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PLoS Comput Biol. 2016 Jun 28;12(6):e1004890. doi: 10.1371/journal.pcbi.1004890. eCollection 2016 Jun.

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.

Author information

1
Rice University, Houston, Texas, United States of America.
2
IBM Computational Biology Center, Yorktown Heights, New York, United States of America.
3
The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
4
Arizona State University, Tempe, Arizona, United States of America.
5
Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
6
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
7
Institute for Systems Biology, Moscow, Russia.
8
Sage Bionetworks, Seattle, Washington, United States of America.
9
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

Abstract

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

PMID:
27351836
PMCID:
PMC4924788
DOI:
10.1371/journal.pcbi.1004890
[Indexed for MEDLINE]
Free PMC Article

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