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PubMed2026

Beyond mutations: epigenetic and fragmentomic landscapes of cfDNA in lung cancer.

INTRODUCTION: Lung cancer is the most frequently diagnosed cancer worldwide and the leading cause of cancer-related mortality. Cell-free DNA (cfDNA) has emerged as a powerful biomarker in cancer detection. Early diagnostics efforts often leverage cancer-associated mutations present in cfDNA, but beyond such mutation-based assays, recent advances have shed light on other non-mutational features. The analysis of cfDNA epigenetic profiles and fragmentation patterns, known as 'fragmentomics,' has revealed a wealth of data to explore in noninvasive lung cancer diagnosis. AREAS COVERED: This review will explore this new narrative, summarizing the current understanding and use of cfDNA epigenetic modifications and fragmentomic patterns, while integrating findings to illustrate their vast potential in early-stage detection and therapeutics. By considering a range of epigenetic and fragmentomic features, cfDNA methylation (5mC, 5hmC), histone modifications, size profiles, and end signatures, this review highlights how the multidimensional integration of such signals shows promise in refining early-stage lung cancer and guiding therapeutic decisions. EXPERT OPINION: cfDNA epigenetic and fragmentomic analyses represent a transformative frontier in lung cancer diagnostics and monitoring. While these approaches demonstrate significant potential, most studies are limited by modest cohort sizes and reports of survival benefits, underscoring the need for large-scale validation and deeper mechanistic understanding.

Humans↗

PubMed2026

fRagmentomics: an R package for integrating cell-free DNA fragment features with mutational status to support liquid biopsy interpretation.

SUMMARY: Liquid biopsy offers a non-invasive approach to study tumor-derived genetic material circulating in plasma. Beyond genetic alterations, the fragmentomic features of cell-free DNA-such as fragment size, genomic position, and end-motifs-provide valuable insights into the biological and clinical context of DNA release. fRagmentomics is a user-friendly R package designed to characterize cfDNA fragments overlapping one or multiple small mutations of any type, starting from an aligned sequencing file (BAM). It supports multiple mutation input formats, accommodates one-based and zero-based genomic conventions, resolves mutation representation ambiguities, and accepts any reference file in FASTA format. For each fragment overlapping a mutation of interest, fRagmentomics outputs fragment-level features including its fragment size, end-motifs, and mutational status, along with additional fragment-level or read-level information. The package implements an indel-aware and optionally soft-clip-preserving fragment size computation that improves accuracy over conventional size estimates based solely on aligned positions. AVAILABILITY AND IMPLEMENTATION: fRagmentomics is licensed under GNU General Public License v3.0 and available at https://github.com/ElsaB-Lab/fRagmentomics, https://anaconda.org/elsab-lab/r-fragmentomics and https://bioconductor.org/packages/fRagmentomics, with documentation and a tutorial. CONTACT: yoann.pradat@gustaveroussy.fr, elsa.bernard@gustaveroussy.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Software↗

PubMed2026

Non-invasive strategy for gastric cancer detection: Integration of cell-free DNA fragmentomics and protein biomarkers.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide, however, accurate and non-invasive diagnostic modalities for GC remain limited. Cell-free DNA (cfDNA) fragmentomics has emerged as a promising tool for cancer cell detection. Here we develop a gastric cancer detection model, named GaFraD model. The GaFraD model uses four cfDNA fragmentomics features, including fragment size ratio (FSR), copy number variation (CNV), 9-bp end motif (Motif), and fragment size at transcription start sites (TF). This model achieves an area under the receiver-operating characteristic curve (AUC) of 0.970 (95% CI: 0.944 - 0.990), a sensitivity of 95.0% and a specificity of 80.9%. By combining the GaFraD model and conventional protein biomarkers CA19-9 and PG-I/PG-II, the CONFIRM model was generated. The CONFIRM model attained an AUC of 0.986 (95% CI: 0.966 - 1.000), a sensitivity of 95.0% and a specificity of 95.6% in detecting GC. Moreover, the CONFIRM model achieved remarkable performance (AUC = 0.983, sensitivity 95.6%, specificity 94.2%) in distinguishing patients with early-stage GC from controls. Our work showed the high discriminatory power in distinguishing GC patients from controls, indicating the clinical potential of using cfDNA fragmentomics combined with protein biomarkers for non-invasive GC detection. The results of the study provide a new avenue for early, accurate, and non-invasive clinical diagnosis of GC.

