Evidence map›Paper›PMID 32644817›Full record

ReviewJCO clinical cancer informatics2020

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.

Shruti Rao, Beth Pitel, Alex H Wagner, Simina M Boca, Matthew McCoy, Ian King, Samir Gupta, Ben Ho Park, Jeremy L Warner, James Chen and 5 more

Open access · hybridAbstract readReview
In one paragraph

Review in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

20 citing papers in PubMed, 37 citations in OpenAlex.

  1. Review
  2. Article
  3. Observational
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Fighting Cancer around the World: A Framework for Action.Healthcare (Basel, Switzerland) · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

15 authors at 9 institutions in 2 countries.

Shruti RaoInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC.
Beth PitelDivision of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.
Alex H WagnerMcDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO.
Simina M BocaInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC.
Matthew McCoyInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC.
Ian KingLaboratory Medicine Program, University Health Network and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Samir GuptaInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC.
Ben Ho ParkDivision of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
Jeremy L WarnerDepartments of Medicine and Biomedical Informatics, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
James ChenDivision of Medical Oncology, Department of Biomedical Informatics, The Ohio State University, Columbus, OH.
Peter K RoganDepartments of Biochemistry and Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.
Debyani ChakravartyKravis Center of Molecular Oncology, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY.
Malachi GriffithMcDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO.
Obi L GriffithMcDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO.
Subha MadhavanInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC.
Georgetown University Medical Center · USJames S. McDonnell Foundation · USGeorgetown University · USVanderbilt University Medical Center · USMayo Clinic · USMolecular Oncology (United States) · USThe Ohio State University · USUniversity Health Network · CAWestern University · CA

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI Louis M Weiner · 1990 to 2026
$71.5M
The Clinical Genome Resource – Advancing genomic medicine through biocuration and expert assessment of genes and variants at scaleU24HG009650 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JONATHAN S BERG, Jessica Ezzell Hunter · 2021 to 2026
$30.0M
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
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
Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision MedicineU24CA237719 · NCI · WASHINGTON UNIVERSITY · PI GRIFFITH, OBI L. · 2019 to 2023
$3.8M
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
Integration of 3D primary tumor drug-profiling with patient-specific drug gene networks for recommending targeted cancer therapies (2 of 2).R21CA220398 · NCI · GEORGETOWN UNIVERSITY · PI BOCA, SIMINA MARIA · 2017 to 2019
$497k
TOOLS FOR NORMALIZING AND INTERPRETING THE CLINICAL ACTIONABILITY OF GENOMIC VARIANTSK99HG010157 · NHGRI · WASHINGTON UNIVERSITY · PI WAGNER, ALEX HANDLER · 2019 to 2020
$226k
NCI NIH HHS P30 CA008748NCI NIH HHS P30 CA051008NCI NIH HHS R21 CA220398NCI NIH HHS U01 CA209936NCI NIH HHS U24 CA237719NHGRI NIH HHS K99 HG010157NHGRI NIH HHS R00 HG007940NHGRI NIH HHS U01 HG007437NHGRI NIH HHS U24 HG009650NHGRI NIH HHS U41 HG009650
6 · The paper itself

Abstract

purposeThe cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning.

methodsIn this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards.

resultsMolecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence.

conclusionAdvances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.

Indexed as

Artificial IntelligenceNeoplasmsGenomicsHumansInformation DisseminationKnowledge Bases

Identifiers

PMID32644817
PMCPMC7397775
OpenAlexW3041023111

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.