Sci Prog. 2025 Jul-Sep;108(3):368504251383055. doi: 10.1177/00368504251383055. Epub 2025 Sep 24.

ABSTRACT

ObjectiveTo synthesize recent molecular advances that inform diagnosis, risk-stratification, and perioperative treatment in early-stage and locally advanced non-small cell lung carcinoma (NSCLC), with emphasis on comprehensive genomic profiling, minimal residual disease (MRD) detection by circulating tumor DNA (ctDNA), and the translation of biomarkers into targeted and immunotherapy strategies.MethodsSystematic review registered in PROSPERO (CRD420251076423). Searches of PubMed, Scopus, Web of Science, and Embase (January 2015-April 2025) followed PRISMA 2020/PRISMA-S. From 4640 records, 890 duplicates were removed; 3750 titles/abstracts were screened; 150 full texts were assessed; 75 studies met inclusion criteria. Risk of bias used Newcastle-Ottawa Scale (NOS) for observational studies and Cochrane RoB 2 tool for randomized controlled trials; certainty was summarized with GRADE where applicable.ResultsActionable alterations (e.g. EGFR, ALK, KRAS, MET, RET, BRAF, NTRK) are prevalent in early-stage NSCLC and comparable to advanced disease, supporting routine comprehensive genomic profiling in curative-intent settings. Next-generation sequencing (NGS) and ctDNA enable the detection of MRD, earlier relapse prediction, and dynamic treatment monitoring. Perioperative strategies integrating targeted therapy and immunotherapy (e.g. adjuvant EGFR-TKI, neoadjuvant chemo-immunotherapy) improve pathological and disease-free outcomes in selected biomarker-defined populations. Evidence profiles generally show low-to-moderate risk of bias and moderate-to-high certainty for key outcomes related to profiling and MRD, with heterogeneity across platforms and endpoints.ConclusionsMolecular advances-particularly broad NGS and ctDNA-based MRD-are reshaping the perioperative management of early and locally advanced NSCLC, enabling precision selection for targeted and immunotherapy approaches. Standardization of testing workflows and reporting, and cost-effective implementation are priorities for equitable adoption and for future trials that combine NGS, MRD, and multi-omic/AI-driven risk stratification.

PMID:40990633 | PMC:PMC12461064 | DOI:10.1177/00368504251383055

Epigenomes. 2025 Sep 5;9(3):34. doi: 10.3390/epigenomes9030034.

ABSTRACT

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy, characterized by low tumor cellularity, a dense stromal response, and intricate cellular and molecular interactions within the tumor microenvironment (TME). Although bulk omics technologies have enhanced our understanding of the molecular landscape of PDAC, the specific contributions of non-malignant immune and stromal components to tumor progression and therapeutic response remain poorly understood. Methods: We explored genome-wide DNA methylation and transcriptomic data from the Cancer Genome Atlas Pancreatic Adenocarcinoma cohort (TCGA-PAAD) to profile the immune composition of the TME and uncover gene co-expression networks. Bioinformatic analyses included DNA methylation profiling followed by hierarchical deconvolution, epigenetic age estimation, and a weighted gene co-expression network analysis (WGCNA). Results: The unsupervised clustering of methylation profiles identified two major tumor groups, with Group 2 (n = 98) exhibiting higher tumor purity and a greater frequency of KRAS mutations compared to Group 1 (n = 87) (p < 0.0001). The hierarchical deconvolution of DNA methylation data revealed three distinct TME subtypes, termed hypo-inflamed (immune-deserted), myeloid-enriched, and lymphoid-enriched (notably T-cell predominant). These immune clusters were further supported by co-expression modules identified via WGCNA, which were enriched in immune regulatory and signaling pathways. Conclusions: This integrative epigenomic-transcriptomic analysis offers a robust framework for stratifying PDAC patients based on the tumor immune microenvironment (TIME), providing valuable insights for biomarker discovery and the development of precision immunotherapies.

PMID:40981070 | PMC:PMC12452622 | DOI:10.3390/epigenomes9030034

J Liq Biopsy. 2025 Aug 6;9:100320. doi: 10.1016/j.jlb.2025.100320. eCollection 2025 Sep.

