Abstract 4954: Decoding tumor microenvironment to enhance NSCLC targeted therapy

Background: Tyrosine kinase inhibitors (TKI) have yielded promising responses in non-small-cell lung cancer (NSCLC) with EGFR mutations and ALK translocations. However, these and other targeted therapies are limited by intrinsic and acquired drug resistance. The previous study from our group investigated tumor autonomous resistance mechanisms by developing patient-derived cancer models (PDCs). In this study, we aimed to decipher the non-autonomous resistance mechanisms via tumor microenvironment by developing patient-derived fibroblast (PDF) models. Method: Cancer-associated fibroblast cell lines are established directly from individual EGFR mutant NSCLC biopsies. These cell lines, as representative of each patient9s tumor microenvironment, are further subjected to functional analysis. An imaging-based high-throughput platform is developed to screen for non-autonomous resistance by co-culturing PDC and PDF models in vitro. In the parallel, two independent approaches are performed to further identify mechanisms underlying the non-autonomous resistance. These include a drug screen to determine the pathway maintaining the cancer cells9 survival, and a secretomic analysis on the PDFs to identify the plausible cytokine(s) responsible for the resistance. Result: By co-culturing screening, non-autonomous resistance can be found in a wide spectrum of models. The subsequent drug screen reveals both a canonical HGF dependent and novel HGF independent mechanisms contributing to EGFR TKI resistance. Both of these can be explained by the PDF9s variable cytokine secretion and can be overcome by specific therapeutic combinations. Moreover, the microenvironment-driven EGFR TKI resistance has also been validated in vivo. And the prevalence of the identified cytokine is further tested in clinical specimens. Conclusion: PDFs provide a new avenue to explore non-autonomous resistance for targeted therapy. Applying this approach, we identified both the canonical HGF dependent and novel HGF independent mechanisms that putatively conferring EGFR TKI resistance. Taking EGFR TKI therapy as a paradigm, these findings will be valuable to optimize targeted therapy and to inform the design of personalized pharmaceutical interventions. Citation Format: Haichuan Hu, Lecia Sequist, Zosia Piotrowska, David Kodack, Aaron Hata, Matt Niederst, Cyril Benes, Jeffrey Engelman. Decoding tumor microenvironment to enhance NSCLC targeted therapy [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 4954.