Technique and Technology: Machine Learning

Dr. Sanju Sinha earned his Bachelor of Technology in Bioengineering at the Indian Institute of Technology in Guwahati, India. He recently completed his postdoctoral research and PhD in computational biology at the National Cancer Institute (NCI) with Dr. Eytan Ruppin with a co-mentorship of Dr. Brid Ryan during his PhD His PhD was earned in a joint University of Maryland and NCI program.

“At the core of my work is the desire to make a lasting impact on patient’s lives, offering patients not just better treatment, but an opportunity to avoid the disease altogether. Sanford Burnham Prebys is renowned for its work in understanding aging and developing new drugs—two areas that are key to my research. This makes it the perfect place for what I’m hoping to achieve.”

Education

2021: PhD, Computational Biology, University of Maryland and National Cancer Institute
2016: B.Tech., Bioengineering, Indian Institute of Technology, Guwahat

Honors and Recognition

2023: Top Five Outstanding NCI Postdoctoral Fellow 
2023: Transition to Industry Fellowship 
2021: Emerging Leaders of Computational Oncology by MSKCC. 
2020: NCI Outstanding PhD award
2020: NCI CCR milestone 
2019: NCI Fellows Award for Research Excellence 

Related Disease
Cancer

Phenomena or Processes
Aging, Cancer Biology

Techniques and Technologies
Bioinformatics, Computational Biology, Drug Discovery, Machine Learning

Developing cancer preventive therapies using the power of AI.

At the heart of our research is the mission to develop cancer-preventive therapies using the power of artificial intelligence. Our current focus lies in dissecting the role of aging in cancer susceptibility, a crucial factor often overlooked in conventional research. We employ computational tools to analyze multi-omics data to understand the aging-induced alterations in the tissue microenvironment that increase cancer risk. But we don’t stop at understanding these changes. We apply this knowledge in designing preventive therapy candidates that specifically target these alterations. Drawing on my experience in machine learning, drug discovery, and precision oncology, our lab is on a quest to reimagine the drug discovery pipeline.

“As we aim to push the boundaries of cancer prevention research, we are hiring individuals eager to contribute to this mission at multiple levels including postdoctoral researchers, experienced computational biologists, and, PhD students.”

Sanju Sinha’s Research Report

My key contributions to the field of computational oncology during my PhD and postdoctoral tenure are outlined below:

DeepTarget: A Computational Tool for Decoding the Mechanism of Action for Cancer Drugs
DeepTarget, our innovative computational tool, integrates data from genetic and drug screens to intricately understand the mechanism of action of cancer drugs. It identifies both primary and secondary drug targets, paving the way for a comprehensive understanding of drug function, its optimal indications, and clinical potential.

Revealing the Potential Cancer Risks associated with Genetic Editing
Through computational analyses of vast genetic screens, we shed light on the inherent selection potential of specific cancer gene mutations associated with CRISPR-Knockout editing. This work has significantly enhanced our understanding of the risks associated with gene editing.

First Single-cell based Precision Oncology Framework: A Proof-of-concept
Our innovative approach to precision oncology utilizes single-cell transcriptomics to predict patient treatment responses and detect resistance. We demonstrated its efficacy using patient-derived primary cells and three recent single-cell clinical cohorts, providing a powerful tool for the next generation of precision oncology approaches.

Why High Tumor Mutation Burden Biomarker of Immunotherapy is only effective for Certain Cancer Types?
We unveiled the microenvironment context that can determine if the high-tumor mutation burden (TMB) biomarker will be effective in a certain cancer type – High M1 Macrophage levels and Low Resting Dendritic Cells. Based on this, we also predicted the rare tumor types with High-TMB where immunotherapy is most likely to be successful.

Uncovering Therapeutic Prospects for African Americans: A Step Forward in Inclusive Cancer Research
Our work revealed distinct biological differences in tumors across African American and European American patients. Most notably, we identified a higher prevalence of homologous recombination deficiency in African American patients, pointing towards promising, personalized treatment options.