Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Erhan Bilal, Janusz Dutkowski, Justin Guinney, In Sock Jang, Benjamin A. Logsdon, Gaurav Pandey, Benjamin A. Sauerwine, Yishai Shimoni, Hans Kristian Moen Vollan, Brigham H. Mecham, Oscar M. Rueda, Jorg Tost, Christina Curtis, Mariano J. Alvarez, Vessela N. Kristensen, Samuel Aparicio, Anne Lise Børresen-Dale, Carlos Caldas, Andrea Califano, Stephen H. FriendTrey Ideker, Eric E. Schadt, Gustavo A. Stolovitzky, Adam A. Margolin

Research output: Contribution to journalArticle

55 Scopus citations

Abstract

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Original languageEnglish (US)
Article numbere1003047
JournalPLoS computational biology
Volume9
Issue number5
DOIs
StatePublished - May 2013

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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  • Cite this

    Bilal, E., Dutkowski, J., Guinney, J., Jang, I. S., Logsdon, B. A., Pandey, G., Sauerwine, B. A., Shimoni, Y., Moen Vollan, H. K., Mecham, B. H., Rueda, O. M., Tost, J., Curtis, C., Alvarez, M. J., Kristensen, V. N., Aparicio, S., Børresen-Dale, A. L., Caldas, C., Califano, A., ... Margolin, A. A. (2013). Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling. PLoS computational biology, 9(5), [e1003047]. https://doi.org/10.1371/journal.pcbi.1003047