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dc.contributor.authorHudson, Andrew M
dc.contributor.authorYates, Tim
dc.contributor.authorLi, Yaoyong
dc.contributor.authorTrotter, Eleanor W
dc.contributor.authorFawdar, Shameem
dc.contributor.authorChapman, Philip J
dc.contributor.authorLorigan, Paul C
dc.contributor.authorBiankin, A
dc.contributor.authorMiller, Crispin J
dc.contributor.authorBrognard, John
dc.date.accessioned2014-11-04T10:43:38Zen
dc.date.available2014-11-04T10:43:38Zen
dc.date.issued2014-09-25en
dc.identifier.citationDiscrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery. 2014: Cancer Resen
dc.identifier.issn1538-7445en
dc.identifier.pmid25256751en
dc.identifier.doi10.1158/0008-5472.CAN-14-1020en
dc.identifier.urihttp://hdl.handle.net/10541/333685en
dc.description.abstractCancer genome sequencing is being employed at an increasing rate to identify actionable driver mutations that can inform therapeutic intervention strategies. A comparison of two of the most prominent cancer genome sequencing databases from different institutes (CCLE and COSMIC) revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.38% conformity). The main reason for this discrepancy is inadequate sequencing of GC-rich areas of the exome. We have therefore mapped over 400 regions of consistent inadequate sequencing (cold-spots) in known cancer-causing genes and kinases in which neither institute find mutations. We demonstrate, using a newly identified PAK4 mutation as proof of principle, that specific targeting and sequencing of these GC-rich cold-spot regions can lead to the identification of novel driver mutations in known tumor suppressors and oncogenes. We highlight that cross-referencing between genomic databases is required to comprehensively assess genomic alterations in commonly used cell lines and that there are still significant opportunities to identify novel drivers of tumorigenesis in poorly sequenced areas of the exome. Finally we assess other reasons for the observed discrepancy, such as variations in dbSNP filtering and the acquisition/loss of mutations, to give explanations as to why there is discrepancy in pharmacogenomic studies given recent concerns with poor reproducibility of data.
dc.languageENGen
dc.language.isoenen
dc.rightsArchived with thanks to Cancer researchen
dc.titleDiscrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery.en
dc.typeArticleen
dc.contributor.departmentSignalling Networks in Cancer, Cancer Research UK Manchester Institute.en
dc.identifier.journalCancer Researchen
refterms.dateFOA2020-05-01T13:08:38Z
html.description.abstractCancer genome sequencing is being employed at an increasing rate to identify actionable driver mutations that can inform therapeutic intervention strategies. A comparison of two of the most prominent cancer genome sequencing databases from different institutes (CCLE and COSMIC) revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.38% conformity). The main reason for this discrepancy is inadequate sequencing of GC-rich areas of the exome. We have therefore mapped over 400 regions of consistent inadequate sequencing (cold-spots) in known cancer-causing genes and kinases in which neither institute find mutations. We demonstrate, using a newly identified PAK4 mutation as proof of principle, that specific targeting and sequencing of these GC-rich cold-spot regions can lead to the identification of novel driver mutations in known tumor suppressors and oncogenes. We highlight that cross-referencing between genomic databases is required to comprehensively assess genomic alterations in commonly used cell lines and that there are still significant opportunities to identify novel drivers of tumorigenesis in poorly sequenced areas of the exome. Finally we assess other reasons for the observed discrepancy, such as variations in dbSNP filtering and the acquisition/loss of mutations, to give explanations as to why there is discrepancy in pharmacogenomic studies given recent concerns with poor reproducibility of data.


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