bims-fragic 2025-03-30 papers

Issue of 2025–03–30
two papers selected by
Laura Mannarino, Humanitas Research

Cancer Res. 2025 Mar 26. OF1-OF12

Integrating Plasma Cell-Free DNA Fragment End Motif and Size with Genomic Features Enables Lung Cancer Detection.

Early detection of lung cancer is important for improving patient survival rates. Liquid biopsy using whole-genome sequencing of cell-free DNA (cfDNA) offers a promising avenue for lung cancer screening, providing a potential alternative or complementary approach to current screening modalities. Here, we aimed to develop and validate an approach by integrating fragment and genomic features of cfDNA to enhance lung cancer detection accuracy across diverse populations. Deep learning-based classifiers were trained using comprehensive cfDNA fragmentomic features from participants in multi-institutional studies, including a Korean discovery dataset (218 patients with lung cancer and 2,559 controls), a Korean validation dataset (111 patients with lung cancer and 1,136 controls), and an independent Caucasian validation cohort (50 patients with lung cancer and 50 controls). In the discovery dataset, classifiers using fragment end motif by size, a feature that captures both fragment end motif and size profiles, outperformed standalone fragment end motif and fragment size classifiers, achieving an area under the curve (AUC) of 0.917. The ensemble classifier integrating fragment end motif by size and genomic coverage achieved an improved performance, with an AUC of 0.937. This performance extended to the Korean validation dataset and demonstrated ethnic generalizability in the Caucasian validation cohort. Overall, the development of a deep learning-based classifier integrating cfDNA fragmentomic and genomic features in this study highlights the potential for accurate lung cancer detection across diverse populations. Significance: Evaluating fragment-based features and genomic coverage in cell-free DNA offers an accurate lung cancer screening method, promising improvements in early cancer detection and addressing challenges associated with current screening methods.

DOI: https://doi.org/10.1158/0008-5472.CAN-24-1517

Future Oncol. 2025 Mar 25. 1-12

Cost-effective shallow genome-wide sequencing for profiling plasma cfDNA signatures to enhance lung cancer detection.

BACKGROUND: Lung cancer (LC) screening via low-dose computed tomography (LDCT) faces challenges including high false-positive rates and low patient compliance. Circulating tumor DNA (ctDNA)-based tests offer a minimally invasive alternative but are limited by high costs and low sensitivity, particularly in early-stage detection. This study introduces a cost-effective, shallow genome-wide sequencing approach for LC detection by profiling multiple cell-free DNA (cfDNA) signatures.
METHODS: We developed a multimodal cfDNA assay with shallow sequencing coverage (0.5×) that integrates fragmentomic, nucleosome, end-motif, and copy number alteration analyses. A machine-learning model trained on a discovery cohort (99 LC patients, 168 healthy controls) and validated on an independent cohort (58 LC patients, 71 controls) demonstrated robust performance.
RESULTS: The ensemble model exhibited outstanding performance, achieving an AUC of 0.97 and a specificity of 92% in both the discovery and validation cohorts, with sensitivities of 94% and 90%, respectively. Notably, it outperformed hotspot mutation-based assays and the multi-cancer SPOT-MAS assay in sensitivity across all LC stages.
CONCLUSIONS: This assay provides a cost-effective, accurate, and minimally invasive method for LC detection, addressing the limitations of current screening methods. It represents a promising complementary tool to improve early detection and patient outcomes in LC.

Keywords: Lung cancer; cfDNA; copy number alteration; end-motif; fragmentomic; nucleosome; shallow genome-wide sequencing

DOI: https://doi.org/10.1080/14796694.2025.2483154