Advertisement

Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

Open AccessPublished:May 06, 2021DOI:https://doi.org/10.1016/j.trsl.2021.04.010
      This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N = 24 and validation N = 22). Response was defined as endoscopic and histologic healing at 4–6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.85 and inadequate response was predicted with a 90% positive predictive value and 82% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients’ baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.

      Abbreviations:

      AUC (area under the curve), CD (Crohn's disease), IBD (inflammatory bowel disease), JAKi (Janus kinase inhibitor), LCC (largest connected component), PPV (positive predictive value), ROC (receiver operating characteristic), UC (ulcerative colitis), TNFi (tumor necrosis factor-ɑ inhibitor)
      AT A GLANCE COMMENTARY

       Background

      Selecting the right drug for each patient results in faster recovery, less pain, and improved quality of life, particularly in progressive diseases such ulcerative colitis (UC). However, response to targeted therapies is only 20%–50%.

       Translational Significance

      We developed a classifier using machine learning and network medicine approaches and validated that it is predictive of infliximab response in a cross-cohort analysis. Network analyses suggest this molecular signature reflects a biology of infliximab response common among UC patients. Further development of such a classifier into a diagnostic test could help identify patients who will not have an adequate response to targeted therapies, identify novel disease mechanisms and define new targets for therapeutic development.

      INTRODUCTION

      Ulcerative colitis (UC) is a chronic, relapsing disease characterized by diffuse mucosal inflammation of the colon.
      • Langan R.C.
      • Gotsch P.B.
      • Krafczyk M.A.
      • Skillinge D.D.
      Ulcerative colitis: diagnosis and treatment.
      UC is part of a larger spectrum of chronic relapsing diseases of the intestinal tract classified as inflammatory bowel disease (IBD), which also includes Crohn's disease (CD). IBD is a growing health problem, and the estimated prevalence is 568 cases per 100,000 persons in the US and 827 cases per 100,000 persons in Europe.
      • Kappelman M.D.
      • Moore K.R.
      • Allen J.K.
      • Cook S.F.
      Recent trends in the prevalence of Crohn's disease and ulcerative colitis in a commercially insured US population.
      Approximately 20% of patients with UC present symptoms before age 20.
      • Kelsen J.
      • Baldassano R.N.
      Inflammatory bowel disease: the difference between children and adults.
      UC is diagnosed based on clinical presentation and endoscopic evidence of inflammation in the rectum that extends proximally into the colon. Clinical manifestations of active disease include bloody diarrhea, rectal urgency, abdominal pain, weight loss, and malaise.
      • Langan R.C.
      • Gotsch P.B.
      • Krafczyk M.A.
      • Skillinge D.D.
      Ulcerative colitis: diagnosis and treatment.
      The treatment goal in UC is to induce remission and maintain a corticosteroid-free remission,
      • Rubin D.T.
      • Ananthakrishnan A.N.
      • Siegel C.A.
      • Sauer B.G.
      • Long M.D.
      ACG clinical guideline.
      which often requires use of a targeted therapy. Approved targeted therapies include anti-integrin ɑ4β7 (eg, vedolizumab), anti-interleukin-12/23 (eg, ustekinumab), tumor necrosis factor inhibitor (TNFi; eg, adalimumab, infliximab, and golimumab) and Janus kinase inhibitor (JAKi; eg, tofacitinib) therapies.
      Selecting the right drug for individual patients from day one of treatment results in faster recovery, less pain, and improved quality of life, particularly in chronic progressive diseases such as UC. However, response to targeted therapies in UC are as low 20%–50%. Clinical response in UC clinical trials is defined as a decline in Mayo score of ≥3 points and either a ≥30% relative decrease from baseline with at least a 1-point decrease in rectal bleeding or a rectal bleeding score of 0 or 1.
      • Rubin D.T.
      • Ananthakrishnan A.N.
      • Siegel C.A.
      • Sauer B.G.
      • Long M.D.
      ACG clinical guideline.
      ,
      • Schroeder K.W.
      • Tremaine W.J.
      • Ilstrup D.M.
      Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. A randomized study.
      This definition of response is less stringent than the treatment goals of clinical remission (Mayo score of 0–2) and mucosal healing (endoscopic score of 0–1). This highlights the urgent need to develop precision medicine tools for UC patients so that a therapy suitable to each patient's disease biology can be prescribed.
      Analysis of the map of human disease biology called the Human Interactome can be used to interpret a patients’ unique molecular signature in order to identify which therapy will work for which patient based on each individual's unique biology.
      • Mellors T.
      • Withers J.B.
      • Ameli A.
      • et al.
      Clinical validation of a blood-based predictive test for stratification of response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.
      Analysis of the topology and dynamics of the Human Interactome can reveal the underlying biological processes regulating many of the most common and difficult to treat diseases. This has resulted in the ability to discover novel targets, reprioritize known targets, and develop new biomarkers to predict drug response.
      • Mellors T.
      • Withers J.B.
      • Ameli A.
      • et al.
      Clinical validation of a blood-based predictive test for stratification of response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.
      ,
      • Gysi D.M.
      • Do Valle I.
      • Zitnik M.
      • et al.
      Network medicine framework for identifying drug repurposing opportunities for COVID-19.
      Application of this technology is especially useful in human complex diseases such as autoimmune conditions like rheumatoid arthritis and UC. The current goal is to develop diagnostic tests to predict which patients will have an inadequate response to targeted therapies and define new drug targets and pathways for novel therapeutic development.

