Clinical Pharmacology & Therapeutics

Impact of Antihypertensive Treatment on Outcomes of Adjuvant Bevacizumab for Ovarian Cancer ( IATRO ), Results from a Nationwide Emulated Clinical Trial

Antiangiogenic therapy with bevacizumab improves outcomes in ovarian cancer but induces hypertension, leading to major adverse cardiovascular events (MACE). While calcium channel blockers (CCBs) and angiotensin‐converting enzyme inhibitors (ACEi) are recommended for managing bevacizumab‐associated hypertension, their impacts on cancer progression and cardiovascular outcomes are unclear. This study compared the effects of CCBs and ACEi on progression‐free survival (PFS) in ovarian cancer patients treated with adjuvant bevacizumab. The incidence of MACE and overall survival (OS) were also evaluated. We conducted an emulated clinical trial using data from January 1, 2011, to January 1, 2021, from the French National Health Data System (SNDS), covering 98.8% of the French population. Patients with FIGO stage III to IV ovarian cancer who underwent cytoreductive surgery and adjuvant chemotherapy with bevacizumab, treated with CCBs or ACEi monotherapy within 6 months after surgery, were included. Out of 4,165 patients treated with bevacizumab, 454 met inclusion criteria for the main analysis: 273 in the CCBs group and 181 in the ACEi group. CCBs use was associated with a longer median PFS compared to ACEi (21.8 vs. 18.2 months) and a higher 3‐year PFS rate (difference of 8.2 percentage points, 95% CI: 2.0%; 14.8%). No significant difference in OS was observed between groups. Cardiovascular complications were more frequent with CCBs compared to ACEi, particularly congestive heart failure (difference in 3‐year incidence of MACE: −4.5 percentage points; 95% CI: −8.2%; −1.1%). These findings emphasize the need for a balanced approach to managing hypertension in cancer patients, considering both oncologic and cardiologic outcomes.

Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab

Pharmacogenomics studies how genes influence a person's response to treatment. When complex phenotypes are influenced by multiple genetic variations with little effect, a single piece of genetic information is often insufficient to explain this variability. The application of machine learning (ML) in pharmacogenomics holds great potential — namely, it can be used to unravel complicated genetic relationships that could explain response to therapy. In this study, ML techniques were used to investigate the relationship between genetic variations affecting more than 60 candidate genes and carboplatin‐induced, taxane‐induced, and bevacizumab‐induced toxicities in 171 patients with ovarian cancer enrolled in the MITO‐16A/MaNGO‐OV2A trial. Single‐nucleotide variation (SNV, formerly SNP) profiles were examined using ML to find and prioritize those associated with drug‐induced toxicities, specifically hypertension, hematological toxicity, nonhematological toxicity, and proteinuria. The Boruta algorithm was used in cross‐validation to determine the significance of SNVs in predicting toxicities. Important SNVs were then used to train eXtreme gradient boosting models. During cross‐validation, the models achieved reliable performance with a Matthews correlation coefficient ranging from 0.375 to 0.410. A total of 43 SNVs critical for predicting toxicity were identified. For each toxicity, key SNVs were used to create a polygenic toxicity risk score that effectively divided individuals into high‐risk and low‐risk categories. In particular, compared with low‐risk individuals, high‐risk patients were 28‐fold more likely to develop hypertension. The proposed method provided insightful data to improve precision medicine for patients with ovarian cancer, which may be useful for reducing toxicities and improving toxicity management.