bims-fragic 2025-04-27 papers

Issue of 2025–04–27
three papers selected by
Laura Mannarino, Humanitas Research

Clin Cancer Res. 2025 Apr 24.

CHIP'ing Signal from Noise in Liquid Biopsy.

The presence of clonal hematopoiesis (CH) in cell-free DNA (cfDNA) analysis can distort the interpretation of results and impact cancer treatment decisions. This CCR Translations discusses the importance of distinguishing the origin of cfDNA variants as tumoral or hematopoietic and its potential clinical implications.

DOI: https://doi.org/10.1158/1078-0432.CCR-25-0569

Nucleic Acids Res. 2025 Apr 22. pii: gkaf313. [Epub ahead of print]53(8):

DirectHRD enables sensitive scar-based classification of homologous recombination deficiency.

Ruolin Liu, Eugenia Roberts, Heather A Parsons, Elizabeth H Stover, Atish D Choudhury, Justin Rhoades, Timothy Blewett, David D Yang, Joyce F Liu, Erica L Mayer, Viktor A Adalsteinsson.

Homologous recombination deficiency (HRD) is a predictive biomarker for efficacy of PARP (poly ADP-ribose polymerase) inhibition and platinum chemotherapy for cancer patients but remains challenging to detect. The discovery of patients without pathogenic mutations in known HR genes but exhibiting genomic scars indicative of HRD led to the FDA approval of the first scar-based HRD test. Despite advancements in whole genome sequencing (WGS) and integration of large training datasets with machine learning models, current methods lack the sensitivity required for detecting HRD scars in low tumor purity samples, especially in liquid biopsies. Here, we describe DirectHRD, a genomic scar-based HRD classifier based on WGS. Compared to other WGS-based methods, DirectHRD exclusively utilizes a highly specific type of HRD scar-small deletions with microhomology-and its associated signatures in a probabilistic framework. We applied DirectHRD to 501 tumor and 90 cell-free DNA (cfDNA) samples from 4 cancer types: breast, ovarian, prostate, and pancreas. Among all 501 tumor biopsies, DirectHRD achieved 100% detection of HRD with high specificity (>90%). Across all 90 cfDNA samples, the method achieved an area under the curve of 0.87 and demonstrated the ability to detect HRD at tumor fractions as low as 1%, making it 10 times more sensitive than state-of-the-art methods.

DOI: https://doi.org/10.1093/nar/gkaf313

Per Med. 2025 Apr 21. 1-10

Genomics and the early diagnosis of lung cancer.

Francesco Pepe, Tancredi Didier Bazan Russo, Valerio Gristina, Andrea Gottardo, Giulia Busuito, Giuliana Iannì, Gianluca Russo, Claudia Scimone, Lucia Palumbo, Lorena Incorvaia, Giuseppe Badalamenti, Antonio Galvano, Viviana Bazan, Antonio Russo, Giancarlo Troncone, Umberto Malapelle.

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages, resulting in poor survival rates. Early detection significantly improves outcomes, yet current screening methods, such as low-dose computed tomography (LDCT), are limited by high false-positive rates, radiation exposure, and restricted eligibility criteria. This review highlights the transformative potential of genomic and molecular technologies in advancing the early detection of LC. Key innovations include liquid biopsy tools, such as circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) analysis, which offer minimally invasive approaches to detect tumor-specific genetic and epigenetic alterations. Emerging biomarkers, including methylation signatures, cfDNA fragmentomics, and multi-omics profiles, demonstrate improved sensitivity and specificity in identifying early-stage tumors. Advanced platforms like next-generation sequencing (NGS) and machine-learning algorithms further enhance diagnostic accuracy. Integrated approaches that combine genomic data with LDCT imaging and artificial intelligence (AI) show promise in addressing current limitations by improving risk stratification and nodule characterization. The review also explores multi-cancer early detection assays and precision diagnostic strategies tailored for diverse at-risk populations. By leveraging these advancements, clinicians can achieve earlier diagnoses, reduce unnecessary procedures, and ultimately decrease LC mortality.

Keywords: Lung cancer early detection; Multi-omics technologies; NGS; artificial intelligence in oncology; cfDNA; genomic biomarkers; liquid biopsy; molecular diagnostics

DOI: https://doi.org/10.1080/17410541.2025.2494982