Boosting the apoptotic response of high-grade serous ovarian cancers with CCNE1 amplification to paclitaxel in vitro by targeting APC/C and the pro-survival protein MCL-1.

Ovarian cancer exhibits the highest mortality rate among gynecological malignancies. Antimitotic agents, such as paclitaxel, are frontline drugs for the treatment of ovarian cancer. They inhibit microtubule dynamics and their efficiency relies on a prolonged mitotic arrest and the strong activation of the spindle assembly checkpoint (SAC). Although ovarian cancers respond well to paclitaxel, the clinical efficacy is limited due to an early onset of drug resistance, which may rely on a compromised mitosis exit associated with weakend intrinsic apoptosis. Accordingly, we aimed at overcoming SAC silencing that occurs rapidly during paclitaxel-induced mitotic arrest. To do this, we used a specific anaphase-promoting complex/cyclosome (APC/C) inhibitor to prevent a premature mitotic exit upon paclitaxel treatment. Furthermore, we investigated the role of the antiapoptotic BCL-2 family member MCL-1 in determining the fate of ovarian cancer cells lines with CCNE1 amplification that are challenged with clinically relevant dose of paclitaxel. Using time-laps microscopy, we demonstrated that APC/C and MCL-1 inhibition under paclitaxel prevents mitotic slippage in ovarian cancer cell lines and restores death in mitosis. Consistent with this, the combinatorial treatment reduced the survival of ovarian cancer cells in 2D and 3D cell models. Since a therapeutic ceiling has been reached with taxanes, it is of utmost importance to develop alternative strategies to improve the patient's survival. Thus, our study provides not only elements to understand the causes of taxane resistance in CCNE1-amplified ovarian cancers but also suggests a new combinatorial strategy that may improve paclitaxel-based efficacy in this highly lethal gynecological disease.

Medical comorbidities in bipolar disorder (BIPCOM): clinical validation of risk factors and biomarkers to improve prevention and treatment. Study protocol.

BACKGROUND: BIPCOM aims to (1) identify medical comorbidities in people with bipolar disorder (BD); (2) examine risk factors and clinical profiles of Medical Comorbidities (MC) in this clinical group, with a special focus on Metabolic Syndrome (MetS); (3) develop a Clinical Support Tool (CST) for the personalized management of BD and medical comorbidities. METHODS: The BIPCOM project aims to investigate MC, specifically MetS, in individuals with BD using various approaches. Initially, prevalence rates, characteristics, genetic and non-genetic risk factors, and the natural progression of MetS among individuals with BD will be assessed by analysing Nordic registers, biobanks, and existing patient datasets from 11 European recruiting centres across 5 countries. Subsequently, a clinical study involving 400 participants from these sites will be conducted to examine the clinical profiles and incidence of specific MetS risk factors over 1 year. Baseline assessments, 1-year follow-ups, biomarker analyses, and physical activity measurements with wearable biosensors, and focus groups will be performed. Using this comprehensive data, a CST will be developed to enhance the prevention, early detection, and personalized treatment of MC in BD, by incorporating clinical, biological, sex and genetic information. This protocol will highlight the study's methodology. DISCUSSION: BIPCOM's data collection will pave the way for tailored treatment and prevention approaches for individuals with BD. This approach has the potential to generate significant healthcare savings by preventing complications, hospitalizations, and emergency visits related to comorbidities and cardiovascular risks in BD. BIPCOM's data collection will enhance BD patient care through personalized strategies, resulting in improved quality of life and reduced costly interventions. The findings of the study will contribute to a better understanding of the relationship between medical comorbidities and BD, enabling accurate prediction and effective management of MetS and cardiovascular diseases. TRIAL REGISTRATION: ISRCTN68010602 at https://www.isrctn.com/ISRCTN68010602 . Registration date: 18/04/2023.