Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most patients are treated with little or no understanding of whether they will respond to an individual therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow for: real-time decision making, identification of rare subpopulations with therapeutic resistance, analysis of very small samples (e.g. MRD), and maintains viability individual cells for downstream assays to characterize phenotypic, genotypic, transcriptional and other determinants of sensitivity. The overall goal of our U54 application is to address this need using new strategies for predicting therapeutic response in which paired phenotypic and genomic properties are measured at the single-cell level. Phenotypic properties will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer- term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal architectures based on therapeutic response will be established across each tumor sample by incorporating molecular and physical parameter data from large numbers of cells. In settings of deep treatment response, pre-treatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The cells that exhibit particular functional properties (e.g. phenotypic non-responders) will be isolated and analyzed for genomic determinants of these properties. These data will then be incorporated into mathematical models to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors responsible for treatment failure. By pursuing this approach, our center will establish a framework that enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens and computational approaches that result in testable predictions.
Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Better information about which treatment to offer an individual patient (i.e., precision medicine) could improve efficacy while sparing patients from the toxicity of therapies that offer no benefit. The overall goal of our U54 application is utilize new technology platforms for predicting therapeutic response in which phenotypic and genomic properties are measured at the single cell level. By pursuing this goal, our center will establish a framework that enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens and computational approaches that result in testable predictions. Over the 5-year award, we plan to expand accessibility of these platforms to clinical studies across leukemias, colon and pancreatic cancers, and potentially a broader range of tumor types.
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| Mead, Benjamin E; Ordovas-Montanes, Jose; Braun, Alexandra P et al. (2018) Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types. BMC Biol 16:62 |
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| Cetin, Arif E; Stevens, Mark M; Calistri, Nicholas L et al. (2017) Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation. Nat Commun 8:1613 |
| Shalek, Alex K; Benson, Mikael (2017) Single-cell analyses to tailor treatments. Sci Transl Med 9: |
