ArticleJournal of translational medicine2024
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.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Inferring tissue origin from cell-free DNA in metastatic cancer.Cancer metastasis reviews · 2026Review
- Generalizable cancer detection from ultra-low-pass WGS via deep contextual modeling of cfDNA sequences.Molecular biomedicine · 2026Article
- DNA Methylation as a Programmable Information Layer: From Molecular Marks to Disease State Engineering.International journal of molecular sciences · 2026Review
- Clinical validation of a multi-model blood cfDNA methylation assay for early-stage gastrointestinal cancer screening.Journal of advanced research · 2026Article
- TiSMeD: A tissue-specific methylation and expression database for biomarker and translational applications.Molecular therapy. Nucleic acids · 2026Article
- A liquid biopsy-based multi-methylation marker panel for minimally invasive gastric cancer screening.Clinical epigenetics · 2026Article
- Design and Testing of Root-Specific Synthetic Promoters by Machine Learning.International journal of molecular sciences · 2026Article
- Harnessing viral footprints in circulating free DNA (cfDNA) for early cancer detection: A focus on liquid-biopsy-based screening.International journal of cancer · 2026Review
- Detection of primary cancer types via fragment size selection in circulating cell-free extrachromosomal circular DNA.Genome medicine · 2026Article
- Nanotechnology-Assisted Molecular Profiling: Emerging Advances in Circulating Tumor DNA Detection.International journal of nanomedicine · 2026Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Plasma cell-free DNA methylation profile before afatinib treatment is associated with progression-free and overall survival of patients with epidermal growth factor receptor gene mutation-positive non-small cell lung cancer.Clinical epigenetics · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
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12 authors.
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Abstract
backgroundCell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.
methodsWe constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.
resultsOur final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.
conclusionsIn conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.
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