bims-fragic 2026-02-01 papers

Issue of 2026–02–01
four papers selected by
Laura Mannarino, Humanitas Research

Bioinformatics. 2026 Jan 29. pii: btag041. [Epub ahead of print]

DNAvi: Integration, statistics, and visualization of cell-free DNA fragment traces.

Anja Hess, Dominik Seelow, Helene Kretzmer.

SUMMARY: DNAvi is a Python-based tool for rapid grouped analysis and visualization of cell-free DNA fragment size profiles directly from electrophoresis data, overcoming the need for sequencing in basic fragmentomic screenings. It enables normalization, statistical comparison, and publication-ready plotting of multiple samples, supporting quality control and exploratory fragmentomics in clinical and research workflows.
AVAILABILITY AND IMPLEMENTATION: DNAvi is implemented in Python and freely available on GitHub at https://github.com/anjahess/DNAvi under a GNU General Public License v3.0, along with source code, documentation, and examples. An archived version is available under https://doi.org/10.5281/zenodo.18097730.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btag041

Biomedicines. 2026 Jan 12. pii: 158. [Epub ahead of print]14(1):

Liquid Biopsy in Early Screening of Cancers: Emerging Technologies and New Prospects.

Hanyu Zhu, Zhenyu Li, Kunxin Xie, Sajjaad Hassan Kassim, Cheng Cao, Keyu Huang, Zipeng Lu, Chenshan Ma, Ying Li, Kuirong Jiang, Lingdi Yin.

Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation.

Keywords: CTCs; MCED; cfDNA; fragmentomics; liquid biopsy; multimodal AI

DOI: https://doi.org/10.3390/biomedicines14010158

Clin Cancer Res. 2026 Jan 27.

Epigenetic Instability Based Metrics in Cell-Free DNA for Early Cancer Detection.

Sara-Jayne Thursby, Zhicheng Jin, Jacob Blum, Andrei Gurau, Michaël Noë, Robert B Scharpf, Victor E Velculescu, Leslie Cope, Malcolm Brock, Stephen Baylin, Thomas Pisanic, Hariharan Easwaran.

PURPOSE: Cancers present significant DNA methylation changes, which arise in a stochastic manner, marked by extensive epigenetic variation, indicative of high epigenetic instability. We aimed to evaluate the utility of epigenetic instability for cell-free DNA (cfDNA)-based cancer detection.
EXPERIMENTAL DESIGN: Through analysis of cancer DNA methylation datasets (n=2,084), we identified a set of 269 CGI regions that robustly captures this instability in a cancer-specific manner. We developed metrics to measure this epigenetic instability, termed the Epigenetic Instability Index (EII), for cancer screening via cfDNA methylation.
RESULTS: Machine learning classifiers employing the EII of these 269 regions efficiently identified breast and lung cancer from cfDNA, differentiating even stage IA LUAD with ~81% sensitivity and early-stage breast cancer with ~68% sensitivity, both at 95% specificity.
CONCLUSION: Our studies demonstrate that quantifying epigenetic instability is a novel, capable approach to distinguishing cancer from normal cases using cfDNA, performing better than standard approaches using absolute methylation changes. The epigenetic instability-based approaches for cancer detection developed here, along with their validation in independent datasets, support further development and the potential for future clinical application of these strategies in cancer screening.

DOI: https://doi.org/10.1158/1078-0432.CCR-25-3384

J Cell Mol Med. 2026 Feb;30(3): e71019

Emerging Role of ctDNA Fragmentomics and Epigenetic Signatures in the Early Detection, Minimal Residual Disease Assessment, and Precision Monitoring of Renal Cell Carcinoma.

Hossam Kamli, Najeeb Ullah Khan.

Renal cell carcinoma (RCC) presents a significant global health challenge, with a substantial proportion of patients diagnosed with advanced or metastatic disease due to the limitations of current diagnostic imaging and the lack of validated non-invasive biomarkers. These conventional methods, including computed tomography and magnetic resonance imaging, often lack the sensitivity and specificity to differentiate benign from malignant small renal masses reliably or to detect minimal residual disease (MRD) post-treatment. This review explores the transformative potential of liquid biopsy, explicitly focusing on circulating tumour DNA (ctDNA) fragmentomics and epigenetic signatures, to overcome these clinical hurdles. This review also explores how the analysis of ctDNA fragmentation patterns-such as size distribution, end motifs, and nucleosome footprints-provides a mutation-independent method to enhance RCC detection, even in low-shedding tumours. Concurrently, RCC-specific epigenetic alterations, particularly DNA methylation profiles, offer particular biomarkers for early detection, tumour classification, and prognostication. This Review examines evidence that integrating these multi-analyte approaches-combining fragmentomic and epigenetic data-synergistically improves diagnostic accuracy, enables sensitive MRD assessment, and allows precision monitoring of treatment response and tumour evolution. Despite existing technical and biological challenges, the convergence of ctDNA fragmentomics and epigenetic profiling heralds a new era for the non-invasive, dynamic, and personalised management of RCC, promising to improve patient outcomes through earlier intervention and tailored therapeutic strategies.

Keywords: ctDNA; early detection; epigenetic signatures; fragmentomics; minimal residual disease; precision monitoring; renal cell carcinoma

DOI: https://doi.org/10.1111/jcmm.71019