Evidence map›Paper›PMID 29888062›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018

Standardizing And Democratizing Access To Cancer Molecular Diagnostic Test Data From Patients To Drive Translational Research.

Subha Madhavan, Deborah Ritter, Christine Micheel, Shruti Rao, Angshumoy Roy, Dmitriy Sonkin, Matthew Mccoy, Malachi Griffith, Obi L Griffith, Peter Mcgarvey and 1 more

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Subha MadhavanInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
Deborah RitterInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
Christine MicheelVanderbilt University School of Medicine, Nashville,TN.
Shruti RaoInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
Angshumoy RoyBaylor College of Medicine and Texas Children's Hospital, Houston, TX.
Dmitriy SonkinNational Cancer Institute, Rockville, MD.
Matthew MccoyInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
Malachi GriffithThe McDonnell Genome Institute, Washington University, St. Louis, MO.
Obi L GriffithThe McDonnell Genome Institute, Washington University, St. Louis, MO.
Peter McgarveyInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
Shashikant KulkarniBaylor College of Medicine and Texas Children's Hospital, Houston, TX.

Funding

The Clinical Genome Resource - Expert Curation and EHR IntegrationU41HG009650 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BERG, JONATHAN S · 2017 to 2020
$13.3M
DEVELOPMENT OF INFORMATICS RESOURCES FOR INTERPRETATION OF CLINICALLY ACTIONABLE VARIANTS IN CANCERU01CA209936 · NCI · WASHINGTON UNIVERSITY · PI GRIFFITH, OBI L. · 2016 to 2018
$1.0M
Integrated Analysis & Interpretation of Whole Genome Exome & Transcriptome SequenR00HG007940 · NHGRI · WASHINGTON UNIVERSITY · PI GRIFFITH, MALACHI · 2017 to 2019
$724k
NCI NIH HHS U01 CA209936NHGRI NIH HHS R00 HG007940NHGRI NIH HHS U41 HG009650
6 · The paper itself

Abstract

In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing to develop better treatment plans with targeted therapies. To truly achieve precision oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. Through the NIH-funded Clinical Genome Resource (ClinGen), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, a Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations was developed. Methodological and technology development to standardize and map MolDx data to the MVLD standard are presented here. Also described is a novel community engagement effort through disease-focused taskforces to provide usecases for technology development.

Identifiers

PMID29888062
PMCPMC5961792

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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.