This technology is a cancer detection assay designed to non-invasively diagnose patients by analyzing a small blood sample for biomarkers and signature genes, leveraging deep neural network modeling to identify early and late stage cancers with high accuracy.
Early and precise detection of cancers is critical for improving therapeutic responses and increasing overall treatment success rates. There is strong evidence that multi-omic analysis including genomics and epigenomics provide unprecedented insight into pathology, allowing physicans to apply precision medicine approaches best tailored to treat a patient’s specific disease. Unfortunately, the current methods for diagnosing cancer and identifying tumor orign predominately rely on scans or biopsies, which are either unable to capture important -omic information such as specific methylation states, or highly invasive.
This diagnostic is a blood-based, biopsy-free method for identifying various cancers by analyzing cell-free DNA (cfDNA) released from tumors and circulating in the blood. By applying machine learning to ss-cfMeDIP-Seq data, users can determine methylation states, identify biomarkers, and screen for signature genes of specific cancers. Built on a deep neural network-based model, this technology has the potential to rapidly diagnose patients, characterize patient specific states, and provide physicians with critical information essential for precision therapies, all while generating a library of cancer-specific cfDNA biomarkers.
This technology has been validated in liver and brain cancer patients.
IR CU23111
Licensing Contact: Joan Martinez
