PhD, University of Toronto
*Please note: Dr.Paul Boutros has moved to UCLA and is no longer recruting new MBP graduate students.*
At A Glance
- Biological Foci: personalized medicine, cancer genomics, biomarkers, prostate cancer
- Technologies: DNA-sequencing, RNA-sequencing, imagining
- Techniques: Computational biology, machine-learning
- Global leader in prostate cancer biomarkers and genomics
- Setting the standards for the analysis of cancer genomic data
- Major focus on data visualization
- 2013 Assistant Professor, Department of Pharmacology & Toxicology, University of Toronto
- 2012 Assistant Professor, Department of Medical Biophysics, University of Toronto
- 2011 Principal Investigator, Informatics & Biocomputing, Ontario Institute for Cancer Research
- 2009 Fellow, Informatics & Biocomputing, Ontario Institute for Cancer Research
- 2009 PhD Medical Biophysics, University of Toronto
- 2004 BSc Chemistry, University of Waterloo
Dr. Paul Boutros pursued his undergraduate education at the University of Waterloo in Chemistry. During the co-op portion of his degree he worked for a wide range of organizations, including the Federal Government, a water-purification company and Petro-Canada. But he found his true calling during a work-term spent at Michigan State University developing computer models of how cells respond to drugs and toxins. In 2004, he started his PhD at the Ontario Cancer Institute in Toronto. During his studies he received several awards, including the CIHR/Next Generation First Prize and the Invitrogen Canada Young Investigator Silver Award. He began his independent research career with an appointment at the Ontario Institute for Cancer Research in 2009. Paul is now a Principal Investigator in Informatics & Biocomputing at OICR, and an Assistant Professor in the Departments of Pharmacology & Toxicology and Medical Biophysics at the University of Toronto. He is a Prostate Cancer Canada Rising Star, and has received CIHR and TFRI New Investigator Awards. His work focuses on the development of clinically useful biomarkers using genomic and data science techniques like next-generation sequencing, clinical and cellular imaging, machine-learning, crowd-sourcing and cloud-computing.
Cancer is not a single disease. Each person's tumour is unique, shaped by the patient's basal germ-line genetics, by the stochastic mutations that cause cancer, and by myriad epigenetic and micro-environmental factors at play inside the human body. Given all these sources of variability, it may be surprising that most patients receive essentially the same treatment, with slight variations.
The underlying goal of our team’s research is to improve survival-rates of cancer patients by predicting for each individual the most effective treatment possible. We do so by using large genomic datasets, such as those generated by microarray experiments or by next-generation genome-sequencing studies (PMID: 26430161). These can be targeted at almost any aspect of the cell, including DNA (methylation, mutations, translocations and copy-number abnormalities), RNA (abundances and splicing events), proteins (abundances or post-translational modifications), and small metabolites (abundances). We develop sophisticated computational models from these datasets, with the aim of predicting clinical behaviour. Our models are then validated independently, and where possible advanced forward as candidate clinical tools.
Key Findings: Biomarkers for Prostate Cancer
Approximately one in seven Canadian men will develop prostate cancer over their lifetime, and the disease is likely to become the most diagnosed cancer in the country over the next decade. Unlike most tumour types, prostate cancer patients are both frequently over-treated (receiving therapies that do not contribute to survival) and under-treated (receiving less therapy than is optimal). Our team focuses on using big-data techniques to better understand prostate cancer biology, and using these insights to develop clinically actionable biomarkers. We have performed the several of the first genomic studies of the disease (PMIDs: 26544944, 26005866) and have used those insights to develop actionable biomarkers integrating measurements of tumour oxygen usage and genomics (PMID: 25456371) and from circulating blood (PMID: 22619380). We are now developing a series of biomarkers using genomic and mitochondrial sequencing, RNA-sequencing, methylation profiling, ChIP-seq, MS-based proteomics and several types of imaging in patient cohorts of several hundred patients. These candidate biomarkers and the underlying biology driven by them will be validated in several patient series of hundreds to thousands of patients to help bring them into clinical practice.
Key Findings: Biological Data Science
To support our work in biomarker development, the team also develops a series of techniques in biological data science. We lead the ICGC-TCGA Dream Somatic Mutation Calling Challenge – a series of crowd-sourced benchmarking projects that are developing the standards for analyzing big biological data (PMIDs: 25314947, 24675517). For example, we have created identified the major sources of error in the identification of point-mutations in DNA sequencing data (PMID: 25984700) in a crowd-sourced challenge supported by Google and Annai bio-systems. Similarly we work on the visualization of big data (http://labs.oicr.on.ca/boutros-lab/software/bpg) and on the quality-assessment of genomic data (PMIDs: 22513995, 23146350, 23169800, 23537167, 25173705).
Students in my lab come from a broad range of research backgrounds, including biochemistry, mathematics, biology, and computer science. Students take on and lead significant projects, gaining a deep understanding of cancer biology, but also of cutting-edge technologies and all within an environment of professional software-development and statistical analysis. Mentorship includes a students-only journal club, regular meetings, and tight integration with clinical and other colleagues to help in networking.
