Method for determining responsiveness to Anti-tumor necrosis factor therapy in psoriasis treatment

Abstract

To provide a method for determining responsiveness to anti-tumor necrosis factor therapy in psoriasis treatment.SOLUTION: The present disclosure relates to the development of methods for predicting the efficacy of anti-TNF agents in the treatment of psoriasis. More specifically, the present disclosure provides novel biomarkers and biomarker combinations for predicting the efficacy of an anti-TNF drug in the treatment of psoriasis, followed by treatment with the anti-TNF drug if the biomarker level indicates efficacy of the anti-TNF treatment in the subject.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present disclosure generally relates to methods for determining responsiveness to anti-tumor necrosis factor (TNF) therapy in the treatment of psoriasis. In some aspects, the present disclosure provides one or more biomarkers associated with predicting the effectiveness of anti-TNF therapy in a subject with psoriasis. [Background technology]

[0002] Psoriasis is an immune-mediated disease that affects the skin and joints of over 100 million individuals worldwide. Because psoriasis patients have activated tumor necrosis factor alpha (TNFα) in both lesional skin and blood (Arican et al., Mediators Inflamm. 2005; 2005(5):273-9; Johansen et al., J Immunol. 2006; 176(3):1431-8), at least five different drugs that inhibit tumor necrosis factor (TNF) have been developed and approved for the treatment of moderate to severe psoriasis and psoriatic arthritis (Leonardi et al., N Engl J Med. 2003; 349(21):2014-22; Gordon et al., J Amer. Acad Dermatol 2006;55(4):598-606; Chaudhari et al., Lancet. 2001;357(9271):1842-7; Kavanaugh et al., Arthritis Rheum. 2009;60(4):976-86; Blauvelt et al., J Eur Acad Dermatol Venereol. 2019 Mar;33(3):546-552. doi:10.1111 / jdv.15258. Epub 2018 Oct 14), making it one of the most commonly used biological classes for the treatment of psoriasis.

[0003] Etanercept (i.e., ENBREL®) was one of the first anti-TNF drugs approved for the treatment of psoriasis. Etanercept is a fusion protein consisting of TNF receptor-2 fused to an IgG1 Fc chain that binds to and neutralizes soluble TNFα (Gisondi et al., Autoimmun Rev. 2007;6(8):515-9). Studies have shown the effectiveness of etanercept in reducing TNFα expression in both non-lesional and lesional skin, as well as in reducing the expression of TNFα-inducible receptors after treatment (Caldarola et al., Int J Immunopathol Pharmacol. 2009;22(4):961-6). Inhibition of TNF has also been shown to be associated with a reduction in Th17 responses and, therefore, an improvement in epidermal hyperplasia in lesional skin (Zaba et al., J Exp Med. 2007;204(13):3183-94). Previous studies have demonstrated that Th17 immune responses are inactivated among responders to etanercept (Zaba et al., J Allergy Clin Immunol. 2009;124(5):1022-10 e1-395.). A double-blind study of 672 patients showed that etanercept increased the proportion of patients achieving PASI 75 (i.e., a 75% improvement in the Psoriasis Severity Index) during 12 weeks of treatment (Leonardi et al., N Engl J Med. 2003;349(21):2014-22). However, similar to other anti-TNFα agents, patient outcomes to etanercept treatment are variable, with PASI 75 responses ranging from 23% to 60% across studies (Leonardi et al., N Engl J Med. 2003;349(21):2014-22; Krueger et al., J Amer Acad Dermatol 2006;54(3 Suppl 2):S112-9; Tyring et al., Arch Dermatol. 2007;143(6):719-26; Paller et al., J Amer Acad Dermatol 2010;63(5):762-8;Kivelevitch et al.,Biologics.2014;8:169-82).Furthermore, the mechanisms involved remain unclear and are not fully explained by psoriasis susceptibility genes (Foulkes et al., Br J Dermatol. 2017;177(2):344-5; Tsoi et al., J Allergy Clin Immunol. J Allergy Clin Immunol. 2018;141(2):805-808, Epub. (2017 Oct 13). Assessing drug response before treatment can improve the efficiency of treating mild to severe forms of psoriasis, limit the risk of unnecessary drug exposure, and reduce the economic burden on patients.

[0004] Precision medicine, which aims to provide patients with personalized healthcare based on their characteristics, is an emerging research topic for various human diseases (Aziz et al., Crit Rev Oncol Hematol. 2017;118:70-8; Chan et al., Int J Mol Sci. 2017 Nov 15;18(11)2423; Flamant et al., Therap Adv Gastroenterol. 2018;11:1-15; Horton et al., J Pers Med. 2017;7(3):7; Tavakolpour, Immunol Lett. 2017;190:130-8). Advances in genomics have facilitated precision medicine by using sophisticated technologies to effectively identify biomarkers and evaluate treatment options. However, despite extensive transcriptomic studies, its application to complex skin diseases, including psoriasis, remains extremely limited (Tsoi et al., J Allergy Clin Immunol. J Allergy Clin Immunol.2018;141(2):805-808,Epub 2017 Oct 13;Li et al.,J Invest Dermatol.2014;134(7):1828-38;Tsoi et al,Genome Biol.2015;16:24;Gudjonsson et al.,J Invest Dermatol.2009;129(12):2795-804;Gudjonsson et al.,J Invest Dermatol.2010;130(7):1829-40). To date, the art has not disclosed a method for predicting the effectiveness of anti-TNF therapy in treating psoriasis. Thus, there is a strong need in the art for a method for reliably predicting whether an anti-TNF agent will significantly aid in the prognosis and management of patients with psoriasis. The following disclosure provides details of such biomarkers and their uses. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Arican et al.,Mediators Inflamm.2005;2005(5):273-9 [Non-patent document 2] Johansen et al.,J Immunol.2006;176(3):1431-8. [Non-patent document 3] Leonardi et al.,N Engl J Med.2003;349(21):2014-22 [Non-patent document 4] Gordon et al.,J Amer Acad Dermatol 2006;55(4):598-606 [Non-patent document 5] Chaudhari et al., Lancet.2001;357(9271):1842-7 [Non-patent document 6] Kavanaugh et al.,Arthritis Rheum.2009;60(4):976-86 [Non-Patent Document 7] Blauvelt et al.,J Eur Acad Dermatol Venereol.2019 Mar;33(3):546-552.doi:10.1111 / jdv.15258.Epub 2018 Oct 14 [Non-patent document 8] Gisondi et al.,Autoimmun Rev.2007;6(8):515-9. [Non-Patent Document 9] Caldarola et al,Int J Immunopathol Pharmacol.2009;22(4):961-6. [Non-Patent Document 10] Zaba et al.J Exp Med.2007;204(13):3183-94 [Non-Patent Document 11] Zaba et al.,J Allergy Clin Immunol.2009;124(5):1022-10 e1-395. Summary of the Invention [Means for solving the problem]

[0006] The method described herein was developed to provide a means for predicting the outcome of psoriasis treatment with anti-TNF agents. This disclosure provides effective risk assessment for drug response by applying statistical modeling using RNA-seq along with in vitro genomic data on cytokine responses to a cohort of psoriasis patients treated with etanercept. This disclosure demonstrates that transcriptome data correlates with treatment outcomes, demonstrating that changes in gene expression in healthy-appearing, non-lesional skin most accurately predict treatment response. Previous studies focused on areas of skin inflammation to identify biomarkers. Using an integrated approach, a method for assessing future drug response has been developed.

[0007] In some aspects, the present disclosure provides methods for treating psoriasis in a subject, the method comprising measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject, wherein the level of the biomarker is increased or decreased compared to an average baseline level of the biomarker in non-lesional skin of a population of psoriasis patients, indicating that the subject will be responsive to treatment with an anti-TNF agent; and administering an effective amount of the anti-TNF agent to the subject indicated to be responsive to treatment.

[0008] The present disclosure provides a method for treating psoriasis in a subject, comprising measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, wherein the at least one biomarker is selected from the group consisting of: (a) ubiquitin-specific protease 18 (USP18), wherein the level of USP18 is increased compared to the control level; (b) type II cytoskeletal 2 epidermal keratin (KRT2), wherein the level of KRT2 is reduced compared to the control level; (c) interleukin 4 receptor (IL4R), wherein the level of IL4R is increased compared to the control level; (d) sex-determining region Y-box transcription factor 5 (SOX5), wherein the level of SOX5 is reduced compared to the control level; (e) helicase C domain 1-induced interferon (IFIH1), wherein the level of IFIH1 is increased compared to a control level; or (f) A combination of any two or more biomarkers from (a) to (e), an increased or decreased level of the biomarker in the subject relative to the control level predicts that the subject will be responsive to treatment with an anti-TNF agent; the control level is the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with an anti-TNF agent; administering an effective amount of an anti-TNF agent to a subject predicted to be responsive to the treatment.

[0009] In some embodiments, at least one biomarker is USP18. In some embodiments, at least one biomarker is KRT2. In some embodiments, at least one biomarker is IL4R. In some embodiments, at least one biomarker is SOX5. In some embodiments, at least one biomarker is IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, and IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1 as set forth in any one of the combinations in Table 1.

[0010] In some embodiments, the level of USP18 is increased by at least about 86% or more compared to control levels. In some embodiments, the level of KRT2 is reduced by at least about 99% or more compared to control levels. In some embodiments, the level of IL4R is increased by at least about 36% or more compared to control levels. In some embodiments, the level of SOX5 is reduced by at least about 89% or more compared to control levels. In some embodiments, the level of IFIH1 is increased by at least about 49% or more compared to control levels.

[0011] In some embodiments, the level is indicative of the level of a nucleic acid or protein present in a biological sample. In some embodiments, the nucleic acid is a deoxyribonucleic acid. In some embodiments, the nucleic acid is a ribonucleic acid.

[0012] In some embodiments, the anti-TNF agent is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab, or a biosimilar thereof. In some embodiments, the anti-TNF agent is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion.

[0013] In some aspects, the psoriasis is plaque psoriasis, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis.

[0014] In some embodiments, the methods further comprise administering to the subject at least one additional treatment or medication for psoriasis.

[0015] In some embodiments, the subject is a human subject.

[0016] In some embodiments, the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing.

[0017] In some embodiments, subjects treated with an anti-TNF agent show an improvement in PASI by about 12 weeks of treatment. In some embodiments, the improvement in PASI is observed sooner than 12 weeks after treatment. In some embodiments, the improvement in PASI is at least about a 10% improvement in PASI.

[0018] The present disclosure provides a method for prognosing responsiveness to an anti-TNF agent in the treatment of psoriasis in a subject, the method comprising measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, wherein the at least one biomarker is selected from the group consisting of: (a) Ubiquitin-specific protease 18 (USP18), (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e); comparing this level to a control level, the control level being the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with an anti-TNF agent; an increase in the level of USP18, ILR4, and / or IFIH1 in the subject relative to a control level of the biomarker predicts that the subject will be responsive to treatment with an anti-TNF agent; If the level of KRT2 and / or SOX5 in the subject is decreased compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with an anti-TNF agent; if the level of USP18, ILR4, and / or IFIH1 in the subject is not increased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with an anti-TNF agent; and / or When the levels of KRT2 and / or SOX5 in the subject are not reduced compared to the control levels of the biomarkers, the subject is predicted to be non-responsive to treatment with an anti-TNF agent.

[0019] In some embodiments, the method for prognosing responsiveness further comprises administering an effective amount of an anti-TNF agent to treat a subject predicted to be responsive to treatment with an anti-TNF agent.

[0020] In some embodiments, at least one biomarker is USP18. In some embodiments, at least one biomarker is KRT2. In some embodiments, at least one biomarker is IL4R. In some embodiments, at least one biomarker is SOX5. In some embodiments, at least one biomarker is IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1, as set forth in any one of the combinations in Table 1.

[0021] In some embodiments, the level of USP18 is increased by at least about 86% or more compared to control levels. In some embodiments, the level of KRT2 is reduced by at least about 99% or more compared to control levels. In some embodiments, the level of IL4R is increased by at least about 36% or more compared to control levels. In some embodiments, the level of SOX5 is reduced by at least about 89% or more compared to control levels. In some embodiments, the level of IFIH1 is increased by at least about 49% or more compared to control levels.

