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An in vitro model and the underlying pathways of sinonasal inverted papilloma development.

ABSTRACT: Recently, the specific association between Sinonasal inverted papilloma (SIP) and EGFR exon 20 mutations has been reported. To investigate the link between specific EGFR mutations and SIP development, we established organotypic raft culture system using nasal polyp-derived immortalized NP2 (iNP2) cells expressing EGFR exon 20 mutants or an exon 19 mutant, and SIP-derived iIP4 cells harboring P772_H773insPYNP mutation. In the raft culture, iIP4 cells showed the inverted growth pattern characteristic to SIP. Interestingly, iNP2 cells expressing EGFR exon 20 duplication mutants, S768_D770dup and N771_H773dup, but not of EGFR exon 19 mutant, E746_A750del, showed the inverted growth pattern. Enhanced activation of the PI3K/AKT signaling pathway was observed in iNP2_S768_D770dup and iIP4 cells, while increased MAPK signaling was found in iNP2_N771_H773dup. Increased cell migration and invasion were found in all cells carrying EGFR mutations when compared to iNP2 cells, and this effect was inhibited by either PI3K or MEK inhibitor. Notably, iNP2 cells expressing the N771_H773dup mutant showed the highest migration and invasion abilities. These results suggest that specific mutations in EGFR exon 20 play a crucial role in SIP development, partially though hyper-activation of the PI3K/AKT and MAPK signaling pathways. This study presents the first in vitro model for SIP development, which could facilitate further investigations into SIP pathogenesis and preclinical studies for new therapeutic agents.

SUBMITTER: Nukpook T

PROVIDER: S-EPMC10611779 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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An in vitro model and the underlying pathways of sinonasal inverted papilloma development.

Nukpook Thawaree T Kiyono Tohru T Ekalaksananan Tipaya T Kasemsiri Pornthep P Teeramatwanich Watchareporn W Vatanasapt Patravoot P Chaiwiriyakul Surachat S Nakahara Tomomi T Pientong Chamsai C

Scientific reports 20231027 1

Recently, the specific association between Sinonasal inverted papilloma (SIP) and EGFR exon 20 mutations has been reported. To investigate the link between specific EGFR mutations and SIP development, we established organotypic raft culture system using nasal polyp-derived immortalized NP2 (iNP2) cells expressing EGFR exon 20 mutants or an exon 19 mutant, and SIP-derived iIP4 cells harboring P772_H773insPYNP mutation. In the raft culture, iIP4 cells showed the inverted growth pattern characteris ...[more]

PMID: 37891239

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Project description:Objective To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic–radiological features can produce a superior diagnostic performance. Methods The database of 247 patients with SNIP (n=106) or MST (n=141) were analyzed. Dataset from scanner A were randomly divided into training set (n=135) and test set 1 (n=58) in a ratio of 7:3, and dataset from scanner B and C were used as an additional independent test set 2 (n=54). Fourteen clinic-radiological features were analyzed by using univariate analysis, and those with significant differences were applied to construct clinical model. Based on the radiomics features extracted from single sequence (T2WI or CE-T1WI) and combined sequence, four commonly used classifiers (logistic regression (LR), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN)) were employed to constitute twelve different machine learning models, and the best-performing one was confirmed as the optimal radiomics model. Furthermore, a combined model incorporated best radiomics feature subsets and clinic-radiological features was developed. The diagnostic performances of these models were assessed by the area under the receiver operating characteristic (ROC) curve (AUC) and the calibration curves. Results Five clinic-radiological features (age, convoluted cerebriform pattern sign, heterogeneity, adjacent bone involvement and infiltration of surrounding tissue) were considered to be significantly different between the tumor groups (P < 0.05). Among the twelve machine learning models, the T2WI-SVM model exhibited optimal predictive efficacy for classification tasks on the two test sets, with the AUC of 0.878 and 0.914, respectively. For three types of diagnostic models, the combined model achieved highest AUC of 0.912 (95%CI: 0.807-0.970) and 0.927 (95%CI: 0.823-0.980) for differentiation of SNIP and MST in test 1 and test 2 sets, which performed prominently better than clinical model (P=0.011, 0.005), but not significantly different from the optimal radiomics model (P=0.100, 0.452). Conclusion The machine learning model based on T2WI sequence and SVM classifier achieved best performance in differentiation of SNIP and MST, and the combination of radiomics features and clinic-radiological features significantly improved the diagnostic capability of the model.

Dataset Information

An in vitro model and the underlying pathways of sinonasal inverted papilloma development.

Publications

An in vitro model and the underlying pathways of sinonasal inverted papilloma development.

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