- The Cancer Genome Atlas Research Network (2015) “The molecular taxonomy of primary prostate cancer” Cell 163(4):1011-1025 (PMID: 26544944)
- Boutros PC*, Fraser M+, Harding NJ+, de Borja R+, Trudel D+, Lalonde E, Meng A, Hennings-Yeomans PH, McPherson A, Sabelnykova VY, Zia A, Fox NS, Livingstone J, Shiah YJ, Wang J, Beck TA, Have CL, Chong T, Sam M, Johns J, Timms L, Buchner N, Wong A, Watson JD, Simmons TT, P’ng C, Zafarana G, Nguyen F, Luo X, Chu KC, Prokopec SD, Sykes J, Dal Pra A, Berlin A, Brown A, Chan-Seng-Yue MA, Yousif F, Denroche RE, Chong LC, Chen GM, Jung E, Fung C, Starmans MH, Chen H, Govind SK, Hawley J, D’Costa A, Pintilie M, Waggott D, Hach F, Lambin P, Muthuswamy LB, Cooper C, Eeles R, Neal D, Tetu B, Sahinalp C, Stein LD, Fleshner N, Shah SP, Collins CC, Hudson TJ, McPherson JD, van der Kwast T, Bristow RG* (2015) “Spatial genomic heterogeneity within localized, multi-focal prostate cancer” Nature Genetics 47(7):736-745 (PMID: 26005866)
- Ewing AD*, Houlahan KE*, Hu Y*, Ellrott K, Caloian C, Yamaguchi TN, Bare JC, P’ng C, Waggott D, Sabelnykova VY, ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants, Kellen MR, Norman TC, Haussler D, Friend SH, Stolovitzky G, Margolin A, Stuart JM, Boutros PC (2015) “Combining accurate tumour genome simulation with crowd-sourcing to benchmark somatic single nucleotide variant detection” Nature Methods 12(7):623-630 (PMID: 25984700)
- The Cancer Genome Atlas Research Network (2015) “Comprehensive genomic characterization of head and neck squamous cell carcinomas” Nature 517(7536):576-582 (PMID: 25631445)
- Starmans MH, Pintilie M, Chan-Seng-Yue M, Moon NC, Haider S, Nguyen F, Lau SK, Liu N, Kasprzyk A, Wouters BG, Der SD, Shepherd FA, Jurisica I, Penn LZ, Tsao MS, Lambin P, Boutros PC (2015) “Integrating RAS status into prognostic signatures for adenocarcinomas of the lung” Clinical Cancer Research 21(6):1477-1486 (PMID: 25609067)
- Lalonde E*, Ishkanian AS*, Sykes J, Fraser M, Ross-Adams H, Erho N, Dunning MJ, Lamb AD, Moon NC, Zafarana G, Warren AY, Meng X, Thoms J, Grzadkowski MR, Berlin A, Have CL, Ramnarine VR, Yao CQ, Malloff CA, Lam LL, Xie H, Harding NJ, Mak DY, Chu KC, Chong LC, Sendorek DH, P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC*, Bristow RG* (2014) “Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” Lancet Oncology 15(13):1521-1532 (PMID: 25456371)
- Yao CQ, Nguyen F, Haider S, Starmans MHW, Lambin P, Boutros PC (2015) “The Transcriptomic Profile of Ovarian Cancer Grading” Cancer Medicine 4(1):56-64 (PMID: 25314936)
- Weinreb I*, Piscuoglio S*, Martelotto LG*, Waggott D*, Ng CK, Perez-Ordonez B, Harding NJ, Alfaro J, Chu KC, Viale A, Fusco N, da Cruz Paula A, Marchio C, Sakr RA, Lim R, Thompson LD, Chiosea SI, Seethala RR, Skalova A, Stelow EB, Fonseca I, Assaad A, How CH, Wang J, de Borja R, Chan-Seng-Yue M, Howlett CJ, Nichols AC, Wen YH, Katabi N, Buchner N, Mullen L, Kislinger T, Wouters BG, Liu FF, Norton L, McPherson JD, Rubin BP, Clarke BA*, Weigelt B*, Boutros PC*, Reis-Filho JS* (2014) “Hotspot activating PRKD1 somatic mutations in polymorphous low-grade adenocarcinomas of the salivary glands” Nature Genetics 46(11):1166-1169 (PMID: 25240283)
- Chong LC, Albuquerque MA, Harding NJ, Caloian C, Chan-Seng-Yue M, de Borja R, Fraser M, Denroche RE, Beck TA, van der Kwast T, Bristow RG, McPherson JD, Boutros PC (2014) “SeqControl: Process Control for DNA Sequencing” Nature Methods 11(10):1071-1075 (PMID: 25173705)
- Fox NS, Starmans MH, Haider S, Lambin P, Boutros PC (2014) “Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences” BMC Bioinformatics 15(1):170 (PMID: 24902696)