| dc.description.abstract | Immune checkpoint therapies, particularly monoclonal antibodies binding to the immune inhibitory molecules programmed death-1 (PD-1) and its ligand PD-L1 as well as cytotoxic T lymphocyte associated protein 4 (CTLA-4), have yielded impressive clinical benefits in a variety of cancer types by potentiating the anti-tumor immune response. However, not all patients respond to these therapies, and the tumor or patient characteristics that underlie robust responses to immune checkpoint therapy remain unknown. Advances in genetic sequencing technology and in silico analysis tools have provided a valuable toolbox for investigating the variation in patient tumor genotype and phenotype and how this impacts response to anti-cancer therapies. In this study, I analyze whole exome and whole transcriptome sequencing data from pre-treatment and post-treatment tumors of more than patients with annotated outcomes to immune checkpoint therapy to identify potential correlative markers of response and resistance to cancer immunotherapies. These studies vary from analysis of a large cohort of patients of a single cancer type focusing largely on whole exome characteristics to a longitudinal, in-depth, multi-dimensional view of multiple tumors obtained longitudinally through a single patient’s course of treatment to a meta-analysis combining multiple cancer histologies and immune checkpoint therapy types under a unified genetic analysis pipeline. Using these methods, I identify multiple biological pathways of interest, including formation of tumor-specific neoantigens, interferon-γ-related pathways, major histocompatibility complex (MHC) variability, phosphatase and tensin homology (PTEN) signaling, and ultraviolet-light and smoking- related molecular signatures. These features likely interact in complex and intertwining ways to influence a patient tumor’s interactions with the immune system, and where possible, I pursue experimental studies with collaborating researchers to further delineate and validate these interactions. This study highlights potential genes and molecular mechanisms that may indicate promising avenues to better understand the cancer-immune interaction and thus improving patient selection for immune checkpoint and identifying areas of improvement for future immune checkpoint therapies. | |
