Abstract 832: Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials

Abstract

Abstract Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms, and the methodology for constructing and optimizing predictive signatures has been less prominently explored. Focusing on the “use case” of a two-arm clinical trial for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, we present tools for the construction of signatures predictive of progression-free survival based on cross-validated multivariate Cox models. The work is organized around a specific approach called subtype correlation, which leverages a priori knowledge of molecular subtypes of tumors for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case, the resulting signature stratifies the patient population into “sensitive” and “relatively-resistant” groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas. Citation Format: Joachim Theilhaber, Marielle Chiron, Jennifer Dreymann, Donald Bergstrom, Jack Pollard. Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 832.