October 8, 2019
OICR is proud to welcome Dr. Tricia Cottrell to Ontario’s cancer research community.
Dr. Tricia Cottrell, who is an immunologist and pathologist by training, is focused on the interplay between cancer cells and the immune system. She maps these complex interactions, as patients undergo treatment, to develop new biomarkers that can better predict the course of a patient’s disease.
Joining OICR from Johns Hopkins University in Baltimore, MD, Cottrell brings unique expertise in studying the tumour immune microenvironment, specifically in lung cancer. Here, she discusses her transition and her new appointments at the Canadian Cancer Trials Group, Queen’s University and OICR.
How did you become interested in the field of immuno-oncology?
The idea of harnessing the immune system to control and eliminate cancer fascinates me.
My PhD research on the autoimmune disease scleroderma left me eager to find ways to study immune responses in human tissue. While pursuing this research through my anatomic pathology residency, I stumbled upon the revolution happening in cancer immunotherapy. There are a lot of interesting intersections between cancer immunology and autoimmunity, and I knew I wanted to dig in.
What problems and questions are you working to solve?
Generally, I look at different features of the immune response to cancer and find patterns in these features that are associated with a response to therapy. I’m addressing the question: can we predict which patients are most likely to respond to treatment?
When we have tools to answer that question, we can help patients decide which treatment is best suited for their unique disease.
How are you addressing those big questions?
As a pathologist, I start with simple observations made through a microscope. Then, I use techniques like multiplex immunofluorescence to understand the cells and molecules driving the patterns I see in the tissue. Finally, I integrate these observations with other –omics analyses of the same sample, like DNA or RNA profiling, in pursuit of better biomarkers. The ultimate goal is to have biomarkers that can accurately predict which therapy or combination of therapies is most likely to empower a patient’s immune system to eliminate their cancer.
Through these studies, we also identify patterns and molecular characteristics in the tumours of patients who respond poorly to treatment. We can use this knowledge to find mechanisms of resistance, or the ways that the cancer can evade treatment. Then we can develop new therapies to address these mechanisms.
You’ve been recognized and awarded for your research on several occasions. What is an achievement that most people don’t know about?
I never anticipated that my research as a pathologist would lead me to analyzing big data. I’m quite proud that I learned some computer programming and I continue to integrate new technologies and cutting-edge analytic approaches into my research.
A specific achievement I am proud of is developing a method to measure the response of lung cancer patients to checkpoint blockade therapy using microscopic features of their tumours. This method is now being validated in a large clinical trial and has been shown to work in other cancer types as well. We are currently investigating its potential as a pan-tumour biomarker that would allow unprecedented standardization of clinical trials across different cancer types.
Why did you choose to relocate to Kingston?
I was looking for an opportunity to expand my research focusing on patients enrolled in clinical trials. Kingston offered that opportunity through an appointment with the Canadian Cancer Trials Group (CCTG), which is based at Queen’s University where I am also an Assistant Professor.
At CCTG, I get to participate in the design of clinical trials, including arranging tissue collection and planning the correlative science (the study of the relationship between biology and clinical outcomes) that goes along with those trials. My goal is to make sure my research will be translatable to the clinic, or in other words – to find solutions that can be applied in practice.
I’m also personally very excited about the opportunity for my family to be here in Canada.
What are you looking forward to over the next year?
I look forward to maintaining my existing collaborations while broadening my research scope. I’ll be working to establish a laboratory-based platform that produces high-quality, large-scale multiplex immunofluorescence data from tumour tissue specimens. I also look forward to laying the groundwork for a data integration and analysis pipeline for tissue-based immunology studies.
Most of all, I’m excited to begin growing my own lab group. I hope to foster a collaborative team environment with individuals from diverse backgrounds in pathology, biology, immunology, bioinformatics and more.
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.