bims-fragic Biomed News
on Fragmentomics
Issue of 2025–06–08
three papers selected by
Laura Mannarino, Humanitas Research
  1. cfDNAFE: Comprehensively extracting multi-omics features of cell-free DNA for noninvasive diagnosis.
  2. cfTools: an R/Bioconductor package for deconvolving cell-free DNA via methylation analysis.
  3. GUIDE: a prospective cohort study for blood-based early detection of gastrointestinal cancers using targeted DNA methylation and fragmentomics sequencing.
  1. Methods. 2025 Jun 03. pii: S1046-2023(25)00134-3. [Epub ahead of print]
    cfDNAFE: Comprehensively extracting multi-omics features of cell-free DNA for noninvasive diagnosis.
    Wanxin Cui, Junjie You, Wenlong Jie, Zihao Li, Xiaoqing Peng.
      The tissues-of-origin of circulating cell-free DNA (cfDNA) holds great promise for non-invasive diagnosing cancers, monitoring allograft rejection, and prenatal testing. Many features for inferring the tissues-of-origin of cfDNAs are being revealed from different angles, including genetics, epigenetics, and fragmentomics, with whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data of cfDNA. However, it lacks integrative toolkits for automatically extracting the revealed features from the WGS and WGBS data of cfDNA samples. Here, we propose cfDNAFE, a comprehensive and easy-to-use python package for extracting multi-omics features from the aligned cfDNA sequencing data. It covers three aspects: cfDNA genetic features, cfDNA methylation features, and cfDNA fragmentation features, including 13 types of feature profiles. The genetic features include substitution mutations, mutation signatures and copy number variations. The methylation features are the proportions of methylated fragments, unmethylated fragments, and mixed methylated fragments on cell-type-specific markers. The fragmentation features related to the fragment sizes, end/breakpoint motifs, and nucleosome positions are also integrated. To verify the functions of cfDNAFE, we perform analysis on the WGS/WGBS data of cfDNA samples based on the feature profiles extracted by cfDNAFE. The comparison between the cfDNA samples of hepatocellular carcinoma (HCC) patients and normal controls suggests HCC cfDNA samples exhibit significant difference in fragment size related features and breakpoint/end motif patterns, and obtain significant higher OCF values in the liver-specific open regions than the health controls. Conclusively, cfDNAFE is a most comprehensive toolkit which covers the most features for inferring the tissues-of-origin of cfDNAs in existing studies up to date. It will facilitate researchers to build machine learning models for auxiliary diagnosis based on these features. Availability and implementation: https://github.com/Cuiwanxin1998/cfDNAFE.
    Keywords:  cell-free DNA; fragmentation; methylation; mutations; noninvasive diagnosis
    DOI:  https://doi.org/10.1016/j.ymeth.2025.05.013
  2. Bioinform Adv. 2025 ;5(1): vbaf108
    cfTools: an R/Bioconductor package for deconvolving cell-free DNA via methylation analysis.
    Ran Hu, Shuo Li, Mary L Stackpole, Qingjiao Li, Xianghong Jasmine Zhou, Wenyuan Li.
       Motivation: Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.
    Results: To address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies.
    Availability and implementation: cfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools.
    DOI:  https://doi.org/10.1093/bioadv/vbaf108
  3. Mol Cancer. 2025 Jun 05. 24(1): 163
    GUIDE: a prospective cohort study for blood-based early detection of gastrointestinal cancers using targeted DNA methylation and fragmentomics sequencing.
    Ao Huang, De-Zhen Guo, Zhi-Xi Su, Yun-Shi Zhong, Liang Liu, Zhi-Guo Xiong, Dong-Li He, Bin Yan, Quan-Lin Li, Zhen Feng, Wen-Quan Wang, Pin-Xiang Lu, Meng-Jiang He, Zhi-Peng Qi, Qi Guo, Jian-Wen Cheng, Shi-Yu Zhang, Wei Guo, Qing Li, Guo-Yong Lin, Hui-Chuan Sun, Shuang-Jian Qiu, Qi-Ye He, Jia Fan, Ajay Goel, Rui Liu, Gang Jin, Xin-Rong Yang, Jian Zhou.
       BACKGROUND: Gastrointestinal (GI) cancers are among the most prevalent and lethal malignancies worldwide. Early, non-invasive detection is essential for timely intervention and improved survival. To address this clinical need, we developed GutSeer, a blood-based assay combining DNA methylation and fragmentomics for multi-GI cancer detection.
    METHODS: Genome-wide methylome profiling identified 1,656 markers specific to five major GI cancers and their tissue origins. Based on these findings, we designed GutSeer, a targeted bisulfite sequencing panel, which was trained and validated using plasma samples from 1,057 cancer patients and 1,415 non-cancer controls. The locked model was blindly tested in an independent cohort of 846 participants, encompassing both inpatient and outpatient settings across five hospitals.
    RESULTS: In the validation cohort, GutSeer achieved an area under the curve (AUC) of 0.950 [95% Confidence Interval (CI): 0.937-0.962] for cancer detection, with 82.8% sensitivity (95% CI: 79.5-86.0) and 95.8% specificity (95% CI: 94.3-97.2). It detected 92.2% of colorectal, 75.5% of esophageal, 65.3% of gastric, 92.9% of liver, and 88.6% of pancreatic cancers. The independent test cohort included 198 early-stage cancers (stage I/II, 66.4%) and 63 advanced precancerous lesions. GutSeer maintained robust performance, with 81.5% sensitivity (95% CI: 77.1-85.9) for GI cancers and 94.4% specificity (95% CI: 92.4-96.5). It also demonstrated the ability to detect advanced precancerous lesions in the colorectum, esophagus, and stomach as a single, non-invasive blood test.
    CONCLUSIONS: By integrating DNA methylation and fragmentomics into a compact panel, GutSeer outperformed genome-wide sequencing in both accuracy and clinical applicability. Its high sensitivity for early-stage GI cancers and practicality as a non-invasive assay highlights its potential to revolutionize early cancer detection and improve patient outcomes.
    TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05431621.
    Keywords:  Cell-free DNA (cfDNA); Fragmentomics; Gastrointestinal cancer; Methylation; Multi-cancer early detection (MCED); Multi-dimensional features
    DOI:  https://doi.org/10.1186/s12943-025-02367-x
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