taxane and Hypertension

Sorafenib is a multikinase inhibitor with antiangiogenic/antiproliferative activity. In this randomized, double-blind, placebo-controlled phase III trial, we assessed first- or second-line capecitabine with sorafenib or placebo in patients with locally advanced/metastatic HER2-negative breast cancer resistant to a taxane and anthracycline and with known estrogen/progesterone receptor status.. Treatment with sorafenib with capecitabine, compared with capecitabine with placebo, did not prolong median PFS (5.5 vs. 5.4 months; hazard ratio [HR], 0.973; 95% confidence interval [CI], 0.779-1.217; P = .811) or overall survival (OS; 18.9 vs. 20.3 months; HR, 1.195; 95% CI, 0.943-1.513; P = .140); or enhance overall response rate (ORR; 13.5% vs. 15.5%; P = .515). Any grade toxicities (sorafenib vs. placebo) included palmar-plantar erythrodysesthesia syndrome (PPES; 79.2% vs. 59.6%), diarrhea (47.3% vs. 37.8%), mucosal inflammation (15.4% vs. 6.7%), and hypertension (26.2% vs. 5.6%). Grade 3/4 toxicities included PPES (15.4% vs. 7.1%), diarrhea (4.2% vs. 6.4%), and vomiting (3.5% vs. 0.7%).. The combination of sorafenib with capecitabine did not improve PFS, OS, or ORR in patients with HER2-negative advanced breast cancer. Rates of Grade 3 toxicities were higher in the sorafenib arm.

Topics: Administration, Oral; Aged; Anthracyclines; Antineoplastic Combined Chemotherapy Protocols; Breast Neoplasms; Bridged-Ring Compounds; Capecitabine; Diarrhea; Disease-Free Survival; Double-Blind Method; Drug Administration Schedule; Drug Resistance, Neoplasm; Female; Hand-Foot Syndrome; Humans; Hypertension; Kaplan-Meier Estimate; Middle Aged; Niacinamide; Phenylurea Compounds; Placebos; Receptor, ErbB-2; Sorafenib; Taxoids; Treatment Outcome

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.

Topics: Antineoplastic Combined Chemotherapy Protocols; Bevacizumab; Carboplatin; Female; Humans; Hypertension; Ovarian Neoplasms; Taxoids