      MATERIAL AND METHODS

       Cohort description

      Training cohort, GSE14580
      • Arijs I.
      • Li K.
      • Toedter G.
      • et al.
      Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
      : Twenty-four patients with active UC, refractory to corticosteroids or immunosuppression, underwent colonoscopy with biopsies from diseased colon within a week prior to the first intravenous infusion of 5 mg infliximab per kg body weight. Response to infliximab was defined as endoscopic and histologic healing at 4–6 weeks after first infliximab treatment (8 patients as responders and 16 patients as inadequate responders). This data also included 6 healthy controls. Total RNA was isolated from colonic mucosal biopsies, labeled, and hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays.
      Validation cohort, GSE12251:
      • Arijs I.
      • Li K.
      • Toedter G.
      • et al.
      Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
      Twenty-two patients underwent colonoscopy with biopsy before infliximab treatment. Response to infliximab was defined as endoscopic and histologic healing at week 8 (12 patients as responders and 11 patients as inadequate responders). For one patient, data from 2 samples taken at different timepoints were available. RNA was isolated from pre-infliximab biopsies, labeled and hybridized to Affymetrix Human Genome U133 Plus_2.0 Array.
      This study was performed in accordance with the principles outlined in the Declaration of Helsinki.

       Microarray analysis

      The 2 datasets were downloaded using GEOquery R package. Before-treatment gene expression data were extracted by setting the visit time point to baseline. Probe IDs were converted to gene Entrez ID using the hgu133plus2.db database. The 2 datasets were merged by the common probe IDs. Batch effects were removed using ComBat from the sva R package.
      • Leek J.T.
      • Johnson W.E.
      • Parker H.S.
      • Jaffe A.E.
      • Storey J.D.
      The sva package for removing batch effects and other unwanted variation in high-throughput experiments.
      ,
      • Johnson W.E.
      • Li C.
      • Rabinovic A.
      Adjusting batch effects in microarray expression data using empirical Bayes methods.
      To retain the biological differences between responders and inadequate responders, cohort-specific biomarkers were derived prior to applying ComBat.

       Human Interactome

      The Human Interactome, previously described,
      • Mellors T.
      • Withers J.B.
      • Ameli A.
      • et al.
      Clinical validation of a blood-based predictive test for stratification of response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.
      ,
      • Menche J.
      • Sharma A.
      • Kitsak M.
      • et al.
      Disease networks. Uncovering disease-disease relationships through the incomplete interactome.
      contains experimentally determined physical interactions between proteins. These interactions include, regulatory, metabolic, signaling, and binary interactions. The Human Interactome amalgamates data from more than 300 thousand interactions among 18 thousand proteins.

       Identification of classifier genes

      For all genes, the Pearson correlation between the gene expression values and the response to treatment was determined.
      • Santolini M.
      • Romay M.C.
      • Yukhtman C.L.
      • et al.
      A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure.
      The signal-to-noise ratio of each gene correlation was calculated by randomly shuffling the response outcome 100 times. Selected genes were then mapped onto the consolidated Human Interactome and the largest connected component (LCC) was determined.

       Classifier design and validation

      Genes identified as discriminatory between responders and inadequate responders to infliximab that were in the LCC were used as features of a probabilistic neural network.
      • Gonzalez-Camacho J.M.
      • Crossa J.
      • Perez-Rodriguez P.
      • Ornella L.
      • Gianola D.
      Genome-enabled prediction using probabilistic neural network classifiers.
      Classifier training was performed using the R package pnn. The classifier training included in-cohort validation using a leave-one-sample-out cross-validation where the classifier was blind to the response outcome of that left-out patient. The classifier was trained using the default smoothing parameter (σ = 0.8). The classifier was validated on the validation cohort where the training cohort was used for feature selection and classifier training and the validation cohort was used for independent validation.
      The classifier provided a probability for each patient reflecting whether or not that individual responded to infliximab treatment. The log-likelihood ratio of response and inadequate response probabilities were used to define a score for each patient and draw the receiver operating characteristic (ROC) curves by comparing the score to actual response outcomes. The area under the curve (AUC) determined the performance of the classifier. In cross-cohort assessment, the trained classifier was blind to the clinical outcomes of the validation cohort patients.

       Response module randomization

      Response module was comprised of the largest connected component formed by top genes when derived from both training and validation cohorts. One of the shared genes (UBC) between the 2 cohorts’ top gene sets was a high degree node in the Human Interactome, that could have caused a high degree of perceived connectedness between the LCC genes from the 2 cohorts. To control and correct for the effect of the high degree node and the many shared nodes between the gene sets, nodes were randomly assigned to one cohort while the shared genes were preserved between 2 sets during the randomization.

