Evidence map›Paper›PMID 27234388›Full record

ReviewTranslational respiratory medicine2013

Oncogenic driver mutations in lung cancer.

Susan Y Luo, David Cl Lam

Open access · hybridAbstract readReview
In one paragraph

Review in Translational respiratory medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

30 citing papers in PubMed, 63 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Translational cancer research · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Notch Transduction in Non-Small Cell Lung Cancer.International journal of molecular sciences · 2020
    Review
  11. Article
  12. Article
  13. Journal of oncology · 2020
    Review
  14. Detection ofBioMed research international · 2020
    Article
  15. Article
  16. Observational
  17. Article
  18. Article
  19. HighOncotarget · 2018
    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

2 authors at 1 institution in 1 country.

Susan Y LuoDepartment of Medicine, University of Hong Kong, 102 Pokfulam Road, Hong Kong, SAR, China.
David Cl LamDepartment of Medicine, University of Hong Kong, 102 Pokfulam Road, Hong Kong, SAR, China. dcllam@hku.hk.
University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is a heterogeneous and complex disease. Genomic and transcriptomic profiling of lung cancer not only further our knowledge about cancer initiation and progression, but could also provide guidance on treatment decisions. The fact that targeted treatment is most successful in a subset of tumors indicates the need for better classification of clinically related molecular tumor phenotypes based on better understanding of the mutations in relevant genes, especially in those oncogenic driver mutations. EGFR gene mutations, KRAS gene mutations, EML4-ALK rearrangements and altered MET signaling are widely recognized alterations that play important roles in both the biological mechanisms and the clinical sensitivity to treatment in lung cancer. In this article, we reviewed the discovery of the clinical values of these oncogenic driver mutations and the clinical studies revealing the prognostic and predictive values of these biomarkers for clinical sensitivity and resistance to anti-EGFR therapy or other targeted therapies. These form the basis of personalized treatment in lung cancer based on biomarker profiles of individual tumor, leading to therapeutic advancement and betterment.

Indexed as

ALKDriver mutationsEGFRLung cancer

Identifiers

PMID27234388
PMCPMC6733434
OpenAlexW2097823004

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.