Celebrating Students at UHN

Over 120 summer students shared their research with the UHN community at STAR Research Day.

Image Caption: (Clockwise from top left) Samridhi Bhardwaj presents her poster; Isabella Gouthro, poster winner; sign at Research Day; Aisha Elgendy shares her poster; Utovia Stewart presents her poster; poster winners Alexander Ball, Nicole Wiesner, and Allona Kaye Lim; (Middle) poster presentations at Research Day.

UHN’s Summer Training and Research (STAR) Program celebrated its students through STAR Research Day—an opportunity for students to share their research experiences via engaging poster presentations.

Research Day marks the culmination of the STAR Program, a comprehensive summer research initiative led by UHN’s Office of Research Trainees (ORT). The program provides summer students the chance to participate in cutting-edge research while gaining essential professional development skills in research design and integrity, scientific communication, networking, and career exploration in science and health care.

At this year’s Research Day, held on August 7, 2025, 123 students presented posters. An additional 22 students shared their work virtually using the online platform VoiceThread. The poster competition, adjudicated by UHN graduate students, postdoctoral researchers, and staff, recognized five students for outstanding posters:

● Nicole Wiesner — supervised by Dr. Andrew McPartlin

● Isabella Gouthro — supervised by Dr. Chung-Wai Chow

● Alexander Ball — supervised by Dr. Kathryn Howe

● Allona Kaye Lim supervised by Dr. Ivan Yeung

● Sunny Yun – supervised by Dr. Suneil Kalia

In total, 107 students completed the STAR Program, marking a summer of growth, innovation, and community building within UHN’s dynamic research environment.

Funding for A Healthier World

UHN researchers received over $20.7 million in CIHR’s Spring 2026 Project Grant competition.

Research Spotlight

Read the latest bi-monthly newsletter that highlights advancements from UHN researchers.

Retaining a Vital Nursing Workforce

Clearer roles and stronger career support could help retain registered practical nurses.

Image Caption: Registered practical nurses are playing an increasingly important role in Canadian hospitals, highlighting the need for role clarity, professional development opportunities, and workplace supports that promote retention.

Understanding Advanced Breast Cancer

Study shows blood-based DNA analysis can classify and characterize advanced breast cancer.

Image Caption: Patterns of DNA methylation—chemical markers on DNA that regulate its expression—are tissue- and cancer-site-specific. This information can be obtained by analyzing fragments of cell-free DNA circulating in the bloodstream.

Researchers from UHN’s Princess Margaret Cancer Centre (PM) have developed a new approach for blood-based testing that could lead to new ways to detect and classify metastatic breast cancer—breast cancer that has spread to other tissues and organs—without the need for invasive tissue biopsies.

Breast cancer is the most diagnosed cancer in women worldwide and is a leading cause of cancer-related mortality. Treatment decisions are typically based on the results of tissue biopsies that determine characteristics of the tumour, such as whether it is sensitive to female hormones (estrogen) or the presence of the protein human epidermal growth factor receptor 2 (HER2) in tumour cells.

However, tissue biopsies are invasive procedures and, depending on the tumour location, are not always possible. In addition, samples may not accurately reflect the differences that can exist between multiple tumours in different parts of the body or how the cancer changes over time.

There is a need for less invasive ways to evaluate breast cancer that can also assess how it changes over time. One promising approach, known as a liquid biopsy, uses a blood sample instead of tumour tissue. By analyzing tiny pieces of tumour DNA that circulate in the bloodstream (circulating tumour DNA, “ctDNA”), liquid biopsies could give doctors a more complete picture of the cancer throughout the body and help guide treatment decisions over the course of a patient's disease.

Liquid biopsy tests that analyze ctDNA have been adopted in many settings to characterize tumour mutations. However, they are not yet widely used to provide information about other tumour features. Functional liquid biopsies, which aim to capture the dynamic states of cancer cells, represent a major area of ongoing research.

To build this type of functional liquid biopsy test, the research team led by Dr. David Cescon, PM Clinician Scientist, evaluated breast cancer-related patterns in ctDNA. They analyzed blood samples from 79 patients with various forms of metastatic breast cancer, as well as a large public dataset of specific DNA modifications called methylation. Methylation is when a small chemical group, called a methyl group, is added to DNA, impacting whether genes are turned "on" or "off".

Integrating patient samples and the public dataset, the researchers looked for patterns of DNA methylation to identify a signature strongly linked to different forms of breast cancer. They then looked at whether the signature could be detected in blood samples using a specialized technique to analyze methylation patterns in cell-free DNA. Following testing and validation across several groups, the signatures could reliably be used to detect breast cancer, distinguish it from other types of cancer, and determine whether tumours were estrogen-responsive or not—known as estrogen receptor positive or negative, respectively.

