Multi-cancer early detection: from promise to practice and the next frontier

Yongjie Xu; Wanqing Chen

doi:10.20892/j.issn.2095-3941.2025.0664

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Figure 1
Workflow for artificial intelligence (AI)-integrated multi-cancer early detection (MCED) screening. This diagram illustrates a practical workflow for population-level MCED screening enhanced by AI. The process begins with AI-driven risk stratification to identify high-risk individuals, thereby improving the economic utility of screening. This targeted high-risk cohort then undergoes AI-augmented MCED testing. Positive test results are integrated into standardized diagnostic and therapeutic pathways, whereas negative results trigger routine follow-up with periodic re-screening. After the initial testing phase, all participants are enrolled in a program of AI-powered digital biomarker monitoring for longitudinal surveillance.

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Table 1

Summary of liquid biopsy markers for multi-cancer screening

Biomarkers	Advantages	Limitations	Representative research/products	Evaluation
cfDNA methylation	High TOO High cancer specificity High early signal sensitivity	Weak signal in some cancer types (such as sarcoma) Requirement for large panels to cover multiple cancers	Galleri⁷, OverC¹⁰	This is the core marker of MCED, which has been extensively studied and clinically translated. Methylation-based NGS is the preferred technology for MCED.
ctDNA mutation	Driver gene mutations High value in targeted therapy	Low mutation abundance in early stages CHIP interference No TOO	CancerSEEK¹⁴	This biomarker must be combined with other markers to improve sensitivity. Tissue tracing cannot be performed.
cfDNA fragmentation	No need to explore tumor-specific markers Low cost	Requirement for large-scale training sets for verification Relatively low TOO accuracy	DELFI¹⁸, MERCURY¹²	Combining this emerging supplementary technology with methylation can optimize MCED’s performance.
cfRNA	Dynamic monitoring of TME High stability of exosomal RNA	Potential sample degradation No standardized processes	ThromboSeq¹⁵	This direction is promising, but bottlenecks of stability and standardization must be addressed. This marker is currently in the stage of case-control development.
Proteins	Clinical validation, e.g., PSA Rapid detection	Low specificity (single marker) Weak early signals	OncoSeek¹⁷	The value of traditional markers is limited, and proteomics based on new technology combined with other markers is a growing trend.
Metabolomics	Indicative of tumor metabolic reprogramming Low cost	Significant individual variation Lack of cancer type-specific markers	MNALCI¹⁶	This biomarker is in early research stages and requires larger-scale studies for verification.
Multi-omics	Improved performance Feature complementarity	High cost Complex data	AlphaLiquid¹⁹	The features are complex and difficult to explain biologically, but they can be used as a supplementary application. Cost-effectiveness must be balanced.

TOO, tissue of origin; MCED, multi-cancer early detection; NGS, next-generation sequencing; CHIP, clonal hematopoiesis of indeterminate potential; TME, tumor microenvironment.