bims-fragic 2026-10-11 papers

Issue of 2026–10–11
four papers selected by
Laura Mannarino, Humanitas Research

Epigenomics. 2026 Oct 03. 1-13

Cell-free DNA fragmentomics: from chromatin biology to clinical stratification.

Tien-Dung Bui, Minh-Thu Phung, Teh-Ying Chou, Nguyen Quoc Khanh Le.

Plasma cell-free DNA (cfDNA) is fragmented nonrandomly, reflecting the chromatin architecture of its source cells and the nucleases that process it. The fragmentome can therefore provide an indirect readout of cellular regulatory state. This review examines how DNA methylation, chromatin accessibility, nucleosome positioning, protein occupancy, and nuclease activity shape cfDNA fragmentation and considers their implications for oncology. We organize fragmentomic measurements into four classes: coverage and protection, fragment size, fragment-end properties, and diversity and entropy metrics. We discuss the genomic contexts in which these features are informative, including repetitive and transposable elements, and compare platforms by sequencing requirements, feature portability, and the fragment populations captured by different library chemistries. We then review applications in cancer screening, tissue-of-origin inference, molecular subtyping, prognosis, minimal residual disease detection, and treatment response monitoring. A central challenge for clinical translation is attributing fragmentomic changes to their tissue of origin.

Keywords: Cell-free DNA; cancer detection; chromatin accessibility; circulating tumor DNA; end motif; fragmentomics; liquid biopsy; nucleosome positioning

DOI: https://doi.org/10.1080/17501911.2026.2740396

J Liq Biopsy. 2026 Dec;14 100503

A cfDNA fragmentomics-based machine learning model for predicting treatment response and recurrence in locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

Kun Miao, Song Wang, Guofeng Sun, Hua Bao, Yang Shao, Zhijun Zeng.

Neoadjuvant chemoradiotherapy (nCRT) is standard treatment for locally advanced rectal cancer (LARC), but responses vary substantially. We developed Fragmentia-AI Response, a machine learning model that uses cell-free DNA (cfDNA) fragment features to predict pathological response and recurrence risk without relying on somatic mutations. We analyzed 102 patients with LARC treated with nCRT followed by surgery, using 510 longitudinal plasma samples collected at five perioperative time points. The model was trained on post-nCRT (T4) cfDNA from 62 patients to predict pathological complete response (pCR), then evaluated in an internal validation cohort (N = 40) and an independent external cohort (N = 96). Associations with tumor regression grade and recurrence-free survival (RFS) were assessed. The model achieved AUCs of 0.909 (95% confidence interval [CI]: 0.812-1.000) in the training cohort, 0.903 (95% CI: 0.785-1.000) in the internal validation cohort, and 0.835 (95% CI: 0.754-0.916) in the external cohort. Model scores were higher in patients with pCR than in those with non-pCR (Wilcoxon p < 0.001) and were strongly associated with tumor regression grade (Jonckheere-Terpstra test p < 0.001). Higher post-nCRT and post-surgery scores were associated with improved RFS (log-rank p = 0.012 and p = 0.024, respectively). Fragment-level analyses revealed a selective enrichment of 50 - 100 bp cfDNA fragments and a higher proportion of subnucleosomal particles in pCR patients, suggesting a biologically grounded signal underlying model prediction. Fragmentia-AI Response is a mutation-independent cfDNA fragmentomics model that showed reproducible discrimination of pathological response and associations with recurrence risk in LARC, supporting further evaluation as a minimally invasive biomarker for treatment stratification and surveillance.

Keywords: Neoadjuvant; Pathological complete response; Rectal cancer; Recurrence

DOI: https://doi.org/10.1016/j.jlb.2026.100503

iScience. 2026 Oct 16. 29(10): 117621

Decomposition-driven biomarker identification enhances non-invasive early cancer detection.

Weizhong Chen, Ying-Hong An, Rui-Qing Fu, Hai-Xiang Sun, Wen-Jing Zheng, Fang Wang, Hua Chen, Wei Li, Min-Jie Xu, Cheng-Cheng Ma, Zhi-Xi Su, Rui Liu, Xin-Rong Yang.

Accurate marker selection in cell-free DNA (cfDNA) is essential for non-invasive early cancer detection, yet conventional approaches based on bulk tissue data often yield markers with limited efficacy in blood-based assays, due to the sparse and heterogeneous nature of tumor-derived cfDNA released into the bloodstream during early tumorigenesis. Here, we present a component decomposition framework that leverages non-negative matrix factorization (NMF) to resolve distinct methylation components from bulk reduced representation bisulfite sequencing (RRBS) data of five gastrointestinal cancers and their para-carcinoma tissues. This approach demonstrates that cancer-specific signals can be recovered at the component level, enabling the identification of cancer-specific and tissue-of-origin markers with significantly improved performance in cfDNA. Extending this framework to transcriptomic data further validates its utility for cfRNA-based biomarker discovery and molecular subtyping. Together, our framework offers a robust and scalable solution to improve biomarker identification for early cancer detection and patient stratification.

Keywords: TOO marker; biomarker; data decomposition; diagnostics; early cancer detection; marker selection

DOI: https://doi.org/10.1016/j.isci.2026.117621

Nat Commun. 2026 09 04. pii: 10522. [Epub ahead of print]17(1):

Fragmentation patterns of human telomeric chromatin in plasma cfDNA.

Brandon Buck, Abigail Weirich, Stephanie Eramo, Rachel Lopez, Laura K White, Peter Kabos, Craig Forester, Srinivas Ramachandran.

The specialized chromatin structure at telomeres protects the linear ends of chromosomes from DNA surveillance machineries. Here, we demonstrate that circulating cell-free DNA (cfDNA) from plasma can map chromatin structure at telomeres. We find that the telomeric 6-mer repeats (TTAGGG/CCCTAA) are the most abundant circulating 6-mers in cfDNA. Telomeric sequences in cfDNA contain subnucleosomal footprints distinct from the rest of the genome, arising from specific cleavages in the C-rich strand, and a nucleosome repeat length of 145 bp, markedly shorter than the ~170 bp observed genome-wide. The abundance of cfDNA telomeric footprints decreases with age, and this decline is exacerbated by Dyskeratosis Congenita (DC), a telomere biology disorder. Promoter subnucleosome enrichment from cfDNA identifies DC-specific gene signatures that reflect disease states and show enrichment towards chromosome ends. In summary, we demonstrate that cfDNA captures telomere chromatin structure and its genome-wide impact non-invasively, including disease-specific signatures in DC.

DOI: https://doi.org/10.1038/s41467-026-77352-z