Abstract 2171: Leveraging global gene expression patterns to identify gene sets that predict expression of large numbers of unmeasured genes
Authors: James Rudd, Rene A. Zelaya, Eugene Demidenko, Casey S. Greene, Jennifer A. Doherty
Journal: Cancer Research (2015)

Abstract Large collections of formalin-fixed paraffin embedded tissue are a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale, and a reduced set of “candidate” genes must be selected and assayed with platforms such as NanoString nCounter®. Using the known expression correlation structure within a given tissue (high grade serous ovarian cancer; HGSC), we sought to determine whether reduced sets of directly measured genes could accurately predict a maximized number of unmeasured genes. To maximize the number of unmeasured genes that can be inferred from reduced set assays, we developed an algorithm with three key parameters: the number of genes to directly measure; Pearson correlation thresholds between genes (rP); and the number of directly measured genes that must be correlated with the unmeasured genes (ie, redundancy). We evaluated this algorithm across a range of parameter values: 10-400 directly assayed genes, redundancy of 1-3, and |rP| of 0.60, 0.65, and 0.70. In a training partition of the Cancer Genome Atlas (TCGA) HGSC Affymetrix U133 Plus 2.0 gene expression data (n = 386), we used the selected directly measured genes to build linear models of predicted gene expression. We then evaluated the predicted expression values using true expression values from the following HGSC datasets: TCGA testing partition (n = 159); GSE9891 (Australian, U133 Plus 2.0 array, n = 264); and GSE32062 (Japanese, Agilent 4×44k microarray, n = 258). After restricting to genes with median absolute deviation (MAD) &amp;gt; 0.5 and using our most conservative parameters (|rP| = 0.7; redundancy = 3), 400 directly measured genes predicted an additional 198 unmeasured genes, with average Spearman rank coefficients (rS) and bootstrapped standard errors between predicted and true expression values of 0.854 (0.005) in the testing partition of TCGA, 0.871 (0.006) in the Australian data, and 0.832 (0.010) in the Japanese data. Removing MAD filtering predicted 332 unmeasured genes but lowered accuracy, with respective average rS values of 0.800 (0.009), 0.816 (0.011), and 0.750 (0.015). Relaxing redundancy to 2 and |rP| to 0.65 predicted 701 unmeasured genes, but respective average rS values decreased to 0.732 (0.007), 0.733 (0.008), and 0.686 (0.009). The number of predicted genes increases as the parameters become less conservative, with a concomitant decrease in accuracy. In summary, we show that for a given disease type a minimal set of directly measured genes can be used to maximize the amount of gene expression information captured in data sets across populations and assay platforms. Genes selected using this method can be combined with candidate gene sets as a cost-effective way to increase the amount of gene expression information obtained in large studies where using a genome-wide measurement platform is not feasible. Citation Format: James Rudd, Rene A. Zelaya, Eugene Demidenko, Casey S. Greene, Jennifer A. Doherty. Leveraging global gene expression patterns to identify gene sets that predict expression of large numbers of unmeasured genes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2171. doi:10.1158/1538-7445.AM2015-2171