Evidence map›Paper›PMID 24910641›Full record

ReviewFrontiers in genetics2014

Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial.

Nicolas Servant, Julien Roméjon, Pierre Gestraud, Philippe La Rosa, Georges Lucotte, Séverine Lair, Virginie Bernard, Bruno Zeitouni, Fanny Coffin, Gérôme Jules-Clément and 13 more

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

34 citing papers in PubMed, 95 citations in OpenAlex.

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  14. Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2020
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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

23 authors at 3 institutions in 1 country.

Nicolas ServantInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Julien RoméjonInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Pierre GestraudInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Philippe La RosaInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Georges LucotteInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Séverine LairInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Virginie BernardINSERM U932, Paris France.
Bruno ZeitouniInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Fanny CoffinInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Gérôme Jules-ClémentInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France ; INSERM U932, Paris France.
Florent YvonInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Alban LermineInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Patrick PoulletInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Stéphane LivaInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Stuart PookInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Tatiana PopovaInstitut Curie, Paris France ; INSERM U830, Paris France.
Camille BaretteInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France ; Institut Curie, Informatic Department, Paris France.
François Prud'hommeInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France ; Institut Curie, Informatic Department, Paris France ; Institut Curie, Sequencing Facility ICGex, Paris France.
Jean-Gabriel DickInstitut Curie, Informatic Department, Paris France.
Maud KamalInstitut Curie, Translational Research Department, Paris France.
Christophe Le TourneauINSERM U900, Paris France ; Mines ParisTech, Fontainebleau France ; Department of Medical Oncology, Institut Curie, Paris France.
Emmanuel BarillotInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
Philippe HupéInstitut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France ; CNRS UMR144, Paris France.
Inserm · FRInstitut Curie · FRCentre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.

Indexed as

bioinformaticsclinical trialoncologyprecision medicinesequencingSHIVA

Identifiers

PMID24910641
PMCPMC4039073
OpenAlexW1984650538

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.