Integration of Clinical and Gene Expression Data Has a Synergetic Effect on Predicting Breast Cancer Outcome

Integration of Clinical and Gene Expression Data Has a Synergetic Effect on Predicting Breast Cancer Outcome

Van Vliet, M.H.
Horlings, H.M.
Van de Vijver, M.J.
Reinders, M.J.T.
Wessels, L.F.A.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2012-07-11

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.

http://resolver.tudelft.nl/uuid:4e71e296-6bda-4aa2-a4e9-0d3cf8d87e7f

https://doi.org/10.1371/journal.pone.0040358

Public Library of Science

1932-6203

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0040358

PLoS ONE, 7 (7), 2012

Institutional Repository

journal article

© 2012 The Author(s)
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