Lung cancer causes more deaths worldwide than the next top three cancers combined. While targeting oncogenic drivers has shown widespread clinical utility, therapeutic responses have also been associated with tumor suppressor genotypes. Despite advances in clinical cancer genome sequencing and the development of many targeted therapies, understanding the relationship of tumor genotype to therapeutic responses remains a major obstacle to translating existing drugs into effective cancer treatments. There is a growing appreciation in cancer biology for the importance of the tumor microenvironment in cancer growth and therapy responses, underscoring the need to assess tumor response to therapy in the native tumor microenvironment. Genetically- engineered mouse models have emerged as particularly rigorous systems with which to test oncology therapies. However, a major limitation of existing mouse model is the relatively small number of different genotypes that can be generated. We have established a novel multiplexed somatic genome-editing approach that will allow the quantification of genotype-specific drug responses. This in vivo approach will increase the precision and scope of translational cancer pharmacogenomics studies. To quantify the effect of tumor suppressor gene inactivation on lung cancer growth in a high-throughput manner, I will use a method combining somatic Cas9-mediated gene inactivation with existing genetically-engineered mouse models, to generate 12 different lung tumor genotypes in parallel in individual mice. To quantify the exact size of each tumor and determine the size distribution of each genotype of tumors, I will induce tumors with barcoded vectors and use high-throughput sequencing and statistical approaches to determine the number of cancer cells in each tumor. I will quantify the responses of different genotypes of tumors to several therapies that have been shown to have genotype-specific effects in lung adenocarcinoma models. This will extend our understanding of the genetic determinants of treatment responses and define the experimental and statistical parameters to enable the most efficient use of these models for translational studies. By performing pre-clinical trials for targeted therapies across different tumor genotypes in parallel, I will generate a pharmacogenomic map connecting lung adenocarcinoma genotype to targeted therapy response. I will uncover sensitive and resistant genotypes, validate these genotype-specific effect, and investigate the cellular and molecular mechanisms of drug sensitivity and resistance. My studies will uncover novel biological insights that enable designing combinatorial therapy or developing novel therapies in order to overcome therapy resistance.
Lung cancer causes more deaths worldwide than the next top three cancers combined. Understanding the relationship of tumor genotype to therapeutic responses (pharmacogenomic analysis) remains a major obstacle to translating existing drugs into effective cancer treatments in the clinic. Here, I develop a high- throughput and cost-effective system for in vivo pharmacogenomic analysis of lung adenocarcinoma that is ideally positioned to become a mainstay of pre-clinical/co-clinical trial design and radically improves our ability to assign the correct drugs to the correct patients.