Abstract 5134: Integrative analysis of gene expression, CpG island methylation, miRNA and gene copy number in cancer.
Authors: Marchi Fabio A. 
Published: August 2014

Recently, a large number of specific genomic alterations between individuals have been described in cancer studies. Despite of these advances, the impact of several aberrations in the patient's physiology is still poorly defined. We have developed a method that integrates genomic, transcriptomic and epigenetic alterations by detecting key mutations that drive cancer progression. Our score guided algorithm searches for genes that are both recurrently aberrant in copy number, miRNA and methylation data and associated with variance of expression patterns in tumor samples. This proposed method uses concepts derived from gene network inference and address a recurrent problem in this approach. Usually, the main limitation of network inference methods is related a small number of samples with large dimensionalities and the noisy nature of the expression measurements. To address these limitations, alternatives are necessary to obtain better accuracy on the inference problem. One of them, presented in this study, is the integration of different data in order to reduce errors during the gene prediction. The algorithm has a regulatory module separated into layers that contains four-dimensional dataset consisting of gene expression, CGH-array, large-scale methylation and miRNA expression profiles measured on the same samples. Weights are applied in the data matrices, selecting the genes of different layers that are likely to contribute jointly to a specific gene expression profile. To identify key gene predictors the algorithm selects the best combination with the highest score, and also considers the role of rare copy number variation, amplification and homozygous deletion. In the context of the gene interaction network, the algorithm performs a protein-protein interaction (PPI) with the selected genes and the “hubs” are also connected to their associated aberrant neighbors, allowing the study of gene alteration pathway. With the aim to test this score-based algorithm, it was used 31 penile carcinomas model. Penile cancer is a rare neoplasm usually associated with low socioeconomic status, poor personal hygiene and HPV infection (particularly 16 and 18 subtypes in 40-50% of cases). Our analysis identified some regions with known and recurring alterations related to immune response and inflammation pathway, such as JUN (transcript downexpression, hypermethylation, and miR-133/miR-204/miR-139 overexpression) and IL-22 (copy number gains, transcript overexpression, miR-155 overexpression). miR-155 overexpression is reported as responsible for enhanced tumor growth and IL-22 overexpression. DNMT1 presented the best score in HPV positive cases, with transcript overexpression, hypomethylation and hsa-let-7c overexpression. In overall, these data demonstrate the ability of the integrative data algorithm to identify drivers in penile carcinogenesis.

Citation Format: Fabio A. Marchi. Integrative analysis of gene expression, CpG island methylation, miRNA and gene copy number in cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5134. doi:10.1158/1538-7445.AM2013-5134