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dc.contributor.authorFernandez-Navarro, Pablo L 
dc.contributor.authorLópez-Nieva, Pilar
dc.contributor.authorPiñeiro-Yañez, Elena
dc.contributor.authorCarreno-Tarragona, Gonzalo
dc.contributor.authorMartinez-López, Joaquín
dc.contributor.authorSánchez Pérez, Raúl
dc.contributor.authorAroca, Ángel
dc.contributor.authorAl-Shahrour, Fatima 
dc.contributor.authorCobos-Fernández, María Ángeles
dc.contributor.authorFernández-Piqueras, José
dc.date.accessioned2020-02-14T11:47:20Z
dc.date.available2020-02-14T11:47:20Z
dc.date.issued2019-10-26
dc.identifier.citationBMC Cancer. 2019 Oct 26;19(1):1005.es_ES
dc.identifier.issn1471-2407es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/9095
dc.description.abstractBACKGROUND: Acute T-cell lymphoblastic leukaemia (T-ALL) is an aggressive disorder derived from immature thymocytes. The variability observed in clinical responses on this type of tumours to treatments, the high toxicity of current protocols and the poor prognosis of patients with relapse or refractory make it urgent to find less toxic and more effective therapies in the context of a personalized medicine of precision. METHODS: Whole exome sequencing and RNAseq were performed on DNA and RNA respectively, extracted of a bone marrow sample from a patient diagnosed with tumour primary T-ALL and double negative thymocytes from thymus control samples. We used PanDrugs, a computational resource to propose pharmacological therapies based on our experimental results, including lists of variants and genes. We extend the possible therapeutic options for the patient by taking into account multiple genomic events potentially sensitive to a treatment, the context of the pathway and the pharmacological evidence already known by large-scale experiments. RESULTS: As a proof-of-principle we used next-generation-sequencing technologies (Whole Exome Sequencing and RNA-Sequencing) in a case of diagnosed Pro-T acute lymphoblastic leukaemia. We identified 689 disease-causing mutations involving 308 genes, as well as multiple fusion transcript variants, alternative splicing, and 6652 genes with at least one principal isoform significantly deregulated. Only 12 genes, with 27 pathogenic gene variants, were among the most frequently mutated ones in this type of lymphoproliferative disorder. Among them, 5 variants detected in CTCF, FBXW7, JAK1, NOTCH1 and WT1 genes have not yet been reported in T-ALL pathogenesis. CONCLUSIONS: Personalized genomic medicine is a therapeutic approach involving the use of an individual's information data to tailor drug therapy. Implementing bioinformatics platform PanDrugs enables us to propose a prioritized list of anticancer drugs as the best theoretical therapeutic candidates to treat this patient has been the goal of this article. Of note, most of the proposed drugs are not being yet considered in the clinical practice of this type of cancer opening up the approach of new treatment possibilities.es_ES
dc.description.sponsorshipThis research was made possible through funding by the Spanish Ministry of Science, Innovation and Universities (RTI2018–093330-B_100); Spanish Ministry of Economy and Competitiveness (SAF2015–70561-R); MINECO/FEDER, EU; BES-2013-065740); Ramón Areces Foundation (CIVP19S7917); the Autonomous Community of Madrid, Spain (B2017/BMD-3778; LINFOMAS-CM); the Spanish Association Against Cancer (AECC, 2018; PROYE18054PIRI); and the Institute of Health Carlos III, ISCIII (ACCI-CIBERER-17). Institutional grants from the Ramón Areces Foundation and the Santander Bank to the Severo Ochoa Molecular Biology Center (CBMSO) are also acknowledged. These projects only provide financial support for our experiments.es_ES
dc.language.isoenges_ES
dc.publisherBioMed Central (BMC) es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCancer genomicses_ES
dc.subjectDruggable genomees_ES
dc.subjectIn silico prescriptiones_ES
dc.subjectNext-generation sequencing technologieses_ES
dc.subjectPanDrugses_ES
dc.subjectPersonalized precision medicinees_ES
dc.subjectPrecision oncologyes_ES
dc.subjectT-ALLes_ES
dc.subjectTargeted therapyes_ES
dc.subjectTranslational bioinformaticses_ES
dc.titleThe use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic dataes_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID31655559es_ES
dc.format.volume19es_ES
dc.format.number1es_ES
dc.format.page1005es_ES
dc.identifier.doi10.1186/s12885-019-6209-9es_ES
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España) 
dc.contributor.funderMinisterio de Economía y Competitividad (España) 
dc.contributor.funderFundación Ramón Areces 
dc.contributor.funderComunidad de Madrid (España) 
dc.contributor.funderAsociación Española Contra el Cáncer 
dc.contributor.funderInstituto de Salud Carlos III 
dc.contributor.funderBanco Santander 
dc.description.peerreviewedes_ES
dc.identifier.e-issn1471-2407es_ES
dc.relation.publisherversionhttps://doi.org/10.1186/s12885-019-6209-9es_ES
dc.identifier.journalBMC canceres_ES
dc.repisalud.centroISCIII::Centro Nacional de Microbiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/RTI2018–093330-B_100es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/SAF2015–70561-Res_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/BES-2013-065740es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/CIVP19S7917es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/B2017/BMD-3778es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PROYE18054PIRIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/ACCI-CIBERER-17es_ES
dc.rights.accessRightsopen accesses_ES


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Atribución 4.0 Internacional
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