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Open Access Article Issue
Bevacizumab and Paclitaxel in Advanced, Hormone Receptor-Positive Breast Cancer: Multifactor Dimensionality Reduction Methodology to Identify Best Overall Survival
Oncology Research 2026, 34(5): 13
Published: 22 April 2026
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Background

The treatment of advanced hormone receptor-positive (HR+) breast cancer has seen relevant changes in last years. However, bevacizumab remains an option when combined with paclitaxel, but no certified pharmacogenetic profiles are now usable for the prediction of its response in breast cancer patients. This study aimed to explore the pharmacogenetic interactions among single nucleotide polymorphisms (SNPs) of genes involved in the angiogenic process and their impact on progression-free survival (PFS) and overall survival (OS) in hormone receptor-positive (HR+) metastatic breast cancer subjects administered with bevacizumab plus paclitaxel, or with paclitaxel alone (clinicaltrial.gov identifier NCT01935102).

Methods

Germline DNA extracted from blood samples was analyzed using real-time polymerase chain reaction to investigate SNPs. The multifactor dimensionality reduction (MDR) analysis was employed to assess interactions between these genetic variants. A total of 168 eligible patients were analyzed. Among these, 106 patients received both paclitaxel and bevacizumab, while 62 received paclitaxel alone.

Results

In the combination therapy group, MDR analysis identified two pharmacogenetic interaction profiles involving specific genotypes of vascular endothelial growth factor-A(VEGF-A) rs833061 and vascular endothelial growth factor receptor-2 (VEGFR-2) rs1870377. Patients with a favorable genetic profile had a median PFS (mPFS) of 22.9 months, compared to 8.7 months in those with an unfavorable profile (p = 0.001). Cox proportional hazards analysis displayed an adjusted hazard ratio of 0.443 (95% CI: 0.284–0.691; p < 0.0001). The median OS (mOS) was 50.2 months for the favorable profile vs. 23.5 months for the unfavorable (p = 0.003), with an adjusted hazard ratio (HR) of 0.404 (95% CI: 0.249–0.657; p < 0.0001). In the 62 subjects administered with just paclitaxel, no significant differences in PFS (p = 0.820) or OS (p = 0.143) were observed between favorable and unfavorable genetic profiles.

Conclusions

The MDR analysis of VEGF-A rs833061 and VEGFR-2 rs1870377 genotypes can detect a subgroup of bevacizumab-administered+ metastatic breast cancer patients with improved PFS and OS.

Open Access Review Issue
Pharmacological Phase I Clinical Trials in Pediatric Brain Tumors (1990–2024): A Historical Perspective
Oncology Research 2025, 33(10): 2603-2656
Published: 26 September 2025
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Central nervous system (CNS) tumors are the most common solid tumors in pediatric patients and the leading cause of childhood cancer-related mortality. Their rarity compared to adult cancers has made enrolling sufficient cases for clinical trials challenging. Consequently, pediatric CNS tumors were long treated with adult protocols despite distinct biological and clinical characteristics. This review examines key aspects of phase I pediatric oncology trials, including study design, primary outcomes, and pharmacological approaches, along with secondary considerations like clinical responses and ethical aspects. Firstly, we evaluated all phase I trial protocols focusing on pediatric CNS tumors with accessible results published in scientific databases (Pubmed, Scopus, Embase, Web of Science, and Google Scholar) from 1990 to November 2024. Secondly, we searched EudraCT and ClinicalTrials.gov on 30 November 2024 for ongoing trials. Our search yielded 60 completed phase I studies and 15 trials in progress. Dividing them by chronological order revealed that study designs and the response assessments evolved as the understanding of CNS tumor biology increased. Despite advancements improving diagnosis, management, and prognostication, mortality remains high, and morbidity persists. Notably, pediatric pharmacokinetics and pharmacodynamics differ from adults, complicating trial comparisons and dosage optimization. Future efforts should focus on large-scale clinical data collection to enhance trial efficiency.

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