Overall, the findings suggest that blood-based DNA analysis may provide clinically useful information through a less invasive approach to monitor metastatic breast cancer. This work complements an ongoing breast cancer liquid biopsy research program led by Dr. Cescon’s group at PM. The program aims to develop and evaluate new approaches that use blood samples to better understand the current state of a person’s cancer, potentially enabling more effective and individualized treatment.

Dr. Sasha Main is a former doctoral student at UHN’s Princess Margaret Cancer Centre and co-first author of the study.

Mitchell Elliott is a doctoral student at UHN’s Princess Margaret Cancer Centre and co-first author of the study.

Althaf Singhawansa is a Research Technician at UHN’s Princess Margaret Cancer Centre and co-first author of the study.                           

Dr. David Cescon is a PM Clinician Scientist at UHN’s Princess Margaret Cancer centre and an Associate Professor in the Department of Medicine at the University of Toronto. He is the corresponding author of the study.

This work was supported by the Canadian Cancer Society, the Canadian Institutes of Health Research, the Ontario Institute for Cancer Research, the Hold’em for Life Fellowship Program, the Canadian Association of Medical Oncology, Komen Foundation, ASCO Conquer Cancer Foundation, Breast Cancer Research Foundation, the Canadian Epigenetics, Environment and Health Research Consortium, and The Princess Margaret Cancer Foundation.

Mitchell Elliott reports personal fees from Roche, Gilead, and Knight Therapeutics outside the submitted work. Dr. David Cescon reports personal fees from AstraZeneca, Daiichi Sankyo, Exact Sciences, GenomeRx, Gilead, GlaxoSmithKline, Inivata/NeoGenomics, Knight Therapeutics, Lilly, Merck, Novartis, Pfizer, Roche, and SAGA and grants, nonfinancial support, and other support from AstraZeneca, GenomeRx, Guardant Health, Grail, Gilead, GlaxoSmithKline, Inivata/NeoGenomics, Knight, Merck, Pear Bio, Pfizer, ProteinQure, RayzeBio, and Roche all outside the submitted work, as well as a patent for methods pending.

For a full list of competing interests, see the manuscript.

Main SC, Elliott MJ, Singhawansa A, Zou J, Zeng Y, Cheng N, Yu C, Hilton JF, Awadalla P, He HH, Bedard PL, Lupien M, Bratman SV, Cescon DW. Circulating cell-free DNA methylation profiling enables detection, distinction, and estrogen receptor status classification of advanced breast cancer. Cancer Res. 2026 Jun 20; OF1-OF16. doi:10.1158/0008-5472.CAN-26-0076.

Understanding Spine Surgery Outcomes

Study finds overall osteoarthritis burden may influence recovery after lumbar spine surgery.

Image Caption: Osteoarthritis (OA) affects nearly 15% of all Canadians. Although OA in the spine is common, most research is focused on OA in appendage joints, such as knees, hands, and hips. For those with spinal OA, the symptoms can be debilitating, seriously impairing quality of life. (Original images c/o UHN's Schroeder Arthritis Institute)

New research from UHN's Schroeder Arthritis Institute suggests that patients with more extensive lumbar disc degeneration may be less likely to benefit from surgery for lumbar spinal stenosis, a narrowing of the spinal canal in the lower back, caused by osteoarthritis (LSS-OA). The findings could help surgeons and patients make more informed treatment decisions and set realistic expectations before surgery.

LSS occurs when osteoarthritis and age-related changes in the spine narrow the space around nerves in the lower back, causing symptoms such as pain, numbness, weakness, and difficulty walking. Surgery is designed to decompress the spine and relieve pressure on these nerves. However, up to 40% of patients do not experience meaningful improvement after surgery.

To better understand this disparity in outcomes, the researchers analyzed data from 119 patients who underwent surgery for LSS-OA. Using MRI scans, the team assessed the extent of disc degeneration throughout each patient's lumbar spine. Before surgery and one year afterward, patients’ pain scores and level of disability were assessed through various questionnaire-style assessments. Participants also self-reported OA symptoms in other joints.

Dr. Raja Rampersaud, the first author of this study, and his colleagues found that about 25% of patients had a high burden of lumbar disc degeneration. Of these patients, only 50% achieved a clinically meaningful improvement in disability after surgery. In contrast, nearly 75% of patients with lower levels of degeneration achieved meaningful improvement.

