Earlier this month, The Hive reported on the winners of the latest Prize4Life competition, which provided anonymized patient data in the hopes that big data hackers could shed light on how ALS progresses. Dr. Melanie Leitner, the chief scientific officer at Prize4Life, shares a little bit more about how this innovative program is merging big data, crowdsourcing, and life sciences to help improve patient treatment.
Big data is more than just a trend: It is a superhighway to solutions for some of the world’s cruelest diseases.
Amyotrophic lateral sclerosis, also known as ALS, or Lou Gehrig’s disease, is one of those diseases. Moreover, it is also referred to as an “orphan’’ disease because it is often overlooked when it comes to funding for research. It’s a vicious cycle: because ALS is as yet incurable, with only one modestly effective treatment on the market, many problem solvers would rather put their talents and time toward diseases that seem to offer more hope. This in turn contributes to the perception that the disease is hopeless.
Prize4Life happens to disagree. We don’t think ALS is hopeless, but we know that its incredibly challenging nature requires new and different thinking. So we took our challenge to the cloud, and looked for solutions from the crowd. Here’s what happened:
The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge (or ALS Prediction Prize) has a long name but a simple goal: to find better ways of predicting disease progression – a continuing mystery in ALS. On average, people with ALS live about 1,000 days once they are diagnosed. But some live less than 2 years and a few live much longer – sometimes for decades. The variability of disease progression makes designing and interpreting clinical trials difficult, which in turn makes the development of viable treatments even harder.
Now, anyone with quantitative abilities, be they an engineer or an atmospheric chemist, can help in the fight against ALS, as was proven through the ALS Prediction Prize – a crowdsourced approach using the InnoCentive platform, which crowdsources innovation problems to the world’s smartest people. The ALS Prediction Prize winners, who were among more than 1,000 participating individuals and teams with interesting and diverse backgrounds, have developed computer algorithms that predict a given patient’s disease status within a year’s time based on three months of data. This solution is important because it could impact how future clinical trials for ALS therapies are designed and conducted, fostering faster breakthroughs in effective treatments for the disease. By making clinical trial data available to a global community of data scientists, researchers, and computer mavens, we continue to find ways to speed up the process of drug discovery, while driving down the cost, through better clinical trials. This is a source of enormous untapped hope for patients and scientists.
But our work has just begun in the journey toward finding a cure. The ALS Prediction Prize is based on the PRO-ACT database, which was developed in collaboration with the Northeast ALS Consortium (NEALS) and the Neurological Clinical Research Institute at Massachusetts General Hospital, and with funding from the ALS Therapy Alliance. A subset of this database was made available to participants in our ALS Prediction Prize, and the full PRO-ACT dataset will be made available to the global scientific community for research on December 5, 2012. That expanded dataset will contain clinical data from over 8,500 ALS patients from multiple completed clinical trials, ten times more than had been available to anyone previously.
The ALS Prediction Prize proved that this database can be used as a viable research tool for new minds worldwide to contribute to the ALS cause. By building and making this database available, we and our many partners further expand the possibilities for breakthroughs that can be found through a crowdsourced approach. In this way, we hope to encourage the global research community to adopt this orphan disease and deliver hope to thousands of patients around the world.
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