Evidence map›Paper›PMID 26848271›Full record

ArticleOncoTargets and therapy2016

Prognostic implications of immunohistochemistry markers for EGFR-TKI therapy in Chinese patients with advanced lung adenocarcinoma harboring EGFR mutations.

Ruiguang Zhang, Yan Li, Xiu Nie, Xiaorong Dong, Gang Wu

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

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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

4 citing papers in PubMed.

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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.

Ruiguang ZhangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Yan LiDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Xiu NieDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Xiaorong DongCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Gang WuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEpidermal growth factor receptor (EGFR) mutations in non-small-cell lung cancer predict dramatic clinical responses to tyrosine kinase inhibitor (TKI) treatment. The conclusion on EGFR mutation-specific antibodies by immunohistochemistry (IHC) is not consistent. We evaluated the clinical availability of EGFR mutation-specific antibodies, investigating the prediction role of mutant EGFRs and other IHC markers in TKI therapy in patients with advanced lung adenocarcinoma. MATERIALS AND

methodsWe analyzed 637 primary lung adenocarcinomas from an unselected Chinese population. For IHC, antibodies against EGFR exon 19 E746_A750 deletions, exon 21 L858R mutations, thyroid transcription factor-1 (TTF-1), and Napsin-A were applied. Positivity was defined as staining score >0.

resultsSpecificities of E746_A750 and L858R antibodies were 99.6% and 99.3%, while sensitivities were 86.0% and 82.7%, respectively. Tumors with Napsin-A positivity, TTF-1 positivity, EGFR mutations, and lepidic pattern showed a lower marker of proliferation index (Ki67). Higher expression scores of mutant EGFR protein, TTF-1 positivity, lower Ki67 proliferation index, and lepidic pattern were associated with longer progression-free survival.

conclusionHigh scores of mutant EGFR, Napsin-A positivity, TTF-1 positivity, lower Ki67 index, and lepidic pattern were favorable predictors for TKI therapy in patients with advanced lung adenocarcinoma.

Indexed as

epidermal growth factor receptor-tyrosine kinase inhibitorgrowth patternKi67Napsin-Aprognosisprogression-free survivalthyroid transcription factor-1

Identifiers

PMID26848271
PMCPMC4723025

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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.