2026-04-29T06:45:20Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/90952024-11-29T19:15:08Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_2173col_20.500.12105_19608col_20.500.12105_19597

00925njm 22002777a 4500 dc Fernandez-Navarro, Pablo L author López-Nieva, Pilar author Piñeiro-Yañez, Elena author Carreno-Tarragona, Gonzalo author Martinez-López, Joaquín author Sánchez Pérez, Raúl author Aroca, Ángel author Al-Shahrour, Fatima author Cobos-Fernández, María Ángeles author Fernández-Piqueras, José author 2019-10-26 BACKGROUND: 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. BMC Cancer. 2019 Oct 26;19(1):1005. 1471-2407 http://hdl.handle.net/20.500.12105/9095 31655559 10.1186/s12885-019-6209-9 1471-2407 BMC cancer Cancer genomics Druggable genome In silico prescription Next-generation sequencing technologies PanDrugs Personalized precision medicine Precision oncology T-ALL Targeted therapy Translational bioinformatics The use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic data