Evidence map›Paper›PMID 36245822›Full record

ReviewOncology letters2022

Drug resistance mechanisms and progress in the treatment of EGFR-mutated lung adenocarcinoma.

Ruizhu Sun, Zhansheng Hou, Yankui Zhang, Bo Jiang

Open access · diamondAbstract readReview
In one paragraph

Review in Oncology letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
8.4field-weighted citation impact, top 2% 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

40 citing papers in PubMed, 57 citations in OpenAlex.

  1. Article
  2. Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Exceptional Response to Immunotherapy-based Treatment in CompoundJournal of immunotherapy and precision oncology · 2025
    Article
  10. Recent Advances in the Use ofPharmaceutics · 2025
    Review
  11. Article
  12. Construction and clinical significance of prognostic risk markers based on cancer driver genes in lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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

4 authors at 1 institution in 1 country.

Ruizhu SunDepartment of Medical Care, The Third Affiliated Hospital of Kunming Medical University, College of Clinical Oncology, Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
Zhansheng HouDepartment of Medical Care, The Third Affiliated Hospital of Kunming Medical University, College of Clinical Oncology, Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
Yankui ZhangDepartment of Medical Care, The Third Affiliated Hospital of Kunming Medical University, College of Clinical Oncology, Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
Bo JiangDepartment of Medical Care, The Third Affiliated Hospital of Kunming Medical University, College of Clinical Oncology, Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
Kunming Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to global cancer data, lung cancer was the leading cause of cancer-related death in 2020. With the diversification of treatment strategies, the survival outcomes of patients with advanced lung cancer have improved significantly, but the 5-year overall survival rate remains <20%. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the preferred treatment for lung adenocarcinoma patients with EGFR-sensitive mutations; however, acquired drug resistance is inevitable. Osimertinib (a third-generation EGFR inhibitor) is the most commonly used drug for cancers with a secondary T790M mutation. Unfortunately, acquired drug resistance against third-generation drugs still emerges. The C797s mutation is the primary acquired resistance mechanism against Osimertinib. Research on fourth-generation EGFR-TKI drugs with a C797s mutation is currently at various experimental stages, and no drug has been approved for clinical use. In addition to the resistance mechanisms described above, HER2 amplification, MET amplification, PIK3A mutation, KRAS mutation, BRAF mutation, transformation to small cell lung cancer, transformation to lung squamous cell carcinoma, and EMT have been reported as mechanisms of acquired drug resistance to first-, second- and third-generation EGFR-TKIs. These mechanisms are noted in a relatively high proportion of tumors, but treatment options are limited. In recent years, immunotherapy has made progress in the treatment of several cancers, including advanced EGFR-mutated non-small cell lung cancer (NSCLC). Due to the relatively high frequency of EGFR mutation in patients with lung adenocarcinoma in China, an increased number of patients develop EGFR-TKI resistance, and subsequent treatment options are critical. This article reviews the mechanisms of drug resistance to different EGFR-TKIs and treatment progression, providing ideas for the follow-up treatment for EGFR-resistant patients.

Indexed as

acquired resistance mechanismfourth generation epidermal growth factor receptor tyrosine kinase inhibitorsimmunotherapynon-small cell lung cancertyrosine kinase inhibitor

Identifiers

PMID36245822
PMCPMC9555020
OpenAlexW4297221746

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.