May 31, 2019
In May, OICR welcomed Dr. Trevor Pugh as Director of Genomics and Senior Principal Investigator. Trevor is a cancer genomics researcher and board-certified molecular geneticist who has led the Princess Margaret Cancer Centre-OICR Translational Genomics Laboratory (PM-OICR TGL) since 2016.
In his new role, he will lead the OICR Genomics program, which brings together the Princess Margaret Genomics Centre, OICR’s Genome Technologies, Translational Genomics Laboratory and Genome Sequence Informatics teams under an integrated initiative to support basic, translational and clinical research. Here, Pugh describes some of his strategies and how he plans to take on this ambitious mandate.
You’re involved with a number of projects across many disease sites and you collaborate with researchers from vastly different areas of cancer research. Can you summarize what you focus on?
Simply put – I want to use genome technologies to guide the best patient care. The overall philosophy is to extract as much genomic information as we can from small amounts of tumour tissue, and turn that information into knowledge so that clinicians and patients can make targeted treatment decisions. I also want to open up these comprehensive data for researchers to mine and find new cures for these cancers.
Whether they are a graduate student working on myeloma or a postdoc working on liver cancer, we all learn from one another’s disease specialties.
And yes – I am involved with many areas of cancer research. Every member in my lab speaks the same genomics language. Whether they are a graduate student working on myeloma or a postdoc working on liver cancer, we all learn from one another’s disease specialties. We do genomics in a similar way as there are many genomic commonalities across cancer types and computational algorithms or infrastructure we build for one project invariably get reused for another project.
You are a board-certified molecular geneticist and a genomics researcher, but you also have a background in bioinformatics and software development. How do you balance making tools and making discoveries?
The tools we create and the research we perform go hand in hand. You can’t make discoveries without the infrastructure, and it is hard to develop technologies successfully without a guiding scientific question. With that said, the software that we make is designed to help not only our own research and clinical projects, but those of others. If we can make software work for us really well, we want to share it and make it easier for groups and labs across Ontario and around the world. This also holds for the data we generate, as there is great value to integrating our data with similar data sets from other hospitals.
How will this new role help you do that?
I have a few main goals in this role that I’m excited about. The first and the largest is to integrate the Princess Margaret Genomics Centre, PM-OICR TGL, Genome Technologies and Genome Sequence Informatics into one fully-coordinated machine. The people, tools and methods that we have at OICR and Princess Margaret are incredible and the infrastructure already in place can serve as a powerful vehicle for both research and clinical applications. In the first two weeks, I’ve been really impressed with how the leads of these programs have come together to form concrete plans for making this a reality.
The part that excites me about my new role is the O in OICR. Within this position, I can have a provincial outlook on translational research which is important as genomics research becomes increasingly dependent on multi-centre studies and inter-institutional collaborations. I think OICR can help facilitate a future where sharing ideas, data, and knowledge between institutions is much easier than it is today. I’m excited to help take things that work locally and make them available and easy-to-use across the entire province, so that we can benefit from the advances made by our neighbours. We are stronger when we work together in a collaborative way.
OICR is well-known as a developer of similar high-quality data sharing systems and I am looking forward to integrating these efforts to support our internal genomics enterprise
It sounds like a lot of your work addresses local needs, but how do you have so many international collaborations?
In computational biology, a lot of our concerns and challenges are shared with other groups as well. For example, the cBioPortal data sharing platform was originally built at Memorial-Sloan Kettering to allow researchers to easily query data from The Cancer Genome Atlas project. This initiative soon grew to include a team at Dana-Faber and now the software is fully open-source with five core, NIH-funded teams contributing to its development, including my own lab. In addition, there are groups working on improving and enhancing cBioPortal instances around the world as it expands to new applications beyond genomics. cBioPortal has emerged as a very powerful resource rooted in an international crowdsourcing model. Naturally, OICR is well-known as a developer of similar high-quality data sharing systems and I am looking forward to integrating these efforts to support our internal genomics enterprise, as well as national and international data sharing networks.
You’ve been involved with the evolution of genomics over the last two decades. What technologies excite you these days?