Cell-free DNA↗

PubMed2026

A cfDNA fragmentomics classifier for noninvasive differentiation of benign and malignant renal masses.

Noninvasive differentiation of malignant and benign renal masses remains a major clinical challenge, particularly for radiologically indeterminate lesions. Here, we developed and validated a plasma cell-free DNA (cfDNA) fragmentomics-based machine learning classifier for renal mass characterization. The model was trained on 331 participants (171 cancer, 160 benign) and independently validated on 144 participants (73 cancer, 71 benign). Three cfDNA fragmentation features, including copy number variation (CNV), fragmentation-based methylation (FRAGMA), and nucleosome footprint (NF), derived from low-pass whole-genome sequencing, were integrated into an ensemble framework. The model achieved strong discriminative performance, with area under the curve (AUC) values of 0.956 in the training cohort and 0.946 in the validation cohort, outperforming individual feature-based models. At a predefined operating threshold corresponding to 90% sensitivity, specificity reached 0.90 and 0.87, respectively. Notably, most cancer samples exhibited low tumor fraction (TF < 3%), yet the model maintained robust performance in low-TF samples (AUCs: 0.952 and 0.941, respectively). Performance remained consistent across tumor stage, grade, and histological subtypes. The classifier also demonstrated potential clinical utility in diagnostically challenging settings, including lipid-poor angiomyolipoma and oncocytoma, with 12 of 13 oncocytoma samples correctly classified in an independent cohort. In addition, the model correctly identified 85.3% of benign masses > 4 cm, for which surgical intervention is more commonly considered, and 84.6% of malignant tumors ≤ 4 cm, for which management can be challenging. Collectively, these findings support cfDNA fragmentomics as a promising noninvasive liquid biopsy approach for renal mass evaluation and clinical decision-making.

Humans↗

PubMed2024

Fragmentomics of plasma mitochondrial and nuclear DNA inform prognosis in COVID-19 patients with critical symptoms.

BACKGROUND: The mortality rate of COVID-19 patients with critical symptoms is reported to be 40.5%. Early identification of patients with poor progression in the critical cohort is essential to timely clinical intervention and reduction of mortality. Although older age, chronic diseases, have been recognized as risk factors for COVID-19 mortality, we still lack an accurate prediction method for every patient. This study aimed to delve into the cell-free DNA fragmentomics of critically ill patients, and develop new promising biomarkers for identifying the patients with high mortality risk. METHODS: We utilized whole genome sequencing on the plasma cell-free DNA (cfDNA) from 33 COVID-19 patients with critical symptoms, whose outcomes were classified as survival (n = 16) and death (n = 17). Mitochondrial DNA (mtDNA) abundance and fragmentomic properties of cfDNA, including size profiles, ends motif and promoter coverages were interrogated and compared between survival and death groups. RESULTS: Significantly decreased abundance (~ 76% reduction) and dramatically shorter fragment size of cell-free mtDNA were observed in deceased patients. Likewise, the deceased patients exhibited distinct end-motif patterns of cfDNA with an enhanced preference for "CC" started motifs, which are related to the activity of nuclease DNASE1L3. Several dysregulated genes involved in the COVID-19 progression-related pathways were further inferred from promoter coverages. These informative cfDNA features enabled a high PPV of 83.3% in predicting deceased patients in the critical cohort. CONCLUSION: The dysregulated biological processes observed in COVID-19 patients with fatal outcomes may contribute to abnormal release and modifications of plasma cfDNA. Our findings provided the feasibility of plasma cfDNA as a promising biomarker in the prognosis prediction in critically ill COVID-19 patients in clinical practice.

Humans↗

PubMed2026

Cell-free DNA genomic and fragmentomic features for early outcome prediction in large B cell lymphoma.

Curative-intent immunochemotherapy fails in ∼30% of patients with large B cell lymphoma (LBCL), yet no validated molecular tool enables early identification of high-risk individuals to guide treatment intensification. Using shallow whole-genome sequencing (sWGS) of plasma cell-free DNA from 190 LBCL patients, we develop and validate the ACT score (aberrations, composition of fragments, and terminal motif analyses), a composite classifier integrating genomic and fragmentomic features from a single post-cycle-1 sample. ACT-positive patients have worse 2-year outcomes versus ACT-negative patients: time-to-progression 29% vs. 83% (hazard ratio [HR]: 4.4, 95% confidence interval [CI]: 1.9-10.0; p = 1.5 × 10-4) and overall survival 47% vs. 93% (HR: 8.7, 95% CI: 3.0-25.4; p = 1.8 × 10-6). The ACT score is independently prognostic of the International Prognostic Index, and their combination identifies the highest risk patients. Unlike mutation-based approaches, this assay requires neither tumor tissue, germline control, nor a baseline plasma sample. Built on open-source tools and sWGS, the ACT score offers a feasible, scalable strategy for early risk stratification in aggressive LBCL.