[0022] In some embodiments, the level is indicative of the level of a nucleic acid or protein present in a biological sample. In some embodiments, the nucleic acid is a deoxyribonucleic acid. In some embodiments, the nucleic acid is a ribonucleic acid.

[0023] In some embodiments, the anti-TNF agent is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab, or a biosimilar thereof. In some embodiments, the anti-TNF agent is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion.

[0024] In some aspects, the psoriasis is plaque psoriasis, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis.

[0025] In some embodiments, the methods further comprise administering to the subject at least one additional treatment or medication for psoriasis.

[0026] In some embodiments, the subject is a human subject.

[0027] In some embodiments, the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing.

[0028] In some embodiments, subjects treated with an anti-TNF agent show an improvement in PASI by about 12 weeks of treatment. In some embodiments, the improvement in PASI is observed sooner than 12 weeks after treatment. In some embodiments, the improvement in PASI is at least about a 10% improvement in PASI.

[0029] The present disclosure provides a kit comprising reagents for measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from a subject with psoriasis prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, the at least one biomarker comprising: (a) Ubiquitin-specific protease 18 (USP18), (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e), The level is a nucleic acid or protein level of the biomarker in the biological sample.

[0030] In some embodiments, the kit further comprises means for comparing the level of the biomarker nucleic acid or protein in the biological sample with a control level, the control level being the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with an anti-TNF agent. In some embodiments, the biological sample is obtained from a biopsy of non-lesional skin from a subject with psoriasis. In some embodiments, the biological sample is obtained from a biopsy of non-lesional skin from a subject with psoriasis prior to treatment with an anti-TNF agent.

[0031] The present disclosure relates to the use of a measurement of the level of at least one biomarker in a biological sample of non-lesional skin from a subject with psoriasis that is increased or decreased compared to a measurement of a control level, the control level being the average level of the biomarker in non-lesional skin in a population of subjects prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, for prognosing the responsiveness of a subject with psoriasis to treatment with an anti-TNF agent, wherein the at least one biomarker is (a) ubiquitin-specific protease 18 (USP18); (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e), an increase in the level of USP18, ILR4, and / or IFIH1 in the subject relative to a control level of the biomarker predicts that the subject will be responsive to treatment with an anti-TNF agent; If the level of KRT2 and / or SOX5 in the subject is decreased compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with an anti-TNF agent; if the level of USP18, ILR4, and / or IFIH1 in the subject is not increased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with an anti-TNF agent; and / or When the levels of KRT2 and / or SOX5 in the subject are not reduced compared to the control levels of the biomarkers, the subject is predicted to be non-responsive to treatment with an anti-TNF agent.

[0032] In some embodiments, at least one biomarker is USP18. In some embodiments, at least one biomarker is KRT2. In some embodiments, at least one biomarker is IL4R. In some embodiments, at least one biomarker is SOX5. In some embodiments, at least one biomarker is IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1. In some embodiments, at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1, as set forth in any one of the combinations in Table 1.

[0033] In some embodiments, the level of USP18 is increased by at least about 86% or more compared to control levels. In some embodiments, the level of KRT2 is reduced by at least about 99% or more compared to control levels. In some embodiments, the level of IL4R is increased by at least about 36% or more compared to control levels. In some embodiments, the level of SOX5 is reduced by at least about 89% or more compared to control levels. In some embodiments, the level of IFIH1 is increased by at least about 49% or more compared to control levels.

[0034] In some embodiments, the level is indicative of the level of a nucleic acid or protein present in a biological sample. In some embodiments, the nucleic acid is a deoxyribonucleic acid. In some embodiments, the nucleic acid is a ribonucleic acid.

[0035] In some embodiments, the anti-TNF agent is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab, or a biosimilar thereof. In some embodiments, the anti-TNF agent is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion.

[0036] In some aspects, the psoriasis is plaque psoriasis, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis.

[0037] In some embodiments, the methods further comprise administering to the subject at least one additional treatment or medication for psoriasis.

[0038] In some embodiments, the subject is a human subject.

[0039] In some embodiments, the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing.

[0040] In some embodiments, subjects treated with an anti-TNF agent show an improvement in PASI by about 12 weeks of treatment. In some embodiments, the improvement in PASI is observed sooner than 12 weeks after treatment. In some embodiments, the improvement in PASI is at least about a 10% improvement in PASI.

[0041] In various aspects, the present disclosure includes methods, kits, and uses of any one biomarker or combination of biomarkers disclosed herein for the prognosis and treatment of psoriasis based on the expression of the biomarker or combination of biomarkers.

[0042] The present disclosure also provides a method for determining whether a biomarker predicts whether a subject with psoriasis will respond to treatment with an anti-TNF agent, the method comprising: (1) measuring the expression levels of one or more TNF-inducible and / or interferon (IFN)-inducible genes in biopsies of non-lesional skin in a population of subjects with psoriasis prior to treatment with the anti-TNF agent, as well as PASI scores; (2) treating the population of subjects with the anti-TNF agent for at least about 12 weeks; (3) tracking transcriptome changes over the course of treatment; and (4) identifying differentially expressed transcripts that are associated with improved PASI scores after adjusting for patient body mass index (BMI), gender, and age.

[0043] The above summary of the invention is not intended to define all aspects of the disclosure, and additional aspects are described in other sections, such as the detailed description below. It should be understood that the entire document is intended to relate to a unified disclosure, and that all combinations of features described herein are contemplated, even if the combinations of features are not found together in the same sentence, paragraph, or section of the document. Other features and advantages of the present invention will become apparent from the detailed description below. However, it should be understood that the detailed description and the specific examples, while indicating specific embodiments of the disclosure, are given by way of example only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from the detailed description.

[0044] The following detailed description, which is provided by way of example and is not intended to limit the invention to the particular embodiments described, can be understood in conjunction with the accompanying drawings, which are incorporated herein by reference. [Brief explanation of the drawings]

[0045] [Figure 1-1]The transcriptome of the longitudinal cohort is shown. Figure 1A illustrates the study design. Figure 1B shows the top two principal components calculated using transcriptome data from all RNA-seq samples. Figure 1C is a heatmap showing the changes in expression profiles over the treatment time course. Forty-six patients were recruited into the cohort, and 42 of those 46 patients provided RNA-seq data both at baseline and at least one of their follow-up visits. [Figure 1-2] Same as above. [Figure 2] The correlation between PASI improvement and baseline expression and expression of USP18 and KRT2 over time is shown. PASI improvement (y-axis) is plotted against baseline expression of USP18 (Figures 2A-B) and KRT2 (Figures 2D-E) in non-lesional (Figures 2A and 2D) and lesional skin (Figures 2B and 2E). Box plots of normalized expression levels of USP18 (Figure 2C) and KRT2 (Figure 2F) in various skin types and time points during the treatment course are shown. Although the correlation between genes (i.e., USP18 and KRT2) and PASI improvement at weeks 2 and 6 was not significant, the direction of the correlation was consistent (Figures 2A and 2D). Interestingly, these associations were not observed when baseline expression levels in lesional skin were used (Figures 2B and 2E). Expression levels of USP18 and KRT2 genes in lesional skin gradually "reverted" over time toward expression levels in non-lesional skin (Figures 2C and 2F). These results indicate that both USP18 and KRT2 are dysregulated in psoriatic skin but can be "restored" to levels in non-lesional skin by etanercept treatment, and that their relative expression levels in non-lesional skin before treatment are associated with future improvement in PASI and are therefore useful for predicting therapeutic response to anti-TNF agents. [Figure 3-1]We demonstrate USP18 as a regulator of the IFN / TNF response. Figure 3A shows immunostaining of USP18 in non-lesional and lesional psoriatic skin, confirming its expression in the epithelial layer. We used siRNA to knock down USP18 expression in keratinocytes and assessed its effect on the TNF / IFN response (Figures 3B-D). Figure 3B shows the effect of USP18 depletion on type I and type II stimulation (x-axis). Figure 3B shows the effect of USP18 depletion on the expression of IL36G and DEFB4 after stimulation with TNF, IL-17A, IFN-α, or IFN-γ (x-axis). Figure 3C shows the effect of USP18 depletion on the expression of myxovirus (influenza virus) resistance 1 (interferon-inducible protein P78; MX1) and interferon-kappa (IFNK) after stimulation with IFN-α or IFN-γ (x-axis). Figure 3D shows the effect of USP18 depletion on the expression of oligoadenylate synthetase-like protein (OASL) and interferon regulatory factor 7 (IRF7) after stimulation with IFN-α or IFN-γ (x-axis). Figures 3E-G show the effect of USP18 overexpression on type I and type II IFN responses. Figure 3E shows the effect of USP18 overexpression on the expression of interleukin-36 gamma (IL36G) and defensin beta 4 (DEFB4) after stimulation with TNF, IL-17A, IFN-α, and IFN-γ. Figure 3F shows the effect of USP18 overexpression on the expression of MX1 and IFNK after stimulation with IFN-α and IFN-γ. Figure 3G shows the effect of USP18 overexpression on the expression of OASL after stimulation with IFN-α and IFN-γ. Figure 3H shows a Western blot confirming the increase in USP18 protein levels in cells transfected with the USP18 plasmid. Data shown include STDV and represent three biological replicates. *p<0.05, **p<0.01, ***p<0.001. [Figure 3-2] Same as above. [Figure 3-3] Same as above. [Figure 3-4] Same as above. [Figure 4]Figure 4 shows the enrichment of various cytokine signatures in various association comparisons. Figure 4A shows the enrichment of signatures between genes showing the strongest differential expression in lesional skin between baseline and follow-up. Figures 4B-C show the enrichment of signatures between expression profiles at baseline in non-lesional skin (b) or lesional skin (c) showing the strongest association with improvement in PASI at follow-up. [Figure 5-1] We provide an assessment of PASI response at week 12 using expression profiles from baseline non-lesional skin. Figures 5A-B show each patient's TNF score in baseline:non-lesional skin (x-axis) plotted against the patient's IFN score, with the color scheme representing PASI improvement by week 12 using either absolute (Figure 5A) or percent (Figure 5B) metrics. Figure 5C shows the AUROC values ​​using various numbers of principal components (PCs) in the model. PCs were calculated using IFN / TNF-inducible genes. Figure 5D shows how precision (solid line) and recall (dotted line) are plotted against the maximum proportion of samples predicted to achieve PASI 75 by week 12. [Figure 5-2] Same as above. [Figure 6] Venn diagram showing the overlap between differentially expressed genes identified in the various comparisons. PN: non-lesional skin; PP: lesional skin. [Figure 7] Normalized expression levels of USP18 in control, non-lesional, and lesional skin from an independent psoriasis transcriptome cohort are shown. [Figure 8] Normalized expression levels of USP18 in keratinocytes under various cytokine stimuli are shown. DETAILED DESCRIPTION OF THE INVENTION

[0046] The present disclosure relates to the identification of various biomarkers, alone or in combination, as predictors of the outcome of psoriasis treatment with anti-TNF drugs. More specifically, the present disclosure provides a rapid and robust method for predicting the effectiveness of anti-TNF drug treatment by measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from a subject suffering from psoriasis, wherein a change in the level of the biomarker in the subject's non-lesional skin compared to the baseline level of the biomarker indicates whether the subject will be responsive to anti-TNF drug treatment. In some embodiments, the method of the present disclosure comprises administering an effective amount of an anti-TNF drug to a subject predicted to be responsive to treatment.

[0047] Before describing any embodiments of the presently disclosed subject matter in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangements of components set forth in the following description or illustrated in the drawings and examples. Accordingly, the disclosure encompasses other embodiments and can be practiced or carried out in various ways.

[0048] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0049] It should be noted herein that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The terms "including," "comprising," "containing," or "having," and variations thereof, are meant to encompass the subsequently listed elements and equivalents thereof, as well as additional subject matter, unless expressly stated otherwise.

