Evidence map›Paper›PMID 25593991›Full record

ArticleOncoscience2014

A framework for genomic biomarker actionability and its use in clinical decision making.

Smruti J Vidwans, Michelle L Turski, Filip Janku, Ignacio Garrido-Laguna, Javier Munoz, Richard Schwab, Vivek Subbiah, Jordi Rodon, Razelle Kurzrock

Abstract read
In one paragraph

Article in Oncoscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Next-generation sequencing, should I use anti-HER2 therapy forTherapeutic advances in medical oncology · 2022
    Article
  6. Article
  7. Genes · 2020
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Utility of Genomic Assessment of Blood-Derived Circulating Tumor DNA (ctDNA) in Patients with Advanced Lung Adenocarcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2017
    Article
  13. Article
  14. An informatics research agenda to support precision medicine: seven key areas.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

9 authors.

Smruti J Vidwans *CollabRx Inc., San Francisco, California, USA.
Michelle L Turski *CollabRx Inc., San Francisco, California, USA.
Filip JankuDepartment of Investigational Cancer Therapeutics - a Phase I Clinical Trials Program, The University of Texas MD Anderson Cancer Center Houston, Texas, USA.
Ignacio Garrido-LagunaHuntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
Javier MunozBanner MD Anderson Cancer Center, Gilbert, Arizona, USA.
Richard SchwabCenter for Personalized Cancer Therapy, Moores Cancer Center, University of California, San Diego, California, USA.
Vivek SubbiahDepartment of Investigational Cancer Therapeutics - a Phase I Clinical Trials Program, The University of Texas MD Anderson Cancer Center Houston, Texas, USA.
Jordi RodonVall d'Hebron Institut d'Oncologia and Universitat Autonoma of Barcelona, Barcelona, Spain.
Razelle KurzrockCenter for Personalized Cancer Therapy, Moores Cancer Center, University of California, San Diego, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing scope and availability of genetic testing options for patients suffering from cancer has raised questions about how to use results of molecular diagnostics to inform patient care. For some biomarkers (e.g. BRAF mutations in melanoma), standards exist that outline treatments for individuals harboring aberrations in the biomarker; however for the vast majority of genomic abnormalities, few guidelines exist. Clinical decision making and the therapeutic approach for a patient with a given cancer characterized by aberrations in different genes may be aided by the use of a biomarker actionability framework that provides levels of evidence regarding whether and how a molecular abnormality can be considered a therapeutically relevant biomarker. A gene may be considered theoretically actionable if it has a basis of actionability, such that clinically available drugs can target a gene product that drives the cancer or is differentially expressed in tumor versus normal elements. Herein, we discuss a possible framework for developing guidelines for actionability, as they relate to genomically-based cancer therapeutics.

Indexed as

BiomarkerClinical decision makingPredictive biomarkersTargeted therapies

Identifiers

PMID25593991
PMCPMC4278279

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.