Modeled reductions in late-stage cancer with a multi-cancer early detection test

Abstract

Background: Cancer is the second leading cause of death globally, with many cases detected at a late stage when prognosis is poor. New technologies enabling multi-cancer early detection (MCED) may make “universal cancer screening” possible. We extend single-cancer models to understand the potential public health effects of adding a MCED test to usual care. Methods: We obtained data on stage-specific incidence and survival of all invasive cancers diagnosed in persons aged 50–79 between 2006 and 2015 from the US Surveillance, Epidemiology, and End Results (SEER) program, and combined this with published performance of a MCED test in a state transition model (interception model) to predict diagnostic yield, stage shift, and potential mortality reductions. We model long-term (incident) performance, accounting for constraints on detection due to repeated screening. Results: The MCED test could intercept 485 cancers per year per 100,000 persons, reducing late-stage (IIIþIV) incidence by 78% in those intercepted. Accounting for lead time, this could reduce 5-year cancer mortality by 39% in those intercepted, resulting in an absolute reduction of 104 deaths per 100,000, or 26% of all cancer-related deaths. Findings are robust across tumor growth scenarios. Conclusions: Evaluating the impact of a MCED test that affects multiple cancer types simultaneously requires modeling across all cancer incidence. Assuming MCED test metrics hold in a clinical setting, the aggregate potential to improve public health is significant. Impact: Modeling performance of a MCED test in a representative population suggests that it could substantially reduce overall cancer mortality if added to usual care.