ABSTRACT

Over the past decade, liquid biopsy has progressively expanded its role in oncology, supported by mounting evidence demonstrating an increasing number of clinical applications. At the 2025 American Society of Clinical Oncology (ASCO) Annual Meeting, liquid biopsy emerged as a central theme across multiple sessions, with more than 700 abstracts, investigating the clinical utility of liquid biopsy across a wide range of tumor types and disease stages. Applications presented included cancer screening, minimal residual disease (MRD) detection, management of metastatic disease, and potential use for matching patients to clinical trials. This editorial, authored on the behalf of the Young Committee of the International Society of Liquid Biopsy (ISLB) highlights the result of selected studies, grouped by tumor type.

PMID:40980343 | PMC:PMC12447415 | DOI:10.1016/j.jlb.2025.100320

Am J Clin Exp Immunol. 2025 Aug 15;14(4):237-240. doi: 10.62347/EBRY4326. eCollection 2025.

ABSTRACT

Cell-free DNA (cfDNA) fragmentomics has emerged as a powerful and noninvasive approach for cancer detection, characterization, and monitoring. By analyzing genome-wide fragmentation patterns - including fragment length distributions, end motifs, nucleosome footprints, and copy number variations - cfDNA fragmentomics provides high-resolution insights into tumor-specific biological signals even at low tumor burden. This technology offers advantages over conventional mutation-based assays by capturing aggregate structural and epigenomic alterations without requiring prior knowledge of driver mutations. In non-small cell lung cancer (NSCLC), cfDNA fragmentomics enables early detection, discrimination of malignant pulmonary nodules, and post-surgical monitoring of minimal residual disease. Recent studies have demonstrated that fragmentomic risk scores can accurately stratify recurrence risk and improve prognostic sensitivity beyond traditional genomic assays. In hepatocellular carcinoma (HCC), integration of fragment size selection, CNV profiling, and end-motif analysis has led to high-performing models for early diagnosis, particularly in high-risk populations. Moreover, cfDNA fragmentomics has proven effective in detecting malignant transformation in patients with neurofibromatosis-associated peripheral nerve sheath tumors, distinguishing benign from premalignant or malignant lesions with high precision. Expanding beyond these major cancers, fragmentomic approaches have demonstrated diagnostic potential in gastric, urological, hematologic, and pediatric malignancies. Notably, the DELFI-TF (DNA Evaluation of Fragments for early Interception-Tumor Fraction) framework has shown prognostic relevance by correlating pre-treatment cfDNA features with survival outcomes in colorectal and lung cancer patients, outperforming conventional imaging. All of these results highlight the translational importance of cfDNA fragmentomics as a cutting-edge precision oncology tool. Its continued integration into clinical workflows may redefine early cancer detection, facilitate subtype-specific interventions, and enable real-time, individualized treatment monitoring.

PMID:40977920 | PMC:PMC12444407 | DOI:10.62347/EBRY4326

Adv Lab Med. 2025 Jun 16;6(3):259-276. doi: 10.1515/almed-2025-0010. eCollection 2025 Sep.

ABSTRACT

Blood-based circulating tumor DNA (ctDNA) analysis has emerged as a highly relevant non-invasive method for molecular profiling of solid tumors, offering valuable information about the genetic landscape of cancer. Somatic mutation analysis of ctDNA is now used clinically to guide targeted therapies for advanced cancers. Recent advancements have also revealed its potential in early detection, prognosis, minimal residual disease assessment, and prediction/monitoring of therapeutic response. In recent years, significant progress has been made with the development of various PCR and NGS-based methods designed for assessing gene variants in ctDNA of patients with cancer. However, despite the transformative possibilities that ctDNA analysis presents, challenges persist. Standardization of preanalytical and analytical protocols, assay sensitivity, and the interpretation of results remain critical hurdles that need to be addressed for the widespread clinical implementation of ctDNA testing. In addition to somatic mutations, emerging studies on DNA methylation (epigenomics) and fragment size patterns (fragmentomics) in several types of biological fluids are yielding promising results as non-invasive biomarkers for effective cancer management. This review addresses the clinical applications of somatic gene variants in ctDNA, emphasizes their potential as cancer biomarkers, and highlights essential factors for successful implementation in clinical laboratories and cancer management.

PMID:40977813 | PMC:PMC12446922 | DOI:10.1515/almed-2025-0010

Cell Rep Methods. 2025 Sep 19:101183. doi: 10.1016/j.crmeth.2025.101183. Online ahead of print.

ABSTRACT

Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study.

PMID:40975055 | DOI:10.1016/j.crmeth.2025.101183