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BioMed Research International
Volume 2014, Article ID 171892, 10 pages
http://dx.doi.org/10.1155/2014/171892
Research Article

Pathway-Driven Discovery of Rare Mutational Impact on Cancer

1Interdisciplinary Program in Bioinformatics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul 151-742, Republic of Korea
2Samsung Genome Institute, Samsung Medical Center, Irwon-ro 81, Seoul 136-710, Republic of Korea
3Department of Statistics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul 151-742, Republic of Korea

Received 31 January 2014; Accepted 14 March 2014; Published 4 May 2014

Academic Editor: FangXiang Wu

Copyright © 2014 TaeJin Ahn and Taesung Park. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene itself. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; the philosophy of our method is iteratively combining rare mutations until no more mutations can be added under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutational events. Breast cancer patients with somatic mutation and mRNA expression were analyzed by our approach. Our approach is shown to sensitively capture mutations that change pathway level mRNA expression, concurrently discovering important mutations previously reported in breast cancer such as TP53, PIK3CA, and RB1. In addition, out of 15,819 genes considered in breast cancer, our approach identified mutational events of 32 genes showing pathway level mRNA expression differences.