MUIDSI Dissertation Defense: IDENTIFICATION OF IMMUNE-RELATED GENE SIGNATURES TO EVALUATE IMMUNOTHARAPEUTIC RESPONSE IN CANCER PATIENTS USING EXPLORATORY SUBGROUP DISCOVERY

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients’ health outcome remains a challenging task. 

We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immuno-targeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease.  

Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.

Please contact Robert Sanders (sandersrl@missouri.edu) for Zoom information.