PARP inhibitors have changed the management of advanced high-grade epithelial ovarian cancer (EOC), especially homologous recombinant (HR)-deficient advanced high-grade EOC. However, the effect of PARP inhibitors on HR-proficient (HRP) EOC is limited. Thus, new therapeutic strategy for HRP EOC is desired. In recent clinical study, the combination of PARP inhibitors with anti-angiogenic agents improved therapeutic efficacy, even in HRP cases. These data suggested that anti-angiogenic agents might potentiate the response to PARP inhibitors in EOC cells. Here, we demonstrated that anti-angiogenic agents, bevacizumab and cediranib, increased the sensitivity of olaparib in HRP EOC cells by suppressing HR activity. Most of the γ-H2AX foci were co-localized with RAD51 foci in control cells. However, most of the RAD51 were decreased in the bevacizumab-treated cells. RNA sequencing showed that bevacizumab decreased the expression of CRY1 under DNA damage stress. CRY1 is one of the transcriptional coregulators associated with circadian rhythm and has recently been reported to regulate the expression of genes required for HR in cancer cells. We found that the anti-angiogenic agents suppressed the increase of CRY1 expression by inhibiting VEGF/VEGFR/PI3K pathway. The suppression of CRY1 expression resulted in decrease of HR activity. In addition, CRY1 inhibition also sensitized EOC cells to olaparib. These data suggested that anti-angiogenic agents and CRY1 inhibitors will be the promising candidate in the combination therapy with PARP inhibitors in HR-proficient EOC.

Logistic regression classifiers' normalized coefficient and confidence interval

IntroductionPreoperative differential diagnosis of clinical stage I uterine sarcoma (US) is essential for surgical intervention. Many studies have been done using CT or MRI imaging for machine learning prediction models but not with blood biomarkers. We aimed to develop a new model for diagnosis and prognosis prediction in the US using preoperative blood biomarkers and patient age.MethodsOverall, 143 US patients and 210 benign uterine myoma (UM) patients were randomly assigned to the ‘training and test’ cohort. 78(55%) cases were on clinical stage I. 30 preoperative peripheral blood parameters and patient’s age was surveyed. The Random Forest (RF) classifier was used to construct an algorithm. The accuracy, the area under the receiver operating characteristic curve (AUC), and the variable importance were calculated in the test cohort. The Ethics Committee approved this study.ResultsThe accuracy and AUC values for segregating stage I US from UM were 87% and 0.89, respectively. Variable important parameters for this classifier included age, CRP, and Hematocrit. Additionally, they were 85% and 0.95 in leiomyosarcoma, and 92% and 0.81 in ESS, respectively. Furthermore, unsupervised clustering analysis based on RF showed significant differences in two clusters in clinical stage I US with a median progression-free survival of 47 (3–115) vs. 13 (1–93) months (P < 0.001).Conclusion/ImplicationsThe RF approach using common blood biomarkers and patient age can differentiate its malignancy and prognosis of US patients before primary intervention. This predictive model may provide a clinically useful approach to preoperative diagnosis distinct from conventional imaging techniques.

ObjectiveAn effective treatment strategy for epithelial ovarian cancer (EOC) with homologous recombination (HR)-proficient (HRP) phenotype has not been established, although poly (ADP-ribose) polymerase inhibitors (PARPi) impact the disease course with HR-deficient (HRD) phenotype. Here, we aimed to clarify the cellular effects of paclitaxel (PTX) on the DNA damage response and the therapeutic application of PTX with PARPi in HRP ovarian cancer.MethodsTwo models with different PTX dosing schedules were established in HRP ovarian cancer OVISE cells. Growth inhibition and HR activity were analyzed in these models with or without PARPi. BRCA1 phosphorylation status was examined in OVISE cells by inhibiting CDK1, which was reduced by PTX treatment. CDK1 expression was evaluated in EOC patients treated with PTX-based neoadjuvant chemotherapy.ResultsPTX suppressed CDK1 expression resulting in impaired BRCA1 phosphorylation in OVISE cells. The reduced CDK1 activity by PTX could decrease HR activity in response to DNA damage and therefore increase the sensitivity to PARPi. Immunohistochemistry showed that CDK1 expression was attenuated in samples collected after PTX-based chemotherapy compared to those collected before chemotherapy. The decrease in CDK1 expression was greater with dose-dense PTX schedule than with the conventional PTX schedule.ConculsionsPTX could act synergistically with PARPi in HRP ovarian cancer cells, suggesting that the combination of PTX with PARPi may be a novel treatment strategy extending the utility of PARPi to EOC. Our findings provide cules for future translational clinical trials evaluating the efficacy of PTX in combination with PARPi in HRP ovarian cancer.

