Harnessing the Potential of Real-World Evidence in the Treatment of Colorectal Cancer: Where Do We Stand?

• Published in: Current treatment options in oncology 2024-04, Vol.25 (4), p.405-426
• Main Authors: van Nassau, Sietske C. M. W., Bol, Guus M., van der Baan, Frederieke H., Roodhart, Jeanine M. L., Vink, Geraldine R., Punt, Cornelis J. A., May, Anne M., Koopman, Miriam, Derksen, Jeroen W. G.
• Format: Article
• Language: English
• Subjects: Clinical trials; Colorectal cancer; Colorectal carcinoma; Decision making; Epidemiology; Medicine; Medicine & Public Health; Oncology; Patients; Quality of life; Topical Collection on Lower Gastrointestinal Cancers
• ISSN: 1527-2729; 1534-6277; 1534-5277
• DOI: 10.1007/s11864-024-01186-4

Opinion statement Treatment guidelines for colorectal cancer (CRC) are primarily based on the results of randomized clinical trials (RCTs), the gold standard methodology to evaluate safety and efficacy of oncological treatments. However, generalizability of trial results is often limited due to stringent eligibility criteria, underrepresentation of specific populations, and more heterogeneity in clinical practice. This may result in an efficacy-effectiveness gap and uncertainty regarding meaningful benefit versus treatment harm. Meanwhile, conduct of traditional RCTs has become increasingly challenging due to identification of a growing number of (small) molecular subtypes. These challenges—combined with the digitalization of health records—have led to growing interest in use of real-world data (RWD) to complement evidence from RCTs. RWD is used to evaluate epidemiological trends, quality of care, treatment effectiveness, long-term (rare) safety, and quality of life (QoL) measures. In addition, RWD is increasingly considered in decision-making by clinicians, regulators, and payers. In this narrative review, we elaborate on these applications in CRC, and provide illustrative examples. As long as the quality of RWD is safeguarded, ongoing developments, such as common data models, federated learning, and predictive modelling, will further unfold its potential. First, whenever possible, we recommend conducting pragmatic trials, such as registry-based RCTs, to optimize generalizability and answer clinical questions that are not addressed in registrational trials. Second, we argue that marketing approval should be conditional for patients who would have been ineligible for the registrational trial, awaiting planned (non) randomized evaluation of outcomes in the real world. Third, high-quality effectiveness results should be incorporated in treatment guidelines to aid in patient counseling. We believe that a coordinated effort from all stakeholders is essential to improve the quality of RWD, create a learning healthcare system with optimal use of trials and real-world evidence (RWE), and ultimately ensure personalized care for every CRC patient.