      RESULTS

       Identification of gene expression features predictive of inadequate response to infliximab

      To identify genes whose expression best distinguished responders from inadequate responders to infliximab, 2 publicly available UC patient gene expression datasets were downloaded for which the clinical outcomes data were available.
      • Arijs I.
      • Li K.
      • Toedter G.
      • et al.
      Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
      The training cohort data were analyzed to find genes with significant gene expression deviations between responders and inadequate responders
      • Santolini M.
      • Romay M.C.
      • Yukhtman C.L.
      • et al.
      A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure.
      (response prediction genes). Unlike conventional differential expression methods that look for large fold-changes in gene expression between 2 groups, this analysis investigated small but significant changes—a high signal-to-noise ratio—between the 2 groups (see Material and methods). Genes were ranked by decreasing value of signal-to-noise ratio and the top genes with the highest signal-to-noise ratio were selected as infliximab response discriminatory genes (Fig 1A).
      Fig 1
      Fig. 1Identification of response discriminatory genes. (A) Pearson correlation distribution of gene expression values with response outcomes in observed vs randomized gene expression data. The signal-to-noise ratio of actual and randomized Pearson correlations were derived by dividing the randomized valued by the observed value. (B) Top 200 genes with highest signal-noise-ratio were mapped on the network resulting in observation of a significantly large connected component (LCC) shown in shaded region. (C) Comparing the observed classifier LCC size of 39 with the expected LCC size generated by 100,000 randomizations. (D) Heatmap representing the baseline gene expression values of LCC genes used for classifier training across patients. Red corresponds to higher relative expression values and green corresponds to lower relative expression values.

       Refinement of molecular signature genes using the Human Interactome

      The top genes from the training cohort for which expression values across patients were significantly correlated to clinical outcome after infliximab treatment were selected and mapped onto the Human Interactome network map of protein-protein interactions (see Material and Methods) (Fig 1B-C). The cut-off of the top 180 genes was empirically determined as the minimum number of genes needed for the LCC size to plateau (Supplementary Fig S1). Although these genes were identified from gene expression data only, proteins encoded by these genes formed a significant cluster of 39 proteins on the Human Interactome. The 39-protein LCC on the Human Interactome was significantly larger than expected by chance (z-score of 2.91) (Fig 1B-C and Table I). Z-scores >1.6 have been associated with sub-networks of underlying disease biology.
      • Menche J.
      • Sharma A.
      • Kitsak M.
      • et al.
      Disease networks. Uncovering disease-disease relationships through the incomplete interactome.
      ,
      • Sharma A.
      • Menche J.
      • Huang C.C.
      • et al.
      A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.
      • Barabasi A.L.
      • Gulbahce N.
      • Loscalzo J.
      Network medicine: a network-based approach to human disease.
      • Ghiassian S.D.
      • Menche J.
      • Chasman D.I.
      • et al.
      Endophenotype network models: common core of complex diseases.
      Table IGenes forming the LCC of response signature genes in the training cohort
      ADARNUCKS1
      ANP32BPKM
      ATRXPML
      BRD7PNN
      CAPN1PRKAB1
      CCDC88ARBCK1
      CFAP206RRP15
      CGNSNRPN
      CIRBPSUMO2
      CLTCTFIP11
      EEA1THTPA
      ERICH1TMEM87A
      FAM192ATNK2
      FAM207ATPR
      HHEXTRAPPC4
      KLF3UBA5
      LCA5UBE2D1
      MDC1VPS72
      MDM2YWHAE
      NFAT5
      Abbreviation: LCC, largest connected component.

       Classifier training and blinded cross-cohort validation

      The LCC genes from the training cohort were used to train a probabilistic neural network
      • Specht D.F.
      Generation of polynomial discriminant functions for pattern recognition.
      ,
      • Specht D.F.
      Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification.
      ; an optimum pattern classifier that minimizes the risk of incorrectly classifying an object with high efficiency.
      • Gonzalez-Camacho J.M.
      • Crossa J.
      • Perez-Rodriguez P.
      • Ornella L.
      • Gianola D.
      Genome-enabled prediction using probabilistic neural network classifiers.
      The probabilistic neural networks were trained using the LCC genes and patient data to teach the predictive classifier the appropriate patient outcome (ie, response or inadequate response to infliximab) for each input (ie, gene expression levels of LCC genes) (see Material and Methods section).
      Blinded, independent cross-cohort validation assessed the performance of the predictive classifier. In this analysis, the classifier that was trained on the known data and outcomes from the training cohort was used to predict the outcomes on the validation cohort, ultimately testing the ability of the classifier to accurately predict inadequate response to infliximab in an unseen patient population. The classifier predicted probabilities were converted to a continuous classifier prediction score using log-likelihood ratio (see Material and Methods). ROC curves, which plot the rate of false positives vs the rate of true positives, were used to assess cross-cohort performance (Fig 2A). An AUC of 0.85 was observed for the classifier predicting inadequate response to infliximab among validation cohort patients. The distribution of classifier prediction scores within the validation cohort showed a significant difference between the classifier prediction scores for responders and inadequate responders (P value = 0.004) (Fig 2B). Additionally, the cross-cohort positive predictive value (PPV) and sensitivity were estimated (Fig 2C), which are metrics that describe the accuracy of the inadequate response predictions. At a 90% PPV, the classifier had a sensitivity of 82%.
      Fig 2
      Fig. 2Cross-cohort performance of the response prediction classifier. (A) ROC curve. (B) Classifier predicted scores among validation set patients who are true clinical responders (R) or inadequate responders (IR) to infliximab. (C) Accuracy in predicting inadequate responders to infliximab in the validation cohort.

