Identification of Genetic Markers with Synergistic Survival Effect in Cancer

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Riku Louhimo, Marko Laakso, Tuomas Heikkinen, Susanna Laitinen, Pekka Manninen, Vladimir Rogojin, Minna Miettinen, Carl Blomqvist, Jianjun Liu, Heli Nevanlinna, Sampsa Hautaniemi
Publisher: BioMed Central Ltd
Publication year: 2013
Journal: BMC Systems Biology
Volume number: 7
Issue number: 1


Abstract

Background
Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival.

Results
The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data.

Conclusions
Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival.

Last updated on 2019-14-11 at 02:39