bims-fragic 2025-11-30 papers

bims-fragic

Biomed News

on Fragmentomics

Issue of 2025–11–30
ten papers selected by
Laura Mannarino, Humanitas Research

Cell-free DNA fragmentomics as a non-invasive tool for predicting pathologic response in colorectal cancer.
Cell-free DNA methylation and fragmentomics-based liquid biopsy for accurate esophageal cancer detection.
A chromatin fiber model explains cell-free DNA fragmentation signatures of active regulatory elements.
cfOncoXpress: Tumor gene expression prediction from cell-free DNA whole-genome sequences.
The comings and goings of cell-free DNA: Biological and clinical implications.
Tracing the tissue origin of cell-free DNA through open chromatin footprint.
From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.
Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection.
Whole-genome profiling of age- and sex-associated DNA methylation signatures in human plasma cell-free DNA.
Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.

Am J Clin Exp Immunol. 2025 ;14(5): 262-266

Cell-free DNA fragmentomics as a non-invasive tool for predicting pathologic response in colorectal cancer.

Wenshi Hu, Guoyan Li, Wanyu Dong, Yaojuan Lu, Zhou Li, Qiping Zheng.

  Despite advances in screening and therapy, colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, underscoring the need for early detection and for predicting treatment efficacy. This review highlights circulating cell-free DNA (cfDNA) fragmentomics as a promising non-invasive approach for tumor detection and disease monitoring. We focus on fragmentomic features - such as fragment size distributions, fragment-end motifs, and epigenetic signals - which, when integrated into machine-learning models, have shown strong performance in distinguishing patients with CRC from healthy controls. Emerging evidence indicates that, these signatures may support early-stage detection, track disease progression, and predict pathologic complete response (pCR), thereby enabling more personalized treatment strategies. We also discuss the potential role of fragmentomics in non-operative management, including "watch-and-wait" approaches. However, important gaps remain in clinical translation; prospective trials and standardized assays/analysis pipelines are required to validate these findings and define their real-world utility.

Keywords:  Cell-free DNA; colorectal cancer; fragmentomics; non-invasive

DOI:  https://doi.org/10.62347/YSQL3793
BMC Cancer. 2025 Nov 25. 25(1): 1816

Cell-free DNA methylation and fragmentomics-based liquid biopsy for accurate esophageal cancer detection.

Jingyun Wang, Wenjing Zhang, Haohao Li, Yiting Wang, Jian Qi, Jinfu Nie.

   BACKGROUND: Cell-free DNA is a promising source of biomarkers for early cancer detection and carries tumor-driven methylation and fragmentation features that have achieved good diagnostic efficacy across various cancers. However, there were no studies that detected both of them for esophageal cancer diagnosis.
METHODS: In this study, we analyzed the cfDNA methylation and fragmentation markers for accurate esophageal cancer detection. Using cfMeDIP-seq, we profiled 145 plasma samples from healthy controls and esophageal cancer patients. We used multiple algorithms to identify cfDNA methylation markers and fragmentation markers to evaluate the efficacy of early esophageal cancer detection.
RESULTS: Finally, we identified 25 cfDNA methylation and fragmentation markers and constructed a machine-learning model, which achieved a sensitivity of 99% and specificity of 97.82% in an independent cohort. These results indicate that methylation and fragmentomics biomarkers based on cfMeDIP-seq can accurately distinguish esophageal cancer patients from non-tumor controls.
CONCLUSION: Our study based on cfMeDIP-seq highlights the efficacy of cfDNA methylation and fragmentation histology markers in diagnosing esophageal cancer and provides a direction for subsequent research.

Keywords:  Cancer early detection; Cell-free DNA; Esophageal cancer; Fragmentomics; Methylation; cfMeDIP-seq

DOI:  https://doi.org/10.1186/s12885-025-15150-4
bioRxiv. 2025 Nov 13. pii: 2025.11.06.686988. [Epub ahead of print]

A chromatin fiber model explains cell-free DNA fragmentation signatures of active regulatory elements.