       Baseline gene expression profiles of responders more closely resemble healthy controls

      To further evaluate the 39-LCC classifier genes, the expression was compared between responders and inadequate responders. In general, the changes in gene expression between these 2 patient groups were small and no gene reached a threshold that indicated a significant differential expression (P value ≤0.05 and enrichment of -log(0.05) ≥ 2.99) (Fig 3A). However, the expression of the 39-LCC classifier genes tended toward higher enrichment scores. One interpretation was that the genes with the greatest fold-change were not necessarily the most relevant to treatment response as they could potentially be downstream of master regulators or be altered by indirect/secondary consequences of disease-relevant signaling processes.
      Fig 3
      Fig. 3Differential gene expression of response discriminatory genes. (A) Volcano plot indicating the differential expression for all genes between responders and inadequate responders to infliximab. The 39 genes in the classifier LCC are highlighted in orange. (B) Comparison of the expression of the 39 LCC classifier genes of responders (teal) or inadequate responders (red) vs healthy controls. (C) Unsupervised hierarchical cluster analysis of 39 classifier LCC genes illustrating the relative RNA expression between healthy controls, responders and inadequate responders.
      Next, the expression of the 39-LCC classifier genes was compared to that of healthy controls, comparing the fold changes relative to responders or to inadequate responders. The patients who were inadequate responders showed the largest divergence in gene expression pattern from that of healthy controls (Fig 3B). Unsupervised hierarchical clustering analysis showed that the baseline expression profiles of the patients who responded to infliximab more closely resembled the expression pattern of healthy controls than did the inadequate responders (Fig 3C).

       The UC infliximab response module is a subnetwork on the Human Interactome

      The Human Interactome can serve as a blueprint to better understand the interconnectivity and underlying biology of the inadequate response prediction genes. Thus, the top 180 genes with the highest signal-to-noise ratio between responders and inadequate responders among the validation cohort data were also determined. When the 180 top genes from the training and validation cohorts were mapped simultaneously onto Human Interactome, the genes were not randomly scattered on the network, but instead significantly interacted with each other (z-score of 8.34) forming a common response module LCC (Fig 4) that was significantly larger than the random expectation (99 genes; z-score of 2.64). To account for genes that were shared between the 2 cohort gene lists, including a high-degree node (UBC) on the Human Interactome, a careful randomization was made to estimate the significance of interconnectivity (see Material and Methods). Two proteins in the response module LCC (RBCK1 and SGCB) are direct interaction partners of TNF-ɑ, the protein target of infliximab. Several proteins in the response module LCC were orphan genes that were not previously part of LCCs of the individual cohorts (eg, GEMIN2 and CSTF2) yet were integrated into this response module LCC (Fig 4A). These results showed that even though the response discriminatory genes identified from each cohort were apparently distinct with minimal overlap, their protein products tended to interact significantly on the network, reflecting the existence of an underlying disease biology sub-network, or response module, that defined a molecular signature of non-response to infliximab in UC patients.
      Fig 4
      Fig. 4Apparently distinct gene lists mapped onto the same network region of the Human Interactome indicated a common underlying biology of response. (A) Response module: largest connected component formed by the proteins encoded by the response signature genes from the training and validation cohorts. Proteins encoded by training cohort genes are in blue and those encoded by validation cohort genes are in orange. (B) Distribution of LCC size from random expectation.

