Galleri Test: A Scientific Review of a Blood-Based Multi-Cancer Early Detection Test

In the context of personalized medicine, early detection of cancer remains one of the most important challenges for improving clinical outcomes. Despite advances in treatment, cancer continues to be a leading cause of death worldwide. According to the World Health Organization, more than 10 million cancer-related deaths occur each year, with nearly 70% of cases diagnosed at advanced stages.
Against this background, the Galleri test, developed by GRAIL (USA), represents a novel approach to multi-cancer early detection (MCED). Using a single blood sample, the test is designed to identify signals associated with more than 50 different cancer types. Importantly, Galleri extends beyond conventional screening programs by addressing cancers for which standard diagnostic methods are limited or unavailable, including pancreatic, liver, and ovarian malignancies.



Scientific Basis of the Galleri Test: cfDNA and DNA Methylation

The Galleri test is based on the concept of liquid biopsy, a non-invasive analysis of circulating cell-free DNA (cfDNA) released into the bloodstream during normal cellular turnover, including apoptosis and necrosis of cancer cells. Tumor-derived DNA fragments differ from those of healthy cells primarily through epigenetic alterations, most notably DNA methylation patterns.

DNA methylation involves the addition of methyl groups to cytosine residues, particularly within CpG-rich regions, and plays a central role in regulating gene expression. During carcinogenesis, these patterns become disrupted. Cancer cells often exhibit hypermethylation of tumor suppressor gene promoters alongside global hypomethylation in other genomic regions, creating disease-specific epigenetic signatures.

Evidence published in Annals of Oncology (2020, 2021) supports the use of cfDNA methylation profiling for cancer detection. In validation studies involving more than 4,000 participants, Galleri demonstrated the ability to distinguish cancer-related signals from non-cancer samples using machine learning models trained on large biobank datasets. This approach differs fundamentally from traditional single-marker tests such as PSA or CA-125, which are limited to specific cancer types and are associated with higher false-positive rates.

Detection Workflow and Underlying Technologies

The analytical process behind the Galleri test can be divided into three main stages that combine molecular biology with computational analysis:

cfDNA Extraction and Sequencing
Approximately 10 mL of venous blood is collected, from which cfDNA is isolated and analyzed using next-generation sequencing (NGS). This enables high-resolution assessment of methylation patterns across millions of DNA fragments.

Machine Learning–Based Signal Detection
Proprietary algorithms analyze methylation profiles to determine whether a cancer-associated signal is present. Trained on datasets comprising thousands of cancer and non-cancer samples, the model achieves a reported specificity of 99.6%, corresponding to a false-positive rate of approximately 0.4%. Sensitivity varies by cancer type, reaching 83.7% for pancreatic cancer, 93.5% for liver and biliary tract cancers, and 83.1% for ovarian cancer, with higher detection rates observed at more advanced stages.

Prediction of Tissue of Origin
When a cancer signal is identified, the algorithm estimates the most likely tissue of origin with an accuracy of approximately 93.4%. This information helps guide subsequent diagnostic procedures, such as imaging studies. The reported positive predictive value is around 62%, indicating a substantial likelihood that a positive signal corresponds to an underlying malignancy.

Despite its technical sophistication and high analytical performance, the Galleri test does not eliminate the need for careful interpretation. Once a result is obtained, understanding its clinical significance requires contextual evaluation of laboratory data rather than reliance on an isolated signal.

At this stage, platforms focused on laboratory data interpretation, such as Aima Diagnostics, can play an important role. Rather than providing diagnoses, such platforms help clarify the meaning of individual laboratory values, identify relationships between biomarkers, and support a more integrated understanding of test results.


Clinical Evidence and Real-World Data (2025)

Clinical performance of the Galleri test has been evaluated in large prospective studies, including the PATHFINDER trial (2023, The Lancet), which reported a positive predictive value of 43% and cancer detection in 1.4% of asymptomatic participants.
Further data from PATHFINDER 2, presented at the ESMO Congress in Berlin in October 2025, expanded these findings. In a cohort exceeding 32,000 individuals, the addition of Galleri to standard screening programs increased cancer detection rates by a factor of seven. Among 216 detected cancer signals (0.93%), 133 cases were confirmed, with approximately half identified at early stages. These results are particularly relevant for cancers that lack effective population screening strategies.



Reviewed by clinical advisors.
25.12.2025
Developed with input from clinical experts and laboratory partners
Educational content. Not a substitute for professional medical advice.