Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio

The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer

Establishment and preclinical application of a patient-derived xenograft model for uterine cancer

Background. The patient-derived xenograft (PDX) model is a promising translational platform for duplicating the characteristics of primary tumors. Here, we established and characterized PDX models of uterine cancer to demonstrate their utility for preclinical drug testing. Materials and methods. We generated PDX tumors surgically derived from 58 cases of uterine cancer. Subrenal capsule xenografts and primary tumors were compared using microscopic examination, short tandem repeat analyses, and targeted sequencing analyses. A phosphatidylinositol 3-kinase (PI3K) inhibitor was administered to mice whose PDX tumors harbored a PTEN deletion or PIK3CA mutation. We also generated an orthotopic PDX model using uterine horn implantation. Results. Thirty-three (56.9%) PDXs were successfully generated and passaged to maintain tumors. The histological features of the PDX tumors were stable over subsequent passages. By contrast, the proportions of epithelial and mesenchymal components of carcinosarcoma PDX models varied by generation. Targeted sequencing analyses revealed that all mutated cancer-related genes were stable during establishment and subgrafting. Treatment with a PI3K inhibitor cased a significant decrease in tumor weight in the clear cell carcinoma PDX harboring a frameshift PTEN deletion (p = 0.049) and in the serous carcinoma PDX harboring a missense PI3KCA mutation (p = 0.003) compared with matched controls. We also successfully established orthotopic PDX models (3/3; 100.0%). Conclusions. The histological and genetic features of PDXs were similar to those of primary tumors. This model is a promising translational platform for preclinical testing of new anticancer drugs and will enable the personalized development of therapeutic options for uterine cancer. (c) 2021 Elsevier Inc. All rights reserved