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.

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

Barbara Di Camillo;Sanavia Tiziana;
2016-01-01

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.
2016
12
6
1
16
https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004890&type=printable
Acute Myeloid Leukemia, Prediction algorithms, Machine Learning, Bioinformatics
Noren David P.; Long Byron L.; Norel Raquel; Rrhissorrakrai Kahn; Hess Kenneth; Hu Chenyue Wendy; Bisberg Alex J.; Schultz Andre; Engquist Erik; Liu L...espandi
File in questo prodotto:
File Dimensione Formato  
Noren_etal_2016_PlosCompbio_Suppl.pdf

Accesso aperto

Descrizione: Articolo principale e Supplementary
Tipo di file: PDF EDITORIALE
Dimensione 3.74 MB
Formato Adobe PDF
3.74 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1727735
Citazioni
  • ???jsp.display-item.citation.pmc??? 15
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 20
social impact