Abstract 5334: Integrated analysis of squamous tumors identifies novel targets and dissects gene regulation

Abstract

Abstract Background: Studies by The Cancer Genome Atlas (TCGA) and others have identified regions of somatic copy number alteration (SCNA) in head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC). Both tumor types exhibit frequent SCNAs in chromosomes 3q, 9p, and 11q. Although CDKN2A is the putative target of 9p deletions, numerous targets have been proposed in the 3q amplicon, including SOX2, PIK3CA, and TP63. Because expression levels are affected by underlying genomic events, we hypothesized that an integrated analysis of multiple genomic data types would provide increased ability to identify target genes in SCNA regions when compared to methods based on copy number alone while providing insight into mechanisms regulating expression. Findings may be subsequently validated by examining multiple tumor types or performing training/testing analyses. Techniques: Gene expression (GE), DNA copy number (CN), DNA methylation (ME), and microRNA expression (miR) data were obtained from the TCGA studies of HNSCC and LUSC. Linear models were constructed for each gene to investigate the effect of changes in CN, ME, and miR on GE. Analysis of model output provided an approach to identify target genes in SCNA regions and assess the effect of genomic alterations on expression. Results: Genome-wide GE, CN, ME, and miR data were available for n = 279 (HNSCC) and n = 159 (LUSC) tumor samples. Univariate modeling detected a strong overall association between GE and CN, as measured by the coefficient of determination (model R2). Although some genes found in SCNA studies produced large model R2— e.g. SOX2— a number of genes with large model R2 are relatively unknown. Notable examples include DVL3, and SENP2, which were implicated as driver genes in the 3q amplicon by a recent study of LUSC but not identified in the TCGA report. Remarkably, the model R2 for DVL3 and SENP2 was also high in HNSCC, and such a finding in a distinct tumor type provides validation and merits additional study. Output from models additionally including ME and miR as covariates contributes insight into the diversity of regulation of gene expression, but the strength of the association between GE and CN could mask other effects. Thus we constructed linear models in which the response variable was the residuals from the GE/CN model and the covariates were ME or miR. The TCGA study of glioblastoma noted frequent methylation of MGMT. When modeling MGMT we detected a highly significant association between ME and the GE/CN residuals in both HNSCC and LUSC. These results illustrate a pronounced effect of ME on GE conditional on CN. Conclusion: Linear modeling techniques provide a flexible and powerful basis for performing integrated analysis of genomic data. Our approach produces predicted results when analyzing known cancer genes, highlights lesser known genes for future study, provides insight into gene regulation, and draws attention to genes relevant in multiple tumor types. Citation Format: Vonn Walter, Ying Du, Xiaoying Yin, Wei Sun, Matthew D. Wilkerson, Michele C. Hayward, Ashley H. Salazar, Charles M. Perou, David N. Hayes. Integrated analysis of squamous tumors identifies novel targets and dissects gene regulation. [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 5334. doi:10.1158/1538-7445.AM2014-5334