title: A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.
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.
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: David P
authors.initials: DP
authors.lastname: Noren
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Byron L
authors.initials: BL
authors.lastname: Long
authors.affiliation: IBM Computational Biology Center, Yorktown Heights, New York, United States of America.
authors.forename: Raquel
authors.initials: R
authors.lastname: Norel
authors.affiliation: IBM Computational Biology Center, Yorktown Heights, New York, United States of America.
authors.forename: Kahn
authors.initials: K
authors.lastname: Rrhissorrakrai
authors.affiliation: The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Hess
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Chenyue Wendy
authors.initials: CW
authors.lastname: Hu
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Bisberg
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Andre
authors.initials: A
authors.lastname: Schultz
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Erik
authors.initials: E
authors.lastname: Engquist
authors.affiliation: Arizona State University, Tempe, Arizona, United States of America.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
authors.forename: Xihui
authors.initials: X
authors.lastname: Lin
authors.affiliation: Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Chen
authors.affiliation: Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
authors.forename: Honglei
authors.initials: H
authors.lastname: Xie
authors.affiliation: Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
authors.forename: Geoffrey A M
authors.initials: GA
authors.lastname: Hunter
authors.affiliation: Ontario Institute for Cancer Research, Toronto, Ontario, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.
authors.forename: Paul C
authors.identifier: 0000-0003-0553-7520
authors.initials: PC
authors.lastname: Boutros
authors.affiliation: Institute for Systems Biology, Moscow, Russia.
authors.forename: Oleg
authors.identifier: 0000-0001-6129-6499
authors.initials: O
authors.lastname: Stepanov
authors.affiliation: Sage Bionetworks, Seattle, Washington, United States of America.
authors.forename: Thea
authors.initials: T
authors.lastname: Norman
authors.affiliation: Sage Bionetworks, Seattle, Washington, United States of America.
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Friend
authors.affiliation: IBM Computational Biology Center, Yorktown Heights, New York, United States of America.|Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Stolovitzky
authors.affiliation: The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Steven
authors.initials: S
authors.lastname: Kornblau
authors.affiliation: Rice University, Houston, Texas, United States of America.
authors.forename: Amina A
authors.initials: AA
authors.lastname: Qutub
mesh_terms: D000465:Algorithms; D000690:Amyotrophic Lateral Sclerosis / Q000175:diagnosis* / Q000378:metabolism / Q000628:therapy*; D015415:Biomarkers / Q000378:metabolism; D063045:Crowdsourcing / Q000379:methods*; D006801:Humans; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D020543:Proteome