[0050] "Control" refers to an active, positive, negative, or vehicle control. In the present disclosure, a "control" or "control level" provides a comparison for measuring the level or amount of a biomarker present in the non-lesional skin of a subject. Thus, as used herein, a "control" or "control level" refers to the average level of a biomarker in the non-lesional skin of a population of psoriasis patients at baseline, i.e., day 0, or before any treatment with an anti-TNF agent. In some embodiments, the relative level of nucleic acid expression in a sample from a subject is compared to the control level. Thus, some measurements are expressed relative to the control. In some embodiments, the control level is the average normalized read count of the gene across all samples, i.e., the average control level of the biomarker at baseline. In some embodiments, the psoriasis patient population is at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, or at least about 50 psoriasis patients. In some embodiments, the psoriasis patient population is at least about 35 psoriasis patients. In some embodiments, the population of psoriasis patients is at least about 36 psoriasis patients. In some embodiments, the population of psoriasis patients in the control may be increased.

[0051] "Measuring" or "measuring" means assessing the presence, amount, or level of a substance, e.g., a biomarker, in a clinical or subject-derived sample (including deriving a qualitative or quantitative concentration level of such a substance), or otherwise assessing the value or classification of a clinical parameter of a subject. The recitation of ranges of values ​​herein, unless otherwise indicated herein, merely serves as a shorthand method for referring individually to each individual value within the range and each endpoint, and each individual value and endpoint is incorporated herein as if it were individually referenced herein.

[0052] The terms "level" and "amount" are used interchangeably herein to refer to the concentration of a biomarker present in a biological sample. In some embodiments of the present disclosure, the biological sample is a biopsy or scraping of non-lesional skin in a psoriasis patient (i.e., a subject) or a population of psoriasis patients (i.e., multiple subjects). In some embodiments, nucleic acids and / or proteins are prepared from a sample of non-lesional skin, and the "level" and / or "amount" is the level or amount of a particular nucleic acid and / or protein of interest. In some embodiments, this is the level or amount of a nucleic acid and / or protein biomarker.

[0053] The terms "protein," "polypeptide," and "peptide" are used interchangeably herein to refer to polymers of amino acid residues linked through peptide bonds. The term "protein" typically refers to large polypeptides. The term "peptide" typically refers to short polypeptides.

[0054] The term "nucleic acid" or "nucleic acid sequence" or "nucleic acid molecule" refers to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form. The term "nucleic acid" is used interchangeably with gene, deoxyribonucleic acid, complementary DNA (cDNA), ribonucleic acid, messenger RNA (mRNA), oligonucleotide, and polynucleotide.

[0055] As used herein, a "fragment" of a protein or nucleic acid refers to any portion of a protein or nucleic acid that is smaller than the full-length protein, nucleic acid, or protein expression product. Fragments are deletion analogs of a full-length protein or nucleic acid in which one or more amino acid residues (proteins) or nucleotides (nucleic acids) have been removed from the amino terminus (proteins) or 5' terminus (nucleic acids) and / or the carboxy terminus (proteins) or 3' terminus (nucleic acids) of the full-length protein or nucleic acid.

[0056] Biomarkers In various aspects, the present disclosure includes methods of measuring a biomarker in a biological sample from a subject, wherein the presence of the biomarker at an increased level above a control or a decreased level below a control indicates that the subject will respond favorably to treatment with an anti-TNF agent.

[0057] "Biomarkers" in the context of this disclosure include proteins, nucleic acids, and metabolites, along with their polymorphisms, mutations, variants, transient mutations, subunits, fragments, protein-ligand complexes, and degradation products, protein-ligand complexes, components, related metabolites, and other analytes or sample-derived indicators. Biomarkers include, but are not limited to, ubiquitin-specific protease 18 (USP18), type II cytoskeleton 2 epidermal keratin (KRT2), interleukin-4 receptor (IL4R), SRY box 5 (SOX5), or helicase C domain 1-induced interferon (IFIH1).

[0014] Thus, in some embodiments, the biomarkers include proteins or fragments thereof, or nucleic acids or fragments thereof. In some embodiments, the biomarkers are TNF- or IFN-inducible nucleic acids or proteins. In further embodiments, one or more biomarkers are measured together to provide an array for predicting that a subject will respond positively to anti-TNF therapy during psoriasis treatment. Biomarkers of the present disclosure are any one or more of ubiquitin-specific protease 18 (USP18), type II cytoskeleton 2 epidermal keratin (KRT2), interleukin-4 receptor (IL4R), SRY box 5 (SOX5), or helicase C domain 1-induced interferon (IFIH1). In exemplary aspects, the term "USP18" as used herein refers to a USP18 protein or nucleic acid, the term "KRT2" as used herein refers to a KRT2 protein or nucleic acid, the term "IL4R" as used herein refers to an IL4R protein or nucleic acid, the term "SOX5" as used herein refers to a SOX5 protein or nucleic acid, and the term "IFIH1" as used herein refers to an IFIH1 protein or nucleic acid.

[0058] In some embodiments, the methods include measuring the level of one or more of USP18, KRT2, IL4R, SOX5, and / or IFIH1 protein or nucleic acid in a biological sample. In some embodiments, the methods further include measuring the level of an additional biomarker or combination of biomarkers shown to correlate with improvement of psoriasis in a subject with psoriasis. The present disclosure includes the use of one or more of these biomarkers in methods for predicting the success of anti-TNF treatment in patients with psoriasis.

[0059] The present disclosure includes the use of any one biomarker or combination of biomarkers listed in the Table of Biomarkers below in any of the disclosed methods, kits, uses, etc. For example, the present disclosure, in various embodiments, includes any biomarker or combination of biomarkers exemplified in columns 1-26 of Table 1 below.

[0060] [Table 1]

[0061] Prognostic value of biomarker levels in psoriasis treatment In the present disclosure, a significant increase or decrease in the levels of each of the five biomarkers independently correlates with a positive response to treatment with anti-TNF agents in patients with psoriasis. For each biomarker (gene), expression levels in non-lesional skin at baseline correlated with changes in PASI at week 12, adjusting for the patient's body mass index (BMI), sex, and age (e.g., by treating these variables as covariates in a regression framework). These five biomarkers were selected because they each encode proteins shown to be involved in immune processes and because they are among the top 20 significant genes associated with a positive response to treatment with anti-TNF agents in patients with psoriasis.

[0062] In the present disclosure, biomarker levels were measured in samples from subjects with psoriasis and compared to a control, which was the average level of the biomarker from non-lesional skin from a population of psoriasis patients. In various embodiments, an increased biomarker level is a level significantly greater than the control level. In various embodiments, the increase in the level of the biomarker in the subject is at least or about 1% greater, at least or about 2% greater, at least or about 3% greater, at least or about 4% greater, at least or about 5% greater, at least or about 6% greater, at least or about 7% greater, at least or about 8% greater, at least or about 9% greater, at least or about 10% greater, at least or about 11% greater, at least or about 12% greater, at least or about 13% greater, at least or about 14% greater, at least or about 15% greater, at least or about 16% greater, at least or about 17% greater, at least or about 18% greater, at least or about 19% greater, at least or about 20% greater, at least or about 21% greater, or at least or about 22% greater than the control level. , at least or about 23% larger, at least or about 24% larger, at least or about 25% larger, at least or about 26% larger, at least or about 27% larger, at least or about 28% larger, at least or about 29% larger, at least or about 30% larger, at least or about 35% larger, at least or about 40% larger, at least or about 45% larger, at least or about 50% larger, at least or about 55% larger, at least or about 60% larger, at least or about 65% larger, at least or about 70% larger, at least or about 75% larger, at least or about 80% larger, at least or about 85% larger, at least or about 90% larger, at least or about 95% larger, at least or about 100% larger, or at least 100% or more than 100% larger.In an exemplary embodiment, the control level is the average level of the biomarker in biological samples of non-lesional skin from a population of psoriasis patients prior to treatment with an anti-TNF agent (ie, baseline).

[0063] In further embodiments, the increase in the level of the biomarker in the subject is at least or about 1 / 10 greater, at least or about 1 / 9 greater, at least or about 1 / 8 greater, at least or about 1 / 7 greater, at least or about 1 / 6 greater, at least or about 1 / 5 greater, at least or about 1 / 4 greater, at least or about 1 / 3 greater, at least or about 1 / 2 greater, at least or about 1 fold greater, at least or about 1.5 fold greater, at least or about 2.0 fold greater, at least or about 2.5 fold greater, at least or about 3.0 fold greater, at least or about 3.5 fold greater, at least or about 4.0 fold greater, at least or about 4.5 fold greater, or at least or about 5 fold greater than the control level.

[0064] In other aspects, an increased level of a biomarker in a sample means that the concentration of the biomarker is significantly greater than the control level, with significant difference being calculated according to any method of statistical analysis known to those of skill in the art.

[0065] In the present disclosure, the level of a biomarker is measured in a sample from a subject suffering from psoriasis and compared to the level of the biomarker in a control. In various embodiments, a reduced level of a biomarker is a level that is significantly lower than the control level. In various embodiments, the reduction in the level of a biomarker in a subject is at least or about 1% lower, at least or about 2% lower, at least or about 3% lower, at least or about 4% lower, at least or about 5% lower, at least or about 6% lower, at least or about 7% lower, at least or about 8% lower, at least or about 9% lower, at least or about 10% lower, at least or about 11% lower, at least or about 12% lower, at least or about 13% lower, at least or about 14% lower, at least or about 15% lower, at least or about 16% lower, at least or about 17% lower, at least or about 18% lower, at least or about 19% lower, at least or about 20% lower, at least or about 21% lower, at least or about 22% less, at least or about 23% less, at least or about 24% less, at least or about 25% less, at least or about 26% less, at least or about 27% less, at least or about 28% less, at least or about 29% less, at least or about 30% less, at least or about 35% less, at least or about 40% less, at least or about 45% less, at least or about 50% less, at least or about 55% less, at least or about 60% less, at least or about 65% less, at least or about 70% less, at least or about 75% less, at least or about 80% less, at least or about 85% less, at least or about 90% less, at least or about 95% less, at least or about 100% less, or less than about 100% less. In an exemplary embodiment, the control level is the average level of the biomarker in biological samples from non-lesional skin from a population of psoriasis patients prior to treatment with an anti-TNF agent (i.e., baseline).

[0066] In further embodiments, the decrease in the level of the biomarker in the subject is at least or about 10-fold less, at least or about 9-fold less, at least or about 18-fold less, at least or about 17-fold less, at least or about 16-fold less, at least or about 15-fold less, at least or about 14-fold less, at least or about 13-fold less, at least or about 1 / 2-fold less, at least or about 1-fold less, at least or about 1.5-fold less, at least or about 2.0-fold less, at least or about 2.5-fold less, at least or about 3.0-fold less, at least or about 3.5-fold less, at least or about 4.0-fold less, at least or about 4.5-fold less, or at least or about 5-fold less than the control level.

[0067] In other aspects, a decreased level of a biomarker in a sample means that the concentration of the biomarker is significantly less than the control level. A significant difference is calculated according to any statistical analysis method known to one of skill in the art.

[0068] In some embodiments, the present disclosure provides relative levels of biomarker expression in non-lesional skin at day 0 that predict improvement in psoriasis upon treatment with an anti-TNF agent. By week 12 of treatment with the anti-TNF agent, patients were determined to achieve an average improvement in PASI score of about 10 points from the week 0 biomarker expression levels in non-lesional skin as follows: (1) for USP18, the expression level in non-lesional skin at day 0 is at least approximately 86% greater than the mean expression level of USP18 in non-lesional skin of psoriasis patients at week 0; (2) in the case of IL4R, the expression level in non-lesional skin at day 0 is at least about 36% greater than the mean expression level of IL4R in non-lesional skin of psoriasis patients at week 0; (3) for IFIH1, the expression level in non-lesional skin at day 0 is at least about 49% greater than the mean expression level of IFIH1 in non-lesional skin of psoriasis patients at week 0; (4) in the case of SOX5, the expression level in non-lesional skin at day 0 is at least about 89% less than the mean expression level of SOX5 in non-lesional skin of psoriasis patients at week 0; (5) In the case of KRT2, the expression level in non-lesional skin on day 0 is at least about 99% less than the average expression level of KRT2 in non-lesional skin of psoriasis patients at week 0.

