Evidence map›Paper›PMID 38801833›Full record

ReviewCancer science2024

Histologic transformation of non-small-cell lung cancer in response to tyrosine kinase inhibitors: Current knowledge of genetic changes and molecular mechanisms.

Aya Shiba-Ishii, Noriko Takemura, Hitomi Kawai, Daisuke Matsubara

Abstract readReview
In one paragraph

Review in Cancer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

4 authors.

Aya Shiba-IshiiDepartment of Diagnostic Pathology, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan.ORCID https://orcid.org/0000-0001-6639-4135
Noriko TakemuraDepartment of Diagnostic Pathology, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan.
Hitomi KawaiDepartment of Diagnostic Pathology, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan.
Daisuke MatsubaraDepartment of Diagnostic Pathology, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan.

Funding

Japan Society for the Promotion of Science 19K07455Takeda Science Foundation
6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer death and includes two major types: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC), accounting for 85% and 15% of cases, respectively. Non-small-cell lung cancer harboring actionable driver mutations is generally treated with tyrosine kinase inhibitors (TKIs) molecularly targeting individual oncogenes. Although TKIs have greatly contributed to better clinical outcomes, acquired resistance to them inevitably occurs. Histologic or lineage transformation is a rare but well-documented off-target mechanism associated with acquired resistance, and has been identified in settings following treatment with multiple different TKIs and other drugs. It includes neuroendocrine transformation, squamous cell transformation, and epithelial-to-mesenchymal transition. Here, we review the clinicopathologic features of transformed tumors and current understanding of the key genetic alterations and biologic mechanism of lineage transformation in NSCLC, particularly TKI-triggered transformation.

Indexed as

Carcinoma, Non-Small-Cell LungCell Transformation, NeoplasticDrug Resistance, NeoplasmEpithelial-Mesenchymal TransitionLung NeoplasmsProtein Kinase InhibitorsHumansMutationProtein-Tyrosine KinasesTyrosine Kinase InhibitorsProtein Kinase InhibitorsProtein-Tyrosine KinasesTyrosine Kinase Inhibitorshistologic transformationlineage plasticityNSCLCSCLCtyrosine kinase inhibitor

Identifiers

PMID38801833
PMCPMC11247606

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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.