The study also showed that many patients experienced symptoms beyond the spine. More than half reported pain or stiffness in at least three other joint sites, such as the hips, knees, hands, or feet. Patients with greater joint involvement experienced less improvement after surgery.

Together, the findings suggest that surgical outcomes may depend on both the condition of the surgical site and the overall burden of OA throughout the spine and the rest of the body. A more comprehensive view of OA and musculoskeletal health could support more personalized treatment planning and improve outcomes for those living with LSS-OA. 

Dr. Raja Rampersaud, Krembil Clinician Investigator at UHN’s Schroeder Arthritis Institute and Professor in the Department of Surgery at the University of Toronto’s Temerty Faculty of Medicine, is the first author. 

Dr. Anthony Perruccio, Senior Scientist at UHN’s Schroeder Arthritis Institute, is the senior author. He is also a Professor at the University of Toronto’s Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, and Temerty Faculty of Medicine Department of Surgery.

This work was supported by the J. Bernard Gosevitz Chair in Arthritis Research and UHN Foundation. Dr. Perruccio was supported directly by an award from the Arthritis Society Canada.

Dr. Rampersaud receives royalties from Medtronic outside of the work in this study, and Dr. Kapoor is an Executive Board Member, Treasurer, and President-Elect of the Osteoarthritis Research Society International (OARSI) as well as a Scientific Advisory Board Member for Chiron Inc. 

Rampersaud YR, Power JD, Nahanni C, Fine N, Canizares M, Kapoor M, Perruccio AV. The impact of overall lumbar disc degeneration burden on disability outcomes following surgery for lumbar spinal stenosis due to osteoarthritis. Osteoarthr Cartil Open. DOI: 10.1016/j.ocarto.2026.100766. 

Meet Dr. Benjamin Haibe-Kains @PMResearch

Making AI research transparent for a safe health care system

Image Caption: Dr. Benjamin Haibe-Kains is UHN’s Executive AI Scientific Director and Co-Director, UHN AI Hub, Senior Scientist and Allan Slaight Scientist at UHN’s Princess Margaret Cancer Centre, and Scientific Director of the Cancer Digital Intelligence Program at Princess Margaret Cancer Centre.

Dr. Benjamin Haibe-Kains was completing his PhD when a major cancer research scandal shook the scientific community.

A highly publicized study claiming to predict patients’ responses to chemotherapy was later found to be based on manipulated data, leading to retracted papers, suspended clinical trials and years of wasted research effort.

Watching the case unfold and seeing the consequences profoundly influenced Benjamin’s approach to science. “I suddenly realized that if I'm not being completely open and transparent about what I do and if I make mistakes, it could take years for people to correct them and do what’s right for patients,” he recalls. “And for me, that was not an acceptable risk. So, I decided to invest my effort in the principles of open science.”

Today, with many leadership roles at UHN and its Princess Margaret Cancer Centre (PM), Benjamin is helping shape how artificial intelligence is developed and deployed in health care research, advocating for approaches that are not only innovative, but also trustworthy, transparent, and beneficial to patients.

Guardrails for developing AI models

“Every year, hundreds of studies are published describing predictive models for cancer care, but very few ever make the leap into clinical practice,” says Benjamin. “Pursuing a wrong model not only wastes time and resources, but could also negatively impact patient care.”

This troubling trend prompted Benjamin and a group of international collaborators to set up an industry standard—the Seven Hallmarks of Predictive Oncology—designed to evaluate the quality, reliability, and clinical potential of AI models before they reach the bedside.

“We use these hallmarks to identify models with the greatest potential for clinical translation and to minimize risks before they enter hospitals,” Benjamin explains.

The seven hallmarks of predictive oncology (Graphic credit to Singhal et al., Cancer Discovery AACR Journal)

Together, the hallmarks assess whether a model is built on data that are relevant to patient care and available in real-world settings; whether the design is complex enough to capture the biology of cancer without overfitting the data; whether it has been rigorously benchmarked against existing approaches; whether the study can make accurate predictions for new patients beyond the population on which it was trained; whether clinicians can understand the factors driving its predictions; whether other researchers can reproduce its results; and whether it performs equitably across diverse patient populations and not just groups represented in the training data.

In addition to scientific rigour, each hallmark carries important ethical considerations. “If you push a model that is not sufficiently accurate, you may waste precious health care resources and reduce the likelihood of patient benefit,” he explains. “Ethical considerations must be embedded in every step of AI development.”

Using computational science for cancer discoveries

Cancer research was not part of Benjamin’s original career plan. He studied computer science at the Université Libre de Bruxelles and was initially interested in robotics. His path changed when a supervisor introduced him to the emerging field of bioinformatics.

