Evidence map›Paper›PMID 29218886›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018

ClinGen Cancer Somatic Working Group - standardizing and democratizing access to cancer molecular diagnostic data 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
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants.International journal of molecular sciences · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Review
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  9. Article
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  14. Review
  15. Article
  16. Review
  17. Variant information systems for precision oncology.BMC medical informatics and decision making · 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

11 authors.

Subha MadhavanInnovation Center for Biomedical Informatics, Georgetown University, Washington D.C., USA.
Deborah Ritter
Christine Micheel
Shruti Rao
Angshumoy Roy
Dmitriy Sonkin
Matthew Mccoy
Malachi Griffith
Obi L Griffith
Peter Mcgarvey
Shashikant Kulkarni

Funding

Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized InterventionUL1TR001409 · NCATS · GEORGETOWN UNIVERSITY · PI GONDRE-LEWIS, MARJORIE C, MELLMAN, THOMAS A · 2015 to 2024
$37.8M
The Future of PharmGKB Funding Under NHGRIU41HG009649 · NHGRI · STANFORD UNIVERSITY · PI BUSTAMANTE, CARLOS DANIEL · 2017 to 2020
$13.4M
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
Clinically Relevant Genome Variation DatabaseU01HG007436 · NHGRI · STANFORD UNIVERSITY · PI BUSTAMANTE, CARLOS DANIEL, PLON, SHARON E. · 2013 to 2016
$10.5M
A Knowledge Base for Clinically Relevant Genes and VariantsU01HG007437 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BERG, JONATHAN S, EVANS, JAMES PHILIP · 2013 to 2016
$9.0M
MACE2K - Molecular And Clinical Extraction: A Natural Language Processing Tool for Personalized MedicineU01HG008390 · NHGRI · GEORGETOWN UNIVERSITY · PI MADHAVAN, SUBHA · 2015 to 2017
$1.5M
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
DEFINING THE REGULATORY, NON-CODING, MUTATIONAL LANDSCAPE OF BREAST CANCERK22CA188163 · NCI · WASHINGTON UNIVERSITY · PI GRIFFITH, OBI L. · 2014 to 2016
$515k
NCATS NIH HHS UL1 TR001409NCI NIH HHS K22 CA188163NCI NIH HHS U01 CA209936NHGRI NIH HHS R00 HG007940NHGRI NIH HHS U01 HG007436NHGRI NIH HHS U01 HG007437NHGRI NIH HHS U01 HG008390NHGRI NIH HHS U41 HG009649NHGRI NIH HHS U41 HG009650
6 · The paper itself

Abstract

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). 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 (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in 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. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGen's Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

Indexed as

Access to InformationCarcinoma, Pancreatic DuctalChildComputational BiologyDatabases, GeneticGene Expression ProfilingGenes, p53Genetic VariationHigh-Throughput Nucleotide SequencingHumansMolecular Diagnostic TechniquesNeoplasmsPancreatic NeoplasmsPrecision MedicineTranslational Research, Biomedical

Identifiers

PMID29218886
PMCPMC5728662

What OpenQuestion holds

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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.