bims-fragic 2025-03-09 papers

Issue of 2025–03–09
four papers selected by
Laura Mannarino, Humanitas Research

Nat Rev Cancer. 2025 Mar 04.

Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection.

Daniel C Bruhm, Nicholas A Vulpescu, Zachariah H Foda, Jillian Phallen, Robert B Scharpf, Victor E Velculescu.

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

DOI: https://doi.org/10.1038/s41568-025-00795-x

J Liq Biopsy. 2024 Jun;4 100141

In vitro size-selection of short circulating tumor DNA fragments from late-stage lung cancer patients enhance the detection of mutations and aneuploidies.

Christoffer Trier Maansson, Louise Skov Thomsen, Laura Stokkebro, Julie Gabe Dissing, Maiken Parm Ulhoi, Anders Lade Nielsen, Peter Meldgaard, Boe Sandahl Sorensen.

Introduction: Recent studies have demonstrated differences between the fragment length profiles of cell-free DNA (cfDNA) from cancer patients and healthy individuals. This has led to the development of in vitro size-selection procedures which can isolate the short fragments that are enriched with mutated circulating tumor DNA (ctDNA). This has yet to be investigated in a large cohort of lung cancer patients.
Materials and methods: We used plasma samples from 35 stage III and IV lung cancer patients and performed targeted next-generation sequencing (NGS) and variant calling from cfDNA with and without size-selection of short fragments. We identified clonal hematopoiesis (CH) and germline mutations using targeted NGS on paired buffy coat (BC) samples. In addition, we performed a genome-wide copy-number alteration analysis on the cfDNA samples with and without size-selection.
Results: ctDNA containing tumor mutations had a different fragment length profile compared to cfDNA fragments with CH or germline mutations. In vitro size-selection resulted in a median 1.36-fold (interquartile range (IQR): 0.63 to 2.48) mutational allele fraction (MAF) enrichment of tumor mutations whereas CH/germline mutations had a median 0.95-fold (IQR: 0.62 to 1.05) MAF enrichment. Key oncogenic drivers, including KRAS and EGFR were more likely to have a MAF increase with size-selection. Size-selection also increased the number plasma aneuploidy positive samples from 8 of 35 to 20 of 35.
Conclusion: This study expands the knowledge regarding ctDNA fragmentation in lung cancer patients and we demonstrate that in vitro size-selection can increase MAF of tumor mutations and plasma aneuploidy calls. Size-selection could lead to increased sensitivity of ctDNA detection, which is crucial for clinical implementation of liquid biopsies. This study is the largest of its kind studying cfDNA samples from 35 lung cancer patients containing 109 mutations in total.

Keywords: Fragmentomics; Liquid biopsy; Lung cancer; NGS; Size-selection

DOI: https://doi.org/10.1016/j.jlb.2024.100141

Cancer Rep (Hoboken). 2025 Mar;8(3): e70167

The Method of Minimal Residual Disease Detection With Circulating Tumor DNA and Its Clinical Applications in Colorectal Cancer.

Ming Liu, Tianhao Mu, Jia Gu, Mingyan Xu, Shifu Chen.

BACKGROUND: Colorectal cancer (CRC) remains a significant health concern in the world. The existing standard of care guidelines for CRC surveillance fall short of effectively and timely detecting recurrence or metastasis.
RECENT FINDINGS: In recent years, circulating tumor DNA (ctDNA) has emerged as a promising material for minimal residual disease (MRD) detection. In this article, we provide an exhaustive review of the methods utilized for MRD detection via ctDNA, present evidence supporting the potential of ctDNA MRD as a valuable biomarker in clinical applications, and engage in a discussion regarding ongoing ctDNA MRD-based clinical trials in CRC. Finally, we offer insights into future prospects of ctDNA-based MRD methodological advancements and clinical research.
CONCLUSION: It is foreseeable that more sensitive, flexible, and economical MRD detection methods will emerge with the deeper research on cell-free DNA (cfDNA) genomics, fragmentomics, methylomes, and nucleosome imprinting. At the same time, MRD-guided intervention studies will evolve for revolutionizing the treatment paradigm of CRC.

Keywords: circulating tumor DNA; clinical applications; clinical trials; colorectal cancer; minimal residual disease

DOI: https://doi.org/10.1002/cnr2.70167

JCO Clin Cancer Inform. 2025 Mar;9 e2400224

Computational Modeling for Circulating Cell-Free DNA in Clinical Oncology.

Linh Nguyen Phuong, Sébastien Salas, Sébastien Benzekry.

PURPOSE: Liquid biopsy, specifically circulating cell-free DNA (cfDNA), has emerged as a powerful tool for cancer early diagnosis, prognosis, and treatment monitoring over a wide range of cancer types. Computational modeling (CM) of cfDNA data is essential to harness its full potential for real-time, noninvasive insights into tumor biology, enhancing clinical decision making.
DESIGN: This work reviews CM-cfDNA methods applied to clinical oncology, emphasizing both machine learning (ML) techniques and mechanistic approaches. The latter integrate biological principles, enabling a deeper understanding of cfDNA dynamics and its relationship with tumor evolution.
RESULTS: Key findings highlight the effectiveness of CM-cfDNA approaches in improving diagnostic accuracy, identifying prognostic markers, and predicting therapeutic outcomes. ML models integrating cfDNA concentration, fragmentation patterns, and mutation detection achieve high sensitivity and specificity for early cancer detection. Mechanistic models describe cfDNA kinetics, linking them to tumor growth and response to treatment, for example, immune checkpoint inhibitors. Longitudinal data and advanced statistical constructs further refine these models for quantification of interindividual and intraindividual variability.
CONCLUSION: CM-cfDNA represents a pivotal advancement in precision oncology. It bridges the gap between extensive cfDNA data and actionable clinical insights, supporting its integration into routine cancer care. Future efforts should focus on standardizing protocols, validating models across populations, and exploring hybrid approaches combining ML with mechanistic modeling to improve biological understanding.

DOI: https://doi.org/10.1200/CCI-24-00224