Dataset Information

A new murine esophageal organoid culture method and organoid-based model of esophageal squamous cell neoplasia.

ABSTRACT: Organoids mimic the physiologic and pathologic events of organs. However, no consensus on esophageal organoid (EO) culture methods has been reached. Moreover, organoid models reproducing esophageal squamous cell carcinoma (ESCC) initiation have been unavailable. Herein, we sought to develop an esophageal minimum essential organoid culture medium (E-MEOM) for culturing murine EOs and establishing an early ESCC model. We formulated E-MEOM to grow EOs from a single cell with clonal expansion, maintenance, and passage. We found that EOs cultured in E-MEOM were equivalent to the esophageal epithelium by histological analysis and transcriptomic study. Trp53 knockout and Kras ^G12D expression in EOs induced the development of esophageal squamous neoplasia, an early lesion of ESCC. Here we propose the new formula for EO culture with minimum components and the organoid model recapitulating ESCC initiation, laying the foundation for ESCC research and drug discovery.

SUBMITTER: Zheng B

PROVIDER: S-EPMC8633967 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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A new murine esophageal organoid culture method and organoid-based model of esophageal squamous cell neoplasia.

Zheng Biyun B Ko Kyung-Pil KP Fang Xuefen X Wang Xiaozhong X Zhang Jie J Jun Sohee S Kim Bong-Jun BJ Luo Wenyi W Kim Moon Jong MJ Jung Youn-Sang YS Cervantes Christopher L CL Park Jae-Il JI

iScience 20211115 12

Organoids mimic the physiologic and pathologic events of organs. However, no consensus on esophageal organoid (EO) culture methods has been reached. Moreover, organoid models reproducing esophageal squamous cell carcinoma (ESCC) initiation have been unavailable. Herein, we sought to develop an esophageal minimum essential organoid culture medium (E-MEOM) for culturing murine EOs and establishing an early ESCC model. We formulated E-MEOM to grow EOs from a single cell with clonal expansion, maint ...[more]

PMID: 34877497

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Project description:BackgroundIn view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC.MethodsWe studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation.ResultsThe overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM.ConclusionsDue to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

Project description:Background and aimsHigh-resolution microendoscopy (HRME) is an optical biopsy technology that provides subcellular imaging of esophageal mucosa but requires expert interpretation of these histopathology-like images. We compared endoscopists with an automated software algorithm for detection of esophageal squamous cell neoplasia (ESCN) and evaluated the endoscopists' accuracy with and without input from the software algorithm.MethodsThirteen endoscopists (6 experts, 7 novices) were trained and tested on 218 post-hoc HRME images from 130 consecutive patients undergoing ESCN screening/surveillance. The automated software algorithm interpreted all images as neoplastic (high-grade dysplasia, ESCN) or non-neoplastic. All endoscopists provided their interpretation (neoplastic or non-neoplastic) and confidence level (high or low) without and with knowledge of the software overlay highlighting abnormal nuclei and software interpretation. The criterion standard was histopathology consensus diagnosis by 2 pathologists.ResultsThe endoscopists had a higher mean sensitivity (84.3%, standard deviation [SD] 8.0% vs 76.3%, P = .004), lower specificity (75.0%, SD 5.2% vs 85.3%, P < .001) but no significant difference in accuracy (81.1%, SD 5.2% vs 79.4%, P = .26) of ESCN detection compared with the automated software algorithm. With knowledge of the software algorithm, the specificity of the endoscopists increased significantly (75.0% to 80.1%, P = .002) but not the sensitivity (84.3% to 84.8%, P = .75) or accuracy (81.1% to 83.1%, P = .13). The increase in specificity was among novices (P = .008) but not experts (P = .11).ConclusionsThe software algorithm had lower sensitivity but higher specificity for ESCN detection than endoscopists. Using computer-assisted diagnosis, the endoscopists maintained high sensitivity while increasing their specificity and accuracy compared with their initial diagnosis. Automated HRME interpretation would facilitate widespread usage in resource-poor areas where this portable, low-cost technology is needed.

Dataset Information

A new murine esophageal organoid culture method and organoid-based model of esophageal squamous cell neoplasia.

Publications

A new murine esophageal organoid culture method and organoid-based model of esophageal squamous cell neoplasia.

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