My current research focuses on understanding the molecular and immunological factors that contribute to the duration of response to cancer immunotherapy, particularly checkpoint inhibition. I recently submitted a manuscript titled "Association of heightened host and tumor immunity with prolonged duration of response to checkpoint inhibition across solid tumors." This study identified novel associations between predicted neoantigen burden, enhanced T-cell activity, cytotoxic markers and genetic variants in the EPHA8 gene with prolonged immunotherapy responses in patients with advanced solid tumors. These findings suggest that deeper insights into tumor immunology and host immune responses can inform more precise and personalized treatment strategies for cancer immunotherapy.
Building on this foundational work, my future research aims to expand the scope of these investigations by incorporating a larger and more diverse patient cohort, with the goal of improving the predictive power of immune and genetic biomarkers. I intend to leverage cutting-edge machine learning approaches to integrate multi-omics data (including DNA, RNA, and immune profiling) to uncover more robust predictors of immunotherapy response. By applying machine learning algorithms to large, multi-dimensional datasets, I hope to identify novel biomarkers and refine our understanding of how genetic and immune factors synergistically influence the success of checkpoint inhibition. Ultimately, my research aims to optimize personalized cancer treatment by identifying actionable targets for clinical intervention and improving patient outcomes across a range of solid tumors.
Ph.D. - Indiana University Luddy School of Informatics, Computing, and Engineering, Indianapolis, IN 2016
M.S. - Indiana University Luddy School of Informatics, Computing, Engineering, Indianapolis, IN 2004
Pharm.D. - Ramaiah College of Pharmacy, Bangalore, India 1999