      DISCUSSION

      This study describes a predictive classifier developed using knowledge from the Human Interactome map of protein-protein interactions and a probabilistic neural network machine learning algorithm. The genes correlating with response to infliximab identified from baseline colon biopsy samples were predictive of inadequate response to infliximab in a cross-cohort validation. The patients in this study were all diagnosed with UC, and as such, differences in the biology between these individuals may not manifest in large fold-changes in gene expression. These subtle differences in transcript levels may be overlooked in conventional differential gene expression analyses in favor of genes with a greater observed fold-change between the responder and inadequate responder groups. However, this study identified small but significant changes in gene expression that may contribute to different treatment outcomes. The network-based method to discover biomarkers described in this study ensured that the differentially expressed genes in the classifier were significantly connected to the subnetwork of ulcerative colitis disease biology. This reduces the large number of differentially expressed genes to those most relevant to the biology of treatment response.
      When response discriminatory genes identified separately from the training and validation cohorts were compared, the gene sets showed limited overlap in identity but significant overlap on the Human Interactome. Thus, they are unified in a common response module on the Human Interactome. This observation addresses one of the major concerns of biomarker irreproducibility; studies evaluating response prediction biomarkers rarely report the same genes. Many studies have reported prognostic indicators of response to TNFi therapies in UC.
      • Arijs I.
      • Li K.
      • Toedter G.
      • et al.
      Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
      ,
      • Subramaniam K.
      • Richardson A.
      • Dodd J.
      • et al.
      Early predictors of colectomy and long-term maintenance of remission in ulcerative colitis patients treated using anti-tumour necrosis factor therapy.
      • Garcia-Bosch O.
      • Gisbert J.P.
      • Canas-Ventura A.
      • et al.
      Observational study on the efficacy of adalimumab for the treatment of ulcerative colitis and predictors of outcome.
      • Rismo R.
      • Olsen T.
      • Cui G.
      • et al.
      Mucosal cytokine gene expression profiles as biomarkers of response to infliximab in ulcerative colitis.
      • Olsen T.
      • Goll R.
      • Cui G.
      • Christiansen I.
      • Florholmen J.
      TNF-alpha gene expression in colorectal mucosa as a predictor of remission after induction therapy with infliximab in ulcerative colitis.
      A gene array study of UC mucosal biopsies identified gene panels predictive of response to infliximab with 95% sensitivity and 85% specificity.
      • Arijs I.
      • Li K.
      • Toedter G.
      • et al.
      Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
      A prospective study determined the predictive value of pre-treatment mucosal T cell-related cytokine gene expression profiles in response to infliximab
      • Rismo R.
      • Olsen T.
      • Cui G.
      • et al.
      Mucosal cytokine gene expression profiles as biomarkers of response to infliximab in ulcerative colitis.
      ; expression of transcripts encoding IL-17A and IFN-γ were associated with remission after 3 infliximab infusions (odds ratio or 5.4, P= 0.013 and 5.5, P= 0.011, respectively). These studies developed predictive models using machine learning approaches, calculating mean gene expression values, evaluating the highest fold changes in gene expression and/or taking a pathway-based approach to describe UC disease biology. None of these studies have been developed into a clinical test for care of UC patients. By mapping the response module, network analyses performed in this study enabled identification of biomarkers associated with a specific disease phenotype (inadequate response to infliximab), reduced the noise inherent to gene expression data and eliminated many false positives that can arise from small sample sizes and characteristics specific to demographics of a particular patient cohort. Future analyses and larger cohort studies will explore the use of genes in the aggregated response module to train and validate a TNFi response classifier.
      The proteins encoded by the classifier genes (Table I) are implicated in many biological processes including protein metabolism, cell-cell adhesion, intracellular vesicle transport, and ciliary biology. TNF-ɑ protein production is elevated in UC patients,
      • MacDonald T.T.
      • Hutchings P.
      • Choy M.Y.
      • Murch S.
      • Cooke A.
      Tumour necrosis factor-alpha and interferon-gamma production measured at the single cell level in normal and inflamed human intestine.
      • Murch S.H.
      • Lamkin V.A.
      • Savage M.O.
      • Walker-Smith J.A.
      • MacDonald T.T.
      Serum concentrations of tumour necrosis factor alpha in childhood chronic inflammatory bowel disease.
      • Komatsu M.
      • Kobayashi D.
      • Saito K.
      • et al.
      Tumor necrosis factor-alpha in serum of patients with inflammatory bowel disease as measured by a highly sensitive immuno-PCR.
      • Martinez-Borra J.
      • Lopez-Larrea C.
      • Gonzalez S.
      • et al.
      High serum tumor necrosis factor-alpha levels are associated with lack of response to infliximab in fistulizing Crohn's disease.
      • Yarur A.J.
      • Jain A.
      • Sussman D.A.
      • et al.
      The association of tissue anti-TNF drug levels with serological and endoscopic disease activity in inflammatory bowel disease: the ATLAS study.
      • Braegger C.P.
      • Nicholls S.
      • Murch S.H.
      • Stephens S.
      • MacDonald T.T.
      Tumour necrosis factor alpha in stool as a marker of intestinal inflammation.
      and aspects of general protein metabolism captured by the classifier include chromatin organization, transcription regulation, mRNA splicing, nuclear export, and ubiquitination. Although, cytokine production is primarily regulated at the transcriptional and translational levels following a stimulus, newly formed cytokines are secreted by intracellular trafficking to the plasma membrane
      • Revelo N.H.
      • Ter Beest M.
      • van den Bogaart G.
      Membrane trafficking as an active regulator of constitutively secreted cytokines.
      . Inclusion of classifier features involved in vesicular trafficking suggests that transport of soluble and transmembrane-bound TNF-ɑ to the cell surface may be an important consideration in the efficacy of TNFi therapies,
      • Revelo N.H.
      • Ter Beest M.
      • van den Bogaart G.
