Sohrab Shah, PhD

Titles and Affiliations

Chief, Computational Oncology Service
Department of Epidemiology and Biostatistics
Memorial Sloan Kettering Cancer Center

Areas of Focus

Metastasis
Treatment
Tumor Biology

Research Area

Developing AI models to improve prediction of breast cancer recurrence risk and treatment response.

Impact

Artificial intelligence models trained on biological and medical data hold immense potential for assisting physicians in understanding and predicting patient outcomes. However, to ensure safety and utility, these AI models must be trained and deployed with the utmost care. Dr. Shah and his team have recently discovered that AI models trained on multimodal data –encompassing the tissue, cellular, and molecular levels—outperform models trained on a single data type. Dr. Shah aims to develop AI models using such multimodal data to predict the risk of recurrence after treatment in patients newly diagnosed with breast cancer and to assess the drivers of treatment resistance in metastatic breast cancer.

What’s Next

Dr. Shah and his team will develop artificial intelligence (AI) models to predict risk in early-stage and metastatic breast cancer. The methods they develop will use information routinely collected in the care of patients, including digitized images of tumor tissue. The team will train two AI models: one to predict recurrence after therapy, and one to study how metastatic breast cancers respond to state-of-the-art therapies. The advances made will assist physicians in precise patient stratification, identifying those in need for therapy while avoiding overtreatment, and harness the power of multimodal data to provide effective care for breast cancer patients.

Biography

Sohrab Shah, PhD is the Chief of Computational Oncology in the Department of Epidemiology and Biostatistics and Director of The Halvorsen Center for Computational Oncology at Memorial Sloan Kettering Cancer Center. He holds the Nicholls-Biondi Endowed Chair in Computational Oncology at MSK and is a Susan G. Komen Scholar. Dr. Shah has a joint appointment as a professor in the Department of Physiology, Biophysics, and Systems Biology at Weill Cornell Medical College. Dr. Shah holds a PhD in computer science and started his independent research laboratory in 2009. He oversees the research activity of the Computational Oncology program, which includes eight tenure-track principal investigators, and three laboratory-track members dedicated to advancing computational and data science research for cancer biology and clinical programs.

Dr. Shah’s research laboratory focuses on understanding the principles and processes of cancer evolution. To this end, his laboratory develops and applies computational approaches encompassing advanced machine learning, artificial intelligence, and Bayesian statistical methods combined with single-cell measurement technology. This work has led to advances in understanding drug resistance and tumor evolution in breast cancer and promising new directions to study how breast cancers evolve during and after therapy. Dr. Shah has recently initiated a new program of research in multimodal data integration to capitalize on the institution’s vast clinical and diagnostic data resources, including genomics, radiologic imaging, digital histology, treatment response, and high-resolution single-cell genomics. Dr. Shah is a principal investigator in several national and international collaborative research programs, including Breakthrough Cancer, the NIH-funded Center of Excellence in Genomic Sciences program called the Center for Integrated Cellular Analysis, and the CRUK Grand Challenge program entitled IMAXT: Imaging and Molecular Annotation of Xenografts and Tumors. His BCRF project will focus on artificial intelligence approaches to study the spatial biology of drug resistance in breast cancer.