In:
Thoracic Cancer, Wiley, Vol. 11, No. 8 ( 2020-08), p. 2279-2290
Abstract:
Patient‐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. Methods Fresh 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. Results PDOs 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. Conclusions PDOs 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.
Type of Medium:
Online Resource
ISSN:
1759-7706
,
1759-7714
DOI:
10.1111/1759-7714.13542
Language:
English
Publisher:
Wiley
Publication Date:
2020
detail.hit.zdb_id:
2559245-2
detail.hit.zdb_id:
2625856-0