      Membrane trafficking as an active regulator of constitutively secreted cytokines.
      • Deora A.
      • Hegde S.
      • Lee J.
      • et al.
      Transmembrane TNF-dependent uptake of anti-TNF antibodies.
      • Ha S.D.
      • Martins A.
      • Khazaie K.
      • et al.
      Cathepsin B is involved in the trafficking of TNF-alpha-containing vesicles to the plasma membrane in macrophages.
      Once secreted, cytokines influence a variety of immunological and cellular responses. Although a direct role for primary cilia—the microtubule-based organelles protruding from the surface of most mammalian cells—in immunological signaling remains unsubstantiated, ciliary proteins can specify and regulate inflammatory responses through the NF-κB signaling pathway.
      • Mc Fie M.
      • Koneva L.
      • Collins I.
      • et al.
      Ciliary proteins specify the cell inflammatory response by tuning NFkappaB signalling, independently of primary cilia.
      ,
      • Wann A.K.
      • Knight M.M.
      Primary cilia elongation in response to interleukin-1 mediates the inflammatory response.
      Cytokines in the TNF family are well-characterized inducers of the NF-κB family of transcription factors.
      • Hayden M.S.
      • Ghosh S.
      Regulation of NF-kappaB by TNF family cytokines.
      Finally, inclusion of genes encoding proteins involved in cell-cell adhesion highlights a role for barrier function in determining TNFi inadequate response.
      • Ye D.
      • Ma I.
      • Ma T.Y.
      Molecular mechanism of tumor necrosis factor-alpha modulation of intestinal epithelial tight junction barrier.
      Dysregulation of the cell-cell adhesion perturbs the dynamic, permeable barrier formed by intestinal epithelial cells that normally restricts access to potential antigens in the intestinal lumen. Disrupted barrier function contributes to the pathogenesis of inflammatory bowel diseases and TNF inhibitor treatment can improve intestinal barrier function in IBD.
      • Noth R.
      • Stuber E.
      • Hasler R.
      • et al.
      Anti-TNF-alpha antibodies improve intestinal barrier function in Crohn's disease.
      • Bouma G.
      • Strober W.
      The immunological and genetic basis of inflammatory bowel disease.
      • Liu F.
      • Lee S.A.
      • Riordan S.M.
      • Zhang L.
      • Zhu L.
      Effects of anti-cytokine antibodies on gut barrier function.
      Consistent with the complexity of TNF-ɑ signaling, transcripts predictive of inadequate response to infliximab are similarly diverse.
      There is an interaction between genetic, immune, and environmental factors that is evident in the mucosa gene expression profiles of IBD patients compared to healthy controls and in the genetic risk alleles associated with an increased risk of IBD.
      • Jostins L.
      • Ripke S.
      • Weersma R.K.
      • et al.
      Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease.
      The topological and biological properties of the infliximab response module on the Human Interactome suggests that it is possible to determine a molecular signature for inadequate response to TNFi therapies in patients with UC. TNFi therapies have demonstrated efficacy in the treatment of moderate to severe IBD. However, response rates vary, and initially 40%–60% of patients fail to achieve remission with their initial treatment,
      • Ford A.C.
      • Sandborn W.J.
      • Khan K.J.
      • et al.
      Efficacy of biological therapies in inflammatory bowel disease: systematic review and meta-analysis.
      • Sandborn W.J.
      • van Assche G.
      • Reinisch W.
      • et al.
      Adalimumab induces and maintains clinical remission in patients with moderate-to-severe ulcerative colitis.
      • Zampeli E.
      • Gizis M.
      • Siakavellas S.I.
      • Bamias G.
      Predictors of response to anti-tumor necrosis factor therapy in ulcerative colitis.
      • Rutgeerts P.
      • Sandborn W.J.
      • Feagan B.G.
      • et al.
      Infliximab for induction and maintenance therapy for ulcerative colitis.
      dose escalation is needed in 23%–46% of patients after 12 weeks of treatment
      • Roda G.
      • Jharap B.
      • Neeraj N.
      • Colombel J.F.
      Loss of response to anti-TNFs: definition, epidemiology, and management.
      ,
      • Fausel R.
      • Afzali A.
      Biologics in the management of ulcerative colitis – comparative safety and efficacy of TNF-alpha antagonists.
      and up to 50% of patients who responded initially will have a secondary loss of response after 12 months of therapy.
      • Roda G.
      • Jharap B.
      • Neeraj N.
      • Colombel J.F.
      Loss of response to anti-TNFs: definition, epidemiology, and management.
      ,
      • Fine S.
      • Papamichael K.
      • Cheifetz A.S.
      Etiology and management of lack or loss of response to anti-tumor necrosis factor therapy in patients with inflammatory bowel disease.
      Furthermore, a multicenter, retrospective study reported that 55.6% of UC patients who were primary non-responders to infliximab underwent colectomy within 3.2 years, a surgery that costs an estimated $91,767.
      • Papamichael K.
      • Rivals-Lerebours O.
      • Billiet T.
      • et al.
      Long-term outcome of patients with ulcerative colitis and primary non-response to infliximab.
      ,
      • Wilson M.
      • Lucas A.
      • Cameron A.
      • Luo M.
      Budget impact of adding vedolizumab to a health plan formulary as another first-line biologic option for ulcerative colitis and Crohn's disease.
      Given the need to rapidly manage disease flares and avoid surgery, there is a critical need for a test that can predict which UC patients will benefit from TNFi therapy and who should consider alternative treatment options.
      This network-based approach evaluates protein interactions to select genes that reflect the biology of disease at the individual patient level. The cross-cohort validation of the predictive classifier developed using a response module found in the Human Interactome, suggests the existence of a molecular signature in baseline tissue samples that characterizes UC patients who will have an inadequate response to TNFi therapy. Further development of such a test would decrease the time to treatment response, thus allowing patients to get back to their normal, productive lives sooner while decreasing the burden on supportive family members. Furthermore, this method of biomarker discovery and classifier development can be applied across multiple disease areas with complex phenotypes and datasets containing molecular information. The platform described herein opens new, unprecedented opportunities to create new drug response modules, predict drug response in complex diseases, and achieve the goal of treating patients with the most effective treatment for their unique disease biology.

