Evidence map›Paper›PMID 29848362›Full record

ArticleGenome medicine2018

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.

Elena Piñeiro-Yáñez, Miguel Reboiro-Jato, Gonzalo Gómez-López, Javier Perales-Patón, Kevin Troulé, José Manuel Rodríguez, Héctor Tejero, Takeshi Shimamura, Pedro Pablo López-Casas, Julián Carretero and 4 more

Abstract read
In one paragraph

Article in Genome medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
–field-weighted citation impact
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

51 citing papers in PubMed.

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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

14 authors.

Elena Piñeiro-YáñezSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Miguel Reboiro-JatoComputer Science Department - University of Vigo, Vigo, Spain.
Gonzalo Gómez-LópezSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Javier Perales-PatónSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Kevin TrouléSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
José Manuel RodríguezSpanish National Bioinformatics Institute (INB), Madrid, Spain.
Héctor TejeroSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Takeshi ShimamuraLoyola University Chicago Stritch School of Medicine, Maywood, IL, USA.
Pedro Pablo López-CasasSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Julián CarreteroDepartment of Physiology - University of Valencia, Valencia, Spain.
Alfonso ValenciaSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Manuel HidalgoSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
Daniel Glez-PeñaComputer Science Department - University of Vigo, Vigo, Spain.
Fátima Al-ShahrourSpanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain. falshahrour@cnio.es.ORCID 0000-0003-2373-769X

Funding

Investigation of CXCR7 signaling in EGFR TKI resistant NSCLCR01CA230778 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI SHIMAMURA, TAKESHI · 2019 to 2023
$1.9M
FP7 People: Marie-Curie Actions CIG334361NCI NIH HHS R01 CA230778
6 · The paper itself

Abstract

backgroundLarge-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.

resultsWe present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.

conclusionsPanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

Indexed as

GenomicsPrecision MedicineAntineoplastic AgentsComputational BiologyComputer SimulationGenome, HumanHumansNeoplasmsAntineoplastic AgentsCancer genomicsDruggable genomeIn silico prescriptionPersonalized medicinePrecision oncologyTargeted therapyTranslational bioinformatics

Identifiers

PMID29848362
PMCPMC5977747

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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.