Abstract 1833: Identification of resistance mechanisms to EGFR treatment in the real world using a clinicogenomic database
Authors: Singal Gaurav , Li Gerald , Agarwala Vineeta , Kaushik Gaurav , O'Connell Claire , Cobb Garrett A. , Caron Thomas , Bourque David , Guria Ameet , Frank Shannon , Frampton Garrett , Carson Ken , Abernethy Amy , Miller Vincent A. 
Published: August 2018

Abstract
Introduction
Use of targeted therapies often results in resistance mediated by genomic evolution, but discovery of these mechanisms has historically been opportunistic. A more scalable approach is needed to uncover resistance mechanisms from real world patient experience.
Methods
A real-world clinicogenomic database (CGDB) was created of patients with NSCLC who received Foundation Medicine's FoundationOne next generation sequencing (NGS) assay as part of routine care, for whom electronic health record (EHR) data was available in the Flatiron Health Database. Data from NGS testing and the EHR were linked through a HIPAA compliant de-identification and linking process (Singal et al, ASCO 2017). Patients who received first and second generation EGFR inhibitors (afatinib, cetuximab, erlotinib, gefitinib, lapatinib) were segmented into those undergoing NGS testing before treatment (“pre-treatment cohort”) and those biopsied at least 3 months after treatment start (“post-treatment cohort”). All tumor samples were from different patients; paired samples were not available. The prevalence of EGFR and non-EGFR alterations in pre- vs post-treatment cohorts were compared using Fisher's exact test.
Results
The NSCLC cohort included 2139 patients, of whom 370 were treated with EGFR inhibitors (NGS testing pre-treatment cohort n=237, post-treatment cohort n=133). Of 51 distinct EGFR short variants identified, only T790M was significantly enriched in the post-treatment cohort (3.4% pre-treatment vs 32.3% post-treatment, p &lt; 0.0001). Analysis of non-EGFR genes demonstrated significant post-treatment enrichment of amplifications in both AKT2 (0.84% vs 7.5%, p=0.001) and FGF10 (1.3% vs 6.0%, p=0.02). These findings may represent resistance mechanisms, concordant with prior literature (Lin et al., AJCR 2014). Conversely, the frequency of ERBB2 short variants was significantly lower in the post-treatment cohort (7.2% vs 0%, p=0.0005).
Conclusion
Population-based analyses of a scalable, real-world clinicogenomic database, derived from data generated as part of routine patient care, can recapitulate known and previously hypothesized mechanisms of resistance to EGFR inhibitors. Extension of this approach with increasing sample size and longitudinal follow-up over time may elucidate novel mechanisms of resistance to a broad array of cancer therapies.
Citation Format: Gaurav Singal, Gerald Li, Vineeta Agarwala, Gaurav Kaushik, Claire O'Connell, Garrett A. Cobb, Thomas Caron, David Bourque, Ameet Guria, Shannon Frank, Garrett Frampton, Ken Carson, Amy Abernethy, Vincent A. Miller. Identification of resistance mechanisms to EGFR treatment in the real world using a clinicogenomic database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1833.