Purpose: This study was designed to elucidate the role of amplification at 8q24 in the pathophysiology of ovarian and breast cancer because increased copy number at this locus is one of the most frequent genomic abnormalities in these cancers. Experimental Design:To accomplish this, we assessed the association of amplification at 8q24 with outcome in ovarian cancers using fluorescence in situ hybridization to tissue microarrays and measured responses of ovarian and breast cancer cell lines to specific small interfering RNAs against the oncogene MYC and a putative noncoding RNA, PVT1, both of which map to 8q24. Results: Amplification of 8q24 was associated with significantly reduced survival duration. In addition, small interfering RNA^mediated reduction in either PVT1 or MYC expression inhibited proliferation in breast and ovarian cancer cell lines in which they were both amplified and overexpressed but not in lines in which they were not amplified/overexpressed. Inhibition of PVT1 expression also induced a strong apoptotic response in cell lines in which it was overexpressed but not in lines in which it was not amplified/overexpressed. Inhibition of MYC, on the other hand, did not induce an apoptotic response in cell lines in which MYC was amplified and overexpressed. Conclusions: These results suggest that MYC and PVT1 contribute independently to ovarian and breast pathogenesis when overexpressed because of genomic abnormalities. They also suggest that PVT1-mediated inhibition of apoptosis may explain why amplification of 8q24 is associated with reduced survival duration in patients treated with agents that act through apoptotic mechanisms.Amplification of a region on chromosome 8q24 is one of the most frequent events in carcinomas, including serous ovarian and breast cancers, and has been associated with reduced survival duration in some studies (1, 2). The well-established oncogene MYC maps to this locus and likely contributes to the pathophysiology of cancers in which it is amplified. However, the PVT1 transcript also maps to this region and has been implicated in cancer pathophysiology as well (3). In mouse, for example, the pvt-1 locus is a site of recurrent translocation in plasmacytomas (4, 5) and is a common site of tumorigenic retroviral insertion in lymphomas (6). In humans, the region homologous to pvt-1 is a site of recurrent translocation between chromosomes 2 and 8 (7, 8) and its first exon is coamplified with MYC in colon carcinoma cell lines (9). PVT1 has been suggested as a MYC activator (10); however, little evidence exists to support that role. Moreover, evidence is now emerging that PVT1 may act as a noncoding RNA 12 that is strongly conserved between mouse and human.We now present evidence that both PVT1 and MYC contribute to ovarian and breast cancer pathophysiology when 12 Huppi et al., personal communication.

Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Na€ ve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble meth-ods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival.Conclusions: Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients.NOTE: There were too few early-stage EOC patients with residual tumor. A definition for the significance of bold is P value of < 0.05.

PURPOSE This phase III, multicenter, randomized, open-label study investigated the efficacy and safety of nivolumab versus chemotherapy (gemcitabine [GEM] or pegylated liposomal doxorubicin [PLD]) in patients with platinum-resistant ovarian cancer. MATERIALS AND METHODS Eligible patients had platinum-resistant epithelial ovarian cancer, received ≤ 1 regimen after diagnosis of resistance, and had an Eastern Cooperative Oncology Group performance score of ≤ 1. Patients were randomly assigned 1:1 to nivolumab (240 mg once every 2 weeks [as one cycle]) or chemotherapy (GEM 1000 mg/m2 for 30 minutes [once on days 1, 8, and 15] followed by a week's rest [as one cycle], or PLD 50 mg/m2 once every 4 weeks [as one cycle]). The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), overall response rate, duration of response, and safety. RESULTS Patients (n = 316) were randomly assigned to nivolumab (n = 157) or GEM or PLD (n = 159) between October 2015 and December 2017. Median OS was 10.1 (95% CI, 8.3 to 14.1) and 12.1 (95% CI, 9.3 to 15.3) months with nivolumab and GEM or PLD, respectively (hazard ratio, 1.0; 95% CI, 0.8 to 1.3; P = .808). Median PFS was 2.0 (95% CI, 1.9 to 2.2) and 3.8 (95% CI, 3.6 to 4.2) months with nivolumab and GEM or PLD, respectively (hazard ratio, 1.5; 95% CI, 1.2 to 1.9; P = .002). There was no statistical difference in overall response rate between groups (7.6% v 13.2%; odds ratio, 0.6; 95% CI, 0.2 to 1.3; P = .191). Median duration of response was numerically longer with nivolumab than GEM or PLD (18.7 v 7.4 months). Fewer treatment-related adverse events were observed with nivolumab versus GEM or PLD (61.5% v 98.1%), with no additional or new safety risks. CONCLUSION Although well-tolerated, nivolumab did not improve OS and showed worse PFS compared with GEM or PLD in patients with platinum-resistant ovarian cancer.

Combining bevacizumab with chemotherapy was tolerable and efficacy was acceptable in Japanese patients with advanced epithelial ovarian cancer. Bevacizumab seems to reduce platinum-resistant recurrence and is promising for clear cell carcinoma.

Human epididymis protein 4 (HE4) levels and the Risk of Ovarian Malignancy Algorithm (ROMA) have recently been shown to improve the sensitivity and specificity of epithelial ovarian cancer (EOC) diagnosis. We evaluated HE4 levels and ROMA as diagnostic tools of type I and type II EOC in Japanese women. Women who had a pelvic mass on imaging and were scheduled to undergo surgery were enrolled as ovarian mass patients. Serum levels of carbohydrate antigen 125 (CA125) and HE4 were tested in 319 women (131 benign, 19 borderline, 75 malignant, and 94 healthy controls). CA125, HE4, and ROMA were evaluated for sensitivity and by receiver operating characteristics (ROC) in type I and type II EOC. The results showed that, at 75 % specificity, the sensitivity of CA125 and HE4 for type II was 92.1 % for both markers and for type I was 51.5 % and 78.8 %, respectively. The sensitivities of ROMA (type I, 84.8 % and type II, 97.4 %) were better than those of CA125 and HE4. CA125, HE4, and ROMA were all highly accurate markers for type II. For type I, HE4 and ROMA showed better sensitivity than CA125. ROMA displayed the best diagnostic power for type I and type II including for the early stage of type I. In conclusion, HE4, CA125, and ROMA are valuable markers for type II EOC diagnosis. HE4 and ROMA analyses may improve differentiation between type I EOC and a benign mass. Measurement of combined HE4 and CA125 levels provides a more accurate method for EOC diagnosis.