Cancer is the leading cause of death in Canada. According to the Canadian Cancer Society, an estimated 230,000 people are diagnosed with the disease every year.
University Professor Ming Li, the Canada Research Chair in Bioinformatics, is using deep learning technology to make personalized cancer vaccines accessible to everyone. He began doing cancer research when his wife, Jessie W. H. Zou, was diagnosed with breast cancer. Though she died in 2010, her legacy continues in his research.
His team’s software, DeepImmu, replaces a lot of lab work with AI, and takes the cutting-edge treatment from a million-dollar specialty treatment to a much more affordable process that the average patient can access.
Traditional cancer treatment has consisted of three pillars: surgery, chemotherapy and radiation. These treatments, however, are not always effective, and they include debilitating side effects that can significantly reduce a patient’s quality of life.
In the last 30 years, researchers have been developing a fourth pillar of cancer treatment known as immunotherapy. “This is essentially using your own immune system to fight the cancer,” Professor Li says.
Recent research has revealed that when a cell mutates into a cancer cell, it brings some mutated protein fragments to the cell surface. These distinctive markers are called neoantigens. In a healthy person’s immune system, T-cells routinely recognize these neoantigens and destroy the rogue cells before they can spread further.
“We get mutated cells every day,” Professor Li explains, “but usually our T-cells are good at noticing them and killing them. For people with cancer, however, this mechanism does not always work.”
In the last few years, scientists have developed personalized cancer vaccines that re-train a patient’s T-cells to target their cancer. While some immunotherapy drugs have been around since the 1990s, the new treatment involves specifically identifying the neoantigens in an individual patient’s tumour, then crafting a corresponding personalized vaccine.
The problem with this exciting new treatment? The process of discovering and validating these neoantigens is prohibitively expensive, slow, and inaccurate.
“Every person’s cancer is different,” Professor Li says. “You have to do a lot of time-consuming, labour-intensive experiments that cost millions of dollars.”
- Read the full article on Waterloo News to learn how Professor Li and his team are using deep learning on mass spectrometry data to streamline, automate and significantly reduce the cost of personalized vaccine development.