By Wallace Ravven | UCSF.edu | November 15, 2013
Which would you choose if you were diagnosed with early-stage breast cancer? You could receive a standard chemo regimen, followed three months later by an MRI scan to determine the treatment's success. Or, instead, a genomic analysis of your biopsied breast tissue could help determine a drug regimen tailored to your own genetic makeup.
An MRI scan would assess your progress just three weeks later so, if need be, your oncologist could quickly switch to a different chemo protocol to attack the cancer.
The second approach adds several additional layers of information: your unique genetic profile, the early follow-up MRI, an ongoing assessment of your treatment's effectiveness, and the chance to respond quickly to new information.
Now envision a truly global extension of this multilayered tactic. Beyond improving treatment protocols, this approach also offers a path to further discovery.
A scientist studying how a misfolded protein triggers a degenerative disease might see another lab's recent results online, for example, and learn that the same protein causes a different disorder. Such a link suggests a common molecular mechanism for both conditions – a precious clue that could yield cures.
Or picture experts in disparate fields, using vastly different routes to discovery – molecular analysis, genomics, epidemiology – but each studying resistance to viral attack. With more ready access to each other's results and to novel analytic tools that tap reservoirs of related findings, they transcend their specialties and boost the chance to see the pieces of the puzzle come together.
Such researchers are immersed in a kind of information commons, awash in a virtual library of interconnected laboratory notebooks and volumes of new analytic tools and data. With an increased ability to harvest information automatically and far more powerfully, they can more easily find the connections among discoveries that would otherwise go unrecognized.
Think of it as highly sophisticated scientific crowdsourcing. The shared information and insights create a rich "knowledge network."
"The knowledge network is the ‘integrating center' for precision medicine," says Keith Yamamoto, PhD, vice chancellor for research, a professor of cellular and molecular pharmacology, and a leader of the precision medicine effort under way at UC San Francisco. "For the clinician-researcher, each added level of insight – say, finding the link between a specific genetic signature and an aggressive type of cancer – can lead to new ways to diagnose and treat the disease."
Neuroscientist Kate Rankin and technology expert Joe Hesse are collaborating to advance the knowledge network.
The oncologist treating early-stage breast cancer, for example, might learn of a new online tool to refine analysis of MRI scans. Or a colleague seeing her preliminary progress may suggest a potent new strategy to screen for the best drug. Either way, the search for better treatments just got a little smarter.
"And in fundamental research," says Yamamoto, "the network will be a ‘discovery generator,' revealing new correlations testable in the lab or new clues to mechanisms that drive disease. The pace of research is extraordinary at many levels, but the real payoff lies where insights intersect."