The research performed in the Haibe-Kains Laboratory focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of diseases, with a particular emphasis on cancer. Dr. Haibe-Kains and his team are using publicly available genomic datasets and data generated through his collaboration to better understand the biology underlying diseases and to develop new predictive models in order to significantly improve disease management. Dr. Haibe-Kains's main contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines.
J Clin Invest. 2019 Feb 12;:
Mol Omics. 2019 Feb 05;:
Nat Commun. 2019 01 17;10(1):278
Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer.
PLoS Comput Biol. 2019 Jan;15(1):e1006596
Radiother Oncol. 2019 Jan;130:2-9
The Antiarrhythmic Drug, Dronedarone, Demonstrates Cytotoxic Effects in Breast Cancer Independent of Thyroid Hormone Receptor Alpha 1 (THRα1) Antagonism.
Sci Rep. 2018 Nov 08;8(1):16562
MYC Interacts with the G9a Histone Methyltransferase to Drive Transcriptional Repression and Tumorigenesis.
Cancer Cell. 2018 Oct 08;34(4):579-595.e8
Clin Cancer Res. 2018 Oct 15;24(20):5037-5047
Nucleic Acids Res. 2018 Jan 04;46(D1):D994-D1002
Senior Scientist, Princess Margaret Cancer Centre
Scientific Lead, Data Science Program, Princess Margaret Cancer Center, University Health Network
Scientific Lead, Radiomics Program of the Radiation Medicine Program (RMP), Princess Margaret Cancer Center, University Health Network
Associate Professor, Department of Medical Biophysics, University of Toronto
Adjunct Professor, Department of Computer Science, University of Toronto
Faculty Associate, Ontario Institute of Cancer Research
Faculty Affiliate, Vector Institute