The Cancerguard test is a multi-cancer early detection (MCED) tool with clinical validity established in the ASCEND-2 study, demonstrating robust performance across a broad spectrum of cancers. The Falcon study will provide real-world evidence to support effective integration into existing care pathways and diverse healthcare settings. Recent modeling data suggests that adding the Cancerguard test to standard single-cancer screening could shift diagnoses toward earlier stages and reduce late-stage disease. Over time, these advances are projected to result in fewer cancer-related deaths. Collectively, the clinical validation, real-world adoption, and encouraging modeling data support the value of the Cancerguard test.

With a clinical development program including 9 clinical trials totaling > 380,000 participants and a commercial experience exceeding 400,000 tests, GRAIL’s Galleri is extensively validated in the intended use population, demonstrating high specificity, high sensitivity for the most aggressive cancers,  efficient cancer signal origin-directed diagnostic evaluations, and a favorable safety profile. The randomized NHS-Galleri study demonstrated that addition of Galleri was associated with a marked reduction in Stage IV cancers compared to standard of care screening, establishing its clinical utility.

Efficient multi-cancer detection relies upon high sensitivity for broad cancer detection whilst maintaining high accuracy for each cancer type. 5-hydroxymethylcytosine profiling in cfDNA and tissues shows strong agreement between signals in tumor tissue and cfDNA, confirming that cfDNA reliably mirrors the tumor epigenomics. Two main patterns, one common across different cancer types and another unique to the specific tissues can be used to enhance single-cancer performance within a multi-cancer framework.

Early-stage sensitivity is the defining bottleneck of multi-cancer early detection, and closing it will require looking beyond genomics signals. This talk examines how functional multiomics signals offer an orthogonal window into cancer biology, what that means for detection performance across cancer types and stages, and how risk stratification can support responsible, scalable deployment. The goal isn't to prescribe a single testing approach, but to make the case for a multimodal future in MCED testing.

The development and implementation of MCEDs is one of the most challenging applications of liquid biopsy (LB) blood tests. Currently, there is still a lack of long-term follow-up with meaningful data on survival. The clinical management varies between tumor types, and the blood test needs to be incorporated into the diagnostic workup—localization information will be crucial. Thus, advancements in radiological imaging are important, along with improving the accuracy of LB tests. Multimodal tests combined with sophisticated AI applications may help to improve accuracy of the LB test.

Risk assessment enables tailored, earlier, and more frequent screening for high-risk individuals, including for individuals at a younger age currently not eligible for available screening. Risk assessment has the potential to , significantly improve survival rates through screening as well as through preventive intervention. Although family history, genetics, and lifestyle allow for a personalized lifetime risk to be evaluated, the addition of a blood test for risk assessment allows for a real time risk assessment which may evolve over time. Progress in the development of blood markers for risk assessment across cancer types will be presented.

Over the last decade, Geisinger has conducted multiple studies of multi-cancer detection (MCD) tests. The common thread among studies has been the study team coordinating MCD testing, results disclosure, and diagnostic resolution of positive test results. This presentation will take the lessons from MCD studies and clinical leader input to discuss considerations for implementing a clinical program that fits the needs of clinicians, population health teams, and health insurers.

Multi-cancer detection (MCD) blood tests could transform early cancer detection, but their implementation will impact healthcare spending. We developed a flexible cohort simulation model that assesses the cost-effectiveness of MCD testing alongside usual care (UC) cancer screening. We simulated life trajectories of individuals following guideline-concordant UC screening compared against UC+MCD strategy. We found that the cost-effectiveness of MCD testing is influenced heavily by test cost and specificity, especially for cancers without screening.

Multi-cancer early detection (MCED) tests have the potential to identify cancers earlier, improving outcomes and expanding access. As these tests emerge into an already complex screening landscape, patients are optimistic but emphasize the need to address access, affordability, acceptance, and accountability. Solutions include providing clear communication, building trust, fostering meaningful engagement, and ensuring health system readiness.