Alexis Yang, Garyoung Gary Lee, Surya Chhetri, Razane El Hajj Chehade, Gunsagar Gulati, Medha Pandey, Doris Fu, Yoo-Na Kim, Shahab Sotudian, Hunter Savignano, Sadia D Shahbazi, Chelsea Philpot, Taylor A Hoggood, Doga C Gulhan, Manolis Kellis, Jacob E Berchuck, Sylvan C Baca.

Circulating cell-free DNA (cfDNA) assays are being widely adopted in oncology and maternal-fetal medicine. Patterns of cfDNA fragmentation can provide useful information about gene regulation and expression in human disease from a blood draw. Here, we demonstrate that enhancer RNA expression - a marker of enhancer activity - can be inferred from local patterns of cfDNA fragmentation. We define a transcriptional activation score (TAS) that predicts expression of enhancers and genes based on cfDNA fragment sizes and positions near transcriptional start sites (TSSs). The TAS identifies activity of cancer-associated enhancers in patients with cancer, distinguishes clinically relevant cancer subtypes, and identifies activation of enhancers associated with treatment resistance and therapy response. We propose a simple model to account for our findings based on chromatin fiber structure and the depletion of H1 histone proteins near active TSSs. Our model provides a unified framework that reconciles seemingly conflicting observations from prior fragmentomics studies. Broadly, this work enables blood-based assessments of gene regulation in cancer and non-oncologic diseases to inform pathobiology, diagnosis, and treatment selection.

DOI: https://doi.org/10.1101/2025.11.06.686988
bioRxiv. 2025 Nov 03. pii: 2025.10.31.685915. [Epub ahead of print]

cfOncoXpress: Tumor gene expression prediction from cell-free DNA whole-genome sequences.

Weiling Li, Marjorie Roskes, Alexander Martinez-Fundichely, Ekta Khurana.

Cell-free DNA (cfDNA) fragments in the plasma capture cellular nucleosomal profiles since nucleosome-protected regions escape enzymatic degradation while nucleosome-depleted regions can not. We developed cfOncoXpress, a machine learning framework that uses fragmentation patterns to predict oncogene expression from cfDNA WGS. cfOncoXpress incorporates gene copy number aberrations inferred from cfDNA, including those associated with extrachromosomal DNA. Its application in prostate and breast cancers shows it can predict tumor subtype based on expression of signature genes and activated pathways. cfOncoXpress shows superior performance relative to other state-of-the-art methods and can be used to predict tumor gene expression when tissue biopsies are infeasible.

DOI: https://doi.org/10.1101/2025.10.31.685915
Med. 2025 Nov 21. pii: S2666-6340(25)00353-8. [Epub ahead of print] 100926

The comings and goings of cell-free DNA: Biological and clinical implications.

Yasine Malki, Qing Zhou, Peiyong Jiang, Y M Dennis Lo.

  Cell-free DNA (cfDNA) has emerged as a pivotal biomarker with significant implications across medical fields, including non-invasive prenatal testing and oncology. As cfDNA reflects the physiological and pathological states of the body, a detailed understanding of the biology of cfDNA, including the origins, production, circulation, and clearance, is crucial for advancing its diagnostic applications. This review offers a detailed account of the current understanding of the biology of cfDNA, integrating findings to explore mechanistic insights underlying the production and clearance of cfDNA. We discuss how this interplay is altered in various pathophysiological states-including cancer, pregnancy, systemic lupus erythematosus, infectious diseases, and transplantation-and highlight areas that warrant further characterization. Understanding these processes is essential in studying cfDNA dynamics in health and disease, providing novel insights that could expedite developments that further expand the utility of cfDNA-based diagnostic tests, and pave the way for more personalized applications of cfDNA.

Keywords:  biology of cfDNA; cell death; fragmentomics; liquid biopsy; nucleases; tissue of origin

DOI:  https://doi.org/10.1016/j.medj.2025.100926
Commun Biol. 2025 Nov 25.

Tracing the tissue origin of cell-free DNA through open chromatin footprint.

Lingguo Li, Xiameizi Li, Yongjie Li, Fengxia Su, Yan Zhang, Zhuangyuan Tang, Jinghua Sun, Ya Gao, Xin Jin, Haiqiang Zhang.

