This article is part of the supplement: Selected articles from the 10th International Workshop on Computational Systems Biology (WCSB) 2013: Systems Biology
Identification of genetic markers with synergistic survival effect in cancer
1 Systems Biology Laboratory, Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
2 CSC - IT Center for Science Ltd, Espoo, Finland
3 Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
4 Human Genetics, Genome Institute of Singapore, Singapore, 60 Biopolis Street 02-01 Singapore 138672
5 Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland
BMC Systems Biology 2013, 7(Suppl 1):S2 doi:10.1186/1752-0509-7-S1-S2Published: 12 August 2013
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.
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.
Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival.