Poster demonstrates the highly sensitive and accurate deep learning model for cancer detection and multi-cancer classification combining different types of cf-DNA features
GC Genome, a leading company in Genomic Diagnostics, is pleased to announce that the company presented the deep learning algorithm that detects and classifies multi-cancer using cf-WGS (cell free DNA whole genome sequencing), summarized in a poster session held at the American Association for Cancer Research (AACR) Annual Meeting 2022 in New Orleans, Louisiana.
The data from the presentation show that it detects abnormal patterns of cancer more sensitively and accurately than original liquid biopsy as it applies deep learning algorithm on whole genome sequencing data, which uncovers structural variants with distinct tumor type.
In addition, this biopsy can detect the existence of 9 major cancers (△ lung cancer △ colorectal cancer △ breast cancer △liver cancer △ pancreatic cancer △ cholangiocarcinoma △ head and neck cancer △ ovarian cancer △ esophageal cancer) through whole genome sequencing (WGS) from circulating tumor DNA (ctDNA) in the blood. Further, its ‘Multi-cancer prediction’ model can predict the tumor-derived tissues related to the 6 specific types of cancers. (△ lung cancer △ breast cancer △ liver cancer △ pancreatic cancer △ ovarian cancer △ esophageal cancer)
“We are excited to introduce the AI-based liquid biopsy with GC Genome’s original technology applied,” said Eun-Hae Cho, Chief Technology Officer at GC Genome Research Center, “The data to be presented at this year’s AACR meeting demonstrates a major step forward in our commitment to offering clinicians and patients a highly sensitive multi-cancer screening test where we believe cancer screening can save lives.”
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