Abstract 1178: Genotype-fitness maps guide targeted therapy combination in lung adenocarcinoma
Authors: Zviran Asaf , Bolan Patrick , Brenan Lisa , Goodale Amy , Rotem Denisse , Adalsteinsson Viktor , Piccioni Federica , Johannessen Cory , Landau Dan 
Published: August 2018

Abstract
Targeted EGFR inhibition in lung cancer leads to dramatic responses. Nonetheless, disease evolution to resistance is the rule. As this evolutionary process is fueled by intra-tumoral genetic diversity, a comprehensive mapping of clonal fitness is required to inform strategies to overcome resistance.
To model intra-tumoral diversity in vitro, we performed a genome-wide, over-expression genetic perturbation assay in EGFR-driven non-small cell lung cancer (NSCLC) PC9 cells. Our screen covered 17,255 ORFs (open reading frame constructs) representing 12,728 wildtype and mutated genes, and examined the effects of a first-generation EGFR inhibitor (erlotinib), a third-generation EGFR inhibitor (osimertinib) and a MEK inhibitor (binimetinib) on evolutionary selection, alone or in combination. Specifically, to obtain genotype-to-fitness maps for each drug, we measured clonal abundance up to 4 times during the screen and mathematically resolved their growth behavior. Finally, for each drug, we applied multiple doses to obtain dose-fitness relationships, which may impact tumor evolution in patients due to high inter- and intra-tumoral variability in drug delivery. In total, we have performed 78 genome-wide screens to map the evolutionary landscape of PC9 resistance to targeted therapy.
Erlotinib and osimertinib both result in an overwhelming reduction of fitness, as expected from their clinical benefit. Known clinical resistance mechanisms involving ERBB2, PIK3CA, AXL, and BRAF confer a pronounced fitness advantage in the presence of both drugs, while EGFR (T790M) improves fitness only with erlotinib. Novel resistance mechanisms include alternative tyrosine kinases (NTRK, PDGFRB, CSF1R, KIT), KRAS, G-coupled protein receptors, transcription factors (SOX15, FOXA1), and cellular transporters (ABCG2). Notably, we observe significant divergence in dose-fitness relationships between different resistance mechanisms. For example, PIK3CA confers a modest but persistent advantage across the dose range, in contrast to BRAF which results in a fitness advantage only when sufficiently high drug levels are added.
The fitness landscape for binimetinib resistance appeared to be largely orthogonal to that of the EGFR inhibitors, with decreased fitness noted for many tyrosine kinases as well as KRAS mutations. MEK inhibitor resistance results from RAF family member overexpression (ARAF, BRAF, RAF1) and MAPK activation. The orthogonal resistance landscapes of EGFR inhibition and MEK inhibition translate into highly synergistic effects in drug combination, with effective prediction of combinatorial fitness from the single-agent fitness landscapes using generalized linear models. These results validate this approach as a systematic method to address the combinatorial problem of optimizing drug combinations and doses to directly anticipate and address cancer evolution.
Citation Format: Asaf Zviran, Patrick Bolan, Lisa Brenan, Amy Goodale, Denisse Rotem, Viktor Adalsteinsson, Federica Piccioni, Cory Johannessen, Dan Landau. Genotype-fitness maps guide targeted therapy combination in lung adenocarcinoma [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 1178.