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

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dc.citation.articleNumbere1004890en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorNoren, David P.en_US
dc.contributor.authorLong, Byron L.en_US
dc.contributor.authorNorel, Raquelen_US
dc.contributor.authorRrhissorrakrai, Kahnen_US
dc.contributor.authorHess, Kennethen_US
dc.contributor.authorHu, Chenyue Wendyen_US
dc.contributor.authorBisberg, Alex J.en_US
dc.contributor.authorSchultz, Andreen_US
dc.contributor.authorEngquist, Eriken_US
dc.contributor.authorLiu, Lien_US
dc.contributor.authorLin, Xihuien_US
dc.contributor.authorChen, Gregory M.en_US
dc.contributor.authorXie, Hongleien_US
dc.contributor.authorHunter, Geoffrey A.M.en_US
dc.contributor.authorBoutros, Paul C.en_US
dc.contributor.authorStepanov, Olegen_US
dc.contributor.authorDREAM 9 AML-OPC Consortiumen_US
dc.contributor.authorNorman, Theaen_US
dc.contributor.authorFriend, Stephen H.en_US
dc.contributor.authorStolovitzky, Gustavoen_US
dc.contributor.authorKornblau, Stevenen_US
dc.contributor.authorQutub, Amina A.en_US
dc.date.accessioned2016-09-30T20:52:23Zen_US
dc.date.available2016-09-30T20:52:23Zen_US
dc.date.issued2016en_US
dc.description.abstractAcute 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.en_US
dc.identifier.citationNoren, David P., Long, Byron L., Norel, Raquel, et al.. "A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis." <i>PLoS Computational Biology,</i> 12, no. 6 (2016) Public Library of Science: http://dx.doi.org/10.1371/journal.pcbi.1004890.en_US
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pcbi.1004890en_US
dc.identifier.urihttps://hdl.handle.net/1911/91645en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleA Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosisen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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