I L4 / I L13-driven immune responses are seemingly not required for controlling most viral or bacterial pathogens, but rather for controlling parasitic infections that are not prevalent in developed environments. Thus, blocking this pathway is not broadly immunosuppressive - as has been confirmed in the large clinical experience with dupilumab - in fact, dupilumab treatment is associated with decreased risk of certain types of infections. The realization that the IL4 / IL13 pathway is the critical driver across so many T2ID explains the profound clinical benefit of IL4 / 13 blockade (including for multiple co-morbid diseases in the same patient), which - together with a safety profile for IL4 / IL13 blockade that has allowed for approved use in infants - helps explain the emergence of IL4 / 13 blockade (via dupilumab) as the leading biologic treatment approach across multiple T2IDs.

[0202] 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 individual item were specifically and individually indicated to be so incorporated by reference. If different versions of a sequence are associated with an accession number at different times, the version associated with the accession number at the effective filing date of this application is meant. The effective filing date means the earlier of the actual filing date or filing date of a priority application referring to the accession number if applicable. Likewise, if different versions of a publication, website or the like are published at different times, the version most recently published at the effective filing date of the application is meant unless otherwise indicated. Any feature, step, element, embodiment, or aspect of the present disclosure can be used in combination with any other feature, step, element, embodiment, or aspect unless specifically indicated otherwise. 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 practiced within the scope of the appended claims.

[0203] 65561822

Claims

DOCKET NO.: 38120-4318 (11934W001)- 36 - What is Claimed is:

1. A method of identifying a disease or condition suitable for treatment with dupilumab, the method comprising:a) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPAS, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes;b) screening the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies; andc) identifying a disease or condition in the plurality of disease studies having a differential gene expression that is in the opposite direction from the dupilumab treatment core gene signature;thereby identifying a disease or condition suitable for treatment with dupilumab.

2. The method of claim 1, wherein the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyposis.

3. The method of claim 1, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and / or a q < 0.05 in > 3 out of 5 treatment studies.

4. The method of claim 3, wherein the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group.

5. The method of claim 2, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyposis treatment studies are carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab.65561822DOCKET NO.: 38120-4318 (11934W001)- 37 - 6. The method of claim 2, wherein the differential gene expression for the asthma and grass allergy treatment studies are carried out by comparing the gene expression with allergen challenge to the gene expression without allergen challenge.

7. The method of claim 1, wherein the differential gene expression is analyzed by a microarray or RNASeq.

8. The method of claim 7, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

9. The method of claim 7, wherein the differential gene expression of the atopic dermatitis and chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by microarray.

10. The method of claim 1, wherein screening the dupilumab core gene signature against a whole transcriptome profile from a plurality of disease studies comprises:i) performing a differential gene expression analysis on the whole transcriptome profile for each disease study in the plurality of disease studies; andii) generating a normalized enrichment score (NES) for all diseases in the plurality of disease studies using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature.

11. The method of claim 10, wherein performing a differential gene expression analysis on the whole transcriptome profile for each disease study in the plurality of disease studies is performed for disease versus healthy controls.

12. The method of claim 10, wherein the plurality of disease studies comprises the Gene Expression Omnibus database orthe ArrayStudio DiseaseLand database.

13. The method of claim 10, wherein the NES is generated using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration.

14. The method of claim 13, wherein the NES is generated by:a) ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a ranked gene list (R+);65561822DOCKET NO.: 38120-4318 (11934W001)- 38 - b) identifying hits (i.e., the rank for genes in the core signature) independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values;c) combining R+ and R- and reordering the values by keeping the hits for both 8+ and S-;d) computing a running score by walking down the combined ranking, wherein the running score increases by lvilpl \< l'clpif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene i, and p is the weight for r;e) determining an Enrichment Score (ES) as a maximum deviation from zero along the running score;f) repeating steps a) - e) with a random gene set for 1,000 times to compute the ES null distribution; andg) generating the NES as ES divided by the mean of ES null distribution.

15. The method of claim 14, further comprising computing the statistical significance by comparing the observed ES to the null distribution or sample label (disease / healthy) permutations.

16. The method of claim 14, wherein step a) comprises using Iog2 fold-change or z score.

17. The method of claim 14, wherein R+ and R- are ranked by Iog2 fold-change comparing the mean gene expression in disease samples to the mean gene expression in healthy samples.

18. The method of claim 14, the method comprising computing the NES for all disease studies using a ranked list for each disease study.

19. The method of any one of claims 1 to 18, wherein a disease with significant NES is a disease suitable fortreatment with dupilumab.

20. A method of identifying a subject having a disease or condition suitable for treatment with dupilumab, the method comprising:65561822DOCKET NO.: 38120-4318 (11934W001)- 39 - a) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPAS, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes;b) screening the dupilumab core gene signature against a whole transcriptome profile from the subject; andc) determining whether the subject is suitable for dupilumab treatment.

