Evidence map›Paper›PMID 32881389›Full record

ArticleThoracic cancer2020

EGF+61 A>G polymorphism does not predict response to first-generation EGFR tyrosine kinase inhibitors in lung cancer patients.

Leticia F Leal, Ana C Laus, Rodrigo Cavagna, Flavia E de Paula, Marco A de Oliveira, Dayana M Ribeiro, Fernanda M Hassan, José E Miziara, Eduardo C Albino da Silva, Vinicius D da Silva and 2 more

Open access · goldAbstract read
In one paragraph

Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Meta-analysis ofInternational journal of immunopathology and pharmacology
    Pooled it
  2. Article
  3. 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

12 authors at 1 institution in 3 countries.

Leticia F LealMolecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.
Ana C LausMolecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.
Rodrigo CavagnaMolecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.
Flavia E de PaulaMolecular Diagnosis Center, Barretos Cancer Hospital, Barretos, Brazil.
Marco A de OliveiraDepartment of Epidemiology and Biostatistics, Barretos Cancer Hospital, Barretos, Brazil.
Dayana M RibeiroDepartment of Clinical Oncology, Barretos Cancer Hospital, Barretos, Brazil.
Fernanda M HassanDepartment of Clinical Oncology, Barretos Cancer Hospital, Barretos, Brazil.
José E MiziaraDepartment of Surgery, Barretos Cancer Hospital, Barretos, Brazil.
Eduardo C Albino da SilvaDepartment of Pathology, Barretos Cancer Hospital, Barretos, Brazil.
Vinicius D da SilvaDepartment of Pathology, Barretos Cancer Hospital, Barretos, Brazil.
Pedro De MarchiDepartment of Clinical Oncology, Barretos Cancer Hospital, Barretos, Brazil.
Rui M ReisMolecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.ORCID 0000-0002-9639-7940
Hospital de Câncer de Barretos · BR

Funding

Barretos Cancer Hospital Research FundConselho Nacional de Desenvolvimento Científico e TecnológicoFINEP - CT-INFRA 02/2010Public Ministry of Labor Campinas FINEP - CT-INFRA (02/2010)Public Ministry of Labor Campinas (Research, Prevention, and Education of Occupational Cancer)
6 · The paper itself

Abstract

Epidermal growth factor (EGF) and its receptor (EGFR) play a paramount role in lung carcinogenesis. The polymorphism in the EGF promoter region EGF+61A>G (rs4444903) has been associated with cancer susceptibility, but its role in lung cancer patients treated with tyrosine kinase inhibitors (TKIs) remains unknown. Here, we aimed to evaluate the predictive and prognostic role of EGF+61A>G SNP in lung cancer from Brazilian EGFR-mutated TKI-treated patients. Herein, patients carrying EGFR-sensitizing mutations submitted to TKI treatment (gefitinib/erlotinib) were analyzed (n = 111) for EGF+61A>G genotype by TaqMan genotyping assay. TKI treatment was classified as partial response (PR), stable disease (SD), and disease progression (DP), according to RECIST1.1. Association analysis was assessed by chi-square and Fisher's test (univariate) and multinomial model (multivariate) and survival analysis by Kaplan-Meier method and log-rank test. The EGF+61A>G genotype frequencies observed were: AA = 31.5% (n = 35), AG = 49.6% (n = 55) and GG = 18.9% (n = 21). The allelic frequencies were 56.3% for A, and 43.7% for G and the population was in Hardy-Weinberg equilibrium (P = 0.94). EGF+61A>G codominant model (AA vs. AG vs. GG) was associated with a response to TKIs (P = 0.046), as well as a recessive model (AA vs. AG + GG; P = 0.023). The multinomial regression showed an association between the codominant model (AG) and recessive model (AG + GG) with SD compared with DP (P = 0.01;OR = 0.08; 95% CI = 0.01-0.60 and P = 0.02;OR = 0.12; 95% CI = 0.20-0.72, respectively). No association between genotypes and progression-free or overall survival was observed. In conclusion, the EGF+61 polymorphism (AG and AG + GG) was independently associated with stable disease in lung cancer patients although it was not associated with the overall response rate to first-generation TKIs or patient outcome.

Indexed as

Epidermal Growth FactorGenetic Predisposition to DiseaseHumansLung NeoplasmsMiddle AgedProtein Kinase InhibitorsRetrospective StudiesEpidermal Growth FactorProtein Kinase InhibitorsEGF+61 A>G polymorphismEGFRfirst-generation TKI SNPlung cancerTKI

Identifiers

PMID32881389
PMCPMC7529554
OpenAlexW3083142717

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.