Evidence map›Paper›PMID 32067121›Full record

ArticleClinical and translational medicine2020

EGFR T790M relative mutation purity predicts osimertinib treatment efficacy in non-small cell lung cancer patients.

Qiufan Zheng, Shaodong Hong, Yan Huang, Hongyun Zhao, Yunpeng Yang, Xue Hou, Yuanyuan Zhao, Yuxiang Ma, Ting Zhou, Yaxiong Zhang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Clinical and translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 9% 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, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  7. Observational
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  10. Dynamic cfDNA Analysis by NGS inFrontiers in oncology · 2021
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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

12 authors at 2 institutions in 1 country.

Qiufan ZhengDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Shaodong HongDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Yan HuangDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Hongyun ZhaoDepartment of Clinical Research, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Yunpeng YangDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Xue HouDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Yuanyuan ZhaoDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Yuxiang MaDepartment of Clinical Research, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Ting ZhouDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Yaxiong ZhangDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Wenfeng FangDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China. fangwf@sysucc.org.cn.
Li ZhangDepartment of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China. zhangli@sysucc.org.cn.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CN

Funding

Guangzhou Science and Technology Program key projects 201607020031National Key R&D Program of China 2016YFC0905500National Key R&D Program of China 2016YFC0905503National Natural Science Foundation of China 81772476National Natural Science Foundation of China 81872499Pearl River S and T Nova Program of Guangzhou 201610010048Science and Technology Planning Project of Guangdong Province 2016A020215084Science and Technology Planning Project of Guangdong Province 2017B020227001Young Scientists Fund 81602005Young Teacher Foundation of Sun Yat-sen University 17ykpy81
6 · The paper itself

Abstract

backgroundDespite the impressive anti-tumor activity of osimertinib in epidermal growth factor receptor (EGFR) T790M-positive non-small cell lung cancer (NSCLC) patients, 30-40% of patients still show limited response. There is therefore a need to identify biomarkers that accurately predict the response to osimertinib therapy. In this study, 54 patients with targeted next-generation sequencing of circulating tumor DNA before osimertinib treatment and known T790M positivity were included. We investigated the predictive value of baseline circulating tumor DNA-derived biomarkers on osimertinib therapy.

resultsBaseline maximum somatic allele frequency (MSAF) level was not associated with objective response rate (ORR) (P = 0.886) and progression-free survival (PFS) (P = 0.370) of osimertinib treatment. T790M relative mutation purity (RMP, defined here as the ratio of T790M AF to MSAF) quartiles were found to be significantly associated with ORR (P for trend = 0.002) and PFS (P for trend = 0.006), and a cut off value of 0.24 identified two distinct prognostic groups [Hazard ratio (HR) = 0.36 for low T790M RMP, 95% confidence interval (CI) 0.18-0.72, P = 0.004). Additionally, although T790M relative mutation abundance (RMA, defined as T790M AF/EGFR driver AF) quartiles were not significantly associated with ORR (P for trend = 0.063), a cut off value of 0.30 also identified two distinct prognostic groups (HR = 0.43 for low T790M RMA, 95% CI 0.22-0.85, P = 0.015). However, in multivariate analysis, grouping of T790M RMP showed a better predictive value (HR = 0.46, 95% CI 0.20-1.05, P = 0.066) than T790M RMA (HR = 0.71, 95% CI 0.31-1.61, P = 0.409). Moreover, T790M RMP as continuous covariate was independently predictive of PFS (HR = 0.15, 95% CI 0.03-0.79, P =0.025), while T790M RMA was not (HR = 1.14, 95% CI 0.49-2.66, P =0.766). An external validation cohort further confirmed the T790M RMP was significantly associated with PFS of osimertinib therapy.

conclusionsThis study established the independent predictive role of T790M RMP in NSCLC patients receiving osimertinib treatment.

Indexed as

Allele frequencyNon-small-cell lung cancerOsimertinibT790M mutation

Identifiers

PMID32067121
PMCPMC7026329
OpenAlexW3012216296

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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.