Enhancing TIL Efficacy in NSCLC through Epigenetic Reprogramming and Computational Modeling

ABSTRACT Novel therapeutic protocols are desperately needed to treat non-small cell lung cancer (NSCLC), the deadliest form of cancer world-wide. Immunotherapy is the standard-of-care for most NSCLC patients and has shown great promise. However, response rates are still strikingly low at just 20%. Alternatively, utilizing a patient’s tumor- infiltrating lymphocytes (TIL) is proving to be a viable and highly personalized approach. Research: Hostile tumor microenvironments (TME) contribute to T cell rejection in solid cancers, and it is well known that the TME of NSCLC influences immunotherapy responses. This project proposes “Enhancing TIL efficacy in NSCLC through epigenetic reprogramming and computational modeling”. We recently found that inhibiting epigenetic enzyme EZH2 (EZH2i) is a promising mechanism to improve both TIL expansion and efficacy. Specifically, EZH2i led to improved anti-PD1 immunotherapy response, upregulation of both major histocompatibility complexes, greater pro-T cell cytokine signaling, and myeloid populations became more tumor-eliminating. Further, several studies show that EZH2i limits regulatory T cells. Based on preliminary data, we hypothesize that EZH2i will improve both TIL expansion and infusion outcomes in NSCLC patients (Aim 1). Yet, there are certainly mechanisms of T cell suppression that EZH2i may not overcome. To address and identify these mechanisms, mathematical modeling of cancer has risen as a viable method to analyze dynamical systems. Stochastic Boolean regulatory networks in particular provide a coarse-grained and mechanistic approach to study cellular processes that drive long-term outcomes. Therefore, we further hypothesize that modern sequencing data and methods from mathematical biology can discover alternative mechanisms of T cell suppression that will suggest novel targets for TIL-combination treatment (Aim 2). Candidate: Dr. Daniel Plaugher is an advanced early postdoctoral fellow in the University of Kentucky’s NCI-designated Comprehensive Markey Cancer Center (MCC). He has extensive training in mathematical biology, building dynamic models of cancer systems, and has spent the last 1.5 years fully emersed in bench training. Further training in (1) cancer informatics, (2) tumor immunology, and (3) modern experimental science will enhance his trajectory toward becoming an independent investigator. Mentors/Environment: Dr. Plaugher’s career development plan includes substantial mentorship, didactic and nondidactic training, and experiential learning at MCC. He will receive direct mentorship from Drs. Christine Brainson (primary, lung cancer biology), Ilhem Messaoudi (co-mentor, immunology), and Chi Wang (co-mentor, bioinformatics). He will be advised by Drs. Zhonglin Hao (thoracic oncology), Jinpeng Liu (computer science), and Daniel Abate-Daga (T cell biology). Overall, the objectives of this application will advance lung cancer research both regionally and beyond by providing evidence for future R01 submissions and by supporting Dr. Plaugher’s training as an independent scientist to run a synergistic computational and experimental laboratory.