bims-fragic 2026-04-26 papers

Issue of 2026–04–26
three papers selected by
Laura Mannarino, Humanitas Research

Expert Rev Mol Diagn. 2026 Apr 23.

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.

Keywords: Fragmentomics; Liquid biopsy; early cancer detection; epigenetics; lung cancer

DOI: https://doi.org/10.1080/14737159.2026.2665256

Exp Mol Med. 2026 Apr 21.

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis.

The rapid and accurate detection of multiple cancers presents considerable challenges, especially for stage I disease, due to the low concentration and heterogeneous nature of circulating tumor DNA. Here we introduce an enhanced multicancer screening assay that integrates whole-genome methylation sequencing with an innovative multimodal analytical framework for cell-free DNA. The ensemble machine learning model integrates four specific cell-free DNA characteristics: average methylation fraction, copy number variation, fragment size ratio and fragment size distribution. The model underwent testing on 1415 samples, encompassing eight primary cancer types and healthy controls. The sensitivity was 93.2%, and the specificity was 95%. The test demonstrated effectiveness in detecting cancers at early stages. The sensitivity was 92.3% for stage I and 92.2% for stage II. The multimodal technique successfully combined average methylation fraction's sensitivity to early epigenetic signals with fragmentomic characteristics. This facilitated the differentiation between healthy individuals and those with early stage cancer. The model achieved an accuracy rate of 85.7% in the top 2 category for correctly identifying the tissue of origin. The results confirm that whole-genome methylation sequencing-based multimodal analysis can improve multicancer early detection technology and revolutionize cancer screening methods.

DOI: https://doi.org/10.1038/s12276-026-01674-7

Clin Cancer Res. 2026 Apr 19. OF1-OF16

plasmaCHORD: A Machine Learning Approach to Distinguish Clonal Hematopoiesis-Derived Variants in Liquid Biopsies from Patients with Solid Tumors.

MEDOCC Group

PURPOSE: Targeted next-generation sequencing (NGS) of cell-free DNA (cfDNA) enables comprehensive molecular profiling and can guide the selection of genotype-targeted therapies. However, the detection of variants derived from clonal hematopoiesis (CH) is a significant confounder in liquid biopsies.
EXPERIMENTAL DESIGN: Using a training cohort of 426 variants identified in cfDNA NGS from 225 patients with stage I to IV solid tumors, we developed plasma Clonal Hematopoiesis ORigin Detection (plasmaCHORD), a machine learning model that includes fragment-, variant-, and patient-level features to distinguish between tumor and CH origin for each variant detected by liquid biopsies. Model performance was assessed by comparison with the reference origin for each plasma variant determined from matched white blood cell and tumor NGS. Following the locking of the model parameters, we applied plasmaCHORD to an independent validation cohort of 1,418 plasma variants detected in 114 patients with metastatic cancers, as well as to cfDNA NGS from patients enrolled in a prospective clinical trial (NCT05585684).
RESULTS: plasmaCHORD predicted tumor origin versus CH origin in the training set with high accuracy (AUC = 0.94). In the independent validation cohort, the locked model maintained similar overall accuracy (AUC = 0.9) and demonstrated significant improvement in accuracy for clinically significant genes. When applied to clinically challenging cases in the context of a precision oncology clinical trial, plasmaCHORD precisely determined variant origin, preventing mismatches with genotype-targeted therapies.
CONCLUSIONS: plasmaCHORD, a multifeature machine learning model, can significantly enhance the ability to identify bona fide tumor variants in routine plasma-only NGS, addressing a critical need for implementing liquid biopsy-guided therapy by minimizing misinterpretation caused by CH.

DOI: https://doi.org/10.1158/1078-0432.CCR-25-0976