Tumor-driven immune suppression is a major barrier to successful immunotherapy in ovarian carcinomas (OvCa). Among various mechanisms responsible for immune suppression, arginase-1 (ARG1)-carrying small extracellular vesicles (EVs) emerge as important contributors to tumor growth and tumor escape from the host immune system. Here, we report that small EVs found in the ascites and plasma of OvCa patients contain ARG1. EVs suppress proliferation of CD4 + and CD8 + T-cells in vitro and in vivo in OvCa mouse models. In mice, ARG1-containing EVs are transported to draining lymph nodes, taken up by dendritic cells and inhibit antigen-specific T-cell proliferation. Increased expression of ARG1 in mouse OvCa cells is associated with accelerated tumor progression that can be blocked by an arginase inhibitor. Altogether, our studies show that tumor cells use EVs as vehicles to carry over long distances and deliver to immune cells a metabolic checkpoint molecule – ARG1, mitigating anti-tumor immune responses.

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

Backgroundβ-catenin is the key protein in the WNT signalling pathway and it forms adherent junctions together with E-cadherin. In ovarian carcinoma, abnormal expression of β-catenin, E-cadherin and WNT-1 was observed, but their prognostic and predictive role is unclear. The aim of this study was to clarify the prognostic and predictive role of E-cadherin, β-catenin and WNT-1 in advanced epithelial ovarian carcinoma (AEOC).MethodsThe expression of E-cadherin, β-catenin and WNT-1 was determined by immunohistochemistry in AEOC. The correlation between expression of these proteins and progression-free survival (PFS) and overall survival (OS) was evaluated. Statistical analyses included Kaplan-Meier estimation, log-rank test, Spearman correlation and Cox proportional-hazards model.ResultsIn ovarian cancer, intense expression of E-cadherin, β-catenin and WNT-1 was found. In multivariate analysis, strong membrane β-catenin expression was an independent unfavourable predictor for PFS (HR 2.19, 95% CI 1.09-4.39; p = 0.028), while in univariate analysis, strong membrane β-catenin expression was a prognostic factor for OS in patients with AOC (p = 0.039). In multivariate analysis, only resistance to first-line chemotherapy was an adverse independent prognostic factor for OS (HR 16.84; 95% CI 5.07-55.98; p < 0.0001). Additionally, strong membranous β-catenin expression was associated with resistance to platinum-based chemotherapy (p = 0.027).ConclusionsThese findings support that WNT/β-catenin pathway and E-cadherin are important factors in advanced epithelial ovarian cancer.

Keywords: laser irradiation; skin exposition; dermal effect of laser; UV; IR; laser treatment

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