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Identification of Genetic Markers with Synergistic Survival Effect in Cancer

Riku Louhimo, Marko Laakso, Tuomas Heikkinen, Susanna Laitinen, Pekka Manninen, Vladimir Rogojin, Minna Miettinen, Carl Blomqvist, Jianjun Liu, Heli Nevanlinna, Sampsa Hautaniemi, Identification of Genetic Markers with Synergistic Survival Effect in Cancer. BMC Systems Biology 7(1), S2, 2013.

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.

BibTeX entry:

@ARTICLE{jLoLaHeLaMaRoMiBlLiNeHa13a,
  title = {Identification of Genetic Markers with Synergistic Survival Effect in Cancer},
  author = {Louhimo, Riku and Laakso, Marko and Heikkinen, Tuomas and Laitinen, Susanna and Manninen, Pekka and Rogojin, Vladimir and Miettinen, Minna and Blomqvist, Carl and Liu, Jianjun and Nevanlinna, Heli and Hautaniemi, Sampsa},
  journal = {BMC Systems Biology},
  volume = {7},
  number = {1},
  publisher = {BioMed Central Ltd},
  pages = {S2},
  year = {2013},
}

Belongs to TUCS Research Unit(s): Computational Biomodeling Laboratory (Combio Lab)

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