“That’s when I started to work with clinicians,” Benjamin recalls. “I got hooked by the fact that I could be working to help the patients at the end of the day.”

Working alongside cancer biologists, bioengineers, and clinicians during his PhD, he learned the language of cancer research while applying computational approaches to uncover previously unknown subtypes of breast cancer.

Benjamin later pursued postdoctoral training to develop more sophisticated computational methods, moving beyond simply applying existing ones. At the Harvard School of Public Health and Dana-Farber Cancer Institute, he studied how genes interact in complex networks, deepening his expertise in machine learning and predictive oncology.

After joining Princess Margaret in 2013, Benjamin led a landmark study with collaborators at the Université de Montréal to better understand why some patients respond to immunotherapy while others do not. The team assembled data from 26 clinical trials spanning 12 cancer types and three classes of immunotherapies, bringing together molecular profiles and treatment outcomes from more than 3,600 patients.

“We analyzed each gene individually to determine whether it could predict response to immunotherapy,” Benjamin explains. “This led us to identify a 100-gene signature that was more predictive than previously published approaches across multiple cancer types.”

The team identified two promising new immunotherapy targets by examining the functions of the genes in the signature. The targets are currently being investigated by study co-lead Dr. John Stagg, Director of the McGill Goodman Cancer Centre.

“I have always enjoyed collaborating with scientists around the world, but the highly collaborative culture of Canadian research makes these partnerships especially rewarding and productive,” Benjamin says.

Bringing AI to clinical settings

Benjamin co-leads PM’s Cancer Digital Intelligence (CDI) program with radiation oncologist Dr. Alejandro Berlin to smoothly translate AI technologies into clinical application.

“What we are trying to do is to build a bridge between research and clinical application and foster more innovations within the institute.”

One example is a predictive model developed by Dr. Robert Grant’s team to identify cancer patients who are likely to return to the emergency department for treatment-related complications. CDI helped refine the model and support its deployment in the clinic, where it is currently being evaluated in a “shadow mode” environment alongside routine clinical care.

Another flagship CDI initiative is PMATCH, an AI-enabled platform designed to connect patients with relevant clinical trials more efficiently. The system automatically extracts key information from patient records, evaluates it against trial eligibility criteria, and alerts clinicians to potential matches. Now operating as a pilot at Princess Margaret, PMATCH processes data from more than 200 patients each week, with plans to expand the platform beyond Ontario.

“We want to see whether this technology can be used across multiple hospitals, to help more patients access clinical trials regardless of where they receive care,” Benjamin says.

Humans have a lot to give in this AI-driven world

When it comes to concerns about the rapid pace at which artificial intelligence is transforming society, Benjamin's advice is to embrace the technology while understanding its limitations.

“We need to learn how to use these tools effectively without giving up on our own creativity or judgment,” he says.

Benjamin believes AI excels at finding unexpected connections across different fields because of its broad knowledge base. What it struggles to replicate, he argues, is the kind of disruptive creativity from people who challenge conventional thinking and pursue entirely new directions. He cites examples like Albert Einstein and Marie Curie, iconoclasts whose transformative discoveries fundamentally changed how people understood the world.

“There will be an interplay between the creativity of humans trying to think out-of-the-box, and the creativity of AI to connect the dots in various fields.”

Specifically, biomedical research is entering a new era with the rise of AI co-scientists and automated laboratories. “We are at the stage to reimagine the scientific process where AI and human capabilities complement and strengthen one another,” says Benjamin. “Researchers who adapt and harness the potential of these technologies will be well positioned to shape what comes next and drive future innovation.”

Trust will play a critical role in the health care system. “AI may work 90 per cent of the time, but for the remaining 10 per cent, if humans are not in the loop and people don't trust the system anymore, there is no way we can fix the problem,” he says.

For that reason, he underscores the effort to assess the quality, reliability, and robustness of AI-related technologies before they can be used in a safe and responsible way in the clinic.

“Human-in-the-loop will remain essential for a long time,” he says. “Many complex health care decisions cannot be fully driven by AI. Clinicians, administrators, nurses and other health care professionals will continue to play a crucial role in the responsible use and evaluation of these new technologies.”

Meet PMResearch is a story series that features Princess Margaret researchers. It showcases the research of world-class scientists, as well as their passions and interests in career and life—from hobbies and avocations to career trajectories and life philosophies. The researchers that we select are relevant to advocacy/awareness initiatives or have recently received awards or published papers. We are also showcasing the diversity of our staff in keeping with UHN themes and priorities.