      ACKNOWLEDGMENTS

      Conflict of Interest: All authors have read the journal's policy on disclosure of potential conflicts of interest. J.B.W., A.S., V.R.A., and S.D.G. are full-time employees and shareholders of Scipher Medicine Corporation. M.S. is a consultant of Scipher Medicine Corporation.
      Author contributions are as follow: S.D.G. designed and carried out the analysis. J.B.W. carried out the biological interpretation of the classifier genes. S.D.G., J.B.W., A.S., and V.R.A. wrote the article. M.S. carried signal-to-noise ratio analysis. All authors have read the journal's authorship agreement and the manuscript has been reviewed by and approved by all named authors.
      Data availability statement: All datasets analyzed in this study are available on the Gene Expression Omnibus: GSE14580 and GSE12251.
      The authors would like to thank Lauren Whelton for review of the manuscript and Keith Johnson for providing feedback.

      Appendix. Supplementary materials

      References

        • Langan R.C.
        • Gotsch P.B.
        • Krafczyk M.A.
        • Skillinge D.D.
        Ulcerative colitis: diagnosis and treatment.
        Am Fam Phys. 2007; 76: 1323-1330
        • Kappelman M.D.
        • Moore K.R.
        • Allen J.K.
        • Cook S.F.
        Recent trends in the prevalence of Crohn's disease and ulcerative colitis in a commercially insured US population.
        Digestive Dis Sci. 2013; 58: 519-525
        • Kelsen J.
        • Baldassano R.N.
        Inflammatory bowel disease: the difference between children and adults.
        Inflamm Bowel Dis. 2008; 14: S9-S11
        • Rubin D.T.
        • Ananthakrishnan A.N.
        • Siegel C.A.
        • Sauer B.G.
        • Long M.D.
        ACG clinical guideline.
        Am J Gastroenterol. 2019; 114: 384-413
        • Schroeder K.W.
        • Tremaine W.J.
        • Ilstrup D.M.
        Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. A randomized study.
        New Engl J Med. 1987; 317: 1625-1629
        • Mellors T.
        • Withers J.B.
        • Ameli A.
        • et al.
        Clinical validation of a blood-based predictive test for stratification of response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.
        Network Syst Med. 2020; 3: 91-104
        • Gysi D.M.
        • Do Valle I.
        • Zitnik M.
        • et al.
        Network medicine framework for identifying drug repurposing opportunities for COVID-19.
        2020 (ArXiv)
        • Arijs I.
        • Li K.
        • Toedter G.
        • et al.
        Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis.
        Gut. 2009; 58: 1612-1619
        • Leek J.T.
        • Johnson W.E.
        • Parker H.S.
        • Jaffe A.E.
        • Storey J.D.
        The sva package for removing batch effects and other unwanted variation in high-throughput experiments.
        Bioinformatics. 2012; 28: 882-883
        • Johnson W.E.
        • Li C.
        • Rabinovic A.
        Adjusting batch effects in microarray expression data using empirical Bayes methods.
        Biostatistics. 2007; 8: 118-127
        • Menche J.
        • Sharma A.
        • Kitsak M.
        • et al.
        Disease networks. Uncovering disease-disease relationships through the incomplete interactome.
        Science. 2015; 3471257601
        • Santolini M.
        • Romay M.C.
        • Yukhtman C.L.
        • et al.
        A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure.
        NPJ Syst Biol Applic. 2018; 4: 12
        • Gonzalez-Camacho J.M.
        • Crossa J.
        • Perez-Rodriguez P.
        • Ornella L.
        • Gianola D.
        Genome-enabled prediction using probabilistic neural network classifiers.
        BMC Genom. 2016; 17: 208
        • Sharma A.
        • Menche J.
        • Huang C.C.
        • et al.
        A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.
        Hum Mol Genet. 2015; 24: 3005-3020
        • Barabasi A.L.
        • Gulbahce N.
        • Loscalzo J.
        Network medicine: a network-based approach to human disease.
        Nat Rev: Genet. 2011; 12: 56-68
        • Ghiassian S.D.
        • Menche J.
        • Chasman D.I.
        • et al.
        Endophenotype network models: common core of complex diseases.
        Sci Rep. 2016; 6: 27414
        • Specht D.F.
        Generation of polynomial discriminant functions for pattern recognition.
        IEEE Trans Electron Comput. 1967; EC-16: 308-319
        • Specht D.F.
        Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification.
        IEEE Trans Neural Netw. 1990; 1: 111-121
        • Subramaniam K.
        • Richardson A.
        • Dodd J.
        • et al.
        Early predictors of colectomy and long-term maintenance of remission in ulcerative colitis patients treated using anti-tumour necrosis factor therapy.
        Intern Med J. 2014; 44: 464-470
        • Garcia-Bosch O.
        • Gisbert J.P.
        • Canas-Ventura A.
        • et al.
        Observational study on the efficacy of adalimumab for the treatment of ulcerative colitis and predictors of outcome.
        J Crohn's Colitis. 2013; 7: 717-722
        • Rismo R.
        • Olsen T.
        • Cui G.
        • et al.
        Mucosal cytokine gene expression profiles as biomarkers of response to infliximab in ulcerative colitis.
        Scand J Gastroenterol. 2012; 47: 538-547
        • Olsen T.
        • Goll R.
        • Cui G.
        • Christiansen I.
        • Florholmen J.
        TNF-alpha gene expression in colorectal mucosa as a predictor of remission after induction therapy with infliximab in ulcerative colitis.
        Cytokine. 2009; 46: 222-227
        • MacDonald T.T.
        • Hutchings P.
        • Choy M.Y.
        • Murch S.
        • Cooke A.