[0069] In other words, if a subject is determined to have a USP18 level on day 0 in non-lesional skin that is at least about 86% greater than the control (i.e., the average expression level of the biomarker in non-lesional skin in a population of psoriasis patients at baseline or week 0), or an IL4R level that is at least about 36% greater than the control, or an IFIH1 level that is at least about 49% greater than the control, or an SOX5 level that is at least about 89% less than the control, or a KRT2 level that is at least about 99% less than the control, the subject is predicted to have an average 10-point improvement in PASI score by week 12 of treatment with the anti-TNF agent.

[0070] In some embodiments of the present disclosure, the level of a biomarker in a biological sample (e.g., a biopsy of non-lesional skin from a subject with psoriasis prior to treatment with an anti-TNF agent) is compared to a control level. The control level is the average level of a biomarker from skin biopsies from a population of psoriasis subjects, the skin biopsies being taken on day 0 or prior to treatment with an anti-TNF agent (i.e., baseline). In some embodiments, the population of subjects is optionally matched for other parameters, such as one or more of the following: age, sex, psoriasis severity, etc. In various embodiments, the level of a biomarker is a relative level. In some embodiments, the level of a biomarker is an absolute level.

[0071] Detection and Measurement of Biomarker Levels In various embodiments of the present disclosure, levels of protein biomarkers in biological samples are detected or quantitatively measured by any suitable means known in the art of protein quantification, including, but not limited to, immunoassays (e.g., ELISA, RIA), immunoturbidimetry, rapid immunodiffusion, laser nephelometry, visual agglutination, quantitative Western blot analysis, multiple reaction monitoring mass spectrometry (MRM Proteomics), Lowry assay, Bradford assay, BCA assay, and UV spectroscopic assays such as UV spectroscopic assays. Alternatively, Northern blots may be used to compare mRNA levels.

[0072] In various embodiments of the present disclosure, the level of a nucleic acid biomarker in a biological sample is detected or quantitatively measured by any suitable means known in the art of nucleic acid quantification, including, but not limited to, RNA sequencing (RNA-seq), high-throughput sequencing (HT-seq), PCR, quantitative PCR, qT-PCR, RT-qPCR, digital PCR, real-time PCR, direct digital quantification, serial analysis of gene expression (SAGE), nucleic acid sequence-based amplification (NASBA), transcription-mediated amplification (TMA), branched DNA (bDNA) assays, and / or Northern or Southern blotting.

[0073] As used herein, RNA sequencing, or "RNA-seq," is used in various embodiments of the present disclosure. RNA-seq (also known as whole transcriptome shotgun sequencing (WTSS)) uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given time. In some embodiments, RNA-seq is used to analyze the continuously changing cellular transcriptome, which is the total cellular content of RNA, including mRNA, rRNA, and tRNA. Understanding the transcriptome is key to linking information about a genome to its functional protein expression. RNA-seq is a tool that observes which genes are turned on in a cell, what their expression levels are, and how many times they are activated or shut off, allowing scientists to better understand cellular biology and evaluate changes that may indicate disease. This can provide researchers with crucial information about the function of genes in cells and tissues. In some embodiments, RNA-seq or HT-seq provides the relative levels of biomarkers compared to controls. In some embodiments, biomarker levels are quantified using standard methods used in RNA-seq data analysis, including transcript quantification methods (Conesa et al., Genome Biol 2016;17:13). In some embodiments, relative levels of nucleic acids are measured by nucleic acid measurement methods known in the art. Nucleic acid quantification is typically performed to determine the average concentration of nucleic acid, either DNA or RNA, present in a sample. In some embodiments, quantification is performed by PCR, qPCR, spectrophotometric quantification, and / or ultraviolet fluorescent labeling in the presence of a nucleic acid dye.

[0074] In an exemplary embodiment, 50-bp single-end reads are generated from patient-derived RNA-seq samples. For each sequence file, Trimmomatic is used to trim adapters (Bolger et al., Bioinformatics 2014;30(15):2114-20), and STAR (Dobin et al., Bioinformatics. 2013;29(1):15-21) is used to align the reads to the human genome b37. HTSeq is used to quantify expression levels (Anders et al., Bioinformatics 2015;31(2):166-9).

[0075] In various embodiments, any of these methods are performed using nucleic acid (e.g., DNA, cDNA, RNA, or mRNA) or protein from a biological sample obtained from a skin biopsy from a human subject with psoriasis. In exemplary embodiments, the biological sample is from non-lesional skin. In further exemplary embodiments, the sample is taken before treatment with an anti-TNF agent to measure pre-treatment biomarker levels. In some embodiments, the sample is taken during and after treatment with an anti-TNF agent to measure biomarker levels during or after treatment.

[0076] In some embodiments, the Area Under the Receiver Operating Characteristic (AUROC) is measured. AUROC is a common summary statistic for the status of a predictor in a binary classification task. It is equal to the probability that the predictor will rank a randomly selected positive example higher than a randomly selected negative example. Specificity and sensitivity are best represented by an AUROC curve, which is a plot of the false positive rate on the x-axis and the true positive rate on the y-axis for all possible levels of the marker. A perfect test would have an AUROC curve that is square, showing 100% true positives and no false positives. In this case, the corresponding AUROC is equal to 1. A random test would have an AUROC of 0.5, meaning that there is one false positive for every true positive. A biomarker panel, in various embodiments, includes several biomarkers that are collectively diagnostic or predictive.

[0077] psoriasis In various embodiments, the methods of the present disclosure are applicable to patients suffering from psoriasis. Psoriasis is a genetic, immune-mediated disease that affects 1-3% of the U.S. population. Approximately 30% of patients with psoriasis also suffer from psoriatic arthritis (PsA), which is accompanied by a wide variety of additional symptoms that contribute to the disease burden. Therefore, as used in this disclosure, the term "psoriasis" includes, but is not limited to, plaque psoriasis, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or PsA. Factors such as joint pain, erosive joint damage, enthesitis, and dactylitis, as well as skin and nail psoriasis, further increase the long-term impact on patients' quality of life, physical function, and work capacity.

[0078] Psoriasis is a chronic inflammatory skin disease characterized by the infiltration of activated leukocytes and increased proliferation of epidermal keratinocytes. The importance of immune mechanisms in the pathogenesis of psoriasis has been proposed, and detection of cytokines has been reported in keratinocyte tissue extracts, suction blister fluid, cytoplasmic extracts, and serum of psoriasis patients. In addition to the release of inflammatory cytokines by immune cells that can propagate psoriasis, keratinocytes produce many cytokines, including TNF, with proinflammatory and growth-promoting activities, either spontaneously or after stimulation.

[0079] The wide variety of symptoms makes it difficult to assess the overall disease activity and response to therapy in both psoriasis and PsA.Accurate assessment is therefore essential for clinicians to determine the most appropriate treatment.These assessment problems are particularly applicable to skin diseases, because currently used outcome measures are limited.Subjects or patients suffering from psoriasis are patients who suffer from the classic symptoms of psoriasis and / or have been diagnosed by medical professionals as suffering from psoriasis.

[0080] Non-lesional and lesional skin in psoriasis In various embodiments, non-lesional skin is used in the methods of the present disclosure. Non-lesional skin of psoriasis patients is non-inflamed, normal-looking skin. In various embodiments, non-lesional skin biopsy is performed on any normal-looking, non-inflamed skin that is far from psoriasis lesions. In some embodiments, non-lesional skin biopsy is performed on the buttocks, which is far from any active psoriasis lesions, because such skin biopsies on the buttocks are more cosmetically acceptable and scars are easier to conceal.

[0081] Due to the localized spread of psoriasis, those skilled in the art know not to take samples of non-lesional skin near affected skin. As used herein, a "biological sample" taken from a subject refers, in various embodiments, to a sample of non-lesional skin obtained from a subject. In various embodiments, the sample is a skin punch biopsy obtained from lesional skin. In some embodiments, biopsies are obtained from both non-lesional and lesional skin under local anesthesia (lidocaine 1:10,000 epinephrine). In some embodiments, biopsies are taken at baseline and subsequently at subsequent measurement time points. In some embodiments, biopsies are taken at time 0 or baseline, followed by re-takes at 2, 6, and 12 weeks. In various embodiments, the biological sample or "sample" contains nucleic acids and / or proteins and / or fluids containing organic and / or inorganic metabolites and substances. In some embodiments of the present invention, the sample contains proteins and nucleic acids suitable for measuring protein or nucleic acid levels or for measuring protein or nucleic acid expression levels. In an exemplary embodiment, the amount of RNA is measured using RNA sequencing (RNA-seq).

[0082] Multiple studies have demonstrated that non-lesional skin from psoriasis patients differs from skin from healthy controls by exhibiting increased rates of epidermal proliferation in in vivo xenograft models (Krueger et al., J Clin Invest. 1981;68(6):1548-57), reduced levels of the epithelial barrier proteins filaggrin and loricrin (Kim et al., J Invest Dermatol. 2011;131(6):1272-9), alterations in innate immune response and lipid metabolism genes (Gudjonsson et al., J Invest Dermatol. 2009;129(12):2795-804), and abnormal epithelial barrier repair (Ye et al., J Invest Dermatol. 2014;134(11):2843-6). The cause of these changes in psoriatic skin is unknown, but it has been speculated that they may result from a systemic inflammatory response from increased levels of circulating proinflammatory mediators (Dowlatshahi et al., Br J Dermatol. 2013;169(2):266-82), genetic predisposition (Tsoi et al., Nature Genetics 2012;44(12):1341-8; Tsoi et al., Nat Commun. 2017;8:15382), or a combination of both. Regardless of the mechanism, these proinflammatory signatures in non-lesional skin provide, in some aspects, a unique signature of each patient's overall inflammatory response.

[0083] How to measure psoriasis severity In various aspects of the present disclosure, psoriasis severity is measured by a physician or physician's assistant. There are several systems used in clinical practice to measure psoriasis severity, including, but not limited to, the Lattice System Physician's Global Assessment (LS-PGA), the Psoriasis Area and Severity Index (PASI), also known as the PASI score, the static Physician's Global Assessment (sPGA or PGA), body surface area (BSA), and / or PGAxBSA.

[0084] The PASI score is the most widely used tool for measuring skin involvement and is considered the "gold standard" in clinical trials (Armstrong et al., JAMA Dermatol 2013;149:577-82). The PASI combines an assessment of lesion severity and area of ​​involvement into a single score ranging from 0 (no disease) to 72 (maximum disease). The PASI is an index used to express the severity of psoriasis by combining severity (erythema, induration, and scaling) with the percentage of area involved. The area and severity score for each area is calculated by multiplying the area score by the severity score (maximum 6 × 12 = 72). Each area's contribution to the final PASI is then quantified based on how much of the total body skin surface it occupies. Generally, a PASI score of less than 10 defines mild psoriasis, 10–20 defines moderate psoriasis, and >20 defines severe psoriasis. A 75% reduction in the PASI score (PASI75) is the current benchmark for the primary endpoint of most psoriasis clinical trials, but many consider this endpoint to be overly strict because it places potentially useful therapies at risk of failing to demonstrate efficacy. In some embodiments of the present disclosure, PASI75 is used as an endpoint in assessing psoriasis. However, in some other embodiments of the present disclosure, an absolute change in the PASI score, for example, a change of about 10 points in the PASI score, is used as an endpoint in assessing the improvement or worsening of psoriasis. For example, a reduction in the PASI score of about 10 reflects a 10-point improvement in the patient's PASI score and an improvement in the patient's psoriasis condition. To achieve or predict an average change in the PASI score of about 10 points or more by week 12 of treatment, biomarker expression on day 0 in non-lesional skin is as follows: (1) for USP18, the expression level in non-lesional skin at day 0 is at least approximately 86% greater than the mean expression level of USP18 in non-lesional skin of psoriasis patients at week 0; (2) in the case of IL4R, the expression level in non-lesional skin at day 0 is at least about 36% greater than the mean expression level of IL4R in non-lesional skin of psoriasis patients at week 0; (3) for IFIH1, the expression level in non-lesional skin at day 0 is at least about 49% greater than the mean expression level of IFIH1 in non-lesional skin of psoriasis patients at week 0; (4) in the case of SOX5, the expression level in non-lesional skin at day 0 is at least about 89% less than the mean expression level of SOX5 in non-lesional skin of psoriasis patients at week 0; (5) In the case of KRT2, the expression level in non-lesional skin on day 0 is at least about 99% less than the average expression level of KRT2 in non-lesional skin of psoriasis patients at week 0.

