Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. To date, precision medicine efforts in oncology have focused primarily on genetic or epigenetic biomarkers within an individual tumor. However, not all marker-based predictions guarantee patient response, as many are the result of correlations from population-based studies. Approaches that utilize individual patient tumors specimens for ex vivo drug susceptibility testing are similarly limited by the process of generating a cell line and subsequent effects on drug sensitivity. Existing assays that measure cancer cell growth, such as ATP-based growth assays (CellTiter-Glo), require extended culture and a large volume of tumor cells. This precludes their use for disease monitoring in most patients with cancer. Furthermore, these bulk approaches are ill-suited for characterizing therapeutic susceptibility within subpopulations. Thus, there is a pressing need for rapid and facile approaches to characterize therapeutic sensitivity within individual tumor specimens that capture heterogeneity and can be applied to very small specimens, including minimal residual disease. Project 1 leverages a unique suite of tools to profile the intrinsic factors that inform the responses of individual cancer cells to therapeutic interventions. We will ask to what extent paired phenotypic and transcriptomic measurements can identify pathways that mediate cell autonomous resistance and highlight therapeutic approaches to overcome that resistance. Cancer cells will be isolated from primary tumors or from patient-derived cell lines/xenografts of both leukemias (as a liquid tumor model) and colon/pancreatic cancers (as a solid tumor model). In contrast to Project 2, cells will be measured in isolation without mimicking aspects of the microenvironment. Over a period of many hours, we will examine distinct phenotypic attributes of the cells (mass and mass accumulation rate) and link these attributes to the transcriptome at the single-cell level. We will then determine cell intrinsic mechanisms for resistance by analyzing transcriptomic features of responding and non-responding tumor cells.
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