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Novel analytical methods to interpret large sequencing data from small sample sizes

dc.rights.licenseopenen_US
dc.contributor.authorLICHOU, F.
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorORAZIO, Sébastien
dc.contributor.authorDULUCQ, S.
dc.contributor.authorETIENNE, G.
dc.contributor.authorLONGY, M.
hal.structure.identifierLaboratoire Maladies Rares: Génétique et Métabolisme (Bordeaux) [U1211 INSERM/MRGM]
dc.contributor.authorHUBERT, Christophe
dc.contributor.authorGROPPI, A.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMONNEREAU, Alain
dc.contributor.authorMAHON, F. X.
dc.contributor.authorTURCQ, B.
dc.date.accessioned2020-06-26T09:28:42Z
dc.date.available2020-06-26T09:28:42Z
dc.date.issued2019-08-30
dc.identifier.issn1473-9542en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/8218
dc.description.abstractEnBACKGROUND: Targeted therapies have greatly improved cancer patient prognosis. For instance, chronic myeloid leukemia is now well treated with imatinib, a tyrosine kinase inhibitor. Around 80% of the patients reach complete remission. However, despite its great efficiency, some patients are resistant to the drug. This heterogeneity in the response might be associated with pharmacokinetic parameters, varying between individuals because of genetic variants. To assess this issue, next-generation sequencing of large panels of genes can be performed from patient samples. However, the common problem in pharmacogenetic studies is the availability of samples, often limited. In the end, large sequencing data are obtained from small sample sizes; therefore, classical statistical analyses cannot be applied to identify interesting targets. To overcome this concern, here, we described original and underused statistical methods to analyze large sequencing data from a restricted number of samples. RESULTS: To evaluate the relevance of our method, 48 genes involved in pharmacokinetics were sequenced by next-generation sequencing from 24 chronic myeloid leukemia patients, either sensitive or resistant to imatinib treatment. Using a graphical representation, from 708 identified polymorphisms, a reduced list of 115 candidates was obtained. Then, by analyzing each gene and the distribution of variant alleles, several candidates were highlighted such as UGT1A9, PTPN22, and ERCC5. These genes were already associated with the transport, the metabolism, and even the sensitivity to imatinib in previous studies. CONCLUSIONS: These relevant tests are great alternatives to inferential statistics not applicable to next-generation sequencing experiments performed on small sample sizes. These approaches permit to reduce the number of targets and find good candidates for further treatment sensitivity studies.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enEPICENE
dc.title.enNovel analytical methods to interpret large sequencing data from small sample sizes
dc.title.alternativeHum Genomicsen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1186/s40246-019-0235-1en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed31470908en_US
bordeaux.journalHuman genomicsen_US
bordeaux.page41en_US
bordeaux.volume13en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.teamEPICENE_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
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