[0085] In other words, if a subject is determined to have a USP18 level in non-lesional skin on day 0 that is at least about 86% greater than the control (i.e., the average expression level of the biomarker in non-lesional skin in a population of psoriasis patients at week 0), or an IL4R level that is at least about 36% greater than the control, or an IFIH1 level that is at least about 49% greater than the control, or an SOX5 level that is at least about 89% less than the control, or a KRT2 level that is at least about 99% less than the control, the subject is predicted to have an average 10-point improvement in PASI score by week 12 of treatment with the anti-TNF agent.

[0086] In some embodiments, subjects having USP18 levels that are at least about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, or about 80% greater than control levels are predicted to have some improvement in PASI scores by week 12 of treatment with the anti-TNF agent. In some embodiments, subjects having IL4R levels that are at least about 10%, about 20%, or about 30% greater than control levels are predicted to have some improvement in PASI scores by week 12 of treatment with the anti-TNF agent. In some embodiments, subjects having IFIH1 levels that are at least about 10%, about 20%, about 30%, or about 40% greater than control levels are predicted to have some improvement in PASI scores by week 12 of treatment with the anti-TNF agent. In some embodiments, subjects having SOX5 levels that are at least about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, or about 80% less than control levels are predicted to have some improvement in PASI scores by week 12 of treatment with an anti-TNF agent. In some embodiments, subjects having KRT2 levels that are at least about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% less than control levels are predicted to have some improvement in PASI scores by week 12 of treatment with an anti-TNF agent.

[0087] In some embodiments, at baseline (i.e., time 0), 2, 6, and 12 weeks, patients undergo a complete clinical evaluation by a dermatologist and BSA, PGA, and PASI scores are recorded.

[0088] How to treat psoriasis In various aspects, the present disclosure includes methods for treating psoriasis.

[0089] In some embodiments, the present disclosure provides various anti-TNF agents for treating psoriasis. In various embodiments, any anti-TNF agent is used in the methods or uses of the present disclosure. In some embodiments, the anti-TNF agent is an anti-TNF alpha (anti-TNFα) agent. In some embodiments, the anti-TNF agent is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab, or a biosimilar thereof.

[0090] In some embodiments, the anti-TNF agent is etanercept. Etanercept (e.g., ENBREL®) is a commercially available anti-TNF agent used, inter alia, to treat moderate to severe psoriasis. Etanercept treats autoimmune diseases by acting as a TNF inhibitor, thereby interfering with the soluble inflammatory cytokine TNF. In some embodiments, the anti-TNF agent is infliximab (e.g., REMICADE®). In some embodiments, the anti-TNF agent is adalimumab (e.g., HUMIRA®). In some embodiments, the anti-TNF agent is certolizumab pegol (e.g., CIMZIA®). In some embodiments, the anti-TNF agent is golimumab (e.g., SIMPONI®).

[0091] In some embodiments, the anti-TNF agent is a biosimilar to etanercept. In some embodiments, the biosimilar to etanercept is etanercept-ykro (Eticovo™) or etanercept-szzs (Erelzi™). In some embodiments, the anti-TNF agent is a biosimilar to infliximab. In some embodiments, the biosimilar to infliximab is Remsima™ or infliximab-dyyb (Inflectra™). In some embodiments, the anti-TNF agent is a biosimilar to adalimumab. In some embodiments, the biosimilar to adalimumab is adalimumab-atto (Amjevita™), adalimumab-bwwd (Hadlima™), or adalimumab-adaz (Hyrimoz™). In some embodiments, the anti-TNF agent is a biosimilar to certolizumab pegol. In some embodiments, the biosimilar to certolizumab pegol is Xbrane™. In some embodiments, the anti-TNF agent is a biosimilar to golimumab.

[0092] In other embodiments, the anti-TNF agent is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion. In some embodiments, two or more anti-TNF agents can be combined in a combination therapy. In some embodiments, the anti-TNF agent is delivered with another drug or agent used to treat psoriasis.

[0093] In some aspects, the present disclosure includes additional therapies used in treating psoriasis. In some aspects, such therapies are used in combination with treatment with anti-TNF agents. These therapies can be used simultaneously or sequentially, either before or after treatment with anti-TNF agents. Psoriasis treatments reduce inflammation and clear the skin. In some embodiments, treatments are divided into three main types: topical treatments, phototherapy, and systemic medications. These topical treatments include, but are not limited to, corticosteroids, vitamin D analogs, anthralin, retinoids, calcineurin inhibitors, salicylic acid, coal tar, and moisturizers. These phototherapies include, but are not limited to, sunlight, UVB phototherapy, narrowband UVB phototherapy, Goeckerman therapy, psoralen plus ultraviolet A (PUVA), and excimer laser. These systemic medications include, but are not limited to, retinoids, methotrexate, cyclosporine, thioguanine, or hydroxyurea. Additionally, some psoriasis treatments include alternative medicines, including but not limited to aloe vera, fish oil, and holly barberry.

[0094] Prediction and Treatment Methods In addition to the above utilities, the biomarkers or combinations of biomarkers disclosed herein are useful for determining the effectiveness of a treatment for psoriasis. The phrase "treating psoriasis" includes alleviating psoriasis, and encompasses treating or alleviating any of the symptoms associated with psoriasis.

[0095] In some embodiments, it is useful to select subjects for treatment based on the expression level of a biomarker. Also, in some embodiments, it is useful to select subjects for treatment based on the expression level of a biomarker together with the presence or absence of various clinical parameters, such as PASI score, as described herein. Thus, in one aspect, the present disclosure provides a method of treating psoriasis in a subject suffering from psoriasis, the method comprising: measuring the level of a biomarker or combination of biomarkers in a biological sample isolated from the subject, wherein an increased or decreased level of the biomarker or combination of biomarkers present in the biological sample compared to a control level indicates the probability of successfully treating the subject with an anti-TNF agent; and administering an effective amount of a treatment comprising an anti-TNF agent.

[0096] In some embodiments, methods are provided for prognosing responsiveness to an anti-TNF agent in treating psoriasis in a subject, and for treating psoriasis in a subject with an anti-TNF agent after it has been determined or predicted that the subject will respond favorably to treatment with the anti-TNF agent.

[0097] The present disclosure provides a method for prognosing responsiveness to an anti-TNF agent in the treatment of psoriasis in a subject, the method comprising measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject prior to treatment with the anti-TNF agent, wherein the at least one biomarker is ubiquitin-specific protease 18 (USP18), type II cytoskeleton 2 epidermal keratin (KRT2), interleukin-4 receptor (IL4R), sex-determining region Y-box transcription factor 5 (SOX5), helicase C domain 1-induced interferon (IFIH1), or a combination of any two or more of the foregoing biomarkers, and comparing the level to a control level, wherein the control level is an average of the biomarkers in non-lesional skin in a population of subjects with psoriasis prior to treatment with the anti-TNF agent. wherein when the level of USP18, ILR4, and / or IFIH1 in the subject is increased compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; when the level of KRT2 and / or SOX5 in the subject is decreased compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; when the level of USP18, ILR4, and / or IFIH1 in the subject is not increased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent; and / or when the level of KRT2 and / or SOX5 in the subject is not decreased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent.

[0098] The present disclosure provides a method for treating psoriasis in a subject, comprising measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject prior to treatment with an anti-TNF agent, wherein the at least one biomarker is USP18 and the level of USP18 is increased compared to a control level; the at least one biomarker is KRT2 and the level of KRT2 is decreased compared to a control level; the at least one biomarker is IL4R and the level of IL4R is increased compared to a control level; or the at least one biomarker is SOX5 and the level of SOX5 is decreased compared to a control level. measuring a level of the biomarker in the subject, wherein at least one biomarker is IFIH1 and the level of IFIH1 is increased compared to a control level, or wherein the at least one biomarker is a combination of any two or more of the above biomarkers, and the increased or decreased level compared to the control level predicts that the subject will be responsive to treatment with an anti-TNF agent, and the control level is the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with the anti-TNF agent; and administering an effective amount of an anti-TNF agent to the subject predicted to be responsive to treatment.

[0099] Where a method comprises a combination of steps, all combinations or subcombinations of steps are encompassed within the scope of the present disclosure unless otherwise noted herein.

[0100] For any of the methods provided, the method steps may be performed simultaneously or sequentially. When method steps are performed sequentially, the steps may be performed in any order unless otherwise specified.

[0101] kit In an additional aspect, the present disclosure includes kits comprising reagents packaged in a manner that facilitates their use for measuring biomarkers in a biological sample from a subject suffering from psoriasis. In some variations, such reagents are packaged together. In some variations, the kit further includes an analytical tool for assessing the likelihood that the subject will respond favorably to anti-TNF therapy after taking a measurement of at least one biomarker from the biological sample from the subject.

[0102] In one embodiment, the present disclosure relates to a kit for assaying a sample from a subject to determine the likelihood that the patient will respond positively to anti-TNF therapy, the kit comprising the necessary reagents for selectively detecting the relative levels of a biomarker or combination of biomarkers in the subject and comparing them to a control. In certain embodiments, the biomarkers are USP18, KRT2, IL4R, SOX5, or IFIH1. In certain embodiments, the kit comprises one or more reagents for detecting and / or measuring the relative expression levels of USP18, KRT2, IL4R, SOX5, or IFIH1, or any one or more combinations thereof, in a sample from a subject suffering from psoriasis.

[0103] In certain embodiments, the kits of the present disclosure each contain a device for collecting a biological sample from a subject and reagents for measuring the level of a biomarker in the biological sample. In a further aspect, the kits optionally include instructions contained within the package that describe how to use the reagents packaged within the kit to practice the method.

[0104] In a further aspect of the present invention, a pharmaceutical pack (kit) is provided, the pack comprising an anti-TNF agent and a set of instructions for administering the anti-TNF agent to a subject who has been diagnostically tested and determined to be a subject who will respond favorably to treatment of their psoriasis with the anti-TNF agent. The anti-TNF agent may be any of the anti-TNF agents described herein. In an exemplary aspect, the anti-TNF agent is etanercept.

[0105] In some embodiments, the kit further comprises a set of instructions for using the reagents comprising the kit. In certain embodiments, the kit further comprises a collection of data comprising correlation data between biomarker levels and the probability that a subject will respond favorably to treatment with an anti-TNF agent. Thus, in some aspects, the kit provides a means for measuring the relative level of the biomarker on day 0 and determining the relative increase or decrease in the level of the biomarker from a sample from a subject compared to the control level, i.e., the average level of the biomarker on day 0 from a population of control subjects.

[0106] Each publication, patent application, patent, and other reference cited herein is incorporated by reference in its entirety to the extent not inconsistent with this disclosure.

[0107] The recitation of ranges of values ​​herein, unless otherwise stated herein, is merely intended to serve as a shorthand method for referring individually to each of the separate values ​​within that range and each of its endpoints, and each separate value and endpoint is incorporated herein as if it were individually referenced herein.

[0108] All methods described herein can be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any examples or exemplary language (e.g., "etc.") provided herein merely serves to further clarify the invention and does not impose a limitation on the scope of the invention unless otherwise asserted. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0109] It is understood that the examples and embodiments described herein are for illustrative purposes only, and that various modifications or changes in light thereof will occur to those skilled in the art and are to be included within the spirit and scope of this application and the appended claims. [Example]

[0110] Further aspects and details of the present disclosure will become apparent from the following examples, which are intended to be illustrative and not limiting.

