Evidence map›Paper›PMID 27272900›Full record

ArticlePLoS genetics2016

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making.

Pierre Thouvenot, Barbara Ben Yamin, Lou Fourrière, Aurianne Lescure, Thomas Boudier, Elaine Del Nery, Anne Chauchereau, David E Goldgar, Claude Houdayer, Dominique Stoppa-Lyonnet and 2 more

Abstract read
In one paragraph

Article in PLoS genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Journal of personalized medicine · 2024
    Article
  3. Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. ExploringMicrobial cell (Graz, Austria) · 2021
    Review
  11. Review
  12. Review
  13. Functional analysis of clinicalCold Spring Harbor molecular case studies · 2019
    Article
  14. Article
  15. Article
  16. Functional Interaction BetweenFrontiers in genetics · 2018
    Article
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

12 authors.

Pierre ThouvenotInstitut Curie, PSL Research University, Paris, France.
Barbara Ben YaminInstitut Curie, PSL Research University, Paris, France.
Lou FourrièreInstitut Curie, PSL Research University, Paris, France.
Aurianne LescureInstitut Curie, PSL Research University, Department of Translational Research, the Biophenics High-Content Screening Laboratory, Cell and Tissue Imaging Facility (PICT-IBiSA), Paris, France.
Thomas BoudierSorbonne Universités, UPMC Univ Paris 06, Paris, France.
Elaine Del NeryInstitut Curie, PSL Research University, Department of Translational Research, the Biophenics High-Content Screening Laboratory, Cell and Tissue Imaging Facility (PICT-IBiSA), Paris, France.
Anne ChauchereauINSERM U981, Villejuif, France.
David E GoldgarDepartment of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Claude HoudayerService de Génétique Oncologique, Institut Curie, Paris, France.ORCID 0000-0002-5190-0389
Dominique Stoppa-LyonnetService de Génétique Oncologique, Institut Curie, Paris, France.
Alain NicolasInstitut Curie, PSL Research University, Paris, France.
Gaël A MillotInstitut Curie, PSL Research University, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening.

Indexed as

BRCA1 ProteinBreast NeoplasmsClinical Decision-MakingFemaleGenetic CounselingGenetic Predisposition to DiseaseGenetic TestingGenetic VariationHumansMutation, MissenseOvarian NeoplasmsBRCA1 ProteinBRCA1 protein, human

Identifiers

PMID27272900
PMCPMC4894565

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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.