US 11,987,620 B2
Methods of treating a subject with an alternative to anti-TNF therapy
Keith J. Johnson, Wayland, MA (US); and Susan Ghiassian, Boston, MA (US)
Assigned to SCIPHER MEDICINE CORPORATION, Waltham, MA (US)
Filed by Scipher Medicine Corporation, Waltham, MA (US)
Filed on Nov. 2, 2021, as Appl. No. 17/517,496.
Application 17/517,496 is a continuation of application No. 16/394,046, filed on Apr. 25, 2019, granted, now 11,198,727.
Application 16/394,046 is a continuation of application No. PCT/US2019/022588, filed on Mar. 15, 2019.
Claims priority of provisional application 62/644,070, filed on Mar. 16, 2018.
Prior Publication US 2022/0056121 A1, Feb. 24, 2022
Int. Cl. C07K 16/24 (2006.01); A61P 19/02 (2006.01); C12Q 1/6827 (2018.01); C12Q 1/6851 (2018.01); C12Q 1/6883 (2018.01); G16B 20/20 (2019.01); G16B 40/30 (2019.01); G16B 45/00 (2019.01); G16H 50/30 (2018.01)
CPC C07K 16/241 (2013.01) [A61P 19/02 (2018.01); C12Q 1/6827 (2013.01); C12Q 1/6851 (2013.01); C12Q 1/6883 (2013.01); G16B 20/20 (2019.02); G16B 40/30 (2019.02); G16B 45/00 (2019.02); G16H 50/30 (2018.01); C12Q 2600/158 (2013.01); G01N 2800/52 (2013.01); G01N 2800/60 (2013.01)] 28 Claims
 
1. A method of treating a subject suffering from an autoimmune disease, disorder, or condition with an alternative therapy to 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 non-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 non-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 non-response of the subject to the anti-TNF therapy, selecting the subject to not receive the anti-TNF therapy, and instead to receive the alternative therapy to anti-TNF therapy; and
(d) administering the alternative therapy to anti-TNF therapy to the subject, wherein the alternative therapy to anti-TNF therapy is selected from the group consisting of rituximab, sarilumab, tofacitinib citrate, leflunomide, vedolizumab, tocilizumab, anakinra, and abatacept.