[0111] Example 1 Materials and Methods Patient cohort Patients with moderate-to-severe chronic plaque psoriasis for more than six months participated in three months of open-label, 50 mg biweekly etanercept treatment. The study protocol was approved by the University of Michigan Institutional Review Board and conducted in accordance with Good Clinical Practice requirements and the Declaration of Helsinki. Informed consent was obtained from all participants. Patients were assessed at baseline, and biopsies were obtained under local anesthesia (lidocaine 1:10,000 epinephrine) from both non-lesional and lesional skin at baseline and from lesional skin only at weeks 2, 6, and 12. At weeks 2, 6, and 12, patients underwent a complete clinical evaluation by a dermatologist, and body surface area, physician's global assessment, and Psoriasis Area and Severity Index (PASI) were recorded. This study was registered with clinicaltrials.gov (NCT01971346).

[0112] RNA-seq processing 50-bp single-end reads were generated from 210 RNA-seq samples from 46 patients. Trimmomatic (Bolger et al., Bioinformatics 2014;30(15):2114-20) was used to trim adapters from each sequence file, and STAR (Dobin et al., Bioinformatics. 2013;29(1):15-21) was used to align reads to the human genome b37. Expression levels were quantified using HTSeq (Anders et al., Bioinformatics 2015;31(2):166-9), using only uniquely mapped reads. Of the 46 patients, one patient was excluded due to dropout and two patients were excluded due to lack of baseline (week 0) biopsy samples, leaving 43 patients and 206 RNA-seq samples for subsequent analysis. One patient's final visit was at week 15, and their results were grouped with all other patients' week 12 data at the time of analysis. 28,182 genes were detected, with an average of >= 1 read / sample, and DEseq2 was applied for read normalization (Love et al., Genome Biol. 2014;15(12):550). Principal component analysis was performed using all genes after applying denormalization to the DESeq2-normalized data.

[0113] Linking RNA-seq expression to clinical response Each gene expression profile from baseline non-lesional or lesional skin samples was correlated with changes in Psoriasis Area and Severity Index (PASI), body surface area (BSA), and static Physician Global Assessment (sPGA) at each of the three follow-up visits. Patient age, sex, and baseline BMI were adjusted for and assessed as both percent (%) and absolute (i.e., delta) disease improvement, referenced to week 0 values. A false discovery rate of ≤10% declared associations significant. Differential expression analysis (i.e., comparing non-lesional skin with lesional skin at baseline; comparing lesional skin at baseline with subsequent visits) was performed using the negative binomial distribution in DESeq2, with FDR <=10% and |log2Fold Change| >=1 used as criteria for declaring genes significant.

[0114] Comparison to cytokine signatures in keratinocytes The procedure for identifying cytokine signatures in keratinocytes has been previously described (Tsoi et al., J. Invest. Dermatol. 2019 Jul;139(7):1480-1489. doi:10.1016 / j.jid.2018.12.018. Epub 2019 Jan 11. PMID:30641038). Briefly, 50 normal human keratinocyte samples were obtained from 50 different healthy adults. Keratinocytes were grown in 154CF medium (Thermo Fisher #M154CF500) containing human keratinocyte growth supplement (Thermo Fisher #S0015) in 12-well plates. Once the keratinocytes were grown to confluence, the complete medium (with supplements) was replaced with 154CF basal medium (without supplements). Cells were then stimulated with cytokines (IL-4, IL-13, IFN-α, IFN-γ, TNF-α, and IL-17A (R&D Systems)) individually at a concentration of 10 ng / ml. After 8 hours, cells were harvested, and RNA was isolated using an RNeasy Plus Mini Kit (Qiagen #74136). RNA was analyzed using RNA NanoChips (Agilent Technologies) and sequenced (Sarkar et al., Ann Rheum Dis. 2018, 77:1653-1664). The top 1,000 genes whose baseline expression profiles showed the strongest correlation with future absolute PASI improvement at each of the three follow-up visits were extracted and compared against the cytokine signature using hypergeometric testing to understand their molecular mechanisms.

[0115] Drug response prediction Each patient from the cohort was assigned a TNF or IFN score based on its baseline expression profile in non-lesional skin, and for each gene, i was induced by TNF / IFN in keratinocytes. The relative expression of that gene in patient p was determined by referencing the median across all samples: rp i =gp i / center(g i), where g is the normalized expression. The TNF or IFN score for patient p is then calculated as r across all genes induced by TNF or IFN, respectively. p The PASI score was defined as the upper quartile of the PASI score. To model PASI response at week 12, we used baseline non-lesional skin expression profiles of genes induced by TNF / type I IFN from the keratinocyte experiment (described above). Principal components were used to reduce data dimensionality. To model drug response at week 12 using the PASI75 criteria, logistic regression was applied with leave-one-out to ensure robustness of model assessment (i.e., principal component analysis and regression modeling were performed only on the training data). A 75% reduction in PASI score (PASI75) is the current benchmark for the primary endpoint of most psoriasis clinical trials. The area under the receiver operating characteristic (AUROC) of PASI75 was calculated using various numbers of principal components. Finally, to further evaluate potential clinical significance, we measured precision (the proportion of true positives among predicted PASI75 scores) and recall (the proportion of predicted actual PASI75 scores) as a function of the proportion of top-ranked samples predicted by the model.

[0116] immunohistochemistry Formalin-fixed, paraffin-embedded tissue slides from patients with psoriasis and skin samples from healthy individuals without psoriasis (i.e., healthy or normal controls) were heated at 60°C for 30 minutes, rehydrated, and epitope retrieval was performed using Tris-EDTA (pH 6). The slides were blocked and incubated overnight at 4°C with USP18 primary antibody (1:100; LS-B1182-50; Lifespan Bioscience). The slides were then washed with PBS and incubated with a biotinylated secondary antibody (biotinylated goat anti-rabbit IgG antibody; BA1000; Vector Laboratories) for 30 minutes at room temperature, followed by incubation with fluorescent dye-conjugated streptavidin for 10 minutes at room temperature. Slides were prepared in mounting medium containing 4',6-diamidino-2-phenylindole (DAPI) (VECTASHIELD Antifade Mounting Medium with DAPI, H-1200, VECTOR). Images were acquired using a Zeiss Axioskop 2 microscope and analyzed by SPOT software 5.1. Images provided were representative of at least three experiments.

[0117] RNAi depletion, RNA extraction, qRT-PCR Upon reaching semi-confluence, the keratinocyte medium was changed to Accell Delivery Media (B-005000, Dharmacon) with 1 μM Accell USP18-targeting siRNA (E-004236-00-0005, Dharmacon). After 48 h, cells were stimulated with IFN-α (10 ng / ml, I4276, R&D Systems), TNF-α (10 ng / ml, 210-TA, R&D Systems), IL-17A (20 ng / ml, 7955-IL, R&D Systems), or IFN-γ (10 ng / ml, 285-IF, R&D Systems) for an additional 24 h. RNA was isolated from the cells using the RNeasy Plus kit (74136, Qiagen). qRT-PCR was performed on a 7900HT Fast Real-time PCR system (Applied Biosystems) using TaqMan Universal PCR Master Mix (ThermoFisher 4304437). The primers used in this study are as follows: USP18, Hs00276441_m1; IL36G, Hs00219742_m1; DEFB4, Hs00175474_m1; MX1, Hs00895608_m1; OASL, Hs00984387_m1; IRF7, Hs01014809_g1; IFNK, Hs00737883_m1 (ThermoFisher Scientific).

[0118] Example 2 RNA-seq to profile the transcriptome trajectory of patients initiating etanercept therapy This study involved 46 patients with psoriasis (Figure 1A). Each patient was treated with 50 mg of etanercept (trade name: Enbrel or Benepali) twice weekly. Prior to initiation (baseline / week 0), demographic information (age, sex) and other clinical information, including PASI score, body surface area (BSA), and sPGA, were collected. At baseline, skin punch biopsies were performed on non-lesional and lesional skin for transcriptome profiling using RNA-seq. Additional lesional samples were obtained at follow-up visits at weeks 2, 6, and 12, along with clinical assessments. Forty-two patients had follow-up data (i.e., transcriptome data from both baseline and at least one follow-up visit) and a total of 36 patients completed the study (RNA-seq and clinical data were available at both baseline and week 12 visits).

[0119] A total of 210 RNA-seq experiments were performed on the 46 patients in the cohort. Principal component analysis (PCA) was used to track transcriptome changes over the course of treatment (Figure 1B). As expected, PCA separated non-lesional and lesional skin at baseline. However, the largest principal component in lesional skin (i.e., PC1) changed over time during treatment, so that by week 12 it overlapped with the largest principal component in baseline non-lesional skin in a portion of the cohort. However, some patients still had PC1 values ​​that remained at baseline levels by week 12, despite improvements in PASI scores. Notably, the former group tended to have lower PASI scores than the latter group at week 12 (lower panel of Figure 1B). We further investigated temporal changes in gene expression levels to determine how expression profiles changed over the 12-week treatment period (Figure 1C). Similar to the PCA results, clear differences existed between non-lesional and lesional skin at baseline, but these differences diminished (or became less clear) as etanercept treatment progressed. Indeed, when comparing gene expression levels in lesional skin at baseline with those at follow-up visits, a gradual increase in the number of differentially expressed transcripts was observed (see Table 2 below), followed by a return toward baseline gene expression levels in non-lesional skin (Figure 6). Nevertheless, heterogeneity was observed among patients' transcriptome responses at week 12, consistent with clinical variability (Leonardi et al., N Engl J Med. 2003;349(21):2014-22; Zaba et al., J Allergy Clin Immunol. 2009;124(5):1022-10 e1-395.).

[0120] [Table 2]

[0121] Example 3 Baseline expression profiles in non-lesional skin correlate with improvement in PASI The correlation between baseline expression profiles and clinical findings was examined. The expression level of each gene (from non-lesional or lesional skin) at week 0 was correlated with the change in PASI, BSA, and / or sPGA at each of the three follow-up visits. Both percent (%) and absolute (i.e., delta) disease improvement was assessed with reference to week 0 values.

[0122] Surprisingly, significant associations between gene profiles could only be identified in non-lesional, but not lesional, skin at baseline with improvement in PASI from at least one of the follow-up visits. Using percent change as the metric, 198 genes in the week 0 expression profile were significantly associated (FDR <= 10%) with improvement in PASI at week 12. Using absolute change as the metric, 192 genes in the baseline expression profile were significantly associated with weekly improvement in PASI at week 6 and 391 genes at week 12. No significant results were detected for associations with changes in BSA and sPGA.

[0123] Of the significant genes whose baseline expression correlated with improvement in PASI at week 12, 105 overlapped between the percent and absolute indices. The ubiquitin-specific peptidase USP18 and the type I cytokeratin KRT2 are two notable examples whose expression in non-lesional skin at baseline correlated significantly with improvement in PASI at the follow-up visit (Figure 2A-F).

[0124] USP18 was significantly upregulated in lesional skin at baseline (fold change, FC=2.5; p=1x10 -26 ), and its expression in non-lesional skin at baseline positively correlated with absolute PASI improvement at week 12 (p=9.8x10 -4 a 20% increase from the mean expression level has a mean PASI improvement of 2.3 after adjusting for age, sex, and BMI), and KRT2 was significantly downregulated in lesional skin at baseline (FC=0.32; p=1.3x10 -6), and its expression in non-lesional skin at week 0 was significantly higher than that in absolute (p=1.4x10 -5 a 20% increase from the mean expression level has a mean PASI improvement of 0.99 after adjusting for age, sex, and BMI) and percent (p=5.4x10 -4 ) were inversely correlated with improvement in both PASI scores. The associations between these and PASI improvement at weeks 2 and 6 were not significant, but the direction of the correlations was consistent (Figures 2A and 2D). Interestingly, these associations were not observed when baseline expression levels in lesional skin were used (Figures 2B and 2D). Furthermore, the expression levels of these two genes in lesional skin gradually "recovered" toward those in non-lesional skin over time (Figures 2C and 2F). These results indicate that both USP18 and KRT2 are dysregulated in psoriatic skin but can be "recovered" to non-lesional skin levels by etanercept treatment, and that their expression levels in non-lesional skin before treatment are associated with future PASI improvement.

