Investigator, Instructor (M) RadOnc MGH SteeleLabFaculty, Mass General Research Institute

Instructor in Radiation Oncology Harvard Medical School

MBBS Stanley Medical College, Chennai, India (TNMGR Medical University) 2013

PhD University of Saskatchewan, SK, Canada 2019

M.Sc. Virology National Institute of Virology, Pune, India 2015

bioinformatics; brain tumors; computational biology; computational modeling; immune-oncology; neuro-oncology My research focuses on uncovering the molecular and metabolic vulnerabilities that drive treatment resistance in solid tumors, particularly in the context of metastatic disease. I have identified metabolic dependencies unique to triple-negative breast cancer (TNBC) brain metastases and demonstrated how targeting these pathways can suppress tumor growth in vivo. I have also shown that modulating the tumor microenvironment—for example, through the addition of losartan to chemoradiation—can downregulate pro-invasion and immunosuppressive gene programs in human pancreatic cancer, providing a rationale for combination therapies that reprogram the tumor milieu.

My research group studies the biology of brain metastases, with a particular focus on breast cancer metastasis to the brain. We investigate how tumor cells adapt to the nutrient-restricted, immune-privileged brain microenvironment and how those adaptations can be therapeutically targeted. Using a combination of transcriptomic profiling, CRISPR-based functional genomics, metabolic assays, and in vivo tumor models, we aim to identify actionable vulnerabilities in tumor and stromal compartments.

In addition to brain metastasis, we apply computational biology and systems-level approaches to study other hard-to-treat cancers, including pancreatic ductal adenocarcinoma (PDAC) and glioblastoma (GBM). We integrate multi-omic datasets, including bulk and single-cell RNA-seq, spatial transcriptomics, and proteomics, to define tumor subtypes, map microenvironmental niches, and uncover therapeutic targets.

Our group also works at the interface of computation and clinical translation, developing tools that apply machine learning and natural language processing to clinical data. We are actively building and evaluating AI-powered tools—such as structured chatbots—to enhance patient triage, improve clinical history-taking, and support diagnostic decision-making across healthcare settings. By combining computational discovery, experimental validation, and real-world clinical data analysis, we aim to bridge the gap between bench and bedside to develop precision treatment strategies for patients with aggressive and metastatic cancers.