Despite enormous success in treating several types of cancer, immune checkpoint blocker (ICB) therapy still only shows efficacies in a subset of patients. Identifying novel regulators of immunotherapy response as well as improving the response rate of cancer immunotherapies remain open questions. Recently, we used CRISPR screens in mouse models to investigate T-cell infiltration, proliferation, and killing efficacy, and identified PBAF of the SWI/SNF chromatin remodeling complex as one novel regulator of T-cell mediated cytotoxicity. We also developed a computational model, TIDE, to identify gene signatures of CD8 T-cell dysfunction in immune hot tumors and T-cell exclusion in immune cold tumors. The resulting signatures, computed from tumor profiles in non-immunotherapy setting, show promising results in predicting melanoma and lung cancer patient response to immune checkpoint blockade based on pre-treatment tumor expression profiles. This proposed project aims to improve the TIDE biomarkers, identify novel regulators, and elucidate their mechanisms underlying ICB response.
In Aim 1, we will develop machine learning approaches on large collection of clinical tumor transcriptome profiles from non-ICB settings to refine the TIDE predictive biomarker of ICB response, and develop a web server to comprehensively evaluate different ICB response biomarkers in all the available ICB cohorts.
In Aim 2, we will conduct in vivo CRISPR screens in mouse syngeneic tumor models to identify cancer-cell intrinsic regulators of ICB response, which can serve as novel targets to improve ICB response.
In Aim 3, we will elucidate the mechanism underlying two novel regulators of ICB response and characterize their effects on the tumor immune microenvironment using single-cell RNA-seq, single-cell ATAC- seq, and computational modeling. Our investigative team has combined expertise in computational methodology immunotherapy. immunology and big data mining, functional genomics profiling Our proposed studies, if successfully executed, and translational benefits to cancer immunotherapy. and screening, cancer immunology and could provide new insights into cancer
Cancer immunotherapies have emerged over the last decade as highly promising approaches for cancer treatment, although only subsets of patients respond to immunotherapies. We propose to integrate computational modeling with functional genomics techniques to refine the immunotherapy response biomarkers, identify novel regulators of immunotherapy response, and elucidate their underlying mechanisms, with the potential to improve immunotherapy response.
