Determine genotypic, phenotypic and evolutionary trajectories of treatment resistance to radiotherapy.

About This Grant

ABSTRACT There is an urgent need to nominate biomarkers that are likely to predict the efficacy of radiotherapy and accelerate their clinical translation. Efforts thus far have been limited in large part because the omic features regulating tumor cell survival and their frequency across and within individual cancer types had not been studied. Also, radiation is one of the most utilized therapeutic modalities in cancer. Despite this, radiotherapy dose is still delivered using a ‘one size fits most’ paradigm and has not yet lent itself to personalization based on genetic features. Our group of investigators have expertise that spans several aspects of the study of translational lung radiotherapy including advanced functional genomics, use of pre-clinical models of human tumors, personalized therapeutic interventions, bioinformatics, computational biology, and clinical research. Collectively, we have: (1) completed the largest profiling effort of the genetic vulnerability of cancer cells to irradiation to date (533 cell lines); (2) completed an arrayed unary (one mutation per well) gene variant cellular profiling project that has interrogated ~400 common and rare genetic variants for response to ionizing radiation in cells; (3) developed new functional genomic tools to study genome-phenome associations at scale; (4) developed over 500 genetically and clinically annotated patient derived xenograft (PDXs), subset of ex vivo and 3D models for testing using a radiation platform and (5), developed new computational tools and frameworks to personalize radiation dose treatments. The major themes for this proposal are: (i) analyze matched pre- and post-radiotherapy genomes from patient tissue and using PDX to elucidate mechanisms of de novo and/or acquired resistance; (ii) develop predictive diagnostics related to multi-omic tumor variates in patients receiving radiotherapy for lung cancer; (iii) integrate multi-tiered omic data to accurately predict the probability of local failures after lung radiotherapy; and (iv) identify and validate functionally significant molecular lesions in tumors that impact radiotherapeutic efficacy. This project is explicitly designed to utilize patient and model systems data in iterative and complementary testing cycles. Due to our extensive and sustained efforts in this space, we have an established preclinical understanding of the most salient genetic determinants that regulate radiation sensitivity in lung cancer. Altogether, our highly integrated proposal will come together in an unprecedented effort to advance toward clinical integration new information and capabilities that will ultimately improve outcomes for the multitude of patients receiving lung radiotherapy each year in the US and abroad.