Abstract 1720: Validation of a 15-gene prognostic signature in stage IB non small-cell lung cancer (NSCLC)
Authors: Pancoska Petr , Romkes Marjorie , Nukui Tomoko , Buch Shama , Herazo-Maya Jose , Kaminski Naftali , Dacic Sanja , Weissfeld Joel L. , Bigbee William L. , Shapiro Steven D. , Siegfried Jill M. 
Published: August 2012

A prognostic 15-gene signature for NSCLC was recently published (J Clin Oncol 28, 4417, 2010). We used a novel integrated approach, the network phenotyping strategy (NPS), to evaluate the performance of this signature in our NSCLC patients and to gain insight into NSCLC heterogeneity by analyzing global mRNA/miRNA and DNA methylation microarray data (n=47 Stage IB tumors). The mRNA data was compared between cases with overall survival (OS) &lt;or&gt; 650 days. The mRNA levels of the 15 individual genes are not associated with OS. We hypothesized that the networked gene expression pattern rather than the absolute level of any 1 gene accounts for the disease outcome association. Maximal spanning tree reduction of a graph of all 105 possible pairwise expression relationships, weighted by the correlation coefficients quantifying co-regulation levels, was used to identify the 7 most informative gene pairs. For each pair, cases were dichotomized into groups where gene 1 was expressed at least 1.5x higher or lower relative to gene 2. A 7-partite graph (K7) captured these trend patterns. K7 cycle-decomposition revealed 10 different gene expression patterns (C1-C10) representing nominally different molecular NSCLC subtypes. Characterization of individual tumor expression trend patterns was computed as the difference vector (≥C) from C1-C10. A rule-based algorithm used ΔC to classify cases into 2 OS groups. We also analyzed DNA methylation and miRNA data for these 15 genes. For 2 genes, hypomethylation (≥&lt;0.1) was associated with high expression; for 3 genes, hypermethylation (≥&gt;0.5) was associated with low expression. For 10 genes with low expression and intermediate methylation, the expression data indicated the involvement of additional regulatory mechanisms. For example, HEXIM1 expression (most commonly down-regulated relative to FAM64A), is regulated by miR-623, which is itself a prognostic biomarker of OS in our study. Validation of the published signature was observed in this independent NSCLC cohort using NPS analysis. Not only was this gene signature prognostic, confirming the signature robustness, but the NPS strategy was equally effective as the originally published principal component analysis, both resulting in a significant separation of the Kaplan-Meier OS curves (p&lt;0.001). In addition, our NPS approach facilitates direct clinical interpretation of NSCLC molecular heterogeneity. The value of integrating genomic microarray data for a published gene expression signature set of 15 genes in predicting Stage IB NSCLC prognosis is illustrated. The NPS approach provided mechanistic insight into the regulation of some of these 15 genes. The significance of HEX1M1 as a member of the most common signature profile is not only detected by the gene expression pattern but is also confirmed by a significant association of a miRNA, miR-623, with OS. Supported by NIH 5P50 CA090440, P30 CA047904, UPMC Institutional Funds.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1720. doi:1538-7445.AM2012-1720