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Scientist profiles A-F

SRI Profiles

Jesse Chao, PhD

Scientist

Sunnybrook Health Sciences Centre
2075 Bayview Ave., Room S236
Toronto, ON
M4N 3M5

Phone: 416-480-6100, ext. 3382

Administrative Assistant: Charmi Shah
Phone: 416-480-6100, ext. 3914
Email: charmi.shah@sunnybrook.ca

Education:

  • B.Sc. Honours, 2007, Queen’s University, Canada
  • PhD, 2014, Cell & Developmental Biology, University of British Columbia, Canada

Appointments and Affiliations:

  • Scientist, Biological Platforms, Odette Cancer Research Program, Sunnybrook Research Institute
  • Assistant Professor, Department of Medical Biophysics, University of Toronto

Research Foci:

  • Genomics
  • Cancer biology
  • Computational biology
  • Computer vision and AI

Research Summary:

The Chao lab is focused on improving our understanding of an individual’s risk of cancer. To accomplish this goal, we are integrating wet-lab and computational approaches to develop new predictive technologies.

A person’s genetic predisposition to hereditary cancers can be detected by screening for potentially cancer-causing mutations, also known as genetic variants. If pathogenic variants are found, clinicians can begin monitoring the patient for new lesions. Their family members can also be tested to trace the variant. However, most identified variants cannot be classified due to insufficient clinical data, causing many patients to receive inconclusive genetic testing results. We are addressing this challenge by developing cell-based assays to assess the functional impact of variants from within a tumour context. Further, we use machine learning to classify variants based on the empirical data, turning variants into actionable genetic markers.

To better detect sporadic cancers, we are building precision histology solutions. By using computer vision and deep learning algorithms, we are designing software to detect, segment and classify potentially cancerous lesions in hematoxylin and eosin (H&E) slides. Thus, computers will learn to identify critical tissue- and cell-level features indicative of cancer. We envision this software will become a clinical diagnostic assistant that will accelerate the detection of precancerous lesions.

Our work is driven by close interdisciplinary collaborations with clinician scientists. Together, we hope to transform precision medicine.

Selected Publications:

See current publications list at PubMed

  1. Chao JT*, Roskelley CD, Loewen CJR. MAPS: machine-assisted phenotype scoring enables rapid functional assessment of genetic variants by high-content microscopy. BMC Bioinformatics. 2021 Apr 20;22(1):202. (*corresponding author)
  2. Chao JT, Pina F, Niwa M. Regulation of the early stages of endoplasmic reticulum inheritance during ER stress. Molecular Biology of the Cell. 2021 Jan 15;32(2):109-119.
  3. Chao JT, Hollman R, Meyers WM, Meili F, Matreyek KA, Dean P, Fowler DM, Haas K, Roskelley CD, Loewen CJR. A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants. Cancer Research. 2020 Jul 1;80(13):2775-2789.
  4. Young BP, Post KL, Chao JT, Meili F, Haas K, Loewen C. Sentinel interaction mapping - a generic approach for the functional analysis of human disease gene variants using yeast. Disease Models and Mechanisms. 2020 Jul 8;13(7):dmm044560.
  5. Chao JT, Piña F, Onishi M, Cohen Y, Lai YS, Schuldiner M, Niwa M. Transfer of the Septin Ring to Cytokinetic Remnants in ER Stress Directs Age-Sensitive Cell-Cycle Re-entry. Developmental Cell. 2019 Oct 21;51(2):173-191.e5.

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