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Conference Paper: TCGA whole-exome sequencing data reveals mutational parameters in distinguishing potential cancer drivers

TitleTCGA whole-exome sequencing data reveals mutational parameters in distinguishing potential cancer drivers
Authors
Issue Date2018
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.humgenomics.com
Citation
Human Genome Meeting 2018, Yokohama, Japan, 12-15 March 2018. In Human Genomics, 2018, v. 12 n. Suppl. 1, abstract no. A1 How to Cite?
AbstractBackground: With huge amount of genome-wide mutational data generated by cancer genomic sequencing studies, distinguishing cancer drivers from the vast majority of passengers is important. Existing cancer driver prediction methods capture specific mutational aspects in discriminating potential cancer drivers. We explore the possibility of alterative way in doing the task. Methods: We noted mutational parameters (functional mutation ratio, mutation frequency and sample mutation recurrence) vary differently among mutant genes of different sizes. This led us to develop our novel algorithm (Mutant Gene Ranker - MuGeR), incorporating the comparison of multiple mutational parameters of target gene against the corresponding background derived from a specific subset of genes using a sliding window approach, to estimate the likelihood of target genes for being potential cancer drivers. We applied our MuGeR algorithm on the The Cancer Genome Atlas (TCGA) datasets. Results: Empirical data on the TCGA datasets and comparison with the prioritization results of 4 other existing tools (MuSiC, MuSig, TUSON explorer and DOTS-Finder) suggested satisfactory performance of our MuGeR algorithm. More importantly, we demonstrated the existence of specific pattern for mutational parameters across cancers. Conclusions: Empirical data verified the usefulness of our MuGeR algorithm in identifying potential cancer drivers. Moreover, our in-depth appraisal of TCGA liver hepatocellular carcinoma datasets further highlighted the frequent mutational dysregulation of ubiquitin-related proteasomal degradation in driving hepatocarcinogenesis.
DescriptionOrganized by : Human Genome Organisation (HUGO) International
Persistent Identifierhttp://hdl.handle.net/10722/261196
ISSN
2015 SCImago Journal Rankings: 1.632

 

DC FieldValueLanguage
dc.contributor.authorHo, DWH-
dc.contributor.authorNg, IOL-
dc.date.accessioned2018-09-14T08:54:08Z-
dc.date.available2018-09-14T08:54:08Z-
dc.date.issued2018-
dc.identifier.citationHuman Genome Meeting 2018, Yokohama, Japan, 12-15 March 2018. In Human Genomics, 2018, v. 12 n. Suppl. 1, abstract no. A1-
dc.identifier.issn1479-7364-
dc.identifier.urihttp://hdl.handle.net/10722/261196-
dc.descriptionOrganized by : Human Genome Organisation (HUGO) International-
dc.description.abstractBackground: With huge amount of genome-wide mutational data generated by cancer genomic sequencing studies, distinguishing cancer drivers from the vast majority of passengers is important. Existing cancer driver prediction methods capture specific mutational aspects in discriminating potential cancer drivers. We explore the possibility of alterative way in doing the task. Methods: We noted mutational parameters (functional mutation ratio, mutation frequency and sample mutation recurrence) vary differently among mutant genes of different sizes. This led us to develop our novel algorithm (Mutant Gene Ranker - MuGeR), incorporating the comparison of multiple mutational parameters of target gene against the corresponding background derived from a specific subset of genes using a sliding window approach, to estimate the likelihood of target genes for being potential cancer drivers. We applied our MuGeR algorithm on the The Cancer Genome Atlas (TCGA) datasets. Results: Empirical data on the TCGA datasets and comparison with the prioritization results of 4 other existing tools (MuSiC, MuSig, TUSON explorer and DOTS-Finder) suggested satisfactory performance of our MuGeR algorithm. More importantly, we demonstrated the existence of specific pattern for mutational parameters across cancers. Conclusions: Empirical data verified the usefulness of our MuGeR algorithm in identifying potential cancer drivers. Moreover, our in-depth appraisal of TCGA liver hepatocellular carcinoma datasets further highlighted the frequent mutational dysregulation of ubiquitin-related proteasomal degradation in driving hepatocarcinogenesis.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.humgenomics.com-
dc.relation.ispartofHuman Genomics (Online)-
dc.relation.ispartofHuman Genome Meeting 2018-
dc.titleTCGA whole-exome sequencing data reveals mutational parameters in distinguishing potential cancer drivers-
dc.typeConference_Paper-
dc.identifier.emailHo, DWH: dwhho@hku.hk-
dc.identifier.emailNg, IOL: iolng@hku.hk-
dc.identifier.authorityHo, DWH=rp02285-
dc.identifier.authorityNg, IOL=rp00335-
dc.identifier.doi10.1186/s40246-018-0138-6-
dc.identifier.hkuros291145-
dc.identifier.volume12-
dc.identifier.issueSuppl. 1-
dc.identifier.spageabstract no. A1-
dc.identifier.epageabstract no. A1-
dc.publisher.placeUnited Kingdom-

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