(PQD1) An Iterative Approach for Overcoming Evolving Targeted Therapy Resistance

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DESCRIPTION (provided by applicant): Here, we investigate Provocative Question PQD1: "How does the selective pressure imposed by the use of different types and doses of targeted therapies modify the evolution of drug resistance?" The effectiveness of targeted therapy to prolong survival in cancer patients is limited by the inevitable development of drug resistance. Cancer populations constantly evolve, enabling subpopulations of cells to adapt and ultimately overcome drug treatment. A comprehensive understanding of potential drug-resistance mechanisms and their therapeutic vulnerabilities will form the basis for finding optimal targeted treatment plans of drug-resistant tumors. A common strategy for studying mechanisms of drug resistance is to generate a drug-resistant cancer population under a single selective pressure, and characterize its vulnerabilities using population-averaged assays. However, it is unclear which selective pressures should be varied to encourage the emergence and evolution of uncharacterized drug mechanisms. There are a virtually limitless number of parameters that could be varied, and it is unclear which would be productive to explore. Further, population-averaged assays largely characterize the fittest clones; clinically relevant mechanisms, which may appear at low frequencies in experimental settings, will be missed. As a result, the drug-resistance "landscape" has not been systematically explored. Here, we propose that drug resistance can be broadly surveyed instead by isolating and studying individual drug-resistant clones derived under a small number of selection conditions. We leverage the natural heterogeneity of cancer, traditionally viewed as an impediment for understanding the disease, to reveal the range of possible resistance mechanisms. Our preliminary studies strongly suggest that this strategy will unmask diverse drug mechanisms. To address PQD1, we assess the diversity of resistance mechanisms present in a cancer population and how this diversity changes in response to different selective pressures. In Aim 1, we use a "shotgun" approach for isolating large numbers of resistant clones from cancer populations treated with different targeted therapies. In Aim 2, we map our clonal populations into "resistance classes" defined by common therapeutic vulnerabilities. In Aim 3, we test how our resistant clones evolve under new selective pressures.