Humans↗

PubMed2025

Noninvasive detection and differentiation of gastric malignancy using cell-free DNA biomarkers.

INTRODUCTION: Gastric cancer remains a major global health burden, with high mortality driven by late-stage diagnoses that limit treatment options and reduce survival. Current diagnostic methods such as endoscopy and biopsy are invasive, resource-intensive, and impractical for large-scale early detection. OBJECTIVES: This study aimed to develop and validate an ensemble machine learning model integrating four cell-free DNA (cfDNA) fragmentomic feature classes derived from 5 × whole genome sequencing (WGS) data to non-invasively differentiate malignant gastric cancer from benign gastric lesions in high-risk or symptomatic patients. METHODS: A total of 681 plasma samples were prospectively collected, comprising 329 from patients with gastric cancer or high-grade intraepithelial neoplasia (HGIN) and 352 from individuals with benign gastric conditions. The dataset was divided into a training cohort (n = 333) and a temporally independent validation cohort (n = 348). An external validation cohort of 305 participants was also included. RESULTS: The ensemble model achieved an AUROC of 0.920 in cross-validation testing on the training cohort, 0.912 in the independent validation cohort, and 0.896 (95% CI 0.860-0.932) in the external cohort. At a pre-specified prediction threshold of 0.402, the model demonstrated 93.3% sensitivity and 71.9% specificity in the validation cohort, yielding a PPV of 71.3% and an NPV of 93.5%. In the external cohort, sensitivity and specificity were 91.7% and 69.1%, respectively (PPV 75.7%, NPV 88.8%). Model scores correlated with clinical stage, tumor grade, and histopathological subtype. Approximately 71% of non-cancer patients could have been spared unnecessary endoscopy. CONCLUSIONS: The cfDNA fragmentomics-based ensemble model enables accurate, non-invasive differentiation between gastric cancer and benign gastric lesions in high-risk or symptomatic patients. This approach demonstrates strong potential as a pre-endoscopy triage tool, supporting earlier detection and more efficient use of diagnostic resources.

Humans↗

PubMed2026

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

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.18401705.

Software↗

PubMed2026

Liquid Biopsy of HPV Cell-Free DNA Enables Blood-Based Early Detection and Molecular Stratification of HPV-Associated Cancer and Precancer Stages.

Liquid biopsies targeting circulating tumor DNA enable noninvasive cancer detection but lack sensitivity in pre- and early- cancer stages, where clinical benefits would be greatest. Human papillomavirus (HPV) causes six cancer types, accounting for 5% of all cancers worldwide. Targeting HPV cell-free (cf)DNA offers a compelling opportunity to overcome current liquid biopsy constraints due to its unique tumor-specific origin, lack of sequence homology to the human genome, and the high viral-to-human copy ratio per cell. Utilizing HPV-associated anal cancer and precancer as a model, here we applied a custom, multi-feature HPV whole-genome liquid biopsy to biobanked and prospective screening cohorts spanning the HPV infection-precancer-cancer continuum. HPV cfDNA was detected years before cancer diagnosis and as early as the infection stage, with increasing detection as stages advanced. Genomic hallmarks of HPV malignancy, including HPV integration, PIK3CA mutations, and 3q amplification, were detected exclusively in cancer, while precancers exhibited distinct HPV genotypes. Fragmentomics analysis of HPV cfDNA revealed stage-informative signatures reflecting viral epigenetic changes during carcinogenesis. A unified classifier incorporating genomic and fragmentomics features achieved a mean AUC of 0.77 for identifying cancer and high-grade precancer, stages requiring clinical intervention. Together, these findings demonstrate the feasibility of blood-based screening and molecular risk stratification for HPV-associated cancer and precancer.

Journal Article↗

PubMed2026

How advances in machine learning drive early detection and risk prediction of early-onset colorectal cancer.

Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50, is rising across high- and middle-income settings whilst organised screening stays anchored to older age thresholds. Blood-based liquid biopsy, combined with machine learning, is the most plausible route to early detection in this group because it does not depend on bowel preparation, endoscopy capacity, or adherence to stool-based testing. The gap is structural: incidence climbs fastest in the population below the age at which any guideline-endorsed modality is offered. The analytical challenge is that early-stage tumour-derived signals in plasma are low in abundance and distributed across heterogeneous molecular layers: circulating tumour DNA mutations, aberrant methylation, cfDNA fragmentomics, and small non-coding RNA. Machine learning converts these into a single calibrated probability. This review examines where artificial intelligence (AI)-driven liquid biopsy genuinely adds diagnostic value in EOCRC, distinguishes components in which learned models are decorative from those in which they are mechanistically necessary, and identifies the validation deficit separating research cohorts from deployable clinical tools. It summarises the first-generation tools used clinically for early detection and post-treatment monitoring, then considers analytes from exosome-bound microRNAs to long-read whole-genome sequencing of circulating plasma DNA, which reads cytosine modification natively, resolves methylation and fragmentation on single molecules, and characterises structural events short reads cannot anchor. Any analyte can feed a learned model, but more diverse input yields better discrimination. The central argument is that approved, guideline-included blood tests were validated in populations aged 45 and above, and their performance in younger patients cannot be assumed.

cfDNA fragmentomics↗

PubMed2026

Mitochondrial DNA in lung cancer: From biology to clinical implications.

Mitochondrial DNA (mtDNA) is emerging as a relevant component of the molecular landscape in non-small cell lung cancer (NSCLC). Due to its inherent vulnerability to environmental carcinogens, the mitochondrial genome accumulates alterations-such as D-loop and Electron Transport Chain variants- increasingly identified as potential mediators of tumor development and metabolic shifts. Recent findings highlight potential clinical applications of mtDNA. In diagnostics, emerging models based on cf-mtDNA fragmentomics and tRNA-derived fragments have shown promising capabilities for early-stage diagnosis. Prognostically, somatic variants in Complex I and specific mitochondrial lncRNA signatures have been evaluated as independent indicators of overall survival and metastatic risk. Furthermore, mitochondrial mass may potentially support chemotherapy election. Additionally, horizontal transfer of mitochondria to tumor-infiltrating lymphocytes offers a novel framework for understanding resistance to immunotherapy. While these preliminary results provide a promising roadmap for molecular stratification, their integration into routine practice remains a goal that requires further prospective validation in larger, multi-ethnic cohorts to ensure reproducibility and to distinguish functional drivers from passenger variants. Collectively, these emerging findings suggest that mtDNA analysis represents a valuable complementary approach to precision oncology in lung cancer.

Humans↗

PubMed2026

The role of circulating tumor DNA (ctDNA) to detect minimal residual disease in locally advanced gastroesophageal carcinoma: the BUTTERFLY study.

BACKGROUND: Despite advances in perioperative and neoadjuvant strategies, patients with locally advanced gastroesophageal cancers remain at high risk of recurrence after curative intent treatment. No validated biomarkers are available to detect minimal residual disease (MRD) or to guide post-operative risk-adapted management. Circulating tumor DNA (ctDNA) has emerged as a noninvasive tool for disease monitoring; single-parameter or tumor-informed assays, however, may lack sensitivity in low-tumor burden settings. Multimodal, tumor-agnostic approaches may overcome these limitations. METHODS: The BUTTERFLY study is a prospective, multicenter observational study enrolling patients with stage II-III gastric, gastroesophageal junction, or esophageal cancer treated with perioperative chemotherapy or neoadjuvant chemoradiotherapy followed by surgery. It evaluates the diagnostic performance and prognostic value of an academic, tumor-agnostic, multimodal ctDNA assay for MRD detection and prognostic stratification. Serial plasma samples are collected from baseline through post-operative follow-up and at relapse. Cell-free DNA is analyzed using the Agnostic Liquid Biopsy Multimodal Advancement (ALMA) platform, integrating tumor fraction estimation, somatic copy number alterations, fragmentomic features, single-nucleotide variants, and whole-genome methylation profiling. Multimodal features are combined with clinical variables using machine learning-based models to enhance MRD detection and relapse risk stratification. The primary endpoint includes sensitivity and specificity of ALMA-defined ctDNA/MRD status at the 4-8 weeks after surgery landmark, whereas secondary endpoints assess diagnostic performance at other time points and associations between ctDNA status and dynamics with disease-free survival, overall survival, treatment response, and lead time to recurrence. FUTURE PERSPECTIVES: If validated, this tumor-agnostic, multimodal ctDNA approach may enable earlier molecular relapse detection and support personalized post-operative management strategies.

circulating tumor DNA (ctDNA)↗

PubMed2025

GCfix: a fast and accurate fragment length-specific method for correcting GC bias in cell-free DNA.

