GC Genome-KAIST team Announces Publication of a Groundbreaking AI-Based Liquid Biopsy Technology for Multi-Cancer Early Detection and Localization in Nature Communications
GC Genome Corporation, a leading genomic diagnostics company, today announced the publication of a new study in Nature Communications, showcasing the company’s novel AI-based liquid biopsy technology. The study highlights the unprecedented accuracy of the technology for cancer early detection and tissue-of-origin localization utilizing advanced AI algorithms to analyze mutation density and patterns of cell-free DNA (cfDNA) and epigenomes in collaboration with the Korea Advanced Institute of Science and Technology (KAIST).
Noninvasive screening by cfDNA holds great promise for multi-cancer early detection. The cancer early detection of various types is an important part of cancer treatment since cancer has a better prognosis and survival rate when diagnosed and treated earlier[1].[2]. However, de novo detection of cancer, especially at early stages, remains challenging and the solution for this urgent need is being actively pursued by using cfDNA-based noninvasive cancer screening for Multi-Cancer Early Detection (MCED) and localization of cancer[3].
“Since cancer screening technologies are limited or premature, diagnostic tests are usually performed after symptoms arise, and early intervention opportunities are often missed, leaving few treatment options,” said Dr. Jung Kyoon Choi, Department of Bio and Brain Engineering, KAIST. “We hope that our methods leveraging large-scale tumor genomes and epigenomes as reference data lay the groundwork for accurate cfDNA-based cancer diagnosis at early stages.”
The Nature Communication study includes a total of 2,543 cancer patient samples and 1,241 normal control samples and describes an ensemble algorithm that incorporates genomic and epigenomic profiles of tumor tissues into a deep learning model. This model analyzes mutation distribution and chromatin organization in reference tumor tissues and uses them as model features to detect the existence of cancer and determine the type of cancer in cfDNA samples.
The technology has demonstrated promising results in detecting multiple types of cancer at an early stage. The technology has shown exceptional sensitivity, achieving a 91.1% performance rate based on a 95% specificity and a high level of accuracy, with an 81.7% success rate in predicting both the presence and type of cancer.
“These results suggest that the GC Genome AI-based cancer early detection technology could help reduce cancer deaths by offering a convenient, high-performing test to people,” said Dr. Eun-Hae Cho, MD, Chief Technical Officer at GC Genome. “We will continue our research to improve accuracy and sensitivity and look forward to making our technology accessible to all cancer patients worldwide.”
[Reference]
[1]. Sankaranarayanan, R. et al. Cancer survival in Africa, Asia, and Central America: a population-based study. Lancet Oncology 11, 165–173 (2010). [2]. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68, 394–424 (2018). [3]. Hackshaw, A., Clarke, C. A. & Hartman, A. R. New genomic technologies for multi-cancer early detection: Rethinking the scope of cancer screening. Cancer Cell vol. 40 109–113 Preprint at https://doi.org/10.1016/j.ccell.2022.01.012 (2022).[About GC Genome]
GC Genome is a leading diagnostics company that aims to connect the care and cure to the world by offering genetic diagnosis services for Oncology, Pre&Neonatal, Rare Diseases, and Health Check-ups and suggesting personalized treatment for longer and healthier lives. The company was established in 2013 as a subsidiary of the GC group, a prominent healthcare organization worldwide. GC Genome operates a CAP-accredited laboratory and places the utmost emphasis on R&D. The company has forged robust partnerships with national hospitals and universities and has shown impressive growth momentum by steadily increasing its test volumes.
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