Cancer is one of the most complex diseases to study and treat. There are many unique types of cancer that evolve differently, which can make it difficult to predict progression and select optimal treatment. An estimated 1.7 million new cases of cancer are diagnosed in the U.S. each year, and only about 5% of these patients are enrolled in clinical trials. Despite these low enrollment numbers, cancer drugs are continuously being approved by the FDA, with 19 new cancer drugs and biologics approved last year alone.

Clinical trial results are essential to understand how cancer treatments perform. However, these results may differ from how the drugs perform in the real world as they are tested in highly selected groups of patients under rigorous protocol-driven conditions. Evidence from non-clinical trial sources, such as observational studies based on electronic health record (EHR) data, can help to ensure that new therapies coming to the market are effective in real-world patients treated outside the context of clinical trials.

Elaborating on the use cases for this data, Chris Boone, VP for global medical epidemiology & big data analysis at Pfizer and Viraj Narayanan, VP of life sciences at COTA, Inc., recently co-authored a STAT+ contributed opinion article that explores how real-world evidence can be used to fuel precision medicine and disrupt increasingly outdated clinical trial models. If you are a STAT+ subscriber and want to read the full article, you can view it here.

Real-world evidence plays an increasingly important role in the advancement of personalized medicine, specifically in oncology. Cancer treatments are evolving from a one-size-fits-all approach to a tailored approach that leverages new biologic and molecular insights. By using real-world evidence to understand how patients respond to various treatments, researchers and clinicians can develop therapies and personalize treatment regimens to improve outcomes and outsmart cancer.