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MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis.

Jia P, Wang Q, Chen Q, Hutchinson KE, Pao W, Zhao Z - Genome Biol. (2014)

Bottom Line: Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function.We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots.Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.

ABSTRACT
Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function. We propose a mutation set enrichment analysis (MSEA) implemented by two novel methods,MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns. MSEA methods are evaluated by both simulated and real cancer data. We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots. Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

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Related in: MedlinePlus

Example MSEA-clust output for four candidate genes. In each panel, the top portion shows the MAS score (y-axis) by the MSEA-clust method (see Materials and methods); the bottom part shows the mutation distribution (black triangles) across domains (red/cyan boxes) in the transcript (x-axis). (A)ATP11B in UCEC. (B)FOXA1 in BRCA. (C)FZD6 in UCEC. (D)POLE in UCEC.
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Fig5: Example MSEA-clust output for four candidate genes. In each panel, the top portion shows the MAS score (y-axis) by the MSEA-clust method (see Materials and methods); the bottom part shows the mutation distribution (black triangles) across domains (red/cyan boxes) in the transcript (x-axis). (A)ATP11B in UCEC. (B)FOXA1 in BRCA. (C)FZD6 in UCEC. (D)POLE in UCEC.

Mentions: Beside the known cancer genes in those genes identified by MSEA, there were also novel discoveries, such as new gene-cancer type pairs and novel mutation patterns previously unreported. Some genes had been previously implicated in cancer but were re-found within our results in different cancer types. For example, ABI1 and TRIM33 were both known cancer genes and were included in the CGC list, but their mutation patterns in UCEC have been rarely reported, even in the most recent comprehensive studies [16,43] (Figure 5). There are also genes that had been previously studied for their gene expression changes in cancer but whose mutation patterns had not been explored. For example, the altered expression of gene ATP11B (encoding ATPase, class VI, type 11B) is associated with cisplatin resistance in ovarian cancer [44]. Here we revealed that its mutations clustered around its E1-E2 ATPase domain (Figure 5). We also found that FZD6, a critical gene in the WNT pathway, had mutations clustered in its Frizzled domain in UCEC (Figure 5). In OvCa, although only three genes were significant by MSEA methods, CUL9, which encodes an E3 ubiquitin ligase that binds to p53 [45], was identified with mutation clustering in the cullin domain. Put together, our results complemented the previous understanding of cancer genes [16,36,43] by quantitatively pinpointing mutation hotspots, predicting new gene-cancer type pairs, and providing alternative insights.Figure 5


MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis.

Jia P, Wang Q, Chen Q, Hutchinson KE, Pao W, Zhao Z - Genome Biol. (2014)

Example MSEA-clust output for four candidate genes. In each panel, the top portion shows the MAS score (y-axis) by the MSEA-clust method (see Materials and methods); the bottom part shows the mutation distribution (black triangles) across domains (red/cyan boxes) in the transcript (x-axis). (A)ATP11B in UCEC. (B)FOXA1 in BRCA. (C)FZD6 in UCEC. (D)POLE in UCEC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4226881&req=5

Fig5: Example MSEA-clust output for four candidate genes. In each panel, the top portion shows the MAS score (y-axis) by the MSEA-clust method (see Materials and methods); the bottom part shows the mutation distribution (black triangles) across domains (red/cyan boxes) in the transcript (x-axis). (A)ATP11B in UCEC. (B)FOXA1 in BRCA. (C)FZD6 in UCEC. (D)POLE in UCEC.
Mentions: Beside the known cancer genes in those genes identified by MSEA, there were also novel discoveries, such as new gene-cancer type pairs and novel mutation patterns previously unreported. Some genes had been previously implicated in cancer but were re-found within our results in different cancer types. For example, ABI1 and TRIM33 were both known cancer genes and were included in the CGC list, but their mutation patterns in UCEC have been rarely reported, even in the most recent comprehensive studies [16,43] (Figure 5). There are also genes that had been previously studied for their gene expression changes in cancer but whose mutation patterns had not been explored. For example, the altered expression of gene ATP11B (encoding ATPase, class VI, type 11B) is associated with cisplatin resistance in ovarian cancer [44]. Here we revealed that its mutations clustered around its E1-E2 ATPase domain (Figure 5). We also found that FZD6, a critical gene in the WNT pathway, had mutations clustered in its Frizzled domain in UCEC (Figure 5). In OvCa, although only three genes were significant by MSEA methods, CUL9, which encodes an E3 ubiquitin ligase that binds to p53 [45], was identified with mutation clustering in the cullin domain. Put together, our results complemented the previous understanding of cancer genes [16,36,43] by quantitatively pinpointing mutation hotspots, predicting new gene-cancer type pairs, and providing alternative insights.Figure 5

Bottom Line: Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function.We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots.Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.

ABSTRACT
Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function. We propose a mutation set enrichment analysis (MSEA) implemented by two novel methods,MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns. MSEA methods are evaluated by both simulated and real cancer data. We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots. Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

Show MeSH
Related in: MedlinePlus