Abstract 6323: Identification of genomic alterations related to treatment progression in RWD

Abstract

Abstract Real-world data (RWD) are potentially a highly valuable resource to accelerate drug discovery and improve clinical practice. In particular, datasets combining clinical outcomes and comprehensive genomic profiling (CGP) could uncover new mechanisms of resistance and identify new patient populations for targeted therapies. Here, we used a de-identified clinico-genomic database (CGDB) from routine clinical care based on a clinical EHR-derived data from Flatiron Health linked to tumor sequencing results from Foundation Medicine to assess how prevalence of genomic alterations changes following progression on targeted therapies in the metastatic setting. One of the main challenges we faced is the lack of systematic and longitudinal genomic testing: among the patients receiving a specific treatment, some had a CGP performed prior to treatment, others only around the end of the line of therapy, but only a handful had multiple tests. We therefore established a methodology to assess changes in alteration prevalence and tested how our results are sensitive to cohort building parameters such as timing of testing, duration of therapy, or prior lines of treatment. Our approach successfully recovered three established cases of therapeutic resistance: (1) EGFR T790M in non-small cell lung cancer patients treated with erlotinib or gefitinib; (2) AR alterations following hormonal therapies in prostate cancers; and (3) ESR1 mutations in hormone-receptor positive breast cancer patients progressing on endocrine therapies other than fulvestrant. We delved into the latter case and identified differences in the type of ESR1 mutation that occurred following endocrine therapies if CDK4/6 inhibitors were co-administered or not. On one hand, RWD lack randomization and can thus be subject to confounding variables but on the other hand, RWD have the advantage of including a larger number of patients in a real-world setting compared to clinical trials. Our analysis shows that real-world CGDB can recapitulate the expected emergence of mutations following specific lines of treatment. Our study is a proof of concept of usage of RWD to assess how prevalence of genomic alterations changes following treatment. Our method allowed us to assess differences between patient cohorts and generate novel hypotheses on treatment-related changes in genomic profiles, which can ultimately lead to new drug indications. Citation Format: Nayan Chaudhary, Patricia Luhn, Gunther Jansen, Ciara Metcalfe, Marc Hafner. Identification of genomic alterations related to treatment progression in RWD [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 6323.