Abstract 1998: A <i>sparse</i> prognostic gene signature for stage IB and II patients with completely resected non-small cell lung cancer
Authors: Philip Twumasi-Ankrah, Keyue Ding, Lesley Seymour, Frances A. Shepherd, Ming-Sound Tsao
Journal: Cancer Research (2010)

Abstract Background: In the statistical analysis of gene expression data, the concept sparsity of expression signature suggests that patterns of variation in expression for a larger gene set, G, could be predicted by a smaller subset of genes, g. This may be an oversimplifies the biomedical reality but a simple and meaningful model reduces the risk of overfitting and produces results which are easier to interpret. Here, we extract a sparse gene signature from the set of informative genes, and establish a functional relation between the selected genes and survival. Methods: Microarray analysis was conducted on extracted mRNA from 62 frozen JBR.10 tumor samples from patients who had undergone complete tumor resection using the Affymetrix U133A oligonucleotide microarray. We performed the following analyses on the data: (a) First, the affymetrix CEL data was processed by the BRB ArrayTools version 3.8.0. Data normalization and background correction to adjust for differences in labeling intensities was done by the GCRMA algorithm [1]. Genes showing minimal variation across arrays with expression differing by at least 1.5 fold from the median in less than 20% of the arrays were excluded from the analysis. (b) The ensuing probe sets were interrogated to identify genes whose expression was significantly related to patient survival by computing for each gene, a univariate proportional hazards models [2] evaluated at a significance threshold of 5%. (c) A sparse gene-set was then obtained from the genes correlated with survival by a multivariate Cox regression with an elastic-net [3] constraint on the regression coefficients. (d) Finally, the sparse gene-set was used to derive risk scores for all patients. Median of the risk scores was used to classify patients into low and high risk groups. Results: From the list of 8141 probe sets that passed the filtering criteria, 310 genes were significant at 0.05 level in the univariate analyses. The sparse gene-sets of 7 was obtained by applying the elastic-net variable selection and dimensionality reduction algorithm. The estimate of risk ratio associated with the high risk group versus the low risk group is 8.9 (95% CI: 3.4 − 23.5) with a p-value of 1.172*10−7 from the logrank test. Conclusions: The sparse gene-set provides an efficient set of genes that may be prognostic in patients with completely resected stage IB and Stage II non-small cell lung cancer. Validation is required in independent data sets. References: 1. Wu Z, Irizarry RA., Gentleman R, Murillo FM, Spencer F. (2004). A Model Based Background Adjustment for Oligonucleotide Expression Arrays. J. Amer. Statistical Assoc., 99(468): 909-917. 2. Cox DR. (1972.). Regression models and life-tables (with discussion). J. Roy. Statistical Society B, 34:187-220. 3. Zou H, Hastie TJR. (2005) Regularization and variable selection via the elastic net. J. Roy. Statistical Society B 67(2): 301-320. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1998.