Inhibiting autophagy enhanced mitotic catastrophe-mediated anticancer immune responses by regulating the cGAS-STING pathway.

Given the limitations of the response rate and efficacy of immune checkpoint inhibitors (ICIs) in clinical applications, exploring new therapeutic strategies for cancer immunotherapy is necessary. We found that 5-(3,4,5-trimethoxybenzoyl)-4-methyl-2-(p-tolyl)imidazole (BZML), a microtubule-targeting agent, exhibited potent anticancer activity by inducing mitotic catastrophe in A549/Taxol and L929 cells. Nuclear membrane disruption and nuclease reduction provided favorable conditions for cGAS-STING pathway activation in cells with mitotic catastrophe. Similar results were obtained in paclitaxel-, docetaxel- and doxorubicin-induced mitotic catastrophe in various cancer cells. Notably, the surface localization of CALR and MHC-I and the release of HMGB1 were also significantly increased in cells with mitotic catastrophe, but not in apoptotic cells, suggesting that mitotic catastrophe is an immunogenic cell death. Furthermore, activated CD8+T cells enhanced the anticancer effects originating from mitotic catastrophe induced by BZML. Inhibiting the cGAS-STING pathway failed to affect BZML-induced mitotic catastrophe but could inhibit mitotic catastrophe-mediated anticancer immune effects. Interestingly, the expression of p-TBK1 first increased and then declined; however, autophagy inhibition reversed the decrease in p-TBK1 expression and enhanced mitotic catastrophe-mediated anticancer immune effects. Collectively, the inhibition of autophagy can potentiate mitotic catastrophe-mediated anticancer immune effects by regulating the cGAS-STING pathway, which explains why the anticancer immune effects induced by chemotherapeutics have not fully exerted their therapeutic efficacy in some patients and opens a new area of research in cancer immunotherapy.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.

Background: This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making. Methods: A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts. Results: The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts. Conclusions: The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.