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Computational Cancer Genomics and Precision Oncology

Rutgers Cancer Institute

Dr. Subhajyoti De is a Professor in the Department of Pathology and Laboratory Medicine at Rutgers Robert Wood Johnson Medical School and a principal investigator at the Rutgers Cancer Institute of New Jersey. Trained initially as an engineer at the Indian Institute of Technology Kharagpur, he pursued a PhD at the University of Cambridge, where he developed computational biology approaches to study genetic variation and mutational signatures in human genome evolution.

Following postdoctoral work as a Human Frontier Science Program Fellow at Harvard University, his research has focused on understanding cancer as a somatic evolutionary process, demonstrating that genomic and epigenetic alterations occur in non-random, context-dependent patterns.

His laboratory integrates genomics, computational biology, and systems-level approaches to investigate genomic instability, tumor evolution, and regulatory alterations in cancer. His work aims to improve early detection, patient stratification, and therapeutic strategies, and has been supported by major funding bodies, including the NIH and cancer research foundations.

Dr. Subhajyoti De is a faculty member at Rutgers Cancer Institute. He conducts this training program in his personal capacity and not as a representative of, or on behalf of, Rutgers Cancer Institute.

Programs will be conducted during the following periods: May–June; September–October; February–March.

Main Areas of Interest

Computational cancer genomics and multi-omics data integration
Environmental exposure, genomic instability and mutational processes in cancer
Tumor evolution, heterogeneity, and clonal dynamics
Machine learning and AI applications in cancer biology and precision oncology
Biomarker discovery and translational cancer informatics
Liquid biopsy and non-invasive cancer diagnosis

Specialties

Oncology

Genetics & Genomics

Cancer

Computational Biology

Techniques You Will Learn

Computational cancer genomics: analysis of whole-genome, RNA-seq, and epigenomic datasets
Multi-omics data integration (genomics, transcriptomics, epigenomics) for cancer profiling
Identification of mutational signatures and genomic instability patterns in cancer
Tumor heterogeneity, clonal evolution, and single-cell genomics approaches
Machine learning and AI methods for cancer classification, prediction, and biomarker discovery
Bioinformatics pipelines (QC, alignment, variant calling, annotation, and visualization)
Liquid biopsy data analysis (ctDNA, circulating biomarkers) for non-invasive diagnostics
Translational cancer informatics: linking molecular data with clinical outcomes
Development and validation of computational tools for precision oncology

Modules

Foundations of Cancer Genomics & Genomic Instability

Overview of cancer genomics, mutational processes, and DNA damage/repair
Environmental exposures and their role in genomic instability
Introduction to sequencing technologies and cancer datasets

Multi-Omics Integration & Computational Analysis

Integration of genomic, transcriptomic, and epigenomic data
Data preprocessing, quality control, and pipeline development
Systems biology approaches to cancer pathway analysis

Tumor Evolution & Heterogeneity

Clonal dynamics and tumor evolution models
Single-cell genomics and intra-tumor heterogeneity
Identification of driver mutations and structural variations

AI, Machine Learning & Cancer Informatics

Machine learning models for cancer classification and prognosis
AI-based approaches for large-scale genomic data interpretation
Case studies (e.g., microbial signal detection in tumors using ML approaches like PRISM)

Biomarkers, Liquid Biopsy & Translational Applications

Biomarker discovery and validation using multi-omics data
Liquid biopsy (ctDNA, circulating biomarkers) for early detection and monitoring
Translational applications: precision oncology and clinical decision support
Final project: integrative analysis of a cancer dataset

Program Fee


Total Number of Modules	5
Total Program Cost	$650.00

Who Can Apply

Postgraduate students (MSc/PhD) in fields such as Molecular Biology, Genetics, Bioinformatics, Computational Biology, or related life sciences
Early-career researchers, postdoctoral fellows, and research scientists working in cancer biology or genomics
Professionals from interdisciplinary backgrounds (e.g., data science, computer science, engineering) with experience in biological data analysis
Candidates interested in translational cancer research, multi-omics integration, and AI applications in healthcare

Host Details

Host Name: Dr. Subhajyoti De

Affiliation: Rutgers Cancer Institute

Address: 195 Little Albany Street, New Brunswick, NJ 08903

Website URL:

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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