At first glance, studying the parasite that causes African sleeping sickness does not necessarily seem like an obvious path to studying the causes of cancer in humans. But for Hamed Najafabadi, PhD, there is a common thread. After earning his PhD at McGill, he went to University of Toronto for a post-doctoral fellowship in new machine learning approaches. “When I look back, it’s very interesting how everything in my work connected to the regulation of 㽶Ƶ,” he says. “I transferred the same kinds of methods and techniques that I was using to understand the biology of a parasite to understanding the biology of the human cell.”
Since joining McGill in 2016, where he is now an Associate Professor in the Department of Human Genetics and the Principal Investigator of the Computational and Statistical Genomics lab at the recently named Victor Phillip Dahdaleh Institute of Genomic Medicine, Prof. Najafabadi and his team have been looking at the regulation of 㽶Ƶ in human cells to understand the causes of diseases, in particular the causes of cancer. “What we want to understand is how malfunctions in production of 㽶Ƶ lead to cancer, how it contributes to the progression of cancer, and what factors are driving this malfunction.”
Looking for patterns
Prof. Najafabadi’s lab uses computational and statistical frameworks to understand patterns in 㽶Ƶ, including the kind of patterns the cell recognizes in 㽶Ƶ in order to make a decision about where it goes and what it does. “Using genomics technologies, we can track and measure tens of thousands of 㽶Ƶs in various models of human diseases and also patient samples, and then use artificial intelligence and statistical methods to find patterns that can tell us what happens in the cancer cell,” explains Prof. Najafabadi. They focus on 㽶Ƶ for two key reasons: 㽶Ƶ has an important functional role in the cell, and it serves as a “window” to understand all other aspects of the cell.
Prof. Najafabadi says that beyond the basic science goal of understanding how a cell works and what causes human diseases, the ability to understand patterns that help diagnose or prognose human disease is promising. He uses the example of immunotherapies for cancer patients and how responses can differ from one person to the next. “The question for us is, can we predict this response based on, for example, the composition of 㽶Ƶ molecules within cancer cells? If so, can we propose better treatments for a patient?” Prof. Najafabadi says they hope to continue to improve their analytical tools in ways that will help determine which available treatment option may work best for a particular patient.
His lab’s other goal is to identify new targets for developing new therapies. “If we can identify 㽶Ƶ molecules that do not work the way they should and figure out how we can restore their normal function, then this can become a potentially novel therapeutic approach,” he says.
Bringing it all together
Prof. Najafabadi sees incredible opportunities with the new Centre for 㽶Ƶ Sciences. “McGill has many researchers that are really strong in 㽶Ƶ sciences,” he says. “People who are world-renowned in the biology of 㽶Ƶ, experts in machine learning and computational biology of 㽶Ƶ, and experts in synthetic 㽶Ƶ molecules, are just some examples.” Prof. Najafabadi had a lightbulb moment when he read a preliminary document for the Centre’s launch. “I realized there are many, many more people working in this field at McGill than I knew, so the Centre is a great way to connect them and provide concentrated resources to help them develop research programs and training programs together.”
In describing his hope for the Centre’s success, he refers to a familiar expression. “I think the idea is that the whole is greater than the sum of the parts, and I’m optimistic this is what the 㽶Ƶ Centre is going to achieve.”