Background: Ovarian cancer is the most lethal gynecological cancer, causing over 200,000 deaths worldwide in 2020. Initial standard treatment for primary ovarian cancer is optimal cytoreductive surgery (CRS) preceded and/or followed by intravenous platinum-based chemotherapy. However, most women develop recurrence within the peritoneal cavity and die of disease. Results of the OVIHIPEC 1 trial (2018) showed improved survival of 34% when hyperthermic intraperitoneal chemotherapy (HIPEC) was given immediately following interval-CRS in women with stage III disease. However, it is unknown if the effect of HIPEC is due to hyperthermia, one extra cycle of intraperitoneal (IP) chemotherapy, or other factors. There is also concern that hyperthermia might be associated with an increase in adverse events (AEs) due to a heightened systemic inflammatory response. HyNOVA is a seamless, multi-stage randomized study that attempts to answer these questions by comparing HIPEC to normothermic intraperitoneal chemotherapy (NIPEC), focusing on safety (stage 1), then assessing activity (stage 2) and effectiveness (stage 3). In this initial study, we hypothesize that NIPEC will result in a lower rate of severe AEs compared to HIPEC. Methods This initial stage of HyNOVA is a phase II study of 80 women with International Federation of Gynaecology and Obstetrics stage III epithelial ovarian cancer, with at least stable disease following 3–4 cycles of neoadjuvant chemotherapy, achieving interval-CRS to <2.5 mm residual disease. Participants are randomized 1:1 to receive IP cisplatin 100 mg/m2 for 90 minutes either as HIPEC, heated to 42°C (41.5°C–42.5°C), or NIPEC, at 37°C (36.5°C–37.5°C). The primary outcome is the proportion of AEs ≥ grade 3 occurring within 90 days. Secondary outcomes are AE of interest, surgical morbidity, patient reported outcomes, resource allocation, feasibility, progression-free survival and overall survival. AEs are measured using both CTCAE v5.0 and Clavien-Dindo classification, particularly infection, pain, bowel dysfunction, and anemia. Tertiary outcomes are potential predictive biomarkers measured before and after HIPEC/NIPEC including circulating cell-free tumor DNA, tissue factors, and systemic inflammatory markers. There are 4 participating Australian sites with experience in CRS and HIPEC for peritoneal malignancy. HyNOVA is funded by an MRFF grant (APP1199155).

Epithelial ovarian cancer (EOC) is among the top ten causes of cancer deaths worldwide, and is one of the most lethal gynecological malignancies in high income countries, with incidence and death rates expected to rise particularly in Asian countries where ovarian cancer is among the 5 most common cancers. Despite the plethora of randomised clinical trials investigating various systemic treatment options in EOC over the last few decades, both progression-free and overall survival have remained at approximately 16 and 40 months respectively. To date the greatest impact on treatment has been made by the use of poly (ADP-ribose) polymerase (PARP) inhibitors in women with advanced EOC and a BRCA1/2 mutation. Inhibition of PARP, the key enzyme in base excision repair, is based on synthetic lethality whereby alternative DNA repair pathways in tumor cells that are deficient in homologous recombination is blocked, rendering them unviable and leading to cell death. The Australia New Zealand Gynaecological Oncology Group (ANZGOG) is the national gynecological cancer clinical trials organization for Australia and New Zealand. ANZGOG's purpose is to improve outcomes and quality of life for women with gynecological cancer through cooperative clinical trials and undertaking multidisciplinary research into the causes, prevention and treatments of gynecological cancer. This review summarizes current ovarian cancer research and treatment approaches presented by Australian and New Zealand experts in the field at the 2020 ANZGOG webinar series entitled “Ovarian Cancer systems of Care”.

The impact the metabolic syndrome (MetS) components on the severity of insulin resistance (IR) has not been reported. We enrolled 564 subjects with MetS and they were divided into quartiles according to the level of each component; and an insulin suppression test was performed to measure IR. In males, steady state plasma glucose (SSPG) levels in the highest quartiles, corresponding to body mass index (BMI) and fasting plasma glucose (FPG), were higher than the other three quartiles and the highest quartiles, corresponding to the diastolic blood pressure and triglycerides, were higher than in the lowest two quartiles. In females, SSPG levels in the highest quartiles, corresponding to the BMI and triglycerides, were higher than in all other quartiles. No significant differences existed between genders, other than the mean SSPG levels in males were greater in the highest quartile corresponding to BMI than that in the highest quartile corresponding to HDL-cholesterol levels. The factor analysis identified two underlying factors (IR and blood pressure factors) among the MetS variables. The clustering of the SSPG, BMI, triglyceride and HDLcholesterol was noted. Our data suggest that adiposity, higher FPG and triglyceride levels have stronger correlation with IR and subjects with the highest BMI have the highest IR.
Waist-Hip Ratio ; Triglycerides/blood ; Middle Aged ; Metabolic Syndrome X/*metabolism ; Male ; *Insulin Resistance ; Humans ; Female ; Fasting/blood ; Cholesterol, HDL/blood ; Body Mass Index ; Blood Glucose/analysis ; Aged ; Adult

Background/Aims: Direct‐acting antivirals (DAAs) have been approved for hepatitis C virus (HCV) treatment in patients with end-stage renal disease (ESRD) on hemodialysis. Nevertheless, the complicated comedications and their potential drug-drug interactions (DDIs) with DAAs might limit clinical practice in this special population. Methods: The number, class, and characteristics of comedications and their potential DDIs with five DAA regimens were analyzed among HCV-viremic patients from 23 hemodialysis centers in Taiwan. Results: Of 2,015 hemodialysis patients screened in 2019, 169 patients seropositive for HCV RNA were enrolled (mean age, 65.6 years; median duration of hemodialysis, 5.8 years). All patients received at least one comedication (median number, 6; mean class number, 3.4). The most common comedication classes were ESRD-associated medications (94.1%), cardiovascular drugs (69.8%) and antidiabetic drugs (43.2%). ESRD-associated medications were excluded from DDI analysis. Sofosbuvir/velpatasvir/voxilaprevir had the highest frequency of potential contraindicated DDIs (red, 5.6%), followed by glecaprevir/pibrentasvir (4.0%), sofosbuvir/ledipasvir (1.3%), sofosbuvir/velpatasvir (1.3%), and elbasvir/grazoprevir (0.3%). For potentially significant DDIs (orange, requiring close monitoring or dose adjustments), sofosbuvir/velpatasvir/voxilaprevir had the highest frequency (19.9%), followed by sofosbuvir/ledipasvir (18.2%), glecaprevir/pibrentasvir (12.6%), sofosbuvir/velpatasvir (12.6%), and elbasvir/grazoprevir (7.3%). Overall, lipid-lowering agents were the most common comedication class with red-category DDIs to all DAA regimens (n=62), followed by cardiovascular agents (n=15), and central nervous system agents (n=10). Conclusions HCV-viremic patients on hemodialysis had a very high prevalence of comedications with a broad spectrum, which had varied DDIs with currently available DAA regimens. Elbasvir/grazoprevir had the fewest potential DDIs, and sofosbuvir/velpatasvir/voxilaprevir had the most potential DDIs.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.