Welcome to computational cancer genomics lab!

Unraveling the genetic code of cancer


Welcome to the Computational Cancer Genomics Lab at the Princess Margaret Cancer Center at the University Health Network. We study role of genomic variations in driving tumor progression.

Our lab studies cancer biology by building computational tools with state-of-the art genomics, biophysics, and machine learning techniques. We are looking for enthusiastic researchers and students interested in computational biology to JOIN US!

Rapid declines in sequencing costs have enabled large-scale genome and exome sequencing for various cancer cohorts. A critical shared objective among such studies has been to understand how genomic variants affect tumor etiology. How may we develop robust quantitative models to predict the impact of somatic mutations on gene expression and protein function? Furthermore, how may we leverage these quantitative models to prioritize genomic variants and utilize this knowledge to develop new cancer therapeutics? Our lab is interested in developing integrative methods that use multiple data resources and cross-disciplinary approaches to address questions of this nature.

Previously, we have developed methods that integrate protein structure and protein motion information to evaluate the molecular impact of cancer mutations and identify putative cancer driver genes. Currently, we are building machine learning methods integrating protein structure, cancer genomics, and clinical data to identify novel drug targets and predict drugs' efficacy & side effects among cancer patients.

The canonical model of cancer progression dichotomizes cancer mutations as drivers and passengers. However, our recent analysis of thousands of cancer genomes indicates the presence of a continuum where strong and weak drivers can contribute to cancer progression via epistatic interactions or their aggregated/additive effects. As a follow-up to this work, we are currently developing novel methods to investigate the role of cooperative genetic and cellular level interactions in driving tumor growth and metastases.

The overwhelming majority of cancer mutations fall within non-coding regions of the genome. Clear insights into how non-coding mutations play causal roles in various cancer types remain limited. Similar to non-coding mutations, we have little understanding of how SVs influence cancer progression. My group is interested in building methodologies for understanding the role of non-coding mutations and SVs in different cancer cohorts.

Recent Publications

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to the more commonplace non-inflammatory breast cancers, Xiaotong Li; Sushant Kumar; Arif Harmanci; …. Naoto T. Ueno; Savitri Krishnamurthy; Lajos Pusztai; and Mark Gerstein. Genome Medicine (2021)

Haplotype-resolved diverse human genomes and integrated analysis of structural variation, Peter Ebert; Peter Audano; Qihui Zhu; Bernardo Rodriguez-Martin; …. Sushant Kumar; …... Tobias Marschall; and Evan E. Eichler Science (2021)

SVFX: a machine learning framework to quantify the pathogenicity of structural variants, Sushant Kumar*; Arif Harmanci*; Jagath Vytheeswaran; and Mark Gerstein Genome Biology (2020)

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences, Sushant Kumar*; Jonathan Warrell*; Shantao Li; Patrick McGillivray; William Meyerson; Leonidas Salichos;….. Ekta Khurana; and Mark Gerstein. Cell (2020)

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes, Esther Rhienbay; Morten Nielsen; Federico Abascal; Jermiah Wala;….. Sushant Kumar; …... Jakob Pedersen; and Gad Getz. Nature (2020)

Leveraging protein dynamics to identify cancer mutational hotspots in 3D-structures, Sushant Kumar; Declan Clarke; and Mark Gerstein. PNAS (2019)

For full publication list please check our google scholar page


Sushant Kumar is an Assistant Professor in the Department of Medical Biophysics at the University of Toronto. He is also a scientist at the Princess Margaret Cancer Center. His expertise and research interests are in computational biology/bioinformatics, cancer biology, genomics, machine learning, and biophysics.

Lucy Fuccillo is the research administrative assistant of the lab. Lucy was born and raised in Niagara Falls, Ontario. Subsequently, she moved to Toronto in 1999 to attend George Brown College to study Business Management. She has 30+ years of experience working as a research administrative assistant for various research and clinical organizations. Away from work, Lucy enjoys cycling, working out, and taking long walks with her buddy Watson!

Shaoshi Zhang is a research student in the lab. Shaoshi was born and grew up in Beijing. He did his undergrad in Math applications and Statistics at U of T and then finished his first Master's Degree (MMAI) at Schulich. He will begin another MSc degree in Computer Science at Western focused on ML/AI applications in life sciences.

Grace Hu is a research student in the lab. Grace completed her undergraduate degree in Computer Science and Biology at McGill University, and is currently a medical student at University of Toronto. Her favourite programming language is Python by far, and her least favourite is C.

Yumika Shiba is a bioinformatics analyst in the lab. Yumkia studied Computer science and Biology at McGill University. Her past research revolves around UK Biobank and genotyping transposable elements. She is deeply touched when she hears whispers of data. Aside from research, she loves birds, playing the piano and the ukulele, and biking.

We are looking for enthusiastic researchers and students interested in computational biology and cancer research to JOIN US!


Postdoctoral Associate

Job description: Applications are invited for postdoctoral positions in the computational cancer genomics laboratory at the Princess Margaret Cancer Center. We are interested in working with highly motivated people interested in building tools and analyzing cancer genomes using long-read, single-cell, and cell-free DNA technologies. They will have opportunities to collaborate with a diverse group of experimental and computational biologists at the Princess Margaret Cancer Center. We are looking for candidates with prior experience in computational biology.

Eligibility requirement: A Ph.D. in computational biology, bioinformatics, genomics, computer science, or a related field is required. A strong computational background, proficiency in at least one programming language, knowledge of machine learning and statistics are also needed.

How to Apply: Interested candidates should send a cover letter, CV, link to their GitHub/another codebase, and names of three references to Dr. Kumar (sushantDotkumarAtuhnresearchDotCA).

Graduate and Undergraduate Students

Interested students should send an email to Dr. Kumar (sushantDotkumarAtuhnresearch.ca) to discuss potential projects in the lab.


Computational Cancer Genomics Lab
Princess Margaret Cancer Research Tower
University Health Network, University of Toronto
101 College Street Toronto, ON M5G 0A3