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Identification of genetic markers with synergistic survival effect in cancer.

Louhimo R, Laakso M, Heikkinen T, Laitinen S, Manninen P, Rogojin V, Miettinen M, Blomqvist C, Liu J, Nevanlinna H, Hautaniemi S - BMC Syst Biol (2013)

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

View Article: PubMed Central - HTML - PubMed

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.

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Related in: MedlinePlus

Cluster tree dendrogram for a family of curves and the distance of two alleles. The features (alleles) circled in blue satisfy the equivalence relation. The allele combinations circled in green and red have a distance of /Hmax − H2/. The height of each leaf node is the height of its parent. This is marked with the blue dashed lines for the circled leafs.
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Figure 2: Cluster tree dendrogram for a family of curves and the distance of two alleles. The features (alleles) circled in blue satisfy the equivalence relation. The allele combinations circled in green and red have a distance of /Hmax − H2/. The height of each leaf node is the height of its parent. This is marked with the blue dashed lines for the circled leafs.

Mentions: In the second stage, curves in a family are clustered by complete linkage agglomerative hierarchical clustering using as the distance matrix. The main benefit of the hierarchical clustering is a dendrogram in which leafs are clusters, the leafs contain biological information which can be taken advantage of, and the clusters represent survival curves (Figure 2). In the complete linkage the distances between two clusters are calculated as the maximum distance between any object in the first and any object in the second cluster. We chose complete linkage clustering over single or average linkage because it more effectively distinguishes curves that are farthest away from one another. However, Geninter allows its user to define any alternative method supported by the underlying clustering library.


Identification of genetic markers with synergistic survival effect in cancer.

Louhimo R, Laakso M, Heikkinen T, Laitinen S, Manninen P, Rogojin V, Miettinen M, Blomqvist C, Liu J, Nevanlinna H, Hautaniemi S - BMC Syst Biol (2013)

Cluster tree dendrogram for a family of curves and the distance of two alleles. The features (alleles) circled in blue satisfy the equivalence relation. The allele combinations circled in green and red have a distance of /Hmax − H2/. The height of each leaf node is the height of its parent. This is marked with the blue dashed lines for the circled leafs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750540&req=5

Figure 2: Cluster tree dendrogram for a family of curves and the distance of two alleles. The features (alleles) circled in blue satisfy the equivalence relation. The allele combinations circled in green and red have a distance of /Hmax − H2/. The height of each leaf node is the height of its parent. This is marked with the blue dashed lines for the circled leafs.
Mentions: In the second stage, curves in a family are clustered by complete linkage agglomerative hierarchical clustering using as the distance matrix. The main benefit of the hierarchical clustering is a dendrogram in which leafs are clusters, the leafs contain biological information which can be taken advantage of, and the clusters represent survival curves (Figure 2). In the complete linkage the distances between two clusters are calculated as the maximum distance between any object in the first and any object in the second cluster. We chose complete linkage clustering over single or average linkage because it more effectively distinguishes curves that are farthest away from one another. However, Geninter allows its user to define any alternative method supported by the underlying clustering library.

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

View Article: PubMed Central - HTML - PubMed

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.

Show MeSH
Related in: MedlinePlus