In a press release, Gene Solutions announces the publication of a peer-reviewed study in the BMC Journal of Translational Medicine that validates the analytical capabilities of its AI-driven Tumor-Specific Methylation Atlas (TSMA). The study, which utilised whole-genome bisulfite sequencing and a Graph Convolutional Neural Network, demonstrated significant improvements in tumour origin prediction accuracy for multi-cancer early detection, achieving up to 100% accuracy for breast cancer, 98% for liver cancer, and 93% for colorectal cancer. This innovative approach also optimises sequencing depth and computational resources, making advanced cancer screening more efficient and accessible. Gene Solutions is committed to further expanding TSMA’s capabilities to reduce assay costs and enhance real-world applications in cancer detection.
[Press Release]
HO CHI MINH, VIETNAM – Media OutReach Newswire – 9 August 2024 – Gene Solutions, a pioneering genetic testing company in South-East Asia, is excited to announce the publication of a new peer-reviewed study that validates the analytical capabilities of its innovative, AI-driven Tumor-Specific Methylation Atlas (TSMA). The study, published in BMC Journal of Translational Medicine: “Tissue of origin detection (TOO) for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks”, details the robust analytical validation process which leverages advanced artificial intelligence to deliver precise and reliable tumor origin predictions in multi-cancer early detection.
Firstly, the bioinformatics team from Gene Solutions used whole-genome bisulfite sequencing (WGBS) on five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC) to construct a tumor-specific methylation atlas (TSMA), where 2,945 CpG regions are discovered between tumor types and WBC. The team then implemented a Deep Learning model of Graph Convolutional Neural Network that combines deconvolution scores from the TSMA with other features to achieve an improved tumor of origin prediction accuracy in the validation dataset of 239 low-depth cfDNA samples.
By enhancing the accuracy of tumor identification through AI-driven method, the study opens up mutliple promises when applying in multi-cancer early detection tests:
Improved Data Accuracy: The combination demonstrated exceptional performance in accurately identifying the tumor of origin, notably up to 100%, 98% and 93% accuracies for breast, liver and colorectal cancer.
Reduced Sequencing Depth: By having a guiding atlas, the R&D team can optimize sequencing depth required for tumor identification, making it more efficient and cost-effective. This reduction in sequencing depth not only accelerates the time-to-result but also conserves costly next-generation sequencing resources.
Optimized Analysis Resources: The AI-driven approach optimizes the use of computational resources, reducing the overall cost and time required for tumor analysis. This optimization is a crucial step towards making advanced circulating tumor DNA analysis accessible and affordable for healthcare providers and patients alike.
Dr. Minh Duy Phan, a lead author of the study commented: “The analytical validation of the new tumor methylation atlas and deep learning algorithm marks a significant milestone in circulating tumor DNA analysis for early cancer signal detection. By harnessing the power of AI, we are enhancing the accuracy and efficiency of multi-cancer early detection technology for real-world utility.”
Future Developments:
With further development by our data team, Gene Solutions is committed to expanding the capabilities of the TSMA. The ongoing research and development efforts aim to enable better quality analysis and application in real-life practice to reduce the cost of assays, ensuring that cutting-edge cancer screening tools are within reach for recommended individuals worldwide.
For more in-depth information about the study and the TSMA, please visit https://spotmas.com/blog/pioneering-a-tumor-specific-methylation-atlas-tsma-to-identify-tissue-of-origin-too-in-multi-cancer-early-detection/
References:
Nguyen, T.H., Doan, N.N.T., Tran, T.H. et al. Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks. J Transl Med 22, 618 (2024), doi: 10.1186/s12967-024-05416-z
About Gene Solutions:
Gene Solutions, a pioneering genetic testing company in South-East Asia, empowers healthcare decisions by providing accessible and critical insights. Their services include next-generation sequencing (NGS) tests that support reproductive health and clinical oncology. Gene Solutions specializes in non-invasive prenatal screening (NIPT), minimal residual disease (MRD) monitoring, multi-gene panel liquid biopsy, comprehensive genomic profiling (CGP), and multi-cancer early detection (MCED) tests. Leveraging clinically validated cell-free DNA analysis methods and artificial intelligence applications in bioinformatics, the company is committed to enable precision medicine. With a network of six international standard NGS Laboratories across Southeast Asia, Gene Solutions remains at the forefront of innovative genetic solutions.
The issuer is solely responsible for the content of this announcement.
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This article is informative only and is not intended to be a substitute for professional medical advice, diagnosis, or treatment, and should never be relied upon for specific medical advice.
