Integrating Biological Covariates into Gene Expression-Based Predictors of Radiation SensitivityReport as inadecuate




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International Journal of Genomics - Volume 2017 2017, Article ID 6576840, 9 pages - https:-doi.org-10.1155-2017-6576840

Research Article

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

Correspondence should be addressed to Steven A. Eschrich

Received 30 September 2016; Revised 4 January 2017; Accepted 11 January 2017; Published 8 February 2017

Academic Editor: Bethany Wolf

Copyright © 2017 Vidya P. Kamath et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The use of gene expression-based classifiers has resulted in a number of promising potential signatures of patient diagnosis, prognosis, and response to therapy. However, these approaches have also created difficulties in trying to use gene expression alone to predict a complex trait. A practical approach to this problem is to integrate existing biological knowledge with gene expression to build a composite predictor. We studied the problem of predicting radiation sensitivity within human cancer cell lines from gene expression. First, we present evidence for the need to integrate known biological conditions tissue of origin, RAS, and p53 mutational status into a gene expression prediction problem involving radiation sensitivity. Next, we demonstrate using linear regression, a technique for incorporating this knowledge. The resulting correlations between gene expression and radiation sensitivity improved through the use of this technique best-fit adjusted increased from 0.3 to 0.84. Overfitting of data was examined through the use of simulation. The results reinforce the concept that radiation sensitivity is not driven solely by gene expression, but rather by a combination of distinct parameters. We show that accounting for biological heterogeneity significantly improves the ability of the model to identify genes that are associated with radiosensitivity.





Author: Vidya P. Kamath, Javier F. Torres-Roca, and Steven A. Eschrich

Source: https://www.hindawi.com/



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