Purpose: To determine the efficacy of the therapeutic DNA vaccine GX-188E for inducing regression of cervical intraepithelial neoplasia (CIN) 3. Experimental Design: We conducted a prospective, randomized, multicenter, open-label, phase 2 clinical trial of GX-188E in CIN3 patients positive for HPV type 16/18. The primary endpoint was to determine the histopathological regression to less than or equal to CIN1 at visit 7 (V7; 20 weeks after the first GX-188E injection), and an extension study was pursued until visit 8 (V8; 36 weeks after the first GX-188E injection). HPV sequencing analysis and an ex vivo IFN-γ ELISPOT assay were performed using the collected cervical biopsy and blood samples from patients. Results: In total, 72 patients were enrolled and underwent randomization. Of them, 64 patients were included in per-protocol analysis (V7) and 52 in extension analysis (V8). Our data showed 52% (33/64) at V7 and 67% (35/52) of patients at V8 presented histopathological regression after receiving the GX-188E injection. We found that 73% (V7) and 77% (V8) of the patients with histological regression showed HPV clearance. HPV clearance and histopathological regression were significantly associated at V7 and at V8. Compared to the measurements at V1 (baseline), the patients at V8 with HPV clearance showed significantly higher fold-changes in their IFN-γ ELISPOT responses compared to those without HPV clearance. The HPV sequence analysis revealed that the HPV type 16 E6/E7 variants D25E, V83L, and N29S were inversely associated with histopathological regression at V8. Conclusions: GX-188E is an effective therapeutic vaccine against a cohort containing only CIN3 patients.

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis. We designed a CNN-CAE model that removes the unnecessary information (e.g., calipers and annotations) from ultrasound images and classifies ovaries into five classes. We used fivefold cross-validation to evaluate the performance of the CNN-CAE model in terms of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) was applied to visualize and verify the CNN-CAE model results qualitatively. In classifying normal versus ovarian tumors, the CNN-CAE model showed 97.2% accuracy, 97.2% sensitivity, and 0.9936 AUC with DenseNet121 CNN architecture. In distinguishing malignant ovarian tumors, the CNN-CAE model showed 90.12% accuracy, 86.67% sensitivity, and 0.9406 AUC with DenseNet161 CNN architecture. Grad-CAM showed that the CNN-CAE model recognizes valid texture and morphology features from the ultrasound images and classifies ovarian tumors from these features. CNN-CAE is a feasible diagnostic tool that is capable of robustly classifying ovarian tumors by eliminating marks on ultrasound images. CNN-CAE demonstrates an important application value in clinical conditions.

Background: Exosomal miRNAs regulate gene expression and play important roles in several diseases. We used exosomal miRNA profiling to investigate diagnostic biomarkers of epithelial ovarian cancer (EOC). Methods: In total, 55 individuals were enrolled, comprising healthy (n = 21) and EOC subjects (n = 34). Small mRNA (smRNA) sequencing and real-time PCR (RT-PCR) were performed to identify potential biomarkers. Receiver operating characteristic (ROC) curves were conducted to determine biomarker sensitivity and specificity. Results: Using smRNA sequencing, we identified seven up-regulated (miR-4732-5p, miR-877-5p, miR-574-3p, let-7a-5p, let-7b-5p, let-7c-5p, and let-7f-5p) and two down-regulated miRNAs (miR-1273f and miR-342-3p) in EOC patients when compared with healthy subjects. Of these, miR-4732-5p and miR-1273f were the most up-regulated and down-regulated respectively, therefore they were selected for RT-PCR analysis. Plasma derived exosomal miR-4732-5p had an area under the ROC curve of 0.889, with 85.7% sensitivity and 82.4% specificity in distinguishing EOC patients from healthy subjects (p<0.0001) and could be a potential biomarker for monitoring the EOC progression from early stage to late stage (p = 0.018). Conclusions: Plasma derived exosomal miR-4732-5p may be a promising candidate biomarker for diagnosing EOC.

PurposeThe purpose of this study was to identify the clinical utility of circulating tumor DNA (ctDNA) from ascites and serial plasma samples from epithelial ovarian cancer (EOC) patients.Materials and MethodsUsing targeted next-generation sequencing, we analyzed a total of 55 EOC samples including ctDNA from ascites and serial plasma and gDNA from tumor tissues. Tumor tissues and ascites were collected during debulking surgeries and plasma samples were collected before and after the surgeries. Because one EOC patient underwent secondary debulking surgery, a total of 11 tumor tissues, 33 plasma samples, and 11 ascites samples were obtained from the 10 patients.Results Of the 10 patients, nine (90%) contained somatic mutations in both tumor tissues and ascites ctDNA. This mutational concordance was confirmed through correlation analysis. The mutational concordance between ascites and tumor tissues was valid in recurrent/progressive ovarian cancer. TP53 was the most frequently detected gene with mutations. ctDNA from serial plasma samples identified EOC progression/recurrence at a similar time or even more rapidly than cancer antigen 125, an established serum protein tumor marker for EOC.ConclusionOur data suggest that ascites ctDNA can be used to identify the mutational landscape of ovarian cancer for therapeutic strategy planning.

Acquired paclitaxel (PTX) resistance limits its effectiveness and results in advanced cancer progression. This review investigated whether the inhibition of phosphatidylinositol 3-kinase (PI3K) signaling overcomes paclitaxel resistance in cervical cancer. It was established paclitaxel-resistant cell lines (PTX-R ME180/PTX-R HeLa) and determined the combination index for paclitaxel and PI3K inhibitors (BYL-719/ LY294002) by tetrazolium dye assay. Flow cytometry was used to detect the cell cycle and apoptosis. Migration and invasion were explored by wound healing and transwell assays. Genes related to multiple pathways were assessed by a western blot. It was found that the PI3K pathway was significantly activated in paclitaxel-resistant HeLa and ME180 cells compared to parental cells. PTX + PI3K inhibitor combined therapy showed a synergistic effect by strengthening paclitaxel-induced S and G₂M arrest in PTX-R cell sublines by the inactivation of cyclin A1, cyclin B1, cyclin E, and Cdc2 expression. Moreover, combination therapy significantly enhanced drug sensitivity and apoptosis through the activation of Bax, and cleavage of poly-(ADP-ribose) polymerase compared with paclitaxel alone. In addition, PI3K inhibition also suppressed tumor migration and invasion by targeting β-catenin and matrix metalloproteinase-2/9. The authors suggest that the combination of a PI3K inhibitor with paclitaxel may enhance antitumor activity through a cascade of PI3K signaling events.

This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADC_mean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADC_mean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADC_mean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADC_mean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.