THU0354 MACHINE LEARNING CLASSIFICATION OF SKIN GENE EXPRESSION IDENTIFIES A SUBSET OF SYSTEMIC SCLEROSIS PATIENTS MOST LIKELY TO SHOW CLINICAL IMPROVEMENT IN RESPONSE TO ABATACEPT
Authors: Jennifer Franks, Bhaven Mehta, Veronica Berrocal, Yue Wang, Tammara Wood, Christopher Denton, Robert Lafyatis, David Fox, Dinesh Khanna, Michael Whitfield
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> A prior pilot study of abatacept in SSc with molecular gene expression data in skin (<i>1</i>) found that four of five patients who improved on abatacept, as determined by change in mRSS, were in the inflammatory intrinsic gene expression subset. Improvement was accompanied by a decrease in gene expression for immune pathways, including the CD28 and CTLA4 receptors—the target of abatacept. Here we tested our <i>a priori</i> hypothesis that the inflammatory subset shows a significant decline in mRSS during abatacept therapy. <h3>Objectives</h3> ASSET (Abatacept Systemic SclErosis Trial) is a Phase 2 study designed to assess the efficacy of subcutaneous abatacept to treat diffuse cutaneous systemic sclerosis. We performed RNA-sequencing (RNA-seq) analysis of skin biopsies and analyzed associations between intrinsic molecular subsets and clinical outcomes in the ASSET trial. <h3>Methods</h3> RNA-seq was performed on skin biopsies from 84 participants in the ASSET trial. A machine learning classifier was trained on independent cohorts and used to objectively classify patients into intrinsic gene expression subsets prior to study unblinding (<i>2</i>). Treatment differences in longitudinal outcomes were assessed using linear mixed effect models. <h3>Results</h3> 84 participants were assigned to intrinsic subset at baseline resulting in 33 inflammatory, 18 fibroproliferative, and 33 normal-like samples. Change in mRSS over 12 months was significantly different between the abatacept and placebo treatment arms for the inflammatory (p&lt;0.001) and normal-like (p=0.03) subsets, but there was no difference in the fibroproliferative subset of patients (p=0.47) (Figure 1). For FVC% predicted, the fibroproliferative subset showed a numerical increase in FVC% in the abatacept arm (p=0.19) while all other groups showed decreases in FVC%. All gene expression subsets showed decreases in HAQ-DI in the abatacept arm not observed in the placebo arm, with the most robust changes occurring in the inflammatory (p=0.09) and normal-like (p=0.06) subsets. <h3>Conclusion</h3> There was a marked divergence of the trajectory for mRSS change for the inflammatory subset and no apparent impact for the fibroproliferative subset. In contrast, only the fibroproliferative subset showed a clinically meaningful FVC change for abatacept. For the broader functional measure of HAQ-DI, there were similar impacts of abatacept on all three intrinsic skin gene expression subsets but is numerically greatest for the normal-like and fibroproliferative subsets. Together these data show for the first time in a placebo-controlled trial that intrinsic skin gene expression subsets may predict differential response to a targeted biological therapy, and also that this may reflect impact on different facets of SSc pathogenesis in skin or lung. This suggests that stratification of cases according to intrinsic gene expression subsets may maximize the number of informative SSc cases in clinical trials, and potentially improve future clinical practice. <h3>References</h3> [1] E. F. Chakravarty, et al., Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis. Arthritis Res Ther 17, 159 (2015). [2] J. M. Franks, et al., A Machine Learning Classifier for Assigning Patients with Systemic Sclerosis to Intrinsic Molecular Subsets., Arthritis Rheum. (2019) In Press. <h3>Acknowledgement</h3> This project was supported by NIH/NIAID Clinical ACE grant (5UM1AI110557-05) and an investigator-initiated grant by Bristol-Myers Squibb. <h3>Disclosure of Interests</h3> Jennifer Franks: None declared, Bhaven Mehta: None declared, Veronica Berrocal: None declared, Yue Wang: None declared, Tammara Wood Consultant for: Celdara Medical LLC, Christopher Denton Grant/research support from: GlaxoSmithKline, Inventiva, CSF Behring, Consultant for: Roche-Genentech, Actelion, GlaxoSmithKline, Sanofi Aventis, Inventiva, CSL Behring, Boehringer Ingelheim, Bayer, Robert Lafyatis: None declared, David Fox: None declared, Dinesh Khanna Shareholder of: Eicos Sciences, Inc, Grant/research support from: Bayer, BMS, Pfizer, Horizon, Consultant for: Actelion Acceleron, Arena, Bayer, BI, BMS, CSL Behring, Corbus, Cytori, GSK, Genentech/Roche, Galapagos, Employee of: Elcos Sciences, Inc, Michael Whitfield Shareholder of: Scientific Founder, Celdara Medical LLC, Grant/research support from: Dr. Whitfield has received research contracts from Celdara Medical LLC and UCB Biopharma, Consultant for: Bristol Myers Squib, Celdara Medical, Corbus, and Boerhinger Ingelheim