[0125] To better understand the potential role of USP18 in TNF responses, we performed several functional assays. First, we verified the upregulation of USP18 in lesional skin using an independent transcriptome cohort recently performed (Figure 7). Interestingly, USP18 expression levels were found to be only slightly upregulated in non-lesional skin compared to normal skin (FC=1.5; p=1.2x10). -2 USP18 also positively correlated with other psoriasis cytokines (IL23A and IL36G) in lesional skin (p = 6.3 x 10 -5 and p=8.3x10 -7). Using immunostaining (Figure 3A), we observed the distribution of USP18 protein in both non-lesional and lesional psoriatic skin, confirming its expression in the epidermal layer. Using small inhibitory RNA (siRNA), we knocked down USP18 expression in keratinocytes and assessed its effect on TNF / interferon (IFN) responses. Importantly, effective knockdown of USP18 had an effect on both TNF and IFN stimulation (Figure 3B–D). Upon TNF stimulation, knockdown of USP18 tended to suppress IL36G mRNA expression but tended to induce DEFB4 mRNA expression (upon TNF + IL17 stimulation). Upon IFN stimulation, knockdown of USP18 promoted higher expression of IFN-responsive genes, including MX1, IFNK, OASL, and IRF7. Conversely, overexpression of USP18 (Fig. 3E–G) reduced type I and type II IFN responses (Fig. 3E–G) but potentiated the IL-17A-induced effects on IL36G and the TNF-induced effects on DEFB4 (Fig. 3E). These results suggest that USP18 has an inhibitory effect on IFN responses while promoting some TNF responses (i.e., IL36G mRNA expression).

[0126] Because IFN and TNF have been thought to have counterregulatory roles in psoriasis (Conrad et al., Nat Commun. 2018;9(1):25), lower USP18 expression levels may, in some embodiments, be useful in promoting a higher IFN response and thus a lower dependency on TNF, consistent with the observation that USP18 positively correlates with improvement in PASI over the course of etanercept treatment.

[0127] Example 4 An integrated approach to providing biological and clinical significance using non-lesional skin We used an integrated approach to understand how RNA-seq can be used to inform the biological and clinical implications of etanercept treatment in psoriasis. First, we analyzed the results of in vitro experiments studying the effects of various cytokine stimuli on keratinocytes. Among genes showing the strongest differential expression (up- and down-regulation) in lesional skin between baseline and subsequent follow-up visits, we observed significant (FDR (or log fold change) ≤ 10%) enrichment of IFN, TNF, and IL-17 signatures (Figure 4A), consistent with etanercept's TNF inhibitory effect and its associated negative regulation of Th17 responses (Krueger et al., J. Amer. Acad. Derm. 2006;54(3 Suppl 2):S112-9; Zaba et al., J. Allergy Clin. Immunol. 2009;124(5):1022-10 e1-395).

[0128] Next, we extracted the top 1,000 genes whose baseline expression profiles showed the strongest correlation with future absolute PASI improvement at each of the three follow-up visits and compared them to type I IFN, TNF, and IL-17A keratinocyte cytokine signatures to understand their molecular mechanisms. Strikingly, significant enrichment of TNF and type I IFN signatures relative to the gene expression profile of non-lesional skin at week 0 was found to have the strongest correlation with absolute PASI improvement at weeks 6 and 12 (Figure 4B). Notably, no significant enrichment occurred when using the gene expression profile of lesional skin at week 0. When percentage PASI improvement was used as an index, the same enrichment of type I IFN signatures was observed (e.g., at week 6 for IFN-α and week 12 for IFN-γ), as was enrichment of the IL-17A response at week 12. It is noteworthy that both USP18 and KRT2 were significantly differentially expressed in keratinocytes upon stimulation with TNF, and USP18 was upregulated by IFN (Fig. 8).

[0129] Each patient from the cohort was assigned a TNF or IFN score (see Example 1), and the corresponding cytokine signature loading for that patient's non-lesional skin at baseline was summarized (Figures 5A-B). Among patients with high IFN or TNF scores, a high proportion of PASI improvements were observed, both absolute and percentage-based. Therefore, it was hypothesized that using keratinocyte-derived cytokine signatures as prior information could provide an assessment of drug response. To provide the most robust drug response assessment, a genomic approach was used. In independent experiments, over 2,900 genes were identified as dysregulated upon cytokine stimulation. For each psoriasis patient in the cohort, principal components of baseline non-lesional skin expression levels of >2,900 genes induced in response to treatment with TNF-α, IFN-α, or IFN-γ were obtained.

[0130] To ensure the robustness of the model, we applied logistic regression to these components to model drug response at week 12 using the PASI75 criteria using leave-one-out validation. We were able to obtain an area under the receiver operating characteristic (AUROC) of up to 0.75 (Figure 5C). To further assess potential clinical significance, we measured precision (the proportion of true positives among predicted PASI75 scores) and recall (the proportion of predicted actual PASI75 scores) as a function of the proportion of top-ranked samples predicted by the model (Figure 5D). Accuracy of up to 80% was achieved among the top 20% of samples with the highest PASI75 prediction scores, demonstrating the ability to identify patients most likely to benefit from etanercept treatment using non-lesional skin transcriptomes at baseline.

[0131] Example 5 An integrated approach to identify additional genes important in assessing drug response to etanercept To identify predictors of etanercept treatment outcome, we conducted a longitudinal study of 210 RNA-seq samples from 46 patients with chronic plaque psoriasis treated with etanercept.

[0132] We took an integrated approach to identify the most informative biological features that could assess etanercept drug response at week 12, combined with longitudinal tissue molecular profiling (RNA-seq). These were cross-compared to RNA-seq data from independent cytokine-stimulated keratinocytes in vitro, along with statistical modeling. Associations between baseline gene expression profiles and clinical findings were examined. Expression levels of each gene (from non-lesional skin) were measured using RNA-seq at week 0 and correlated with changes in PASI score at each of three follow-up visits (weeks 2, 6, and 12). Both percent (%) and absolute (i.e., delta) changes in disease improvement were assessed relative to week 0 values.

[0133] Significant associations between baseline non-lesional skin gene profiles and improvement in PASI from at least one follow-up visit were identified only when percent change was used as the metric. Using absolute change as the metric, 198 genes had expression profiles at week 0 that were significantly associated (false discovery rate (FDR) ≤ 10%) with improvement in PASI at week 12. 192 genes had expression profiles at baseline that were significantly associated with improvement in PASI at weeks 6 and 12. 391 genes had expression profiles at baseline that were significantly associated with improvement in PASI at week 12.

[0134] Of the genes whose baseline expression levels were identified as being associated with PASI improvement at week 12, 105 overlapped between percent and absolute indices. USP18, a ubiquitin-specific peptidase, and KRT2, a type I cytokeratin, are two notable examples where expression in non-lesional skin at baseline was significantly associated with PASI improvement at the follow-up visit (Figure 1). USP18 was significantly upregulated in lesional skin at baseline (fold change (FC) = 2.5; p = 1x10 -26 ), and its expression in non-lesional skin at baseline positively correlated with absolute PASI improvement at week 12 (p=9.8x10 -4 KRT2 was significantly downregulated in lesional skin at baseline (FC=0.32; p=1.3x10 -6 ), and its expression in non-lesional skin at week 0 was significantly higher than that in absolute (p=1.4x10 -5 ) and percentage (p=5.4x10 -4 ) were inversely correlated with improvement in both PASI scores. The associations between these and PASI improvement at weeks 2 and 6 were not statistically significant, but the direction of the correlations was consistent (Figures 1A and 1D). Interestingly, these associations were not observed when baseline expression levels in lesional skin were used (Figures 1B and 1D). Furthermore, the expression levels of these two genes in lesional skin gradually "recovered" toward those in non-lesional skin over time (Figures 1C and 1F). These results indicate that both USP18 and KRT2 are dysregulated in psoriatic skin but can be "recovered" to non-lesional skin levels by etanercept treatment, and that their expression levels in non-lesional skin before etanercept treatment are associated with future PASI improvement.

[0135] To model the PASI response at week 12, baseline (week 0) non-lesional skin gene expression profiles of >2,900 genes induced by cytokine stimulation, e.g., TNF / type I IFN (identified by previous keratinocyte experiments), were used as described herein above. For each psoriasis patient in the cohort, a principal component of the baseline non-lesional skin expression levels of >2,900 genes induced in response to treatment with TNF-α, IFN-α, or IFN-γ was obtained.

[0136] To model drug response at week 12 using the PASI75 criteria, logistic regression was applied with leave-one-out to ensure robustness of the model assessment (i.e., principal component analysis and regression modeling were performed only on the training data). The area under the receiver operating characteristic (AUROC) of PASI75 was calculated using various numbers of principal components. To further evaluate potential clinical significance, precision (the proportion of true positives among predicted PASI75 scores) and recall (the proportion of predicted actual PASI75 scores) were measured as a function of the proportion of top-ranked samples predicted by the model. A maximum of 0.75 was obtained within the AUROC (Figure 2). A maximum of 80% predicted response accuracy was achieved among the top 20% of samples with the best PASI75 predictions, demonstrating the ability to identify patients most likely to benefit from etanercept treatment using non-lesional skin transcriptomes at baseline.

[0137] Regression analysis was applied to identify genes whose expression levels correlated with drug response in psoriasis patients who were most likely to benefit from etanercept treatment. USP18, KRT2, IL4R, SOX5, and IFIH1 were identified as the top protein-coding gene candidates for use as biomarkers to indicate whether psoriasis patients would respond favorably to treatment with anti-TNF drugs. To assess the robustness of this approach, the feature selection process was repeated 36 times, excluding different samples each time and evaluating the performance of the excluded samples, achieving an AUROC > 0.75.

[0138] This study demonstrated that modeling in vitro genomic data on cytokine responses identified a significant association between gene profiles from non-lesional skin at baseline and improvement in the Psoriasis Severity Index (PASI) at follow-up visits. Notably, gene profiles from non-lesional, but not inflamed, skin at baseline were the best predictors of treatment response at week 12. These results demonstrate the feasibility of using non-lesional skin to assess drug response in psoriasis and implicate IFN / TNF regulatory factors in anti-TNF responses.

[0139] Example 6 Evaluate improvement in PASI in response to etanercept treatment Each gene expression profile from baseline non-lesional or lesional skin samples was correlated with changes in PASI, body surface area (BSA), and static Physician Global Assessment (sPGA) at each of the three follow-up visits. Each sample was adjusted for patient age, sex, and baseline BMI, and both percent (%) and absolute (i.e., delta) disease improvement was assessed with reference to week 0 (i.e., day 0) values. A false discovery rate of ≤10% declared association significant. Specifically, regression analysis was applied to identify genes whose expression levels correlated with drug response in psoriasis patients most likely to benefit from anti-TNF (e.g., etanercept) treatment. USP18, KRT2, IL4R, SOX5, and IFIH1 were identified as top protein-coding gene candidates for use as biomarkers to indicate whether psoriasis patients will respond favorably to treatment with anti-TNF agents. Age, sex, and BMI were used as covariates in the regression analysis. The p-values ​​for these genes in the association analysis are as follows: USP18:9.81x10 -4 ; KRT2:1.45x10 -5 ; IFIH1:2.12x10 -3 ; SOX5:1.85x10 -3 and IL4R:1.6x10-5 .

[0140] Using USP18 at a level increased by 20% from baseline or mean levels of USP18 in controls (i.e., mean levels in non-lesional skin from psoriasis patients before treatment) as a biomarker for predicting responsiveness to etanercept demonstrated a mean improvement in PASI of 2.3 points in subjects at 12 weeks of treatment with etanercept. Using KRT2 at a level increased by 20% from baseline or mean levels of KRT2 in controls (i.e., non-lesional skin from psoriasis patients) as a biomarker for predicting responsiveness to etanercept demonstrated a mean improvement in PASI of 0.99 points in subjects at 12 weeks of treatment with etanercept. Using IL4R at a level increased by 20% from baseline or mean levels of IL4R in controls (i.e., non-lesional skin from psoriasis patients) as a biomarker for predicting responsiveness to etanercept demonstrated a mean improvement in PASI of 5.5 points in subjects at 12 weeks of treatment with etanercept. Using SOX5 at levels increased by 20% from baseline or mean levels of SOX5 in controls (i.e., non-lesional skin from psoriasis patients) as a biomarker to predict responsiveness to etanercept, a mean improvement in PASI of 2.3 points was demonstrated in subjects at week 12 of treatment with etanercept. Using IFIH1 at levels increased by 20% from baseline or mean levels of IFIH1 in controls (i.e., non-lesional skin from psoriasis patients) as a biomarker to predict responsiveness to etanercept, a mean improvement in PASI of 4.1 points was demonstrated in subjects at week 12 of treatment with etanercept.

