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GC Genome's Groundbreaking Study on cfDNA Fragmentation

GC Genome, a frontrunner in clinical genomics and liquid biopsy solutions, has recently published a pivotal study in Clinical Chemistry that examines the fragmentation patterns of cell-free DNA (cfDNA) across a large cohort of healthy individuals. Conducted with the expertise of Professor Min-Jung Kwon and her team from the Kangbuk Samsung Medical Center, this research provides vital insights into how certain physiological factors can interfere with the detection of cancer-related cfDNA signals.

Study Overview

The investigation focused on a sample size of 1,154 individuals who were confirmed to be cancer-free and had undergone routine health checkups. The primary aim was to correlate cfDNA fragmentation profiles with a total of 65 clinical variables, including demographic data such as age, as well as various biochemical markers. The goal was to identify any potential confounders that might obscure the detection of cancer through cfDNA analysis.

Fragmentomic Features Analyzed

The study derived three essential features from the cfDNA calculations:
1. cfDNA Concentration
2. Short-Fragment Ratio (SFR)
3. Frequency of Cancer-Enriched Motifs (CEMs)

Key Findings

The results unveiled significant physiological factors that alter cfDNA fragmentation patterns, primarily emphasizing the role of liver enzymes—including AST, ALP, and γ-GTP—as well as age. Specifically, it was found that:

• - Elevated levels of AST were shown to closely resemble fragmentomic signatures indicative of cancer, thereby blurring the lines between non-cancerous and cancerous profiles. In fact, the fragmentation size patterns associated with elevated AST bore a striking similarity to those seen in lung cancer patients, achieving a cosine similarity score of 0.98.
• - Age emerged as another critical factor, with a cosine similarity score of 0.52 when comparing age-related patterns to cancer-like profiles.

Importantly, Receiver Operating Characteristic (ROC) analysis confirmed that the physiological variables identified in this study act as confounders, inadvertently reducing the specificity of cfDNA-based cancer detection methods. Consequently, this may increase the probability of false-positive results in non-cancer patients.

Implications of the Study

This groundbreaking study highlights the significant interplay between non-cancer physiological factors and cfDNA signals, underscoring the necessity of incorporating confounder-aware modeling into the development of liquid biopsy tests. A spokesperson from GC Genome remarked,

“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 test, ai-CANCERCH, particularly in reducing false-positive rates and improving test specificity.”

About ai-CANCERCH

Launched in September 2023, ai-CANCERCH is an innovative AI-driven multi-cancer early detection test that utilizes low-cost, minimal blood sample analysis to identify cancer signals across various malignancies. Notably, an upgrade is planned for January 2026 to expand the test's capabilities from detecting six to ten different types of cancer, including colorectal, lung, liver, and breast cancers, among others.

About GC Genome

Established in 2013, GC Genome strives to revolutionize the diagnostics landscape by offering comprehensive genetic testing services focused on Oncology, PreNeonatal analysis, Rare Diseases, and routine Health Check-ups. The company is committed to personalized treatment solutions, ensuring longer and healthier lives for patients. With its state-of-the-art CAP-accredited laboratory at the forefront of research and development, GC Genome continues to grow and expand its testing capabilities around the globe.

Contact for Media Inquiries

• - Sohee Kim
• - Yelin Jun
• - Yoonjae Na
• - GC Biopharma Contact

In conclusion, as GC Genome furthers its commitment to advancing liquid biopsies and cancer detection technologies, this study serves as a catalyst for future research and improvements in early cancer diagnosis.