        Tumour necrosis factor-alpha and interferon-gamma production measured at the single cell level in normal and inflamed human intestine.
        Clin Exp Immunol. 1990; 81: 301-305
        • Murch S.H.
        • Lamkin V.A.
        • Savage M.O.
        • Walker-Smith J.A.
        • MacDonald T.T.
        Serum concentrations of tumour necrosis factor alpha in childhood chronic inflammatory bowel disease.
        Gut. 1991; 32: 913-917
        • Komatsu M.
        • Kobayashi D.
        • Saito K.
        • et al.
        Tumor necrosis factor-alpha in serum of patients with inflammatory bowel disease as measured by a highly sensitive immuno-PCR.
        Clin Chem. 2001; 47: 1297-1301
        • Martinez-Borra J.
        • Lopez-Larrea C.
        • Gonzalez S.
        • et al.
        High serum tumor necrosis factor-alpha levels are associated with lack of response to infliximab in fistulizing Crohn's disease.
        Am J Gastroenterol. 2002; 97: 2350-2356
        • Yarur A.J.
        • Jain A.
        • Sussman D.A.
        • et al.
        The association of tissue anti-TNF drug levels with serological and endoscopic disease activity in inflammatory bowel disease: the ATLAS study.
        Gut. 2016; 65: 249-255
        • Braegger C.P.
        • Nicholls S.
        • Murch S.H.
        • Stephens S.
        • MacDonald T.T.
        Tumour necrosis factor alpha in stool as a marker of intestinal inflammation.
        Lancet. 1992; 339: 89-91
        • Revelo N.H.
        • Ter Beest M.
        • van den Bogaart G.
        Membrane trafficking as an active regulator of constitutively secreted cytokines.
        J Cell Sci. 2020; 133 (jcs234781)
        • Deora A.
        • Hegde S.
        • Lee J.
        • et al.
        Transmembrane TNF-dependent uptake of anti-TNF antibodies.
        MAbs. 2017; 9: 680-695
        • Ha S.D.
        • Martins A.
        • Khazaie K.
        • et al.
        Cathepsin B is involved in the trafficking of TNF-alpha-containing vesicles to the plasma membrane in macrophages.
        J Immunol. 2008; 181: 690-697
        • Mc Fie M.
        • Koneva L.
        • Collins I.
        • et al.
        Ciliary proteins specify the cell inflammatory response by tuning NFkappaB signalling, independently of primary cilia.
        J Cell Sci. 2020; 133 (jcs239871)
        • Wann A.K.
        • Knight M.M.
        Primary cilia elongation in response to interleukin-1 mediates the inflammatory response.
        Cell Mol Life Sci. 2012; 69: 2967-2977
        • Hayden M.S.
        • Ghosh S.
        Regulation of NF-kappaB by TNF family cytokines.
        Semin Immunol. 2014; 26: 253-266
        • Ye D.
        • Ma I.
        • Ma T.Y.
        Molecular mechanism of tumor necrosis factor-alpha modulation of intestinal epithelial tight junction barrier.
        Am J Physiol: Gastrointest Liver Physiol. 2006; 290: G496-G504
        • Noth R.
        • Stuber E.
        • Hasler R.
        • et al.
        Anti-TNF-alpha antibodies improve intestinal barrier function in Crohn's disease.
        J Crohn's Colitis. 2012; 6: 464-469
        • Bouma G.
        • Strober W.
        The immunological and genetic basis of inflammatory bowel disease.
        Nat Rev: Immunol. 2003; 3: 521-533
        • Liu F.
        • Lee S.A.
        • Riordan S.M.
        • Zhang L.
        • Zhu L.
        Effects of anti-cytokine antibodies on gut barrier function.
        Mediat Inflamm. 2019; 20197028253
        • Jostins L.
        • Ripke S.
        • Weersma R.K.
        • et al.
        Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease.
        Nature. 2012; 491: 119-124
        • Ford A.C.
        • Sandborn W.J.
        • Khan K.J.
        • et al.
        Efficacy of biological therapies in inflammatory bowel disease: systematic review and meta-analysis.
        Am J Gastroenterol. 2011; 106 (quiz 660): 644-659
        • Sandborn W.J.
        • van Assche G.
        • Reinisch W.
        • et al.
        Adalimumab induces and maintains clinical remission in patients with moderate-to-severe ulcerative colitis.
        Gastroenterology. 2012; 142 (e251-253): 257-265
        • Zampeli E.
        • Gizis M.
        • Siakavellas S.I.
        • Bamias G.
        Predictors of response to anti-tumor necrosis factor therapy in ulcerative colitis.
        World J Gastrointest Pathophysiol. 2014; 5: 293-303
        • Rutgeerts P.
        • Sandborn W.J.
        • Feagan B.G.
        • et al.
        Infliximab for induction and maintenance therapy for ulcerative colitis.
        New Engl J Med. 2005; 353: 2462-2476
        • Roda G.
        • Jharap B.
        • Neeraj N.
        • Colombel J.F.
        Loss of response to anti-TNFs: definition, epidemiology, and management.
        Clin Transl Gastroenterol. 2016; 7: e135
        • Fausel R.
        • Afzali A.
        Biologics in the management of ulcerative colitis – comparative safety and efficacy of TNF-alpha antagonists.
        Ther Clin Risk Manage. 2015; 11: 63-73
        • Fine S.
        • Papamichael K.
        • Cheifetz A.S.
        Etiology and management of lack or loss of response to anti-tumor necrosis factor therapy in patients with inflammatory bowel disease.
        Gastroenterol Hepatol. 2019; 15: 656-665
        • Papamichael K.
        • Rivals-Lerebours O.
        • Billiet T.
        • et al.
        Long-term outcome of patients with ulcerative colitis and primary non-response to infliximab.
        J Crohn's Colitis. 2016; 10: 1015-1023
        • Wilson M.
        • Lucas A.
        • Cameron A.
        • Luo M.
        Budget impact of adding vedolizumab to a health plan formulary as another first-line biologic option for ulcerative colitis and Crohn's disease.
        Am Health Drug Benefits. 2018; 11: 253-262