Team Taps The Wisdom Of The Crowd To Impact Breast Cancer Prognosis

Two new reports issuing in Science Translational Medicine (STM) today showcase the potential of teams of scientists working together to solve increasingly complex medical problems. The results demonstrate that better predictors of breast cancer progression than those currently available can be rapidly evolved by running open Big Data Challenges such as The Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (BCC).

In breast cancer, a key undertaking is determining those patients whose disease is most likely to progress rapidly and therefore tailor the best course of treatment for them. Currently oncologists are using gene-expression based assays such as MammaPrint and Oncotype Dx, that are based on 10 year old science, and both do better with breast cancer risk prediction than models based only on clinical data.

Dr. Stephen Friend, the Founder of Sage Bionetworks and one of the organizers of the BCC reflects, "Ten years ago, members of our research group used gene expression profiling to build one of the first breast cancer predictors. Mammaprint and Oncotype Dx were developed off of that but further improvement seems to have stalled. We wondered if running a Challenge like BCC would motivate lots ofdifferent groups to tackle this problem, some working collaboratively, and if that might be more fruitful than the current "go it alone" single researcher approach."