Shrabanti Chowdhury, PhD

2026 Early Career Investigator Grant

Icahn School of Medicine at Mount Sinai

Project Summary
Despite decades of research, there is currently no way to predict platinum-refractory (who do not respond to platinum-based therapy) high-grade serous ovarian cancer (HGSOC) prior to treatment. Our recent study (Cell 2023, PMID: 37541199) identified and validated a proteomic signature capable of predicting refractory tumors before chemotherapy. We also found that mechanisms of refractoriness vary across HGSOC subtypes. Building on this, we propose a precision medicine approach that leverages deep learning and subtype-specific biology to improve prediction. With clinical validation using higher throughput assays, these signatures can predict refractory HGSOC patients, and eventually could guide alternative therapies through clinical trials.

Bio
Shrabanti Chowdhury, PhD is an Assistant Professor in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai in New York and is a member of National Cancer Institute’s Clinical Proteomics Tumor Analysis Consortium (CPTAC). She received her PhD in Statistics from University of California, Riverside and is trained as a Biostatistician and Computational Biologist in her post-doctoral work. Her research is focused on integrative proteogenomic analysis methods and their application to complex high-dimensional multimodality cancer data sets, with a major focus on ovarian cancer. Specifically, she develops sophisticated computational/statistical methods for integrating different types of -omics data to understand the molecular mechanism and identify signaling pathways driving the disease that, in turn, can be translated into the clinic. Her current research involves developing a comprehensive pipeline employing advanced artificial intelligence and deep learning techniques for predicting treatment response in ovarian cancer, to enhance prediction accuracy by flexibly modeling and capturing complex correlations in high-dimensional data with a limited sample size. She also develops novel algorithms for learning directed network based on high dimensional -omics data to facilitate system learning, such as cell-cell communication.