Overexpression of ABCG2 confers resistance to pevonedistat, an NAE inhibitor.

Pevonedistat is a potent, selective, first-in-class NEDD8 activating enzyme inhibitor. It is now under multiple clinical trials that investigate its anticancer effect against solid tumors and leukemia. ATP-binding cassette (ABC) transporters are membrane proteins that are involved in mediating multidrug resistance (MDR). In this article, we reveal that pevonedistat is a substrate of ABCG2 which decreases the therapeutic effect of pevonedistat. The cytotoxicity of pevonedistat was significantly weakened in ABCG2-overexpressing cells, and the drug resistance can be reversed by ABCG2 inhibitors. The ATPase assay suggested that pevonedistat can stimulate ABCG2 ATPase activity in a concentration-dependent manner. Pevonedistat showed little effect on the expression level or subcellular localization of ABCG2 after 72 h treatment. Furthermore, a pevonedistat resistance cell line S1-PR was established and overexpressed ABCG2. Generally, our study provides evidence that ABCG2 can be a prominent factor leading to pevonedistat-resistance. Furthermore, ABCG2 may also be utilized as a biomarker to monitor the development of pevonedistat resistance during cancer treatment.

A machine learning model utilizing delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: A prospective multicenter study.

BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC) and to establish a clinically applicable model to guide the extent of surgery. METHODS: This prospective cohort study included 603 patients with PTC from three centers. Clinical, pathological, and ultrasonographic data were collected and utilized to develop a machine learning (ML) model for predicting CCLNM. Model development at the internal center utilized logistic regression along with other ML algorithms. Diagnostic efficacy was compared among these methods, leading to the adoption of the final model (random forest). This model was subject to AI interpretation and externally validated at other centers. RESULTS: CCLNM was associated with multiple pathological factors. The Delphian lymph node metastasis ratio, ipsilateral cervical lymph node metastasis number, and presence of ipsilateral cervical lymph node metastasis were independent risk factors for CCLNM. Following feature selection, a Delphian lymph node-CCLNM (D-CCLNM) model was established using the Random forest algorithm based on five attributes. The D-CCLNM model demonstrated the highest area under the curve (AUC; 0.9273) in the training cohort and exhibited high predictive accuracy, with AUCs of 0.8907 and 0.9247 in the external and validation cohorts, respectively. CONCLUSIONS: We developed a new, effective method that uses ML to predict CCLNM in patients with PTC. This approach integrates data from Delphian lymph nodes and clinical characteristics, offering a foundation for guiding surgical decisions, and is conveniently applicable in clinical settings.

Establishment and Characterization of a Novel Multidrug Resistant Human Ovarian Cancer Cell Line With Heterogenous MRP7 Overexpression.

Ovarian cancer is one of the leading female malignancies which accounts for the highest mortality rate among gynecologic cancers. Surgical cytoreduction followed by chemotherapy is the mainstay of treatment. However, patients with recurrent ovarian cancer are likely to exhibit resistance to chemotherapy due to reduced sensitivity to chemotherapeutic drugs. Adenosine triphosphate (ATP)-binding cassette (ABC) transporters have been extensively studied as multidrug resistance (MDR) mediators since they are responsible for the efflux of various anticancer drugs. Multidrug resistance protein 7 (MRP7, or ABCC10) was discovered in 2001 and revealed to transport chemotherapeutic drugs. Till now, only limited knowledge was obtained regarding its roles in ovarian cancer. In this study, we established an MRP7-overexpressing ovarian cancer cell line SKOV3/MRP7 via transfecting recombinant MRP7 plasmids. The SKOV3/MRP7 cell line was resistant to multiple anticancer drugs including paclitaxel, docetaxel, vincristine and vinorelbine with a maximum of 8-fold resistance. Biological function of MRP7 protein was further determined by efflux-accumulation assays. Additionally, MTT results showed that the drug resistance of the SKOV3/MRP7 cells was reversed by cepharanthine, a known inhibitor of MRP7. Moreover, we also found that the overexpression of MRP7 enhanced the migration and epithelial-mesenchymal transition (EMT) induction. In conclusion, we established an in vitro model of MDR in ovarian cancer and suggested MRP7 overexpression as the leading mechanism of chemoresistance in this cell line. Our results demonstrated the potential relationship between MRP7 and ovarian cancer MDR.