Methods and systems for predicting response to anti-tnf therapies

A method of treating a subject suffering from an autoimmune disease, disorder, or condition with an anti-TNF therapy, the method comprising:

• (a) obtaining a gene expression response signature comprising gene expression levels of a set of genes in a biological sample from the subject, wherein the set of genes are differentially expressed in a first population of subjects who respond to the anti-TNF therapy (“responders”) as compared to a second population of subjects who do not respond to the anti-TNF therapy (“non-responders”);
• (b) applying a trained machine learning classifier to the gene expression response signature to classify the gene expression response signature as being indicative of response of the subject to the anti-TNF therapy, wherein the trained machine learning classifier is obtained at least in part by:
• (i) determining, for each of the set of genes, a significance of correlation with response outcome to the anti-TNF therapy among the responders as compared to the non-responders,
• (ii) selecting a reduced subset of the set of genes with highest significance of correlation with response outcome to the anti-TNF therapy,
• (iii) training a classifier to classify whether the gene expression response signature associated with the reduced subset of the set of genes is indicative of response, wherein the training comprises using (1) gene expression levels of the reduced subset of the set of genes in biological samples from the first population of subjects and the second population of subjects and (2) a responder outcome of the first population of subjects or a non-responder outcome of the second population of subjects,
• (iv) validating the classifier on a second independent cohort of subjects who have received the anti-TNF therapy and have been determined as either responding to the anti-TNF therapy or not responding to the anti-TNF therapy, and
• (v) selecting a cutoff score for the validated classifier, such that the validated classifier achieves a true negative rate (TNR) of at least 0.5 and a negative predictive value (NPV) of at least 0.9;
• (c) based at least in part on the determining in (b) of the indicated response of the subject to the anti-TNF therapy, selecting the subject to receive the anti-TNF therapy;
• and
• (d) administering the anti-TNF therapy to the subject, wherein the anti-TNF therapy is selected from the group consisting of infliximab, adalimumab, etanercept, certolizumab pegol, golimumab, or biosimilars thereof.