21. The method of claim 20, wherein the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyposis.

22. The method of claim 20, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and / or a q < 0.05 in > 3 out of 5 treatment studies.

23. The method of claim 22, wherein the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group.

24. The method of claim 21, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyposis treatment studies are carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab.

25. The method of claim 21, wherein the differential gene expression for the asthma and grass allergy treatment studies are carried out by comparing the gene expression with allergen challenge to the gene expression without allergen challenge.

26. The method of claim 20, wherein the differential gene expression is analyzed by a microarray or RNASeq.

27. The method of claim 26, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.65561822DOCKET NO.: 38120-4318 (11934W001)-40- 28. The method of claim 26, wherein the differential gene expression of the atopic dermatitis and chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by microarray.

29. The method of claim 20, wherein screening the dupilumab core gene signature against a whole transcriptome profile from the subject comprises:i) transforming the whole transcriptome profile from the subject into z-scores; ii) ranking the z-scores; andiii) generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature, thereby representing the dupilumab signature enrichment for the subject.

30. The method of claim 29, wherein the NES is generated using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration.

31. The method of claim 30, wherein the NES is generated by:a) transforming each gene expression within the plurality of genes into a z-score, and ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+;b) identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values;c) combining R+ and R- and reordering the values by keeping the hits for both S+ and S-;d) computing a running score by walking down the combined ranking, wherein the running score increases by / r, / p / ^ies / r / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r;e) determining an Enrichment Score (ES) as a maximum deviation from zero along the running score;65561822DOCKET NO.: 38120-4318 (11934W001)-41 - f) repeat steps a) - e) with a random gene set for 1,000 times to compute the ES null distribution; andg) generating the NES as ES divided by the mean of ES null distribution.

32. The method of claim 31, further comprising computing the statistical significance by determining the 95thpercentile NES from healthy control samples.

33. The method of claim 31, the method comprising computing the NES for all disease studies using a ranked list for each disease study.

34. The method of claim 20, wherein when the NES of the subject is higher than the NES of a healthy control, the subject is suitable for dupilumab treatment.

35. A method of carrying out a clinical trial for dupilumab treatment of a disease or condition, the method comprising using a dupilumab core gene signature as a clinical endpoint for the clinical trial, wherein the dupilumab treatment core gene signature is generated by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

36. The method of claim 35, wherein the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyposis.

37. The method of claim 35, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and / or a q < 0.05 in > 3 out of 5 treatment studies.

38. The method of claim 37, wherein the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group.

39. The method of claim 36, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyposis65561822DOCKET NO.: 38120-4318 (11934W001)-42 -treatment studies are carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab.

40. The method of claim 36, wherein the differential gene expression for the asthma and grass allergy treatment studies are carried out by comparing the gene expression with allergen challenge to the gene expression without allergen challenge.

41. The method of claim 35, wherein the differential gene expression is analyzed by a microarray or RNASeq.

42. The method of claim 41, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

43. The method of claim 39, wherein the differential gene expression of the atopic dermatitis and chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by microarray.

44. The method of claim 35, wherein the clinical trial comprises generating a normalized enrichment score (NES) for the dupilumab treatment core gene signature prior to initiation of treatment of a subject with dupilumab and at least one time point after initiation of treatment of a subject with dupilumab.

45. The method of claim 44, wherein when dupilumab treatment results in a decrease in the NES for the dupilumab treatment core gene signature to an acceptable value, the clinical endpoint has been achieved.

46. The method of claim 44, wherein the NES is generated by:a) ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+;b) identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values;c) combining R+ and R- and reordering the values by keeping the hits for both S+ and S-;65561822DOCKET NO.: 38120-4318 (11934W001)-43 - d) computing a running score by walking down the combined ranking, wherein the running score increases by / r, / p / es / r / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r;e) determining an Enrichment Score (ES) as a maximum deviation from zero along the running score;f) repeat steps a) - e) with a random gene set for 1,000 times to compute the ES null distribution; andg) generating the NES as ES divided by the mean of ES null distribution.

47. The method of claim 46, further comprising computing the statistical significance by comparing the observed ES to the null distribution or sample label (disease / healthy) permutations.

48. The method of claim 46, wherein step a) comprises using Iog2 fold-change to compare gene expression after dupilumab treatment to gene expression prior to initiation of treatment with dupilumab.

49. The method of claim 44, wherein a plurality of samples is obtained from the subject and the NES is generated for each sample.

50. A method 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 comprising:i) generating a dupilumab treatment core gene signature by determining differential gene expression of a dupilumab treatment group and a placebo treatment group for a plurality of treatment studies, and identifying a plurality of genes that are differentially expressed, wherein the plurality of genes comprises ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes;65561822DOCKET NO.: 38120-4318 (11934W001)-44- ii) screening the dupilumab core gene signature against a whole transcriptome profile from the subject; andiii) 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.

