Patient derived models of cancer (PDMC) are supposed to recapitulate clinical human cancer more faithfully, and these preclinical models are being increasingly used for drug discovery and mechanistic studies. However, there have been no systematic studies that compare the PDMCs to understand whether different PDMC growth environments can cause distinct phenotypic and molecular changes to patient- derived cancer cells carrying the same genetic mutations This proposal aims to test an overarching hypothesis that patient-derived cancer cells can undergo distinct epigenetic reprogramming in response to the different PDMC environments, which impact tumor phenotypes such as heterogeneity, chemoresistance, metastasis, and immune adaptation. By assembling a multidisciplinary team consisting of clinicians, geneticists, and engineers, this project will systematically profile the epigenomes of three PDMC colorectal cancer (CRC) models: organoid, patient- derived xenograft (PDX), and humanized immunoproficient PDX. The evolution of the tumor cell epigenetic landscape in PDMC (and vs. original patient tumors) and in response to therapy will be investigated. Whether P matched primary and metastatic CRCs from the same patient remain epigenetically distinct or converge will also be tested. A novel precision CRISPR-based epigenomic editing screening technology will then identify specific epigenetic drivers that contribute to PDMC tumor growth and chemoresistance. If successful, this comprehensive study will systematically characterize the differences between these PDMCs, which will be informative for future basic and translational studies. Furthermore, this study will provide insights into epigenetic regulation of CRC chemoresistance, metastasis, and immune evasion. These insights are important thanks to emerging evidence suggesting that genetic mutation alone cannot account for all phenotypic changes across PDMCs. In contrast to small molecule epigenetic modifiers which affect the genome globally, screening using the novel CRISPR-based epigenomic editing technology will be able to identify specific epigenomic drivers for the first time.
Patient-derived cancer models are being increasingly used for pre-clinical applications, but their differences in recapitulating human cancer are not well understood. This study will systematically characterize the epigenetic reprogramming between different models and uses novel epigenetic editing tools to identify epigenomic drivers that contribute to the phenotypic differences between the models.
