Evidence map›Paper›PMID 38786296›Full record

ArticleDiagnostics (Basel, Switzerland)2024

Spheroids Generated from Malignant Pleural Effusion as a Tool to Predict the Response of Non-Small Cell Lung Cancer to Treatment.

Tsung-Ming Yang, Yu-Hung Fang, Chieh-Mo Lin, Miao-Fen Chen, Chun-Liang Lin

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. 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

5 authors.

Tsung-Ming YangDivision of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan.ORCID 0000-0002-9241-2816
Yu-Hung FangDivision of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan.
Chieh-Mo LinDivision of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan.
Miao-Fen ChenDepartment of Radiation Oncology, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan.
Chun-Liang LinDepartment of Nephrology, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan.ORCID 0000-0002-4500-0878

Funding

Chiayi Chang Gung Memorial Hospital CMRPG6C0111 and CMRPG6I0113
6 · The paper itself

Abstract

backgroundSpheroids generated by tumor cells collected from malignant pleural effusion (MPE) were shown to retain the characteristics of the original tumors. This ex vivo model might be used to predict the response of non-small cell lung cancer (NSCLC) to anticancer treatments.

methodsThe characteristics, epidermal growth factor receptor (EGFR) mutation status, and clinical response to EGFR-TKIs treatment of enrolled patients were recorded. The viability of the spheroids generated from MPE of enrolled patients were evaluated by visualization of the formazan product of the MTT assay.

resultsSpheroids were generated from 14 patients with NSCLC-related MPE. Patients with EGFR L861Q, L858R, or Exon 19 deletion all received EGFR-TKIs, and five of these seven patients responded to treatment. The viability of the spheroids generated from MPE of these five patients who responded to EGFR-TKIs treatment was significantly reduced after gefitinib treatment. On the other hand, gefitinib treatment did not reduce the viability of the spheroids generated from MPE of patients with EGFR wild type, Exon 20 insertion, or patients with sensitive EGFR mutation but did not respond to EGFR-TKIs treatment.

conclusionMulticellular spheroids generated from NSCLC-related MPE might be used to predict the response of NSCLC to treatment.

Indexed as

biomarkerlung cancermalignant pleural effusion

Identifiers

PMID38786296
PMCPMC11120547

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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.