While we're on the subject of cancer, an unfortunately "popular" topic of late in my world, thanks to Patrick McGovern at Lybba (check it out, another great resource for good health and better medicine, especially for the little ones), I've learned about Cancer Commons, which is taking the next step in solving the cancer koan. This cutline carries the gist: "What if the thousands of "N of 1" experiments conducted daily by 30,000 oncologists could be coordinated into a giant adaptive search for better, individualized treatments?"
The move to aggregate data is very popular in medicine these days -- and difficult given the legacy systems of different institutions and the addiction to paper records that leaves the medical field woefully behind industry in its database interoperability.
There are many efforts to address this, e.g. I recently learned about Improve Care Now, which links pediatric GI docs and their data, thus providing them with access to greater research pools than their practices alone can provide. (If you know a child or teen with Crohn's or colitis, check it out--I'm oversimplifying their groundbreaking work here.) And there are the "translational science" awards from NIH that are attempting to accelerate the time between "bench and bedside," typically an astonishing 17 years, this according to a Principal Investigator working on one of these grants.
Comes news now of another approach being tried out in cancer that incorporates data, individualized treatment, and networking. Here's a pull from Cancer Commons' white paper. Having spent so much time online in the past nine months, reading listservs, trying to fathom what clinical trial protocols really hold promise, and whether "our" patient might be a candidate...and feeling, regardless of a very committed circle of friends, alone with the disease nonetheless, I appreciate any effort to pool information and link up researchers, clinicians, and patients. Take a look:
Modern molecular biology supports the hypothesis that cancer is actually hundreds or thousands of rare diseases, and that every patient’s tumor is, to some extent, unique. Although there is a rapidly growing arsenal of targeted cancer therapies that can be highly effective in specific subpopulations, especially when used in rational combinations to block complementary pathways, the pharmaceutical industry continues to rely on large-scale randomized clinical trials that test drugs individually in heterogeneous populations. Such trials are an extremely inefficient strategy for searching the combinational treatment space, and capture only a small portion of the data needed to predict individual treatment responses. On the other hand, an estimated 70% of all cancer drugs are used off-label in cocktails based on each individual physician’s experience, as if the nation's 30,000 oncologists are engaged in a gigantic uncontrolled and unobserved experiment, involving hundreds of thousands of patients suffering from an undetermined number of diseases. These informal experiments could provide the basis for what amounts to a giant adaptive search for better treatments, if only the genomic and outcomes data could be captured and analyzed, and the findings integrated and disseminated.
Toward this end, we are developing Cancer Commons, a family of web-based open-science communities in which physicians, patients, and scientists collaborate on models of cancer subtypes to more accurately predict individual responses to therapy. The goals are to: 1) give each patient the best possible outcome by individualizing their treatment based on their tumor’s genomic subtype; 2) learn as much as possible from each patient’s response, and 3) rapidly disseminate what is learned. The key innovation is to run this adaptive search strategy in "realtime", continuously updating disease and treatment models so that the knowledge learned from one patient is disseminated in time to help the next.
Cancer Commons is being developed one cancer at a time, beginning with melanoma.
