As a kind of gynecological tumor, ovarian cancer is not as common as cervical cancer and breast cancer, but its malignant degree is higher. Despite the increasingly mature treatment of ovarian cancer, the five-year survival rate of patients is still less than 50%. Based on the concept of synthetic lethality, poly (ADP- ribose) polymerase (PARP) inhibitors target tumor cells with defects in homologous recombination repair(HRR), the most significant being the target gene Breast cancer susceptibility genes(BRCA). PARP inhibitors capture PARP-1 protein at the site of DNA damage to destroy the original reaction, causing the accumulation of PARP-DNA nucleoprotein complexes, resulting in DNA double-strand breaks(DSBs) and cell death. PARP inhibitors have been approved for the treatment of ovarian cancer for several years and achieved good results. However, with the widespread use of PARP inhibitors, more and more attention has been paid to drug resistance and side effects. Therefore, further research is needed to understand the mechanism of PARP inhibitors, to be familiar with the adverse reactions of the drug, to explore the markers of its efficacy and prognosis, and to deal with its drug resistance. This review elaborates the use of PARP inhibitors in ovarian cancer.

BackgroundOvarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.PurposeWe aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.MethodsA total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.ResultsML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).ConclusionsBased on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Background: Tumor pathology can assess patient prognosis based on a morphological deviation of tumor tissue from normal. Digitizing whole slide images (WSIs) of tissue enables the use of deep learning (DL) techniques in pathology, which may shed light on prognostic indicators of cancers, and avoid biases introduced by human experience.Purpose: We aim to explore new prognostic indicators of ovarian cancer (OC) patients using the DL framework on WSIs, and provide a valuable approach for OC risk stratification.Methods: We obtained the TCGA-OV dataset from the NIH Genomic Data Commons Data Portal database. The preprocessing of the dataset was comprised of three stages: 1) The WSIs and corresponding clinical data were paired and filtered based on a unique patient ID; 2) a weakly-supervised CLAM WSI-analysis tool was exploited to segment regions of interest; 3) the pre-trained model ResNet50 on ImageNet was employed to extract feature tensors. We proposed an attention-based network to predict a hazard score for each case. Furthermore, all cases were divided into a high-risk score group and a low-risk one according to the median as the threshold value. The multi-omics data of OC patients were used to assess the potential applications of the risk score. Finally, a nomogram based on risk scores and age features was established.Results: A total of 90 WSIs were processed, extracted, and fed into the attention-based network. The mean value of the resulting C-index was 0.5789 (0.5096–0.6053), and the resulting p-value was 0.00845. Moreover, the risk score showed a better prediction ability in the HRD + subgroup.Conclusion: Our deep learning framework is a promising method for searching WSIs, and providing a valuable clinical means for prognosis.

Supramolecular polymers (SPs) based on macrocyclic host molecules as copolymer monomers are considered a promising class of advanced functional materials due to their satisfactory tunable properties and unique host-guest interactions....