Exploring Breast Cancer Systemic Drug Therapy Patterns in Real-World Data

Area of Science:

• Oncology
• Pharmacology
• Health Informatics

Background:

• Breast cancer treatment involves complex medication regimens.
• Understanding real-world drug administration is crucial for optimizing patient care.
• Variability in treatment patterns can impact efficacy and patient outcomes.

Purpose of the Study:

• To analyze real-world medication use and administration patterns in breast cancer patients.
• To compare observed treatment regimens with established clinical guidelines.
• To identify common drug administration sequences and their variations.

Main Methods:

• Retrospective analysis of deidentified patient data from the TriNetX network (2013-2022).
• Utilized rule-based and unsupervised learning (k-means clustering) to extract medication data and administration patterns.
• Classified patients by cancer stage (early vs. late) and receptor status (HR+/ERBB2-, ERBB2+, TN) for analysis.
• Compared derived regimens against National Comprehensive Cancer Network (NCCN) guidelines.

Main Results:

• Common regimens identified for early-stage HR+/ERBB2- and TN breast cancer included cyclophosphamide/docetaxel and cyclophosphamide/doxorubicin followed by paclitaxel.
• For early-stage ERBB2+/HR± breast cancer, carboplatin/docetaxel with trastuzumab and potentially pertuzumab was frequent.
• Observed medication use, number of administrations, and cycle lengths generally aligned with NCCN recommendations across 7,798 patients.

Conclusions:

• Real-world breast cancer medication administration generally aligns with NCCN guidelines.
• Variability exists in specific drug choices and administration schedules in clinical practice.
• Further research can explore the impact of this variability on treatment outcomes.