Thursday, May 9, 2013
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computational model for breast cancer progression
A computational model for
breast cancer progression developed through an open innovation challenge
could lead to better prognostic tests for the disease and help guide the
development of future computational models. An online server was used to host
user-submitted computational algorithms for predicting disease progression
based on a dataset containing expression, copy number and clinical data from
about 2,000 breast cancer samples. The winning model predicted progression on
a validation data set of 184 samples with higher accuracy than that of
previously reported prognostic models for breast cancer. Next steps include
studying the biology underlying predictive features of the model and running
additional challenges in other disease areas using this framework.
Oncotype DX from Genomic Health Inc. and MammaPrint from Agendia B.V. are prognostic
gene expression tests marketed for breast cancer (see DREAM team, page 4).
Published online May 9, 2013
Findings unpatented for
both studies; licensing status not applicable
Margolin, A.A. et al.
Sci. Transl. Med.; published online April 17, 2013;
Contact: Stephen H. Friend, Sage Bionetworks, Seattle, Wash.
Contact: Adam A. Margolin, same affiliation as above
Cheng, W.-Y. et al. Sci. Transl. Med.; published online April 17,
Contact: Dimitris Anastassiou, Columbia University, New
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