Evidence map›Paper›PMID 32633046›Full record

ArticleThoracic cancer2020

Genomic characteristics and drug screening among organoids derived from non-small cell lung cancer patients.

Jing-Hua Chen, Xiang-Peng Chu, Jia-Tao Zhang, Qiang Nie, Wen-Fang Tang, Jian Su, Hong-Hong Yan, Hong-Ping Zheng, Ze-Xin Chen, Xin Chen and 9 more

Open access · goldAbstract read
In one paragraph

Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 8% 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

49 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 citations in OpenAlex.

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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

19 authors at 7 institutions in 3 countries.

Jing-Hua ChenThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Xiang-Peng ChuGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Jia-Tao ZhangThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Qiang NieGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Wen-Fang TangGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Jian SuGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Hong-Hong YanGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Hong-Ping ZhengAccurate International Biotechnology Co., Guangzhou, China.
Ze-Xin ChenAccurate International Biotechnology Co., Guangzhou, China.
Xin ChenAccurate International Biotechnology Co., Guangzhou, China.
Meng-Meng SongGeneplus-Beijing Institute, Beijing, China.
Xin YiGeneplus-Beijing Institute, Beijing, China.
Pan-Song LiGeneplus-Beijing Institute, Beijing, China.
Yan-Fang GuanGeneplus-Beijing Institute, Beijing, China.
Gang LiThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Chu-Xia DengUniversity of Macau. Cancer Centre, Faculty of Health Sciences, University of Macau, Macau, China.
Rafael RosellInstitut d'Investigació en Ciències de la Salut Germans Trias i Pujol Campus Can Ruti (Edifici Muntanya), Ctra. de Can Ruti, Barcelona, Spain.
Yi-Long WuGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Key Laboratory of Lung Cancer Translational Medicine, South China University of Technology & Guangdong Academy of Medical Sciences, Guangzhou, China.
Wen-Zhao ZhongThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.ORCID 0000-0002-8917-8635
Guangdong Academy of Medical Sciences · CNBeike Biotechnology (China) · CNInstitut d'Investigació en Ciències de la Salut Germans Trias i Pujol · ESShantou University · CNSouthern Medical University · CNTwelfth Guangzhou City People's Hospital · CNUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient-derived organoid (PDO) models are highly valuable and have potentially widespread clinical applications. However, limited information is available regarding organoid models of non-small cell lung cancer (NSCLC). This study aimed to characterize the consistency between primary tumors in NSCLC and PDOs and to explore the applications of PDOs as preclinical models to understand and predict treatment response during lung cancer.

methodsFresh tumor samples were harvested for organoid culture. Primary tumor samples and PDOs were analyzed via whole-exome sequencing. Paired samples were subjected to immunohistochemical analysis. There were 26 antineoplastic drugs tested in the PDOs. Cell viability was assessed using the Cell Titer Glo assay 7-10 days after drug treatment. A heatmap of log-transformed values of the half-maximal inhibitory concentrations was generated on the basis of drug responses of PDOs through nonlinear regression (curve fit). A total of 12 patients (stages I-III) were enrolled, and 7 paired surgical tumors and PDOs were analyzed.

resultsPDOs retained the histological and genetic characteristics of the primary tumors. The concordance between tumors and PDOs in mutations in the top 20 NSCLC-related genes was >80% in five patients. Sample purity was significantly and positively associated with variant allele frequency (Pearson r = 0.82, P = 0.0005) and chromosome stability. The in vitro response to drug screening with PDOs revealed high correlation with the mutation profiles in the primary tumors.

conclusionsPDOs are highly credible models for detecting NSCLC and for prospective prediction of the treatment response for personalized precision medicine. KEY POINTS: Lung cancer organoid models could save precious time of drug testing on patients, and accurately select anticancer drugs according to the drug sensitivity results, so as to provide a powerful supplement and verification for the gene sequencing.

Indexed as

AgedCarcinoma, Non-Small-Cell LungDrug Evaluation, PreclinicalFemaleGenomicsHumansLung NeoplasmsMaleMiddle AgedOrganoidsConsistency analysisdrug screeningnon-small cell lung cancerpatient-derived organoidwhole exome sequencing

Identifiers

PMID32633046
PMCPMC7396373
OpenAlexW3040790495

What OpenQuestion holds

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LicenceCC BY
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.