GESTALT Barcoding and Single-cell Transcriptomics of Tumor Cell Evolution in Personalized Tumor Models

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Molecular & Cellular Analysis Technologies
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Not Applicable
PROJECT SUMMARY / ABSTRACTIdentification of somatic mutations in diverse tumor types has grown exponentially with the development ofnext-generation sequencing technologies. However, there is a pressing need to validate putative cancer drivergenes and separate them from coincidental ?passenger? mutations. Further, it is becoming clear that manycancers are highly heterogeneous in terms of the polyclonality of somatic genotypes?often expressingmultiple driver mutations simultaneously or in different subpopulations. Moreover, standard of care treatmentoften induces selective pressures resulting in significant alterations in recurrent populations. We are only inthe beginning stages of validating driver genes in many tumor types and are even further behind in studyingmechanisms of evolution and recurrence in these systems. The central theme of this grant application is togenerate a toolset marrying patient-derived, ?personalized? somatic mutation signatures with genome editing ofsynthetic target arrays for lineage tracing (GESTALT) for the elucidation of the transcriptomic mechanismsleading to tumor diversity. Specifically, we will generate a pipeline for isolating single-cell transcriptomes andGESTALT barcodes to classify and lineage map large numbers of tumor populations over time, including afterthe selective pressures of standard of care treatment. Over the past several years, we have pioneered anelectroporation-based somatic mutation method for rapid, non-invasive, somatic transgenesis for highthroughput validation of tumor driver genes using mosaic analysis with dual recombinase-mediated cassetteexchange (MADR). We will employ novel in vivo MADR models of glioma as a test case for the utilization ofthis system for later use with diverse tumor types. The overall objective of the proposal is to perform advanceddevelopment of this combined MADR-GESTALT approach to allow for generalized use in diverse tumorcontexts and, therefore, demonstrate the potential of this technology to transform cancer research.We propose to carry out this work in three parts. The focus of Specific Aim 1 is to optimize the combinedMADR-GESTALT system for generating tumor cell classification by transcriptome and lineage maps. Themain goal of Specific Aim 2 is to rigorously validate MADR-GESTALT inducible elements in the context ofclinical standard of care treatment. Finally, to prepare for widespread dissemination of these tools, in SpecificAim 3 we will generate and validate knock-in mice with GESTALT elements for tissues not amenable toelectroporation. Successful completion of these experiments will create the foundation for a long-lived,cornerstone toolset for understanding both basic and pathologic mechanisms of the disease as well asproviding definitive, genetic insights into the cellular and transcriptomic mechanisms of progression andrecurrence in a diverse array of cancers.