Recommended by: Anne-Lise Børresen-Dale, Oslo University
David Quigley got his start with a master's degree in bioinformatics, but it was answering an ad posted by the University of California, San Francisco's Alan Balmain for someone with experience in data-mining techniques that prompted Quigley to become a cancer biologist. Now pursuing his PhD simultaneously at the University of California, San Francisco, and Oslo University, most of his work to date has been on the genetics of susceptibility — working out how networks of genes work together to contribute to cancer risk — primarily in models of skin and lung cancer.
Because the problem of susceptibility has yet to be solved, a lot of effort has been spent trying to identify risk loci. But so far, all the common loci that have been identified have only a modest effect on increasing someone's total risk of cancer, Quigley says. "In order to get a fuller picture of how things work together, you need to map together sets of genes that work in common pathways, and basically combine those things together," he adds. To that end, he and Balmain are taking a network-based approach, using tools that allow them to visualize how sets of genes are correlated in terms of expression, and how they may be working together to get a bigger picture of what the likely total contribution of these genes is to cancer susceptibility.
However, the loci the researchers are looking for tend to have very small effects on susceptibility when taken one at a time. "So one major challenge is either dealing with human data — the tumors that we look at, the individuals that we look at are very heterogeneous — and even when we use mouse models, we use a chemical carcinogenesis model, and it takes a long time for the mice the get cancer, and just as humans are, the tumors are very heterogeneous," Quigley says.
And the Nobel goes to…
If Quigley were to win the Nobel Prize, he'd like it to be for being the person to identify the genetic precursors to the development of cancer for that would allow screening to distinguish those tumors that will progress from those that would remain benign.