Plasma cell-free DNA (cfDNA) is a promising biomarker for liquid biopsy, essential for diagnosing and monitoring diseases. Current methods for estimating tissue contributions primarily rely on methylation markers, which can damage cfDNA, limiting clinical use. While research shows cfDNA coverage near transcription start sites (TSS) of actively transcribed genes decreases due to open chromatin, a comprehensive cross-tissue atlas has been lacking. Here, we identify 2549 tissue-specific, highly expressed genes across 12 human tissues and develop the Tissue Contribution Index (TCI) to quantify tissue contributions to plasma cfDNA using TSS coverage. TCI is validated in cfDNA origin models, including pregnant women and transplant recipients, demonstrating high accuracy. We establish reference intervals using plasma cfDNA from 460 healthy individuals and explore TCI's diagnostic utility in monitoring tissue damage and predicting outcomes. This study presents a simple, cost-effective method for tissue deconvolution of cfDNA, advancing liquid biopsy for disease detection and personalized medicine.

DOI: https://doi.org/10.1038/s42003-025-09232-z
Biomark Res. 2025 Nov 28.

From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.

Xinwei Luo, Sijia Xie, Feitong Hong, Xiaolong Li, Yijie Wei, Yuwei Zhou, Wei Su, Yuhe Yang, Lixia Tang, Fuying Dao, Peiling Cai, Hao Lin, Hongyan Lai, Hao Lyu.

  Cancer remains a leading cause of mortality worldwide, with early detection being critical for improving survival rates. Traditional diagnostic methods, such as tissue biopsies and imaging, face limitations in invasiveness, cost, and accessibility, making liquid biopsy a compelling non-invasive alternative. Among liquid biopsy approaches, circulating cell-free DNA (cfDNA) analysis has gained prominence for its ability to capture tumor-derived genetic and epigenetic alterations. This review summarizes key cfDNA biomarkers, including gene mutations, copy number variations (CNVs), DNA methylation, fragmentation patterns, and end motifs (EMs), and highlights their utility in cancer detection and monitoring. By integrating these multi-modal cfDNA biomarkers, feature fusion approaches have not only enhanced the performance of cancer classification models but also stabilized low-abundance signals, thus ensuring more reliable cancer detection and monitoring. Furthermore, the diagnostic power of cfDNA analysis has been further amplified by machine learning (ML), with both traditional ML and deep learning (DL) methods demonstrating strong predictive performance in routine clinical liquid biopsy applications. However, challenges remain, including tumor heterogeneity, standardization of data processing, model explainability, and cost constraints. Future advancements should focus on refining multi-modal feature integration, developing explainable AI (XAI) models, and optimizing cost-effective strategies to enhance clinical applicability. As computational methodologies advance, the integration of cfDNA biomarkers with ML frameworks holds great promise to reshape non-invasive cancer detection by enabling earlier diagnostics, more accurate prognostic evaluation and personalized treatment strategies.

Keywords:  Artificial intelligence; Biomarker discovery; Cell-free DNA; Circulating tumor DNA; Deep learning; Liquid biopsy

DOI:  https://doi.org/10.1186/s40364-025-00874-z
bioRxiv. 2025 Oct 21. pii: 2025.10.20.683167. [Epub ahead of print]

Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection.

Antonio De Falco, Piera Grisolia, Raffaella Giuffrida, Clara Iannarone, Cinzia Graziano, Marianna Scrima, Alessia Maria Cossu, Rossella Tufano, Maria Vanessa Yow, Chloe Marissa Brown, Palak Bajaj, Marco Bocchetti, Pier Vitale Nuzzo, Floriana Morgillo, Francesco Caraglia, Maria Carminia Delle Corte, Gaetano Di Guida, Teresa Troiani, Fortunato Ciardiello, Maria Rosaria Rizzo, Alfonso Fiorelli, Noemi Maria Giorgiano, Davide Arcaniolo, Giampiero Della Rosa, Marco De Sio, Alessia Covre, Anna Maria Di Giacomo, Luana Calabrò, Paolo Fontana, Marzia Mare, Ola Landgren, Damian J Green, Alex Lesokhin, David G Coffey, Nipun Merchant, Jashodeep Datta, Stefano Forte, Michele Maio, Michele Caraglia, Michele Ceccarelli.

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

DOI: https://doi.org/10.1101/2025.10.20.683167
Commun Med (Lond). 2025 Nov 28. 5(1): 503

Whole-genome profiling of age- and sex-associated DNA methylation signatures in human plasma cell-free DNA.

Wei Chen, Jinjin Xu, Guodan Zeng, Rijing Ou, Changlin Yang, Chuang Xu, Yeqin Wang, Xinxin Wang, Qiuyan Li, Chenhui Zhao, Wenwen Zhou, Yu Lin, Wending Pang, Haiqiang Zhang, Jianhua Yin, Yan Zhang, Xin Jin.

BACKGROUND: Age and sex significantly impact DNA methylation patterns, however, existing datasets typically include only a subset of methylation sites in the human genome, hindering our thorough understanding.
METHODS: We recruited 98 generally healthy adults aged from 22 to 77 and investigated the effects of age and sex on plasma cell-free DNA (cfDNA) methylation through whole-genome bisulfite sequencing (WGBS) and association analysis.
RESULTS: Here we show 3,047 age-associated and 1,053 sex-associated CpGs on autosomes, corresponding to 1,587 and 324 genes, respectively. To the best of our knowledge, many of these CpGs are newly discovered to be age- and sex-related at the DNA methylation level. The discovered sex-differential cfDNA methylation patterns on the X chromosome are related to XCI status. Besides, a cfDNA epigenetic clock comprising 125 CpGs is developed, demonstrating relatively high accuracy in predicting chronological age. Tissue-of-origin analysis reveals that cfDNA derived from monocytes/macrophages, granulocytes, and hepatocytes is associated with age and sex. Several individuals with abnormal cfDNA proportions of some specific cell types are found to have individual health problems.
CONCLUSIONS: Our discovered CpGs and genes help to explain age-related and sex-biased diseases such as psychiatric disorders, diabetes, and autoimmune diseases, and we demonstrate the potential of cfDNA methylation signatures as very promising biomarkers for health monitoring for the general population.

DOI: https://doi.org/10.1038/s43856-025-01220-y
Int J Mol Sci. 2025 Nov 13. pii: 10982. [Epub ahead of print]26(22):

Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.

Elisa De Paolis, Alessia Perrucci, Gabriele Albertini Petroni, Alessandra Conca, Matteo Corsi, Andrea Urbani, Angelo Minucci.

  The adoption of liquid biopsy approaches in clinical practice has triggered a significant paradigm shift in the diagnostic, prognostic, and predictive outcomes for cancer patients. Circulating tumor DNA (ctDNA) is considered a valuable biomarker for monitoring tumor burden and its mutational dynamics. In this context, not all cell-free DNA (cfDNA) molecules are derived from tumor cells. Furthermore, due to tumor heterogeneity, not all ctDNA molecules contain cancer-associated alleles, complicating the direct quantification of the circulating tumor allele fraction (cTF) within the total cfDNA. Cancer arises from the accumulation of multiple genetic and epigenetic changes. Each of these molecular features can be exploited as the basis of methodological strategies used in ctDNA quantification. Different layers of omics data, from genomics, evaluating mutational analysis of somatic single-nucleotide variants and copy number alterations, to epigenomics, primarily consisting of the evaluation of methylation profiles and fragmentation patterns, can be used for this purpose. Some of these approaches can be effective in a multi-modal manner. To date, the quantification approaches for estimating cTF vary enormously, making direct comparisons and an assessment of their translational value challenging. Moreover, the lack of regulatory approval for many of these assays is a critical barrier to their widespread clinical adoption. This review explores the different omics approaches described for ctDNA quantification, outlining strengths and limitations, and highlighting their valuable applications in clinical settings.

Keywords:  NGS; cTF; cfDNA; ctDNA; epigenomics; genomics; liquid biopsy; methylation; omics; transcriptomics

DOI:  https://doi.org/10.3390/ijms262210982