Diagnostic accuracy of clinicopathological factors using Machine Learning Algorithms

Objective: Although most cervical cancer is characterized as preventable cancer, its incidence continues to increase in Japan; there is an urgent need to develop novel therapeutics for patients with cervical cancer, especially adolescents and young adults.Our aim is to identify actionable fusion and driver genes as potential therapeutic targets and RNA-based biomarkers in cervical cancer.Methods: We conducted RNA and target sequencing in 116 patients with cervical cancer.For comparison, we used publicly available data registered in cBioPortal (21,789 cases) and Center for Cancer Genomics and Advanced Therapeutics (29,309 cases).We also performed the non-negative matrix factorization (NMF) clustering to classify cervical cancer that received postoperative adjuvant therapy based on recurrence-related genes.Results: We identified 3 cases with FGFR3-TACC3 fusion and 1 with GOPC-ROS1 fusion genes as potential therapeutic targets.Using publicly available data, FGFR3 fusion was detected in 1.5% and 0.5% of patients with cervical cancer, respectively.The frequency of the FGFR3 fusion gene is higher in cervical cancer than in other cancer types, regardless of ethnicity.Expression analysis using NMF clustering was used to identify worse prognosis groups for cervical cancer.The poor prognosis group showed decreased regulation of pathways associated with the immune system and lower level of macrophage M1 related mRNA.Conclusion: RNA-based analysis revealed distribution of FGFR fusions which may be promising targets and identified novel clinically relevant subgroups of patients with cervical cancer.

Pretreatment peripheral blood tests of 334 patients with epithelial ovarian cancer and 101 patients with benign ovarian tumor

Supplementary Data from Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis

Explanation and evaluation of the random forest (RF) classifier. (A) Schematic illustration of the classification of samples by the RF. (B, C) Representative classification trees from the discrimination between malignant and benign tumors. These trees are only representations out of 4,000 trees constructed in the RF classifier. The final class is determined as a result of voting by all 4,000 trees. (D, E) The highest accuracy of prediction (D) and the AUC (E) using different numbers of samples in RF classification between malignant and benign tumors. The mean accuracy and AUC with these 95% confidence intervals were presented for 10 independent sets of randomly selected data of 20%, 40%, 60%, and 80% of patients from the training and test cohorts.

Genetic abnormalities, such as PTEN, PIK3CA, CTNNB1, ARID1A, and ERBB2, which frequently occur in endometrial cancer (EC), are potential therapeutic targets. In 2013, integrated genomic analysis conducted by The Cancer Genome Atlas identified four molecular subtypes, including POLE ultra-mutated, microsatellite instability hypermutated, copy-number low, and copy-number high, which strongly correlate with prognosis. Surrogate markers-based molecular classification methods have been developed to make these molecular classifications accessible and affordable, achieving classification into POLEmut, mismatch repair deficient (MMRd), p53abn, and no specific molecular profile (NSMP) with normal p53 expression. Although POLEmut EC has aggressive pathologic features, there are few cases of advanced and/or recurrence. Therefore, the possibility of de-escalating adjuvant therapy can be considered. Additionally, immune checkpoint inhibitors (ICI) may be a candidate for treating advanced and recurrent POLEmut EC because of their high immunogenicity. MMRd EC shows an intermediate prognosis between those of POLEmut and p53abn EC. MMRd EC is generally characterized by high immunogenicity similar to POLEmut EC, suggesting that ICI can also be a potential therapeutic agent. Among the four molecular subtypes, p53abn EC has the worst prognosis. However, some p53abn tumors have the molecular hallmark of homologous recombination deficiency and could be treated with poly (ADP-ribose) polymerase inhibitors. In addition, some p53abn tumors overexpress the human epidermal growth factor receptor 2, which can also be a potential therapeutic target. NSMP EC are a heterogeneous population because they lack characteristic molecular biological features. Approximately half of the NSMP EC show high expression of estrogen receptor/progesterone receptor, suggesting the possibility of hormonal therapy. In addition, the PI3K/AKT/mTOR pathway frequently altered in EC may be a therapeutic target. This review summarizes the molecular biological characteristics and potential therapeutic agents in molecularly featured EC. Several clinical trials are in progress to stratify EC into molecular classifications and demonstrate the efficacy and safety of molecularly matched treatment and management strategies.

Abstract Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71,756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca2+ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico–driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations. Significance: Comprehensive proteogenomic and in silico analyses of a vast number of VUSs identify a novel set of oncogenic and druggable mutations in the well-characterized RET oncogene.