BackgroundUveal melanoma (UM) is the most common primary intraocular tumor. Hepatic metastasis is the major and direct death-related reason in UM patients. Given that cancer stem-like cells (CSCs) are roots of metastasis, targeting CSCs may be a promising strategy to overcome hepatic metastasis in UM. Salinomycin, which has been identified as a selective inhibitor of CSCs in multiple types of cancer, may be an attractive agent against CSCs thereby restrain hepatic metastasis in UM. The objective of the study is to explore the antitumor activity of salinomycin against UM and clarify its underlying mechanism.MethodsUM cells were treated with salinomycin, and its effects on cell proliferation, apoptosis, migration, invasion, CSCs population, and the related signal transduction pathways were determined. The in vivo antitumor activity of salinomycin was evaluated in the NOD/SCID UM xenograft model and intrasplenic transplantation liver metastasis mouse model.ResultsWe found that salinomycin remarkably obviated growth and survival in UM cell lines and in a UM xenograft mouse model. Meanwhile, salinomycin significantly eliminated CSCs and efficiently hampered hepatic metastasis in UM liver metastasis mouse model. Mechanistically, Twist1 was fundamental for the salinomycin-enabled CSCs elimination and migration/invasion blockage in UM cells.ConclusionsOur findings suggest that targeting UM CSCs by salinomycin is a promising therapeutic strategy to hamper hepatic metastasis in UM. These results provide the first pre-clinical evidence for further testing of salinomycin for its antitumor efficacy in UM patients with hepatic metastasis.

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e20513 Background: Lung cancer is the most incident worldwide, and surveillance of recurrence remains a clinically unmet need. Non-invasive early detection is essential to improve prognosis of lung cancer. DNA methylation-based biomarkers for early cancer detection is promising. This study aims to investigate the methylation biomarkers of ctDNA that could predict the postoperative recurrence in early-stage lung cancer. Methods: The HM450K DNA methylation microarray data of lung adenocarcinoma (LUAD) (492 tumor tissues and 32 normal tissues) and lung squamous cell carcinoma (LUSC) (415 tumor tissues and 43 normal tissues) were downloaded from TCGA Data Portal ( https://portal.gdc.cancer.gov/ ), and healthy individuals were obtained from GEO database (656 whole blood from GSE40279) ( https://www.ncbi.nlm.nih.gov/geo/ ). After processing missing values with impute.knn function in CHAMP package, differences > 0.1 between tumor tissues and control samples (normal tissues or whole blood), standard deviation (SD) < 0.15 for tumor tissues and SD < 0.1 for control samples were nominated as differentially methylated sites (DMS). The adjacent ( < 250 bp) methylation sites were stitched into peaks. MCBs (methylation-correlated blocks) were defined as regions with more than or equal to 3 CpG sites within 100 bp and with DNA methylation sites correlation > 0.5. Results: In TCGA LUAD and LUSC, 658 and 551 DMS were identified between tumor and normal tissues; 999 and 912 DMS were generated by comparing tumor tissues and whole blood. The intersection of DMS located in the methylation canyon created a 203 kbp DNA methylation panel, containing 2521 DMS and 1417 methylation peaks. 37 MCBs were detected in tumor tissues of 30 lung cancer with DNA methylation panel sequencing. In term of 37 MCBs, 4 MCBs located in SHOX2, PTGER4, RASSF1A and ARL8B were identified with significant differences (p < 0.001) between plasma of 50 lung cancer patients and 50 healthy individuals. Furthermore, Real-time Quantitative PCR (qPCR) of the 4 MCB regions were performed in plasma of 30 lung cancer patients and 25 healthy individuals, and a cutoff of 0.414 distinguishing lung cancer from the healthy was calculated by the generalized linear model (GLM) based on the cycle threshold (CT) values of qPCR. The GLM incorporated the 4 MCBs showed robust performances in which the sensitivity, specificity and area under the curve (AUC) were 100%, 95% and 0.95, respectively. Postoperative plasma within 3 months were retrospectively collected from 32 lung cancer patients, and 30 patients with GLM scored less than 0.414 (Maximum: 0.327) had no recurrence within 1 year, while 2 patients with GLM scored more than 0.414 (0.480 and 0.557) were recurred within 1 year. Conclusions: The generalized linear model incorporated the 4 MCBs showed robust performances, and predicted the recurrence of early-stage lung cancer within 1 year after surgery.

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