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Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.

Bertrand D, Chng KR, Sherbaf FG, Kiesel A, Chia BK, Sia YY, Huang SK, Hoon DS, Liu ET, Hillmer A, Nagarajan N - Nucleic Acids Res. (2015)

Bottom Line: Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively).In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient.We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication.

View Article: PubMed Central - PubMed

Affiliation: Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.

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A schematic representation of OncoIMPACT's algorithmic framework. (a) Overview of OncoIMPACT's workflow involving three main stages of data-processing. (b) Depiction of OncoIMPACT's search through a multi-dimensional space to set network and expression parameters (F, fold change of genes; L, length of path; D, degree of nodes). (c) Parsimony-based matching of potential driver and phenotype genes in a bipartite graph to eliminate back-seat drivers. Solid and dashed lines indicate the association of potential driver genes to phenotype genes that were accepted and rejected, respectively.
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Figure 1: A schematic representation of OncoIMPACT's algorithmic framework. (a) Overview of OncoIMPACT's workflow involving three main stages of data-processing. (b) Depiction of OncoIMPACT's search through a multi-dimensional space to set network and expression parameters (F, fold change of genes; L, length of path; D, degree of nodes). (c) Parsimony-based matching of potential driver and phenotype genes in a bipartite graph to eliminate back-seat drivers. Solid and dashed lines indicate the association of potential driver genes to phenotype genes that were accepted and rejected, respectively.

Mentions: OncoIMPACT is designed to integrate information regarding mutations (genomic and epigenomic), changes in cell state (e.g. transcriptome, proteome, epigenome or metabolome) and gene interaction networks to nominate and rank driver cancer mutations in a patient-specific manner (i.e. driver predictions are made for each patient; Figure 1a and Materials and Methods). Briefly, it does so by evaluating the impact of a mutation by associating them to modules of patient-specific deregulated genes through the gene interaction network (step 3 in Figure 1a). A key step in this process is the identification of sentinel phenotype genes frequently deregulated in a cancer subtype (but not typically mutated) and serve to distinguish relevant driver mutations from passengers (step 2 in Figure 1a). The association of mutations to phenotype genes is controlled by three parameters (maximum path length L, maximum gene connectivity D and a perturbation threshold F) that are determined in a data-driven fashion using a statistical maximization approach (step 1 in Figure 1a, b and Materials and Methods). To further differentiate true drivers from back-seat drivers, OncoIMPACT employs the parsimony principle to identify a minimal set of driver mutations for each patient (Figure 1c). Finally, the nominated patient-specific drivers are ranked based on their impact on associated modules. A detailed description for each of the steps in OncoIMPACT can be found in the Materials and Methods section.


Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.

Bertrand D, Chng KR, Sherbaf FG, Kiesel A, Chia BK, Sia YY, Huang SK, Hoon DS, Liu ET, Hillmer A, Nagarajan N - Nucleic Acids Res. (2015)

A schematic representation of OncoIMPACT's algorithmic framework. (a) Overview of OncoIMPACT's workflow involving three main stages of data-processing. (b) Depiction of OncoIMPACT's search through a multi-dimensional space to set network and expression parameters (F, fold change of genes; L, length of path; D, degree of nodes). (c) Parsimony-based matching of potential driver and phenotype genes in a bipartite graph to eliminate back-seat drivers. Solid and dashed lines indicate the association of potential driver genes to phenotype genes that were accepted and rejected, respectively.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402507&req=5

Figure 1: A schematic representation of OncoIMPACT's algorithmic framework. (a) Overview of OncoIMPACT's workflow involving three main stages of data-processing. (b) Depiction of OncoIMPACT's search through a multi-dimensional space to set network and expression parameters (F, fold change of genes; L, length of path; D, degree of nodes). (c) Parsimony-based matching of potential driver and phenotype genes in a bipartite graph to eliminate back-seat drivers. Solid and dashed lines indicate the association of potential driver genes to phenotype genes that were accepted and rejected, respectively.
Mentions: OncoIMPACT is designed to integrate information regarding mutations (genomic and epigenomic), changes in cell state (e.g. transcriptome, proteome, epigenome or metabolome) and gene interaction networks to nominate and rank driver cancer mutations in a patient-specific manner (i.e. driver predictions are made for each patient; Figure 1a and Materials and Methods). Briefly, it does so by evaluating the impact of a mutation by associating them to modules of patient-specific deregulated genes through the gene interaction network (step 3 in Figure 1a). A key step in this process is the identification of sentinel phenotype genes frequently deregulated in a cancer subtype (but not typically mutated) and serve to distinguish relevant driver mutations from passengers (step 2 in Figure 1a). The association of mutations to phenotype genes is controlled by three parameters (maximum path length L, maximum gene connectivity D and a perturbation threshold F) that are determined in a data-driven fashion using a statistical maximization approach (step 1 in Figure 1a, b and Materials and Methods). To further differentiate true drivers from back-seat drivers, OncoIMPACT employs the parsimony principle to identify a minimal set of driver mutations for each patient (Figure 1c). Finally, the nominated patient-specific drivers are ranked based on their impact on associated modules. A detailed description for each of the steps in OncoIMPACT can be found in the Materials and Methods section.

Bottom Line: Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively).In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient.We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication.

View Article: PubMed Central - PubMed

Affiliation: Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.

Show MeSH
Related in: MedlinePlus