Integrating Biological Covariates into Gene Expression-Based Predictors of Radiation Sensitivity

摘要

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 expressionalone to predict a complex trait. A practical approach to this problem is to integrate existing biological knowledge with geneexpression to build a composite predictor. We studied the problem of predicting radiation sensitivity within human cancer celllines 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 usinglinear regression, a technique for incorporating this knowledge. The resulting correlations between gene expression and radiationsensitivity improved through the use of this technique (best-fit adjusted R~2 increased from 0.3 to 0.84). Overfitting of data wasexamined through the use of simulation. The results reinforce the concept that radiation sensitivity is not driven solely by geneexpression, but rather by a combination of distinct parameters. We show that accounting for biological heterogeneity significantlyimproves the ability of the model to identify genes that are associated with radiosensitivity.