September 3, 2019
OICR is proud to welcome Dr. Parisa Shooshtari as an OICR Investigator.
Shooshtari specializes in developing computational, statistical and machine learning methods to understand the biological mechanisms underlying complex diseases, like cancer and autoimmune conditions. She is interested in uncovering how genes are dysregulated in complex diseases by integrating multiple data types and applying machine learning methods to analyze single-sell sequencing data.
Of her many achievements, Shooshtari developed a computational pipeline to uniformly process more than 800 epigenomic data samples from different international consortia. She then built and led a team that developed a web-interface and an interactive genome-browser to make the database publicly available to download and explore.
Shooshtari joins the OICR community with research experience from Yale University and the Broad Institute of MIT and Harvard. She also served as a Research Associate with the Centre for Computational Medicine at the Hospital for Sick Children (SickKids).
Shooshtari recently became an Assistant Professor in the Schulich School of Medicine and Dentistry at Western University, where she officially began her career as an independent researcher. Here, Shooshtari discusses her commitment to collaboration and her transition to professorship.
Your work spans multiple disease areas from autoimmune diseases to cancer, what do these diseases have in common? Is there a specific disease that you’re more interested in?
My work focuses on complex diseases, where instead of one gene causing the disease, there are sometimes tens or hundreds of genes working together to give rise to an ailment.
When it comes to complex diseases, we also know that there are multiple factors that we need to consider, including genetics, epigenetics and environmental factors. We live in an era where we have rich datasets with many different types of data. Each of these data types sheds light upon a different aspect of the disease mechanism, but we need to integrate these data types to gain a comprehensive understanding of how a complex disease works.
I develop computational methods for integrative analysis, so complex diseases are definitely the most interesting to me. I feel lucky to be a researcher at this time when I can help bring these data types together to understand mechanisms of diseases, which in turn will help inform treatment selection or help find new therapeutic strategies.
I am interested in applying our data integration methods to several complex diseases but I am currently working with a few Canadian groups to help better understand Diffuse Intrinsic Pontine Glioma (DIPG) – a type of fatal childhood brain cancer.
Your current collaborators include researchers from Yale, Harvard, MIT, SickKids and other leading organizations. How did you initiate and sustain these collaborations?
At the beginning of my research career, I would reach out to scientists who were working on interesting, challenging and cutting-edge problems. I enjoy working in collaborative environments because I believe the key to success in biomedical research is through collaborations between researchers from diverse backgrounds.
With the support of my collaborators, I’ve been able to learn and shift my focus from theoretical computational sciences to applications of data science in genetics of complex diseases. Now, sometimes collaborators approach me with their rich data, which I’m eager to help analyze.
With your new appointment, what are you looking forward to over the next few years?
I am eager to continue expanding my research program and working with new scientists on exciting cutting-edge problems in genetics and epigenetics of complex diseases. New technologies have revolutionized how we study diseases, and we are transitioning to a point where these new technologies are revolutionizing how we treat diseases. I am confident that we will have better ways of treating these diseases in the future using personalized medicine, and I want to help make that a reality.
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.”
November 8, 2018
Lawrence Heisler, Project Manager in the Genome Sequence Informatics team at OICR, talks about how new technologies are making genetic sequencing faster and cheaper. But turning data into discoveries requires the right behind-the-scenes support. That’s where Heisler’s team comes in.
October 21, 2016
OICR-led study finds four unique genomic signatures in pancreas cancer, uncovers potential of immunotherapies
Pancreas cancer is one of the most aggressive and deadly forms of the disease. According to the Canadian Cancer Society, only 8 percent of pancreas cancer patients survive more than five years after diagnosis. OICR’s PanCuRx Translational Research Initiative has recently published the results of an international collaboration that increases understanding of this complex disease and how to treat it based on a patient’s unique profile.
October 18, 2016
Reactome releases 10,000th annotated human protein, a major milestone that will benefit research community
Open source tools like Wikipedia and Google Maps help us get things done faster in our daily lives. In the same way, researchers rely on a variety of open source tools to help them make discoveries faster. Reactome (www.reactome.org) is one such tool. Researchers use it because it relates human genes, proteins and other biomolecules to the biological pathways and processes in which they participate, helping to facilitate new cancer research breakthroughs. Earlier this month Reactome reached a major milestone when it released its 10,000th annotated human protein to the research community. We spoke to OICR’s Dr. Robin Haw, who is Project Manager and Outreach Coordinator at Reactome, about the history of the project, the importance of this particular milestone and where the project is headed next.