Unfolded protein response (UPR) is crucial for both survival and death of mammalian cells, which is regulated by reactive oxygen species (ROS) and nutrient depletion. In this study, we demonstrated the effect of ROS-accumulation, induced by β-phenethyl isothiocyanate (PEITC), on UPR mediated apoptosis in ovarian cancer cells. We used ovarian cancer cell lines, PA-1 and SKOV-3, with different p53 status (wild- and null- type, respectively). PEITC caused increased ROS-accumulation and inhibited proliferation selectively in ovarian cancer cells, and glutathione (GSH) depletion in SKOV-3. However, PEITC did not cause any effect in normal ovarian epithelial cells and peripheral blood mononuclear cells. After 48 h of PEITC treatment (5 µM), apoptotic cell death was shown to increase significantly in the ovarian cancer cells and not in the normal cells. The key regulator of UPR-mediated apoptosis, CHOP/GADD153 and ER resident chaperone BiP/GRP78 were parallely up-regulated with activation of two major sensors of the UPR (PERK and ATF-6 in PA-1; PERK, and IRE1α in SKOV-3) in response to ROS accumulation induced by PEITC (5 µM). ROS scavenger, N-acetyl-cysteine (NAC), attenuated the effect of PEITC on UPR signatures (P-PERK, IRE1α, CHOP/GADD153, and BiP/GRP78), suggesting the involvement of ROS in UPR-mediated apoptosis. Altogether, PEITC induces UPR-mediated apoptosis in ovarian cancer cells via accumulation of ROS in a cancer-specific manner.

To evaluate impact of germline BRCA mutational status on prognosis in patients with advanced ovarian cancer. A total of 128 patients diagnosed with FIGO stage III-IV high-grade serous ovarian cancer (HGSOC) between 2008 and 2017 and underwent BRCA1/2 gene testing at the time of or within two years from cancer diagnosis were included in this study. We compared patients’ clinicopathological characteristics and survival outcomes after primary treatment according to germline BRCA mutational status. Treatment-related factors that might affect patients’ survival outcome were also investigated. Germline BRCA1/2 mutations were observed in 51 women (39.8%). There were no differences in age and serum CA-125 levels at the time of HGSOC diagnosis, use of neoadjuvant chemotherapy (NAC), extent of debulking surgery, and overall survival (OS) between the BRCA mutation and wild-type BRCA groups. In contrast, the BRCA mutation group displayed longer progression-free survival (PFS) (median, 22.9 vs. 16.9 months, P = 0.001). Multivariate analyses identified germline BRCA1/2 mutation as an independent favorable prognostic factor for PFS (adjusted HR, 0.502; 95% CI, 0.318–0.795; P = 0.003). In the wild-type BRCA group, patients who received NAC as the primary treatment had shorter PFS compared to those who received primary debulking surgery (PDS) (median, 14.2 vs. 16.9 months, P = 0.003). However, in the BRCA mutation group, PFS did not differ between the NAC and PDS groups (P = 0.082). In advanced-stage HGSOC, patients with germline BRCA1/2 mutations have better prognosis with longer PFS than those lacking BRCA mutations. Prognosis after NAC was different according to the BRCA mutational status.

We aimed to develop a diagnostic model identifying ovarian cancer (OC) from benign ovarian tumors using metagenomic data from serum microbe-derived extracellular vesicles (EVs). We obtained serum samples from 166 patients with pathologically confirmed OC and 76 patients with benign ovarian tumors. For model construction and validation, samples were randomly divided into training and test sets in the ratio 2:1. Isolation of microbial EVs from serum samples of the patients and 16S rDNA amplicon sequencing were carried out. Metagenomic and clinicopathologic data-based OC diagnostic models were constructed in the training set and then validated in the test set. There were significant differences in the metagenomic profiles between the OC and benign ovarian tumor groups; specifically, genus Acinetobacter was significantly more abundant in the OC group. More importantly, Acinetobacter was the only common genus identified by seven different statistical analysis methods. Among the various metagenomic and clinicopathologic data-based OC diagnostic models, the model consisting of age, serum CA-125 levels, and relative abundance of Acinetobacter showed the best diagnostic performance with the area under the receiver operating characteristic curve of 0.898 and 0.846 in the training and test sets, respectively. Thus, our findings establish a metagenomic analysis of serum microbe-derived EVs as a potential tool for the diagnosis of OC.

Luo Y, et al. J Gynecol Oncol. 2017 Mar;28(3):e36. https://doi.org/10.3802/jgo.2017.28.e36