Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 12, Issue 6, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1004890
Keywords
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Funding
- NLM Training Program in Biomedical Informatics [T15LM007093-21]
- CPRIT Computational Cancer Training Program
- Sage Bionetworks
- MD Anderson Cancer Center Department of Leukemia
- NSF
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1150645] Funding Source: National Science Foundation
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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.
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