Improving breast cancer survival analysis through competition-based multidimensional modeling.
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...
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2013
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oai:doaj.org-article:99861dbc72c64dee91e8655ddfaa22222021-11-18T05:52:10ZImproving breast cancer survival analysis through competition-based multidimensional modeling.1553-734X1553-735810.1371/journal.pcbi.1003047https://doaj.org/article/99861dbc72c64dee91e8655ddfaa22222013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23671412/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Breast 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.Erhan BilalJanusz DutkowskiJustin GuinneyIn Sock JangBenjamin A LogsdonGaurav PandeyBenjamin A SauerwineYishai ShimoniHans Kristian Moen VollanBrigham H MechamOscar M RuedaJorg TostChristina CurtisMariano J AlvarezVessela N KristensenSamuel AparicioAnne-Lise Børresen-DaleCarlos CaldasAndrea CalifanoStephen H FriendTrey IdekerEric E SchadtGustavo A StolovitzkyAdam A MargolinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 5, p e1003047 (2013) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Biology (General) QH301-705.5 |
| spellingShingle |
Biology (General) QH301-705.5 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 Friend Trey Ideker Eric E Schadt Gustavo A Stolovitzky Adam A Margolin Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| description |
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. |
| format |
article |
| author |
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 Friend Trey Ideker Eric E Schadt Gustavo A Stolovitzky Adam A Margolin |
| author_facet |
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 Friend Trey Ideker Eric E Schadt Gustavo A Stolovitzky Adam A Margolin |
| author_sort |
Erhan Bilal |
| title |
Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| title_short |
Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| title_full |
Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| title_fullStr |
Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| title_full_unstemmed |
Improving breast cancer survival analysis through competition-based multidimensional modeling. |
| title_sort |
improving breast cancer survival analysis through competition-based multidimensional modeling. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/99861dbc72c64dee91e8655ddfaa2222 |
| work_keys_str_mv |
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