A newly published study has identified 'confounders' interfering with cancer signals, key physiological factors that can interfere with cancer-associated cell-free DNA (cfDNA), offering a foundation for improving the accuracy of liquid biopsy tests.
GC Genome, a leading clinical genomics and liquid biopsy company, has led the study published in Clinical Chemistry, which has analysed cfDNA fragmentation patterns in 1,154 healthy individuals
The study, conducted in collaboration with Professor Min-Jung Kwon and her team at Kangbuk Samsung Medical Center, in Seoul, South Korea, examined correlations between cfDNA fragmentomic profiles and 65 clinical variables, including age and liver function markers. The goal was to identify potential confounders that could influence cfDNA-based cancer detection in individuals without cancer.
The study cohort was 1,154 healthy, noncancerous individuals who underwent routine health checkups. Clinical variables included: 65 demographic, hematologic, and biochemical parameters. Three fragmentomic features were derived: cfDNA concentration, short-fragment ratio (SFR), and frequency of cancer-enriched motifs(CEMs).
The key findings were:
- Liver enzymes (including AST, ALP, γ-GTP) and age were identified as major factors altering cfDNA fragmentation patterns
- Elevated AST or age closely resembled cancer-like fragmentomic signatures, blurring the distinction between noncancer and cancer profiles
- AST showed high similarity to fragmentation size patterns seen in lung cancer patients (cosine similarity = 0.98)
- Age showed the highest similarity to cancer-like profiles among clinical variables (cosine similarity = 0.52)
- Receiver Operating Characteristic (ROC) analysis confirmed that these physiological variables can act as confounders by reducing the specificity of cfDNA-based detection, potentially leading to false-positive results.
These findings demonstrate that non-cancer physiological factors can influence cfDNA signals, underscoring the need for confounder-aware modelling approaches in liquid biopsy development.
A GC Genome spokesperson stated: "This study is significant because it uses large-scale data from healthy individuals to identify key confounders that influence cfDNA fragmentation patterns. These insights will play an important role in refining our Multi-Cancer Early Detection (MCED) test, ai-CANCERCH, particularly in reducing false-positive rates and improving test specificity."
- Lee TR, Cho EH, Ahn JM, et al. Impact of Clinical Variables on cfDNA Fragmentomic Signatures and Their Potential as Confounders in Cancer Detection. Clin Chem. 2025 Dec 2. Published online. doi:10.1093/clinchem/hvaf163