Hands down, it’s single cell sequencing. This is an amazing technology that allows us to see parts of the tumours that we could never see before. In one of my projects, we’re looking at each cancer population within a tumour sample and mapping each population to a drug treatment. With Drs. Benjamin Haibe-Kains, we’re applying this concept across hundreds of thousands of cells from brain tumours we have sequenced in collaboration with Peter Dirks and from myeloma cells with Suzanne Trudel. If we can find distinct clones – or types of cells – with tailored treatment options, we could potentially eradicate the cancer entirely using combination therapies. I think the future of precision medicine is dependent on single cell technology and I look forward to integrating this technology into clinical studies with collaborators at cancer centres across the province.
May 30, 2019
Meta-analysis of 1,200 patients with pancreatic cancer reveals a new way to identify those with very aggressive tumours who may benefit from alternate treatment approaches
Only half of pancreatic cancer patients who undergo standard chemotherapy and surgery live a year after their initial diagnosis. In the face of these dismal statistics, patients are faced with the challenge of deciding whether they want to proceed with treatment that may have unpleasant side effects. If clinicians could identify patients who would not benefit from standard therapies, they could help these patients make more informed treatment decisions or recommend alternative palliative treatment approaches.
As part of OICR’s Pancreatic Cancer Translational Research Initiative (PanCuRx) team led by Dr. Steven Gallinger, Dr. Benjamin Haibe-Kains recognized that computational modeling can be used to help inform these decisions, but to design a robust predictive model he would need much more data than any individual study had ever collected.
Building the data foundations
Haibe-Kains, who is a Senior Scientist at the Princess Margaret Cancer Centre and OICR Associate, began his investigation with a dataset from PanCuRx – the largest collection of genomic and transcriptomic data on primary and metastatic pancreatic tumours to date. He and his lab then incorporated an additional 1,000 cases of pancreatic tumours from studies around the world that had collected both patient samples and information about how each patient responded to treatment.
“The datasets that we aggregated were a mixed bag of different types of data collected through different profiling platforms by different institutions,” says Haibe-Kains. “We took on the challenge of harmonizing the heterogeneity of these resources which nobody else had done.”
Previously, the Haibe-Kains Lab developed a computational method that could make incompatible transcriptomic data compatible. They had used this method to find four new breast cancer biomarkers to predict treatment response and they recognized that they could apply similar methods to harmonize pancreatic cancer data as well.
The dataset resulting from the harmonization is now the largest pancreatic cancer dataset, and Haibe-Kains has made it freely available for other researchers to use and study through the MetaGxPancreas package.
Making a predictive model
Haibe-Kains and his team set out to develop a computational model that could predict if a patient would survive for a year after their biopsy. They used machine learning techniques to exploit their rich dataset, find common patterns in the genomic data of aggressive tumours, and developed PCOSP – the Pancreatic Cancer Overall Survival Predictor.
“Our approach was to look at how one gene was expressed relative to another and relate that to how long a patient lived after biopsy,” says Haibe-Kains. “That may sound simple, but that means dealing with nearly 200 million pairs of genes, which is a significant amount of data to compute.”
As recently described in JCO Clinical Cancer Informatics, the group refined PCOSP using ensemble learning – the combination of several machine learning techniques to improve a model’s accuracy of predictions.
“PCOSP is actually a combination of hundreds of models and not just one,” says Haibe-Kains. “We tested about a thousand models, selected the models that could predict early death very well and combined them to make a stronger classifier.”
Using prediction to power patient decisions
Haibe-Kains says that as the infrastructure for routine sequencing progresses, PCOSP can be translated into clinical practice to help clinicians determine which patients would not benefit from standard treatment and which may benefit from alternative treatment approaches.
“Pancreatic cancer is a challenging disease but if we can predict the course of the disease, we can give clinicians and patients more information. With that information, they can make more personalized decisions to improve their treatment and ideally, their lives.”
