Leveraging Real-world data to advance breast cancer research

Real-world data (RWD) has become an increasingly valuable tool for breast cancer research and care in recent years. RWD encompasses data regarding patient health status, treatment patterns, and outcomes that are collected outside of traditional clinical trials. For breast cancer, the most common cancer in women, RWD provides unique insights that are propelling critical advances. At BC Platforms not only do we believe that RWD complements clinical trial data, but we also know that it allows for research on a larger scale, real-world populations and outcomes over longer periods. This provides key insights that can improve breast cancer screening, treatment and patient care overall. Our world-wide data partner network with a catchment of over 70M patient lives, can support breast cancer research globally. The reasons to use RWD in breast cancer research are relevant to gain deeper insights:

Capturing Real-World Experience

A major advantage of RWD is the capacity to better represent real patient populations compared to clinical trials. Participants in trials tend to be younger, healthier, and less racially diverse than typical breast cancer patients. RWD derived from electronic health records, insurance claims, registries, and other sources provides a comprehensive view of outcomes across demographics (Bole et al., 2022). 

RWD also contains long-term data on treatment effectiveness, side effects, and costs over months or years following diagnosis. Clinical trials generally follow patients for weeks or months. Analyses of RWD can reveal how breast cancer therapies perform in real-world settings compared to the controlled environment of trials (Conte et al., 2022).

Accelerating Drug and Test Development 

Pharmaceutical and diagnostic companies integrate RWD into development programs for breast cancer drugs and tests. RWD helps identify patients most likely to benefit from new targeted therapies based on genomic markers or clinical characteristics. It provides expanded access to diverse participants for enrollment in clinical trials (Inoue-Choi et al., 2022). RWD analysis following approval provides further evidence on efficacy, safety, and optimal use of therapies.

Researchers are applying machine learning to rapidly analyze large sets of real-world data. Algorithms can discern patterns and correlations that would take years to identify through manual review. Natural language processing extracts information from unstructured physician notes and reports (Mamtani et al., 2022). These techniques help accelerate hypothesis testing and evidence generation.

Optimizing Care Delivery

Oncology providers are leveraging RWD to improve quality of care. Analysis of real-world treatment patterns reveals gaps between actual and guideline-recommended care that highlight opportunities for practice improvement (Wazir et al., 2022). At the patient level, RWD-derived risk models facilitate identification of high-risk individuals for preventive interventions and monitoring. 

Integrated real-world databases, including cancer registry data linked to genetic and imaging data, provide researchers with rich resources to study factors impacting breast cancer outcomes. For example, the FDA’s Sentinel database contains information on over 100 million patients (Sherman et al., 2022). Analyses will support development of evidence-based, tailored care for breast cancer.

In summary, RWD enables breast cancer research that more closely reflects real patient experiences versus clinical trials alone. Incorporating RWD with data from trials and basic science will lead to essential advances in prevention, diagnosis, treatment, and survivorship care for breast cancer patients. Through our global, federated Data Partner Network, BC Platforms enables analytics and deeper insights. From Data to Health.


Bole, C., Atay, S., & Theriault, R. L. (2022). The utility of real-world data in metastatic breast cancer. Current Breast Cancer Reports. doi: 10.1007/s12609-022-00437-5

Conte, P., Rossi, S., Michelotti, A., Risi, E., Canale, M., Cortesi, L., & Cazzaniga, M. (2022). Harnessing real-world data to optimize clinical management of luminal HER2-negative metastatic breast cancer. Cancer Treatment Reviews, 103, 102303. 

Inoue-Choi, M., Robien, K., & Lazovich, D. (2022). Using real-world data to address disparities in breast cancer prevention trials. Cancer Epidemiology, Biomarkers & Prevention, 31(1), 26-28.

Mamtani, A., & Waljee, A. K. (2022). Applications of real-world data in breast cancer care. JCO Clinical Cancer Informatics, 6, 1-9. 

Wazir, U., Chia, S. K., Ho, W. K., Lim, E. H., Chan, M. Y., & Bhoo-Pathy, N. (2022). Real world data and its role in shaping the management of breast cancer in Asia. Cancer Treatment Reviews, 104, 102421.

Sherman, R. E., Anderson, S. A., Dal Pan, G. J., Gray, G. W., Gross, T., Hunter, N. L., LaVange, L., Marinac-Dabic, D., Marks, P. W., Robb, M. A., … & Califf, R. M. (2021). Real-world evidence—What is it and what can it tell us. New England Journal of Medicine, 385(23), 2293-2297.