The vast majority of protein interactions in the human body are a mystery. Each gene can encode multiple parts, and researchers are working toward a comprehensive understanding of what all those parts are, where they are found within the cells of our bodies and how they connect to each other.

A team co-led by Frederick Roth, Canada Excellence Research Chair in Integrative Biology at the University of Toronto, has created the world’s largest-scale map showing the network formed by proteins and other cellular components that “stick together”. The work has led to the identification of dozens of genes newly thought to be cancer-related.

Describing some 14,000 direct interactions between proteins, Roth’s “human interactome” map is four times larger than any previous map of its kind, and contains greater detail on more high-quality interactions than all previous studies put together. Studies in baker’s yeast have mapped interactions at genome scale, but the new study is the first to approach this scale in humans.

The study also shows that the network of protein interactions in humans covers a much broader range of genes than some past research has suggested. Studies often focus on ‘popular’ proteins that are already known to be tied to disease or to be interesting for other reasons, which has created a bias in our understanding of interactions, Roth says.

Along with Marc Vidal (Dana-Farber Cancer Institute and Harvard Medical School), Roth co-led the international research team that wrote the paper in Cell. Roth is a senior investigator at Mount Sinai Hospital’s Lunenfeld-Tanenbaum Research Institute and a professor at the Donnelly Centre for Cellular and Biomolecular Research, University of Toronto.

Frederick Roth
Canada Excellence Research Chair in Integrative Biology at the University of Toronto

Using lab experiments, the scientists identified interactions and then, using computer modelling, they zoomed in on proteins that ‘connect’ to one or more other cancer proteins.

“We show, really for the first time, that cancer proteins are more likely to interconnect with one another than they are to connect to randomly chosen non-cancer proteins,” says Roth, who is also a Canadian Institute for Advanced Research (CIFAR) senior fellow.

“Once you see that proteins associated to the same disease are more likely to connect to each other, you can use this network of interactions as a prediction tool to find new cancer proteins, and the genes they encode,” says Roth. For example, two known cancer genes encoded two proteins that interacted with CTBP2, a protein encoded at a location tied to prostate cancer, which can spread to nearby lymph nodes. These two proteins are implicated in lymphoid tumours, suggesting that CTBP2 plays a role in the development of lymphoid tumours.

Using their predictive method, the researchers found that 60 of their predicted cancer genes fit into a known cancer pathway. Discoveries like these are crucial for understanding how cancer and other diseases develop and ultimately, how to treat and prevent them.

Roth likens a doctor asked to treat a patient’s disease to a car mechanic. “How can we ask someone to fix a car with an incomplete list of parts and no guidance on how the parts fit together?” he says. “One major conclusion of the paper is that when you look systematically for interactions, you find them everywhere.”