51. The method of claim 50, wherein the plurality of treatment studies comprises eosinophilic esophagitis, atopic dermatitis, asthma, grass allergy, and chronic rhinosinusitis with nasal polyposis.

52. The method of claim 50, wherein the genes in the core gene signature identified from the differential gene expression are selected as having a fold-change > 2, and / or a q < 0.05 in > 3 out of 5 treatment studies.

53. The method of claim 52, wherein the fold-change comprises subtracting the changes in expression in the placebo treatment group from the dupilumab treatment group.

54. The method of claim 51, wherein the differential gene expression for the eosinophilic esophagitis, atopic dermatitis, and chronic rhinosinusitis with nasal polyposis treatment studies are carried out by comparing the baseline gene expression before treatment with dupilumab to the gene expression after treatment with dupilumab.

55. The method of claim 51, wherein the differential gene expression for the asthma and grass allergy treatment studies are carried out by comparing the gene expression with allergen challenge to the gene expression without allergen challenge.

56. The method of claim 50, wherein the differential gene expression is analyzed by a microarray or RNASeq.

57. The method of claim 56, wherein the differential gene expression of the eosinophilic esophagitis, asthma, and grass allergy treatment studies is analyzed by RNASeq.

58. The method of claim 54, wherein the differential gene expression of the atopic dermatitis and chronic rhinosinusitis with nasal polyposis treatment studies is analyzed by microarray.

59. The method of claim 50, wherein screening the dupilumab core gene signature against a whole transcriptome profile from the subject comprises:65561822DOCKET NO.: 38120-4318 (11934W001)-45 - i) transforming the whole transcriptome profile from the subject into z-scores; ii) ranking the z-scores; andiii) generating a normalized enrichment score (NES) for all ranked z-scores using the plurality of genes that are differentially expressed and are in the dupilumab treatment core gene signature, thereby representing the dupilumab signature enrichment for the subject.

60. The method of claim 59, wherein the NES is generated using a gene set enrichment analysis tool that takes both positive and negative gene sets into consideration.

61. The method of claim 60, wherein the NES is generated by:a) transforming each gene expression within the plurality of genes into a z-score, and ordering the plurality of genes that are differentially expressed from the most positive (i.e., most up-regulated) to the most negative (i.e., most down-regulated) values to generate a value of R+;b) identifying hits independently for the positive (i.e., most up-regulated) gene set (S+) in R+, and the negative (i.e., most down-regulated) gene set (S-) in R-, wherein R- is the inversed ranking of R+ with inverted values;c) combining R+ and R- and reordering the values by keeping the hits for both S+ and S-;d) computing a running score by walking down the combined ranking, wherein the running score increases by / r, / p / ^ies / r / pif the ithgene is a hit, or decreases by 1 / (2N-S), where S is the combined total number of genes in S+ and S-; r, is the value for gene / , and p is the weight for r;e) determining an Enrichment Score (ES) as a maximum deviation from zero along the running score;f) repeat steps a) - e) with a random gene set for 1,000 times to compute the ES null distribution; andg) generating the NES as ES divided by the mean of ES null distribution.

62. The method of claim 61, further comprising computing the statistical significance by determining the 95thpercentile NES from healthy control samples.65561822DOCKET NO.: 38120-4318 (11934W001)-46 - 63. The method of claim 61, the method comprising computing the NES for all disease studies using a ranked list for each disease study.

64. The method of claim 50, wherein when the NES of the subject is higher than the NES of a healthy control, the subject is suitable for dupilumab treatment.

65. A kit comprising a plurality of nucleic acid molecules, wherein the plurality of nucleic acid molecules comprise nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.

66. The kit of claim 65, wherein each of the plurality of nucleic acid molecules is linked to a solid support.

67. The kit of claim 66, wherein the solid support comprises a chip, multiwell plate, or a bead.

68. A method of detecting a plurality of nucleic acid molecules in a subject after treatment with dupilumab, wherein the plurality of nucleic acid molecules detected comprises the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes, the method comprising:contacting a biological sample from the subject with a plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes; and detecting the presence or absence of the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3, or any subset thereof comprising at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, or at least 9 genes.65561822DOCKET NO.: 38120-4318 (11934W001)-47 - 69. The method of claim 68, wherein the amount of each of the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPAS, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3 is determined.

70. The method of claim 68 or claim 69, wherein each of the plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to each of nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3 are labeled, and the detection step comprises detecting the label.

71. The method of any one of claims 68 to 70, wherein each of the plurality of nucleic acid molecules comprising nucleotide sequences that are complementary to the nucleic acid molecules encoding ALOX15, POSTN, CDH26, CH25H, CPA3, SERPINB4, CDH3, TPSAB1, IGFBP3, and ADORA3 is linked to a solid support.

72. The method of claim 71, wherein the solid support comprises a chip, multiwell plate, or a bead.65561822

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