MOTIVATION: Cell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its noninvasive nature. However, cfDNA fragmentomics and copy number analysis can be complicated by GC bias. There is a lack of GC correction software based on rigorous cfDNA GC bias analysis. Furthermore, there is no standardized metric for comparing GC bias correction methods across large sample sets, nor a rigorous experiment setup to demonstrate their effectiveness on cfDNA data at various coverage levels. RESULTS: We present GCfix, a method for robust GC bias correction in cfDNA data across diverse coverages. Developed following an in-depth analysis of cfDNA GC bias at the region and fragment length levels, GCfix is both fast and accurate. It works on all reference genomes and generates correction factors, tagged BAM files, and corrected coverage tracks. We also introduce two orthogonal performance metrics for (i) comparing the fragment count density distribution of GC content between expected and corrected samples, and (ii) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics. AVAILABILITY AND IMPLEMENTATION: GCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.

Software↗

PubMed2025

Multiple features of cell-free mtDNA for predicting transarterial chemoembolization response in hepatocellular carcinoma.

BACKGROUND: Transarterial chemoembolization (TACE) is the primary treatment modality for advanced HCC, yet its efficacy assessment and prognosis prediction largely depend on imaging and serological markers that possess inherent limitations in terms of real-time capability, sensitivity, and specificity. Here, we explored whether multiple features of cell-free mitochondrial DNA (cf-mtDNA), including copy number, mutations, and fragmentomics, could be used to predict the response and prognosis of patients with HCC undergoing TACE treatment. METHODS: A total of 60 plasma cell-free DNA samples were collected from 30 patients with HCC before and after the first TACE treatment and then subjected to capture-based mtDNA sequencing and whole-genome sequencing. RESULTS: Comprehensive analyses revealed a clear association between cf-mtDNA multiple features and tumor characteristics. Based on cf-mtDNA multiple features, we also developed HCC death and progression risk prediction models. Kaplan-Meier curve analyses revealed that the high-death risk or high-progression-risk group had significantly shorter median overall survival (OS) and progression-free survival than the low-death risk or low-progression-risk group (all p<0.05). Moreover, the change in cf-mtDNA multiple features before and after TACE treatment exhibited an exceptional ability to predict the risk of death and progression in patients with HCC (log-rank test, all p<0.01; HRs: 0.36 and 0.33, respectively). Furthermore, we observed the consistency of change between the cf-mtDNA multiple features and copy number variant burden before and after TACE treatment in 40.00% (12/30) patients with HCC. CONCLUSIONS: Altogether, we developed a novel strategy based on profiling of cf-mtDNA multiple features for prognosis prediction and efficacy evaluation in patients with HCC undergoing TACE treatment.

Humans↗

PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗

PubMed2026

From detection to action: ctDNA-MRD surveillance and translational strategies in early breast cancer.

Recurrence remains a major cause of mortality in early breast cancer (EBC), and conventional follow-up often identifies relapse only after clinically detectable disease has emerged. Circulating tumor DNA-based minimal residual disease (ctDNA-MRD) testing offers the possibility of detecting molecular relapse earlier and refining recurrence-risk assessment during follow-up. This narrative review examines the evolving role of ctDNA-MRD in EBC, focusing on assay interpretation, longitudinal surveillance, MRD-guided trial design, and clinical implementation. Prospective studies consistently show that postoperative or surveillance ctDNA positivity is associated with an increased risk of recurrence. However, test performance and interpretation vary with assay characteristics and sampling strategies, and whether treatment initiated solely on the basis of MRD positivity can improve patient outcomes remains unresolved. The central challenge is no longer simply to detect residual disease earlier, but to determine when and how that information should influence care. Further prospective validation, assay standardization, clear pathways for uncertain findings, and patient-centered implementation will be needed before ctDNA-MRD can be integrated into routine management of EBC.

circulating tumor DNA↗