Abstract 4125: Robust feature selection and gene committee predictor for breast cancer patient survival prediction
Authors: Yang Howard H. , Lee Maxwell P. 
Published: May 2015

Gene expression signatures have been widely used to predict patient survival. However, predictors developed on training data often failed on validation data. The grand challenge is to improve the generalization of such predictors. We analyzed the breast cancer data from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). There were n=997 patients in the training data and n=995 patients in the validation data. Using the training data, we applied the Cox Proportional Hazards Model to estimate patient survival using the dichotomized expression with adjustment for age, ER, tumor size, and node. Based on the whole transcriptome analysis of the training data using the Cox model for each of 33075 genes, we selected 330 genes (the top 1%). To quantify their reproducibility, we analyzed bootstrap samples (half-samples of the training data) and defined the repetition rate (rep-rate) in two steps. Step1: choose a p-value cutoff (Pc) to determine whether a gene is significant in the analysis of any bootstrap sample. We randomly drew 100 half-samples and did the whole transcriptome analysis for each half-sample. We found 100 Pc's for the top 1% genes. We then choose the geometric average of the 100 Pc's as the desired Pc. Step2: randomly drew 1000 half-samples from the training data. Using Pc, we only needed to do the analysis on the 330 genes for each half-sample with 99% less computation. Based on the analyses of the 1000 half- samples, we computed the rep-rates for the 330 genes. We then selected 50 genes with rep-rates greater than 0.36. Considering each gene as a weak predictor, we used three models to combine the expression of a set of k genes: sum, weighted sum and gene committee. The hazard ratio (HR) data from the analyses of the bootstrap samples were used in 2nd and 3rd model. For the i-th gene we computed mHR(i) (the median HR). We defined a k-dim vector w: w(i)=1 if mHR(i) greater than 1 and w(i)=-1 if mHR(i) less than 1. We then used w as the weights in the weighted sum predictor. For the gene committee, from the binary (0 and 1 for below and above the median, respectively) expression variables for the k genes, we defined k risk variables. If a gene had mHR greater than 1, its risk variable was the same as the binary expression variable. But if a gene had mHR less than 1, let x be the binary expression variable, the risk variable was defined as y=1-x. A committee predictor was defined from the risk variables based on the majority rule. We used the validation data to examine the performance of these three predictors. Using our 50 genes, the sum predictor was not significant. However, the weighted sum had HR=1.53 with 95%CI 1.25-1.87 and P= 4.449e-05. The gene committee was the most significant predictor with HR=1.7, CI 1.39-2.08 and P= 2.552e-07. Using the PAM50 genes, the sum predictor was significant with HR= 1.30, CI 1.07-1.58 and P=7.008e-03. The weighted sum had HR=1.56, CI 1.26-1.93 and P=4.381e-05 and the gene committee had HR=1.55, CI 1.26-1.91 and P= 3.249e-05.

Citation Format: Howard H. Yang, Maxwell P. Lee. Robust feature selection and gene committee predictor for breast cancer patient survival prediction. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4125. doi:10.1158/1538-7445.AM2014-4125