Mixture modeling of DNA copy number amplification patterns in cancer
Jarkko Tikka, Jaakko Hollmen and Samuel Myllykangas
In: International Work-Conference on Artificial Neural Networks (IWANN 2007), 20 June - 22 June 2007, San Sebastián, Spain.

Abstract

DNA copy number amplifications are hallmarks of many cancers. In this work we analyzed data of genome-wide DNA copy number amplifications collected from more than 4500 neoplasm cases. Based on the 0-1 representation of the data, we trained finite mixtures of multivariate Bernoulli distributions using the EM algorithm to describe the inherent structure in the data. The resulting component distributions of the mixtures of Bernoulli distributions yielded plausible and localized amplification patterns. Individual amplification patterns were tested for their role in cancer groups formed with known risk associations. Our detailed analysis of chromosome 1 showed that asbestos-exposure related and hormonal imbalance-associated cancers were clustered and specific chromosome bands, 1p34 and 1q42, were identified. These sites contain cancer genes, which might explain the condition-specific selection of these loci for amplification.

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