Multi-cancer early detection (MCED) tests aim to find signs of cancer in people who may have no symptoms. But cancer can leave many different molecular traces in the blood. Some tests look for genetic mutations. Others examine DNA methylation, fragment characteristics, proteins or RNA.

These approaches capture different aspects of cancer biology, with different strengths and limitations. So where does the evidence stand, and could combining different signals improve cancer detection?

Methylation currently has the most mature evidence base

DNA methylation involves chemical modifications that help regulate gene activity. Cancer can alter methylation patterns across many regions of the genome, creating a broad signal that can help distinguish cancer from normal biology and predict where a cancer originated.

Early work from the Circulating Cell-free Genome Atlas (CCGA) evaluated several ways of detecting cancer from cell-free DNA. Among the approaches studied, whole-genome methylation showed the strongest overall cancer-signal detection and was particularly effective for predicting the likely tissue of origin.[1]

This led to further development of targeted methylation analysis. In a 4,077-participant independent CCGA validation cohort, a targeted methylation approach achieved 99.5% specificity and 51.5% overall sensitivity. Sensitivity increased from 16.8% for stage I cancers to 90.1% for stage IV disease.[2]

The evidence has continued to progress beyond test performance studies. In September 2026, an FDA advisory committee reviewed a premarket approval application for a methylation-based MCED test and voted favorably on its effectiveness, safety and benefit-risk profile. An advisory committee vote is an important regulatory milestone, but it is not itself FDA approval.[3]

A large randomized trial in England also recently evaluated annual screening with a methylation-based MCED test in more than 140,000 adults. The trial did not meet its primary endpoint of reducing the incidence of stage III/IV cancers overall. It did, however, find fewer stage IV cancers and more stage I/II cancers in the screened group. Longer-term follow-up will help determine whether these differences translate into meaningful clinical benefit.[4]

Taken together, methylation currently has the most mature clinical evidence base among molecular MCED approaches, spanning analytical performance, clinical validation, regulatory review and randomized evaluation of screening.

Mutations: a direct tumor signal

Mutation-based testing looks for somatic genetic alterations arising in cancer cells. These alterations can provide a specific molecular signal of tumor DNA.

Its main challenge is abundance. Early cancers may release very little tumor-derived DNA into the bloodstream, so a cancer-associated mutation may be present at an extremely low fraction of total cell-free DNA.

This makes assay design particularly important. Targeted amplicon-based sequencing can concentrate sequencing depth on selected genomic regions, supporting detection of low-level variants from limited cfDNA input.[5] Deep sequencing and error-suppression methods can further improve detection.[6] Sequencing matched white blood cells can also help distinguish tumor-derived mutations from clonal hematopoiesis and other background variants.[7]

The CCGA comparison provides useful context. When the same samples were analyzed using different approaches, mutation-based classifiers with paired white blood cell background removal showed strong cancer-signal detection performance. Whole-genome methylation performed best for predicting cancer-signal origin.[1]

Mutation and methylation thus offer different ways of finding a cancer signal, with different biological and technical considerations. Methylation has accumulated more clinical evidence for MCED to date, while mutation-based testing offers a direct tumor-derived signal and technical strategies for detecting very low-level variants.

Why combine different signals?

Different biomarkers may capture different aspects of cancer biology, which has led researchers to test whether combining them can improve performance.[8]

For example, studies have evaluated methylation alongside proteins and found complementary contributions from the two biomarker classes.[9,10] More recent work has explored adding mutation analysis to methylation and protein signals, with improvements in early-stage detection reported in a case-control study.[11]

Other approaches have combined genomic, methylomic and fragmentomic features from circulating DNA.[12,13] The rationale is straightforward: if one signal is weak or absent in a particular cancer, another may provide complementary information.

