Yan Yi Li is a PhD student in the Department of Biochemistry at Western University. She is conducting BCC-funded breast cancer research under the mentorship of Dr. Pingzhao Hu.
- What specific areas of breast cancer research are you currently focusing on, and why are they important?
My research focuses on finding new treatments for triple-negative breast cancer (TNBC), one of the most aggressive forms of breast cancer and one that still has limited treatment options. I study a protein called Pin1, which acts like a control switch inside cancer cells and helps regulate how they grow and spread. Because Pin1 plays such an important role in cancer, we believe that it is a promising new treatment target for TNBC. To search for possible Pin1-targeted treatments, I use artificial intelligence (AI) and computer-based tools to identify molecules that may be able to block Pin1 activity. This is important because there are millions of possible drug candidates, far too many to test in a lab one by one. By using AI to narrow down the most promising options, this research could help speed up the discovery of new and more effective treatments for breast cancer patients.
- What recent breakthroughs or discoveries in Canadian breast cancer research are exciting to you?
One recent area of Canadian breast cancer research that I find very exciting is the increasing use of AI and computational tools to accelerate drug discovery and cancer research. At Western University, researchers are using interdisciplinary approaches that combine molecular biology, imaging, and computational methods to better understand cancer mechanisms and identify new therapeutic vulnerabilities. Other AI research groups in Canada are also developing machine learning models that can predict how small molecules interact with cancer-related proteins, improving both the speed and accuracy of early drug discovery. What excites me most is how these advances connect directly to my own work in computational drug discovery. They show that AI can successfully handle the complexity of cancer systems and large datasets, which makes me even more motivated about its potential to deliver more targeted and effective therapies in the future.
- How do you see the future of breast cancer prevention, screening, diagnosis, treatment evolving through research?
I see the future of breast cancer research becoming much more personalized, precise, and driven by data. Advances in AI and computational tools will allow researchers to better predict disease risk, identify early signs of cancer, and match patients with the most effective treatments based on their specific tumor biology. In drug development, AI will help explore complex disease mechanisms and speed up the discovery of targeted therapies, especially for difficult-to-treat cancers like TNBC. In the long term, I think breast cancer care will shift from a one-size-fits-all approach to a more targeted strategy that integrates prevention, early detection, and therapy design. This is especially exciting for my field, as computational drug discovery can help identify key targets like Pin1 and design more effective treatments faster and more precisely than traditional methods.
- Can you discuss any challenges or obstacles you’ve encountered in your breast cancer research, and how you’ve overcome them?
One of the biggest challenges in my research is working with very large and complex sets of biological and chemical data. Before we can use this information with artificial intelligence (AI), we first need to carefully organize and clean the data to make sure it is accurate and reliable. Another challenge is making sure the AI predictions are not only suitable from a computer science perspective, but also meaningful in the context of real-world cancer biology. In other words, we want to make sure the results could actually help improve breast cancer treatment in the future. To overcome these challenges, I carefully prepare and test the data and use multiple methods to check that the AI models are making reliable predictions. I also combine AI tools with existing biological knowledge about proteins like Pin1 and triple-negative breast cancer to help make the results more accurate and useful.
- In your opinion, what role can public awareness and advocacy play in advancing breast cancer research and treatment?
Public awareness and advocacy play a critical role in advancing breast cancer research because they help shape funding priorities, research directions, and access to care. Despite major progress, breast cancer – especially aggressive subtypes like TNBC – still faces challenges such as limited targeted therapies and uneven access to clinical trials and advanced treatments. Stronger public engagement helps highlight these gaps and promotes investment in more innovative approaches while also pushing for more equitable healthcare systems. It can also accelerate the translation of research findings into real-world therapies by influencing funding agencies and policy decisions that prioritize unmet clinical needs. Overall, this creates a more supportive environment for advancing research-driven solutions, including emerging approaches like AI-driven drug discovery that aim to identify new targets and improve treatment options for patients.
