Evidence map›Paper›PMID 31655559›Full record

ArticleBMC cancer2019

The use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic data.

Pablo Fernández-Navarro, Pilar López-Nieva, Elena Piñeiro-Yañez, Gonzalo Carreño-Tarragona, Joaquín Martinez-López, Raúl Sánchez Pérez, Ángel Aroca, Fátima Al-Shahrour, María Ángeles Cobos-Fernández, José Fernández-Piqueras

Open access · goldAbstract readCase Reports
In one paragraph

Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Pablo Fernández-NavarroCancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, 28029, Spain.ORCID https://orcid.org/0000-0001-9427-2581
Pilar López-NievaDepartment of Cellular Biology and Immunology, Severo Ochoa Molecular Biology Center (CBMSO), CSIC-Madrid Autonomous University, Madrid, 28049, Spain.ORCID https://orcid.org/0000-0001-6369-2444
Elena Piñeiro-YañezBioinformatics Unit, Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, 28029, Spain.ORCID https://orcid.org/0000-0003-2773-2343
Gonzalo Carreño-TarragonaHematology Department, Hospital Universitario 12 de Octubre, Madrid, 28041, Spain.ORCID https://orcid.org/0000-0002-9570-5542
Joaquín Martinez-LópezHematology Department, Hospital Universitario 12 de Octubre, Madrid, 28041, Spain.
Raúl Sánchez PérezDepartment of Congenital Cardiac Surgery, Hospital Universitario La Paz, Madrid, 28046, Spain.
Ángel ArocaDepartment of Congenital Cardiac Surgery, Hospital Universitario La Paz, Madrid, 28046, Spain.
Fátima Al-ShahrourBioinformatics Unit, Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, 28029, Spain.ORCID https://orcid.org/0000-0003-2373-769X
María Ángeles Cobos-FernándezDepartment of Cellular Biology and Immunology, Severo Ochoa Molecular Biology Center (CBMSO), CSIC-Madrid Autonomous University, Madrid, 28049, Spain.
José Fernández-PiquerasDepartment of Cellular Biology and Immunology, Severo Ochoa Molecular Biology Center (CBMSO), CSIC-Madrid Autonomous University, Madrid, 28049, Spain. jfpiqueras@cbm.csic.es.ORCID https://orcid.org/0000-0003-4520-6785
Hospital Universitario 12 De Octubre · ESHospital Universitario La Paz · ESInstituto de Salud Carlos III · ESSpanish National Cancer Research Centre · ESCentre for Biomedical Network Research on Rare Diseases · ESHospital Universitario Fundación Jiménez Díaz · ES

Funding

Comunidad de Madrid B2017/BMD-3778; LINFOMAS-CMFundación Científica Asociación Española Contra el Cáncer AECC, 2018; PROYE18054PIRIFundación Ramón Areces CIVP19S7917Instituto de Salud Carlos III ACCI-CIBERER-17Ministerio de Ciencia e Innovación RTI2018-093330-B_100Ministerio de Economía y Competitividad BES-2013-065740Ministerio de Economía y Competitividad SAF2015-70561-R
6 · The paper itself

Abstract

backgroundAcute 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.

methodsWhole 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.

resultsAs 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.

conclusionsPersonalized 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.

Indexed as

AdolescentAlternative SplicingAntineoplastic AgentsExomeGene FusionGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansMaleMutationPrecision MedicinePrecursor T-Cell Lymphoblastic Leukemia-LymphomaRNA-SeqSpainTranscriptomeAntineoplastic AgentsCancer genomicsDruggable genomeIn silico prescriptionNext-generation sequencing technologiesPanDrugsPersonalized precision medicinePrecision oncologyT-ALLTargeted therapyTranslational bioinformatics

Identifiers

PMID31655559
PMCPMC6815385
OpenAlexW2981678336

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.