Nevan Krogan thinks genomics has brought us closer to a revolution in cancer treatment than even geneticists realize.

“There’s been a tsunami of genetic data about different cancers,” says the medical geneticist and UCSF professor of cellular and molecular pharmacology.

To make sense of this flood of data, he says, it’s time move beyond simply cataloguing cancer genes and integrate the genomic findings with basic biological research, to create a more holistic understanding of cancer biology. That integration is a defining element of precision medicine and is key to accelerating progress: new insights into the basics of cell function provide new knowledge for geneticists and others working with ‘omic technologies, and to those engaged in clinical discovery, ultimately leading to better therapies.

To this end, Krogan and colleagues at UCSF and UC San Diego have launched an ambitious new project called the Cancer Cell Map Initiative, whose goal is to map out the meaning of the hundreds of cancer mutations that have already been identified in the context of the biology of healthy and diseased cells.

Krogan thinks the initiative will help solve an impasse that currently stymies the field: Despite years of research, there is still no scientific consensus about the specific genetic mutations responsible for most cancers.

The solution, Krogan and colleagues believe, is to view cancer not as a result of specific gene mutations but one of dysfunctional networks of genes and proteins.

Cancers arise because mutated genes in a particular cell produce mutated proteins and interactions, which fail to perform their cellular jobs properly. A few such failures can hijack cascading networks of dozens of interacting proteins, leading the cell to divide uncontrollably, making thousands of copies of itself. Importantly, different genetic components of these networks can fail in different people and still produce the same end result.

Krogan and his colleagues believe the perplexing diversity of cancer genetics can only be understood in the context of these networks.

For instance, scores of different mutations identified in 300 ovarian cancer patients failed to form any recognizable pattern until Krogan and his CCMI colleagues combined them with a map of known gene-protein interactions within the cell. Immediately, the researchers could see that all the various mutations in patients with one particularly aggressive type of ovarian cancer were connected by two specific molecular pathways controlling cell growth.

Such insights into the biological mechanisms behind specific subsets of cancer will be critical, Krogan believes, to a precision medicine approach to diagnosing and treating cancer. Ultimately this strategic approach can be applied beyond cancer, to virtually any other disease, as the relevant genomic data become available.

“As we refine our understanding of how these mutations are affecting specific biological networks, that's going to improve doctors’ predictive power in a clinical setting,” Krogan says. “We're just building tools to make that connection between the basic research and the clinical world.”