Table 1: What Each Biomarker Can Contribute

Fragmentomics examines characteristics of cell-free DNA such as fragment length, end patterns and other features of DNA fragmentation. These signals can provide information about tumor biology and tissue of origin and can be analyzed alongside genomic and methylation features.[12,13]

Proteins provide a different biological signal from DNA and may complement molecular alterations in cell-free DNA.[9,10] RNA can provide information about gene expression and cellular activity that is not captured by DNA sequence alone.[14] Viral DNA may provide another highly specific signal in cancers associated with oncogenic viruses.[15]

Depending on assay design, multiple molecular signals can also be measured from a single blood sample. DNA sequencing can potentially capture mutations, copy-number changes, fragment characteristics and viral sequences, while RNA analysis can add information about gene expression.

From promising biomarker to useful screening test

The development of an MCED test does not end with demonstrating that it can detect cancer. Tests must establish analytical reliability and clinical performance, followed by evidence showing how they perform in real-world populations and whether their use improves meaningful patient outcomes.

The experience with methylation-based MCED illustrates why these stages matter. Strong diagnostic performance can support regulatory review, while randomized trials can address a different question: whether using the test as a screening intervention ultimately benefits patients.

As evidence accumulates, broader clinical adoption and coverage could provide opportunities to study performance across larger and more diverse populations. If future evidence demonstrates meaningful clinical benefit, it could eventually influence clinical guidelines and screening strategies.

The question for the field is becoming broader than which biomarker can detect cancer?

It is which combination of biological signals can improve sensitivity while maintaining the high specificity required for screening, and can that performance translate into better outcomes in the real world?

References

1. Jamshidi A, Liu MC, Klein EA, et al. Evaluation of cell-free DNA approaches for multi-cancer early detection. Cancer Cell. 2022;40(12):1537-1549.e12.

2. Klein EA, Richards D, Cohn A, et al. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann Oncol. 2021;32(9):1167-1177.

3. Molecular and Clinical Genetics Panel of the Medical Devices Advisory. U.S. Food and Drug Administration. September 24, 2026. Accessed September 28, 2026. https://www.fda.gov/advisory-committees/advisory-committee-calendar/september-23-2026-molecular-and-clinical-genetics-panel-medical-devices-advisory-committee-meeting

4. Sasieni P, Johnson P, Round T, et al. Effect of screening with multicancer early-detection test on late-stage cancer diagnosis. N Engl J Med. 2026;(NEJMoa2505723). doi:10.1056/NEJMoa2505723

5. Samorodnitsky E, Jewell BM, Hagopian R, et al. Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing. Hum Mutat. 2015;36(9):903-914.

6. Newman AM, Lovejoy AF, Klass DM, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol. 2016;34(5):547-555.

7. Razavi P, Li BT, Brown DN, et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat Med. 2019;25(12):1928-1937.

8. Cohen JD, Li L, Wang Y, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018;359(6378):926-930.

9. Gainullin VG, Gray M, Kumar M, et al. Abstract 1109: The complementary contributions of methylation and protein biomarker classes in a multi-cancer early detection (MCED) test. Cancer Res. 2026;86(7_Supplement):1109-1109.

10. Douville C, Gainullin V, Arvai K. Organ-specific performance of a multi-analyte, multi-cancer early detection (MCED) blood test in a prospectively-collected cohort. Ann Oncol. 2024;35(2). doi:10.1016/j.annonc.2024.08.2140

11. Gainullin V, Bae J, Guthrie VB, et al. Abstract A056: Performance of multi-biomarker class reflex testing in a prospectively-collected cohort. Clin Cancer Res. 2024;30(21_Supplement):A056-A056.

12. Nguyen VTC, Nguyen TH, Doan NNT, et al. Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization. Elife. 2023;12(RP89083):RP89083.

13. Bao H, Yang S, Chen X, et al. Early detection of multiple cancer types using multidimensional cell-free DNA fragmentomics. Nat Med. 2025;31(8):2737-2745.

14. Larson MH, Pan W, Kim HJ, et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat Commun. 2021;12(1):2357.

15. Chan KCA, Woo JKS, King A, et al. Analysis of plasma Epstein-Barr virus DNA to screen for nasopharyngeal cancer. N Engl J Med. 2017;377(6):513-522.