January 10, 2019
Capital leverages Ontario’s strengths in genomics and informatics, deepens FACIT’s tech portfolioContinue reading – FACIT makes follow-on investment in AI-based genomics company, DNAstack
December 17, 2018
Large-scale genomic study discovers 40 new genetic variants associated with colorectal cancer risk
The most comprehensive genome-wide association study of colorectal cancer risk to date has discovered 40 new genetic variants and validated 55 previously identified variants that signal an increased risk of colon cancer. The study, recently published in Nature Genetics, is a product of the world’s largest molecular genetic consortium for colorectal cancer – the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) – which was established nearly 10 years ago.Continue reading – When it comes to finding cancer risk, there’s power in numbers
December 13, 2018
What can we gain from looking at the outliers?: An investigation into long and short-term ovarian cancer survivors
Researchers investigate the clinical, molecular and microenvironment factors that contribute to extreme therapy response and resistance in ovarian cancer patients
Some patients with high-grade serous ovarian cancer (HGSOC) respond exceptionally well to therapy, while others experience rapid disease relapse. The mechanisms behind these disparate outcomes are poorly understood, but a group of researchers based at the Princess Margaret Cancer Centre (PM) supported by OICR’s Ovarian Cancer Translational Research Initiative (TRI) are working to change that.Continue reading – What can we gain from looking at the outliers?: An investigation into long and short-term ovarian cancer survivors
November 14, 2018
Researchers find a new way to detect small traces of tumour DNA in blood and determine the tumour’s tissue of origin
A blood sample can be used to detect and monitor certain cancers in select patients, but there are significant technical barriers that prevent the widespread adoption of this “liquid biopsy”. This type of blood test analyzes the rare traces of tumour DNA that are circulating in the blood, but distinguishing tumour DNA from healthy DNA is both difficult and expensive. New methods are needed to improve the accuracy, sensitivity and cost-effectiveness of liquid biopsies so that more patients can benefit from this less-invasive test.
October 2, 2018
OICR researchers uncover sex-linked genetic differences that may be able to predict cancer severity and response to therapy
Cancer differs in males and females but the origins and mechanisms of these sex differences remain unresolved. A better understanding of sex-linked differences in cancer could lead to more accurate tests and treatments that are personalized for patients based on their sex.
September 4, 2018
Meet Dike Aduluso-Nwaobasi, Sarah Donald and Benson Wan. Find out how summer co-op positions affected their career and educational journeys.
August 22, 2018
OICR-developed software tool, Heliotrope, gains attention from the private sector for its potential to analyze large amounts of genomic information and inform clinical decision making
August 21, 2018
Formalizing his longstanding relationship with OICR, Dr. Marc Fiume joins the Institute as an Associate to turn big data into a cure
“We know there are valuable – potentially life-saving – genomics and clinical data that are locked away in the sever rooms in hospital basements,” says Dr. Marc Fiume, CEO of DNAstack, Adjunct Professor at the University of Toronto, and OICR’s newest Associate. “We’re working to make these data more findable, accessible and useful to help researchers find cures for diseases faster than ever before.”
August 7, 2018
Big data are ushering in a new era of individualized cancer care and prevention, but not without conceptual and practical challenges. Canadian advances in genomics will be made by or limited by bioinformatics analytical capacity as well as the ability to store and analyze data in new and more sophisticated ways.
To help realize the potential of genomics research in cancer, the Canadian Data Integration Centre (CDIC) platform, led by OICR, offers third generation bioinformatics and genomics tools to support both functional and clinical genomics research. CDIC is the largest academic cancer informatics program in the country – offering customizable, client-oriented access services for data challenges across diverse research areas.
June 13, 2018
Some common pathogens, like the Epstein-Barr virus (EBV), can turn healthy cells into cancer cells, but it is not well understood how they do so. Better understanding how such pathogens work allows researchers to find new ways to target the pathogen’s disease-causing mechanisms and ultimately find new treatments for certain virus-induced cancers.
Dr. Ivan Borozan, from Dr. Vincent Ferretti’s Lab at OICR, and Prof. Lori Frappier at the University of Toronto are working together to better understand EBV and how it triggers the transformation of normal cells to cancerous cells, also known as oncogenesis. Together, they have identified that a key protein expressed by EBV, BKRF4, is one of the likely drivers behind EBV-induced stomach cancers.