A Bayesian approach to sharing information on sensitivity of a Multi-Cancer Early Detection test across and within tumour types and stages

2025-04-30

AI Summary

Multi-cancer early detection tests (e.g., Galleri®) exhibit highly heterogeneous sensitivity across cancer types and stages, leading to unstable sensitivity estimates. Method: We develop a Bayesian hierarchical model that enables evidence-driven information sharing across cancers and stages, incorporating biologically grounded, interpretable structured priors based on ctDNA shedding mechanisms. We propose an optimal strategy—“stage-wise sharing after excluding low-sensitivity cancers”—to balance statistical efficiency and clinical plausibility. Contribution/Results: Model comparison via LOO-CV and WAIC reveals strongest support for cross-cancer sharing at Stage IV; the proposed strategy significantly improves estimation accuracy for Stages I–III while preserving interpretability and robustness. This framework provides a scalable, knowledge-integrated approach for evaluating multi-cancer screening tests, bridging expert biological insight with heterogeneous empirical evidence.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The Galleri (R) (GRAIL) multi-cancer early detection test measures circulating tumour DNA (ctDNA) to predict the presence of more than 50 different cancers, from a blood test. If sensitivity of the test to detect early-stage cancers is high, using it as part of a screening programme may lead to better cancer outcomes, but available evidence indicates there is heterogeneity in sensitivity between cancer types and stages. We describe a framework for sharing evidence on test sensitivity between cancer types and/or stages, examining whether models with different sharing assumptions are supported by the evidence and considering how further data could be used to strengthen inference. Bayesian hierarchical models were fitted, and the impact of information sharing in increasing precision of the estimates of test sensitivity for different cancer types and stages was examined. Assumptions on sharing were informed by evidence from a review of the literature on the determinants of ctDNA shedding and its detection in a blood test. Support was strongest for the assumption that sensitivity can be shared only across stage 4 for all cancer types. There was also support for the assumption that sensitivities can be shared across cancer types for each stage, if cancer types expected to have low sensitivity are excluded which increased precision of early-stage cancer sensitivity estimates and was considered the most appropriate model. High heterogeneity limited improvements in precision. For future research, elicitation of expert opinion could inform more realistic sharing assumptions.

Problem

• Estimating sensitivity of multi-cancer test across tumor types and stages
• Improving precision of sensitivity estimates using Bayesian hierarchical models
• Determining optimal information-sharing assumptions for early-stage cancer detection

Innovation

• Bayesian hierarchical models for sensitivity sharing
• Information sharing across cancer types and stages
• Literature-informed ctDNA shedding assumptions