[0141] The present disclosure has been described in terms of particular embodiments that have been found or are proposed to comprise specific modes for practicing the disclosure. Various modifications and variations of the described invention will become apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the following claims. The present invention provides, for example, the following items. (Item 1) 1. A method for treating psoriasis in a subject, comprising: measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from the subject prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, wherein the at least one biomarker is (a) ubiquitin-specific protease 18 (USP18), wherein the level of USP18 is increased compared to the control level; (b) type II cytoskeletal 2 epidermal keratin (KRT2), wherein the level of KRT2 is reduced compared to the control level; (c) interleukin 4 receptor (IL4R), wherein the level of IL4R is increased compared to the control level; (d) sex-determining region Y-box transcription factor 5 (SOX5), wherein the level of SOX5 is reduced compared to the control level; (e) helicase C domain 1-induced interferon (IFIH1), wherein the level of IFIH1 is increased compared to a control level; or (f) A combination of any two or more biomarkers from (a) to (e), the increased or decreased level of the biomarker in the subject relative to the control level predicts that the subject will be responsive to treatment with the anti-TNF agent; the control level is the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with the anti-TNF agent; administering to said subject predicted to be responsive to treatment an effective amount of an anti-TNF agent. (Item 2) 2. The method of claim 1, wherein the at least one biomarker is USP18. (Item 3) 2. The method of item 1, wherein the at least one biomarker is KRT2. (Item 4) 2. The method of item 1, wherein the at least one biomarker is IL4R. (Item 5) 2. The method of item 1, wherein the at least one biomarker is SOX5. (Item 6) 2. The method of item 1, wherein the at least one biomarker is IFIH1. (Item 7) Item 10. The method of item 1, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1. (Item 8) 2. The method of item 1, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1 listed in any one of the combinations in Table 1. (Item 9) 3. The method of claim 1 or 2, wherein the level of USP18 is increased by at least about 86% compared to the control level. (Item 10) 4. The method of item 1 or 3, wherein the level of KRT2 is reduced by at least about 99% or more compared to the control level. (Item 11) 5. The method of item 1 or 4, wherein the level of IL4R is increased by at least about 36% or more compared to the control level. (Item 12) 6. The method of item 1 or 5, wherein the level of SOX5 is reduced by at least about 89% or more compared to the control level. (Item 13) 7. The method of item 1 or 6, wherein the level of IFIH1 is increased by at least about 49% or more compared to the control level. (Item 14) 14. The method according to any one of items 1 to 13, wherein the level is an indication of the level of a nucleic acid or protein present in the biological sample. (Item 15) 15. The method of claim 14, wherein the nucleic acid is a deoxyribonucleic acid. (Item 16) 15. The method of claim 14, wherein the nucleic acid is a ribonucleic acid. (Item 17) 17. The method of any one of items 1 to 16, wherein the anti-TNF drug is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab. (Item 18) 17. The method of any one of items 1 to 16, wherein the anti-TNF drug is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion. (Item 19) 19. The method according to any one of items 1 to 18, wherein the psoriasis is psoriasis vulgaris, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis. (Item 20) 20. The method of any one of items 1 to 19, further comprising administering to the subject at least one additional treatment or medicament for psoriasis. (Item 21) 21. The method according to any one of items 1 to 20, wherein the subject is a human subject. (Item 22) 22. The method of any one of items 1 to 21, wherein the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing. (Item 23) 23. The method of any one of items 1 to 22, wherein the subject treated with the anti-TNF agent shows improvement in PASI with about 12 weeks of treatment. (Item 24) 24. The method of claim 23, wherein the improvement in PASI is at least about a 10% improvement in PASI. (Item 25) 1. A method for prognosing responsiveness to an anti-TNF agent in the treatment of psoriasis in a subject, comprising: measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from said subject prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, At least one biomarker is (a) Ubiquitin-specific protease 18 (USP18), (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e); comparing said level with a control level, said control level being the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with said anti-TNF agent; when the level of USP18, ILR4, and / or IFIH1 in the subject is increased relative to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; when the level of KRT2 and / or SOX5 in the subject is reduced compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; if the level of USP18, ILR4, and / or IFIH1 in the subject is not increased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent; and / or When the level of KRT2 and / or SOX5 in the subject is not reduced compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent. (Item 26) 26. The method of claim 25, further comprising administering an effective amount of the anti-TNF agent to treat the subject predicted to be responsive to treatment with the anti-TNF agent. (Item 27) 27. The method of claim 25 or 26, wherein the at least one biomarker is USP18. (Item 28) 27. The method of item 25 or 26, wherein the at least one biomarker is KRT2. (Item 29) 27. The method of item 25 or 26, wherein the at least one biomarker is IL4R. (Item 30) 27. The method of item 25 or 26, wherein the at least one biomarker is SOX5. (Item 31) 27. The method of item 25 or 26, wherein the at least one biomarker is IFIH1. (Item 32) 27. The method of item 25 or 26, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, and IFIH1. (Item 33) 27. The method of item 25 or 26, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, and IFIH1 listed in any one of the combinations in Table 1. (Item 34) 28. The method of item 25, 26, or 27, wherein the level of USP18 is increased by at least about 86% or more compared to the control level. (Item 35) 29. The method of paragraph 25, 26, or 28, wherein the level of KRT2 is reduced by at least about 99% or more compared to the control level. (Item 36) 30. The method of paragraph 25, 26, or 29, wherein the level of IL4R is increased by at least about 36% or more compared to the control level. (Item 37) 31. The method of paragraph 25, 26, or 30, wherein the level of SOX5 is reduced by at least about 89% or more compared to the control level. (Item 38) 31. The method of paragraph 25, 26, or 30, wherein the level of IFIH1 is increased by at least about 49% or more compared to the control level. (Item 39) 29. The method according to any one of items 25 to 28, wherein the level is an indication of the level of a nucleic acid or protein present in the biological sample. (Item 40) 40. The method of claim 39, wherein the nucleic acid is a deoxyribonucleic acid. (Item 41) 40. The method of claim 39, wherein the nucleic acid is a ribonucleic acid. (Item 42) 42. The method of any one of items 25 to 41, wherein the anti-TNF drug is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab. (Item 43) 42. The method of any one of items 25 to 41, wherein the anti-TNF drug is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion. (Item 44) 44. The method according to any one of Items 25 to 43, wherein the psoriasis is plaque psoriasis, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis. (Item 45) 45. The method of any one of items 25 to 44, further comprising administering to the subject at least one additional treatment or medicament for psoriasis. (Item 46) 46. ​​The method of any one of items 25 to 45, wherein the subject is a human subject. (Item 47) 47. The method of any one of items 25 to 46, wherein the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing. (Item 48) 48. The method of any one of items 25 to 47, wherein the subject predicted to be responsive to treatment with the anti-TNF agent is expected to show improvement in PASI with about 12 weeks of treatment. (Item 49) 49. The method of item 48, wherein the improvement in PASI is expected to be at least about a 10% improvement in PASI. (Item 50) 1. A kit comprising reagents for measuring the level of at least one biomarker in a biological sample of non-lesional skin isolated from a subject with psoriasis prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, wherein the at least one biomarker comprises: (a) Ubiquitin-specific protease 18 (USP18), (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e), The level is a nucleic acid or protein level of the biomarker in the biological sample. (Item 51) 51. The kit of item 50, further comprising a means for comparing the level of the nucleic acid or protein of the biomarker in the biological sample with a control level, wherein the control level is the average level of the biomarker in non-lesional skin in a population of subjects with psoriasis prior to treatment with the anti-TNF agent. (Item 52) 52. The kit of item 50 or 51, wherein the biological sample is obtained from a biopsy of non-lesional skin from a subject suffering from psoriasis. (Item 53) 1. Use of a measurement of the level of at least one biomarker in a biological sample of non-lesional skin from a subject suffering from psoriasis that is increased or decreased compared to a measurement of a control level, wherein the control level is the average level of the biomarker in non-lesional skin in a population of subjects prior to treatment with an anti-tumor necrosis factor (anti-TNF) agent, for prognosing the responsiveness of a subject suffering from psoriasis to treatment with the anti-TNF agent, wherein the at least one biomarker is (a) Ubiquitin-specific protease 18 (USP18), (b) type II cytoskeletal 2 epithelial keratin (KRT2); (c) interleukin 4 receptor (IL4R), (d) sex-determining region Y-box transcription factor 5 (SOX5); (e) helicase C domain 1-induced interferon (IFIH1), or (f) A combination of any two or more biomarkers from (a) to (e), when the level of USP18, ILR4, and / or IFIH1 in the subject is increased relative to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; when the level of KRT2 and / or SOX5 in the subject is reduced compared to the control level of the biomarker, the subject is predicted to be responsive to treatment with the anti-TNF agent; if the level of USP18, ILR4, and / or IFIH1 in the subject is not increased compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent; and / or When the level of KRT2 and / or SOX5 in the subject is not reduced compared to the control level of the biomarker, the subject is predicted to be non-responsive to treatment with the anti-TNF agent. (Item 54) 54. The use of item 53, wherein the at least one biomarker is USP18. (Item 55) 54. The use of item 53, wherein the at least one biomarker is KRT2. (Item 56) 54. The use of item 53, wherein the at least one biomarker is IL4R. (Item 57) 54. The use of item 53, wherein the at least one biomarker is SOX5. (Item 58) 54. The use of item 53, wherein the at least one biomarker is IFIH1. (Item 59) 54. The use of item 53, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1. (Item 60) 54. The use of item 53, wherein the at least one biomarker is a combination of any two or more of USP18, KRT2, IL4R, SOX5, or IFIH1 as listed in any one of the combinations in Table 1. (Item 61) 55. The use according to item 53 or 54, wherein the level of USP18 is increased by at least about 86% compared to the control level. (Item 62) 56. The use of item 53 or 55, wherein the level of KRT2 is reduced by at least about 99% or more compared to the control level. (Item 63) 57. The use of item 53 or 56, wherein the level of IL4R is increased by at least about 36% or more compared to the control level. (Item 64) 58. The use of item 53 or 57, wherein the level of SOX5 is reduced by at least about 89% compared to the control level. (Item 65) 59. The use of item 53 or 58, wherein the level of IFIH1 is increased by at least about 49% compared to the control level. (Item 66) 66. The use according to any one of items 53 to 65, wherein the level is an indication of the level of a nucleic acid or protein present in the biological sample. (Item 67) 67. The use according to item 66, wherein the nucleic acid is a deoxyribonucleic acid. (Item 68) 67. The use according to item 66, wherein the nucleic acid is a ribonucleic acid. (Item 69) 69. The use of any one of items 53 to 68, wherein the anti-TNF agent is etanercept, infliximab, adalimumab, certolizumab pegol, or golimumab. (Item 70) 69. The use according to any one of items 53 to 68, wherein the anti-TNF agent is thalidomide, lenalidomide, pomalidomide, a xanthine derivative, or bupropion. (Item 71) 71. The use according to any one of Items 53 to 70, wherein the psoriasis is psoriasis vulgaris, guttate psoriasis, inverse psoriasis, intertriginous psoriasis, pustular psoriasis, erythrodermic psoriasis, or psoriatic arthritis. (Item 72) 72. The use according to any one of items 53 to 71, wherein the subject is a human subject. (Item 73) 73. The use of any one of items 53 to 72, wherein the level of the biomarker is measured by immunoassay, Northern blot analysis, reverse transcription quantitative polymerase chain reaction, RNA sequencing, or high throughput sequencing. (Item 74) 74. The use of any one of items 53 to 73, wherein the subject predicted to be responsive to treatment with the anti-TNF agent is expected to show improvement in PASI with about 12 weeks of treatment. (Item 75) 75. The use of item 74, wherein the improvement in PASI is expected to be at least about a 10% improvement in PASI.

Claims

[Claim 1] The invention described in the specification.

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