Evidence map›Paper›PMID 40503037›Full record

ReviewOncology letters2025

EGFR mutations in non-small cell lung cancer: Classification, characteristics and resistance to third-generation EGFR-tyrosine kinase inhibitors (Review).

Zhe Tian, Lilan Cen, Feng Wei, Jue Dong, Yulan Huang, Yi Han, Zhibo Wang, Junhua Deng, Yujie Jiang

Abstract readReview
In one paragraph

Review in Oncology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Central nervous system efficacy of third generationJournal of thoracic disease · 2026
    Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Innovative Amino-Functionalization of Pyrido[2,3-Pharmaceuticals (Basel, Switzerland) · 2025
    Article
  14. 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

9 authors.

Zhe TianDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Lilan CenDepartment of Infectious Disease, Guangxi Academy of Medical Sciences, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Feng WeiDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Jue DongDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Yulan HuangDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Yi HanDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Zhibo WangDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Junhua DengDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.
Yujie JiangDepartment of Pulmonary and Critical Care Medicine, Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Zhuang Autonomous Region 533000, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mutations in EGFR (mEGFRs) in non-small cell lung cancer (NSCLC) are key factors driving tumor development and treatment response. The present article aimed to review the classification, characteristics and molecular mechanisms of resistance to third-generation EGFR-tyrosine kinase inhibitors (EGFR-TKIs) associated with mEGFRs. Activating mutations (such as L858R and exon 19 deletions) are the primary markers of sensitivity to EGFR-TKIs, while rare mutations (such as G719X and S768I) require individualized treatment strategies. Resistance mechanisms are categorized into EGFR-dependent (such as T790M and C797S mutations) and -independent (bypassing signaling activation, epithelial-mesenchymal transition, tumor microenvironment remodeling and epigenetic regulation). Third-generation EGFR-TKIs (such as osimertinib) markedly improve patient survival by selectively targeting the T790M mutation, but novel resistance mutations, such as C797S, limit their long-term efficacy. Combination therapies (such as MET proto-oncogene, receptor tyrosine kinase/EGFR dual-target inhibitors) and fourth-generation TKIs (such as BLU-945) offer novel directions to overcome resistance. Future research should focus on precise subtyping, dynamic monitoring of resistance mechanisms and regulation of the immune microenvironment to advance personalized treatment for NSCLC.

Indexed as

drug resistanceEGFR mutationnon-small cell lung cancertargeted therapythird-generation EGFR-tyrosine kinase inhibitor

Identifiers

PMID40503037
PMCPMC12150204

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.