Liquid Biopsy(MCED)

Multi-cancer Early Detection (MCED)

The Future of Early Detection Starts Here

Detect Cancer at an Early Stage

At GC Genome, we are developing a liquid biopsy assay that can detect cancer at its earlier and more treatable stages. Our innovative approach, empowered by machine learning engines and fortified with strong analytical ability, enables us to identify cancer signals within cell-free DNA (cfDNA) pattern with remarkable precision through a single blood draw.

Finding Signal in Copious Mine of Information

Our cost-effective and rapid turnaround solution begins with low-coverage whole-genome sequencing (lcWGS). Tumor-derived cfDNA possesses a distinctive DNA profile, and analysis of cfDNA fragments from WGS results allows for the identification of specific sizes and signature patterns present at the ends of these fragments. Furthermore, the mutation density of these fragments is assessed. Through this process, we are able to detect cancer signals in copious mine of information.

Cancer detection with Methylation Markers

Methylation markers, also known as DNA methylation markers, are specific regions of DNA that undergo a chemical modification called DNA methylation. In cancer research and diagnostics, methylation markers are used as biomarkers to detect and classify different types of cancers or to identify cancer at an early stage. At GC Genome, we have advanced technology capable of detecting methylation markers with machine learning analysis, allowing us to pioneer a novel approach in cancer detection.

Our Novel Approach, Powered by AI Engine

Our novel approach using artificial intelligence (AI) enable us to read signals above noises of cancer in genomics and epigenomics from the cell-free DNA patterns of individual.

GC Genome’s Multi-Cancer Early Detection (MCED) Liquid Biopsy Assay can detect multiple types of cancer, including Lung, Ovarian, Pancreas, Colorectal, Liver, and Esophageal. Our genome-wide approach uses millions of data to identify not only the presence of cancer but also its tissue of origin(TOO).

Our Publication on Nature Communications

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA

Featured Presentations

AACR 2023

Deep learning algorithm for cancer detection using multimodal characteristics of whole methylome sequencing of cf-DNA

AACR 2022

Deep Learning Algorithm for Multi-cancer Detection and Classification using cf-WGS

ESMO 2022

Deep Learning-based Multimodal Ensemble Algorithms for Multi-cancer Detection