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. 2024 Sep 12;10(18):e37884.
doi: 10.1016/j.heliyon.2024.e37884. eCollection 2024 Sep 30.

Patient-derived organoids and mini-PDX for predicting METN375S-mutated lung cancer patient clinical therapeutic response

Affiliations

Patient-derived organoids and mini-PDX for predicting METN375S-mutated lung cancer patient clinical therapeutic response

Meng Jiang et al. Heliyon. .

Abstract

Lung cancer as a molecularly and histologically high heterogonous disease, there is an urgent need to predict lung cancer patients' responses to anti-cancer treatment, and patient-derived organoids (PDOs) have been recognized as a valuable platform for preclinical drug screening. In this study, we successfully established 26 PDO lines from various subtypes of lung cancers including benign tumor, adenocarcinoma, squamous cell carcinoma, adenosquamous carcinoma, large-cell carcinoma, and small-cell carcinoma. These PDOs were shown to retain the major genomic and histological characteristics of primary tumors and remain stable during long-term culture. With the help of targeted genomic sequencing, we found that lung cancer that harbors METN375S mutation is selectively sensitive to afatinib, and a combination of afatinib and gemcitabine induced synthetic lethality in PDO and mini-PDX models. In summary, our findings demonstrate the potential of PDO in predicting lung cancer drug response, and reveal a promising strategy for METN375S mutant lung cancer treatment.

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Conflict of interest statement

We declare that we have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Establishment of PDOs from different subtypes of lung cancer A. Diagram of the process of establishing PDOs from patient tumors for the subsequent passaging, freezing and drug testing; B. Pie chart showing the subtypes of established 38 PDOs for the lung cancer, also see Table 1. C. The ratio of male to female patients in each subtype D. The scatter plot shows the distribution of patient age among lung cancer subtypes. E. Representive bright-field microscopy images of six subtypes PDOs cultured for day 24. Scale bar, 75 μm. F. Representative bright-field pictures showing growth status of PDO at passage 1, days 0, 7 and 24, the image was obtained from PDO-3; And, representative bright-field pictures showing growth status of PDO at passage 3, 5 and 10; the image was obtained from PDO-3 on day 7 after passage.
Fig. 2
Fig. 2
PDO retention of histological and genetic characteristics of their original tumors. A-F. Representative image of HE staining and IHC staining of six subtypes of lung cancers and paired PDOs using indicated antibodies. G. Targeted genomic sequencing of 425 cancer-related genes was performed using 8 paired primary tumor and organiods, and 70 gene mutations were shown.
Fig. 3
Fig. 3
Identification of PDOs cell types. A-D. Immunofluorescence images showing the expression of PanCK, p63, MUC1, KRT5, KRT7, CC10, Ac-Tub, Arl13b, Epcam. The nuclei were stained with DAPI(Blue). Scale bar, 50 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
Drug response of organoid lines A-D. The PDO lines were seeded in 96 well plate, and treated with different doses of Cisplatin (A), Gemcitabine (B), Capecitabine (C) and 5-Fluorouracil (D) for 72 h, the PDO viability was measured by CELL TITER-GLO (CTG); E-F. The PDO lines (Passage5, Passage10) were treated with various doses of Olaparib (E) and Erlotinib (F) for 72 h, the PDO viability was measured using CTG. G. Heat-map of IC50 values were the four chemotherapeutic drugs treated in the four organoid lines. Where organoids showed multiple relative IC50 values. As expected, organoids varied significantly in their sensitivity to chemotherapy drugs. H-I. IC50 values are the average ± SD of each condition analyzed in triplicate. Data analysis using two-tailed paired student's t-test. Error bars represent SEM of two to three independent experiments. ∗∗p < 0.05.
Fig. 5
Fig. 5
The combination of afatinib and gemcitabine induced synthetic lethality in METN375S mutant lung cancer A. left: CT scan image showing the size of the patient's originating tumor (red line), right: the bronchial placeholder image of the tumor under thoracoscopy; B. Representative staining of HE and Ki67 in primary tissues (patient-3); C. PDO-3 (MET N375S) and PDO-8 (MET WT) were treated with various concentration of Afatinib for 72 h, and the PDOs viability were detected using CTG; D. PDO-3 was treated with a single drug or combination of Afatinib and Gemcitibabine for 72 h, the PDOs viability were detected using CTG; E. Representative bliss synergy score heatmap for three independent experiments is shown. Red, synergy; green, antagonism; white, no effect. Synergyavg = average bliss synergy score. F. PDO-3 was treated with Afatinib (1 μM) and/or gemcitabine (1 μM) for 72 h, indicated proteins expression was analyzed using immunoblot analysis, GAPDH was used as loading control. Full-length blot is shown in the supplementary information, and the gels have been run under the same experimental conditions. G. Cytotoxicity analysis of single drug or combination of Afatinib and Gemcitibabine in mini-PDX model, relative tumor organoid growth was shown; H. The body weight of mice used in G during drug treatment. I-K. Expression of p-MET, p-HER2 and P-ERK were quantified and expressed as mean of total MET, HER2 and ERK ± SD (n = 3). Two-tailed Student's t-test; ∗P < 0.05. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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References

    1. Siegel R.L., et al. Cancer statistics. CA A Cancer J. Clin. 2021;71(1) 2021. - PubMed
    1. Zito Marino F., et al. Molecular heterogeneity in lung cancer: from mechanisms of origin to clinical implications. Int. J. Med. Sci. 2019;16(7):981–989. - PMC - PubMed
    1. Leitao, et al. Pathobiology Journal of Immunopathology Molecular & Cellular Biology; 2018. Heterogeneity in Lung Cancer.
    1. Bedard P., et al. Small molecules, big impact: 20 years of targeted therapy in oncology. Lancet (London, England) 2020;395(10229):1078–1088. - PubMed
    1. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–926. - PMC - PubMed

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