Evidence map›Paper›PMID 35633141›Full record

ArticleThoracic cancer2022

Epidermal growth factor receptor tyrosine kinase inhibitors for de novo T790M mutation: A retrospective study of 44 patients.

John Wen-Cheng Chang, Chen-Yang Huang, Yueh-Fu Fang, Ching-Fu Chang, Cheng-Ta Yang, Chih-Hsi Scott Kuo, Ping-Chih Hsu, Chiao-En Wu

Open access · goldAbstract read
In one paragraph

Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed, 15 citations in OpenAlex.

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  3. Analysis of genomic alternations in epidermal growth factor receptor (EGFR)-T790M-mutated non-small cell lung cancer (NSCLC) patients with acquired resistance to osimertinib therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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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

8 authors at 1 institution in 1 country.

John Wen-Cheng ChangDivision of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Chen-Yang HuangDivision of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Yueh-Fu FangDivision of Thoracic Medicine, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Ching-Fu ChangDivision of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Cheng-Ta YangDivision of Thoracic Medicine, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Chih-Hsi Scott KuoDivision of Thoracic Medicine, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Ping-Chih HsuDivision of Thoracic Medicine, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.
Chiao-En WuDivision of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan.ORCID 0000-0002-1081-8805
Chang Gung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to evaluate possible treatment strategies for patients with de novo T790M mutation-positive (T790M+) non-small-cell lung cancer (NSCLC).

methodsPatients diagnosed with de novo T790M+ NSCLC and treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) between 2011 and 2018 at a regional hospital in Taiwan were retrospectively reviewed. Their clinicopathological characteristics and subsequent treatment information were collected, and potential prognostic factors were identified using univariate and multivariate analyses.

resultsAll tumors with T790M mutations coexisted with sensitizing mutations. Through the last follow-up in May 2021, afatinib and osimertinib demonstrated better progression-free survival (PFS, p < 0.01) and overall survival (OS, p < 0.01) than gefitinib and erlotinib. Additionally, patients with low T790M ratios had better PFS than those with high T790M ratios, implying that the proportion of T790M+ tumors determined the response to EGFR-TKIs. Multivariate analysis confirmed that both EGFR-TKI treatment (osimertinib hazard ratio [HR] 0.06, 95% confidence interval [CI] 0.01-0.30; afatinib HR 0.09, 95% CI 0.02-0.39) and a low T790M ratio (HR 0.29, 95% CI 0.12-0.69) were independently favorable prognostic factors for patients with de novo T790M+ NSCLC. Median PFS was 6.1 (95% CI 4.4-7.8) months. In addition, patients treated with first-generation (1G)/second-generation (2G) EGFR-TKIs followed by osimertinib (n = 8) demonstrated the best OS compared with patients treated with frontline osimertinib (n = 5) or 1G/2G EGFR-TKIs without osimertinib (n = 28, p < 0.01).

conclusionSequential TKIs may represent an alternative option for de novo T790M mutation, particularly frontline afatinib and tumors with low T790M ratios.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsAfatinibErbB ReceptorsHumansMutationProtein Kinase InhibitorsRetrospective StudiesAfatinibErbB ReceptorsProtein Kinase Inhibitorsafatiniblung cancerosimertinibT790M mutation

Identifiers

PMID35633141
PMCPMC9250841
OpenAlexW4281929609

What OpenQuestion holds

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LicenceCC BY
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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.