Evidence map›Paper›PMID 32206561›Full record

ArticleTranslational lung cancer research2020

Rechallenge with erlotinib in osimertinib-resistant lung adenocarcinoma mediated by driver gene loss: a case report.

Lingli Liu, Analyn Lizaso, Xinru Mao, Nong Yang, Yongchang Zhang

Open access · diamondAbstract readCase Reports
In one paragraph

Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 32% 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, 9 citations in OpenAlex.

  1. Article
  2. Article
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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

5 authors at 3 institutions in 1 country.

Lingli LiuDepartment of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China.
Analyn LizasoBurning Rock Biotech, Guangzhou 510300, China.
Xinru MaoBurning Rock Biotech, Guangzhou 510300, China.
Nong YangDepartment of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China.
Yongchang ZhangDepartment of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China.
Burning Rock Biotech (China) · CNCentral South University · CNHunan Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The efficacy and safety of osimertinib have been demonstrated in several clinical trials; however, acquired resistance is an inevitable problem associated with most targeted drugs. Based on previous findings, the mechanism of osimertinib resistance is equivocal, and there is still no consensus on the most optimal treatment strategy after developing resistance, especially for patients detected with no actionable driver mutation. Here, we report the efficacy of erlotinib rechallenge in a patient with advanced lung adenocarcinoma following osimertinib resistance mediated by driver gene loss. Following osimertinib resistance, targeted sequencing of both blood and tissue samples revealed the disappearance of both

Indexed as

case reportepidermal growth factor receptor (EGFR)next generation sequencing (NGS)non-small-cell lung cancer (NSCLC)Resistance mechanismtyrosine kinase inhibitor (TKI)

Identifiers

PMID32206561
PMCPMC7082283
OpenAlexW3005603467

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.