RESUMEN
Esophageal neuroendocrine carcinoma (E-NEC) is an aggressive disease with a poor prognosis. The present study aimed to assess the role of surgery in the treatment of patients with resectable E-NEC, and identify a microRNA (miRNA/miR) signature in association with positive postoperative outcomes. Between February 2017 and August 2019, 36 patients with E-NEC who underwent curative surgery at the Japan Neuroendocrine Tumor Society partner hospitals were enrolled in the study. A total of 16 (44.4%) patients achieved disease-free survival (non-relapse group), whereas 20 (55.6%) patients developed tumor relapse (relapse group) during the median follow-up time of 36.5 months (range, 1-242) after surgery with a 5-year overall survival rate of 100 and 10.8%, respectively (P<0.01). No clinicopathological parameters, such as histological type or TNM staging, were associated with tumor relapse. Microarray analysis of 2,630 miRNAs in 11 patients with sufficient quality RNA revealed 12 miRNAs (miR-1260a, -1260b, -1246, -4284, -612, -1249-3p, -296-5p, -575, -6805-3p, -12136, -6822-5p and -4454) that were differentially expressed between the relapse (n=6) and non-relapse (n=5) groups. Furthermore, the top three miRNAs (miR-1246, -1260a and -1260b) were associated with overall survival (P<0.01). These results demonstrated that surgery-based multidisciplinary treatment is effective in a distinct subpopulation of limited stage E-NEC. A specific miRNA gene set is suggested to be associated with treatment outcome.
RESUMEN
Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.