Kohei Hamada

A Deep Learning–Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has not been used in routine clinical practice because of reproducibility issues. The current study developed two convolutional neural network models to detect TILs and to determine their spatial location in whole slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8

Myeloid activation clears ascites and reveals IL27-dependent regression of metastatic ovarian cancer

Patients with metastatic ovarian cancer (OvCa) have a 5-year survival rate of <30% due to the persisting dissemination of chemoresistant cells in the peritoneal fluid and the immunosuppressive microenvironment in the peritoneal cavity. Here, we report that intraperitoneal administration of β-glucan and IFNγ (BI) induced robust tumor regression in clinically relevant models of metastatic OvCa. BI induced tumor regression by controlling fluid tumor burden and activating localized antitumor immunity. β-glucan alone cleared ascites and eliminated fluid tumor cells by inducing intraperitoneal clotting in the fluid and Dectin-1-Syk–dependent NETosis in the omentum. In omentum tumors, BI expanded a novel subset of immunostimulatory IL27+ macrophages and neutralizing IL27 impaired BI efficacy in vivo. Moreover, BI directly induced IL27 secretion in macrophages where single agent treatment did not. Finally, BI extended mouse survival in a chemoresistant model and significantly improved chemotherapy response in a chemo-sensitive model. In summary, we propose a new therapeutic strategy for the treatment of metastatic OvCa.