Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Drug Des Devel Ther. 2026 Sep 5;20:543657. doi: 10.2147/DDDT.S543657. eCollection 2026.

ABSTRACT

Metabolic dysfunction-associated steatohepatitis (MASH) is not solely a disorder of hepatocellular lipid accumulation, but a multicellular disease driven by coordinated metabolic stress, sterile inflammation, fibrogenesis, and niche remodeling. Recent therapeutic progress with the provisional approval of resmetirom and semaglutide has validated MASH as a tractable clinical target. However, many experimental agents have shown limited or inconsistent efficacy, particularly for regression of hepatic fibrosis or cirrhosis, reflecting the biological heterogeneity and dynamic cellular architecture of the disease. Distinct from conventional pathway- or drug class-based reviews, we summarize emerging therapeutics through a liver cell-centered framework, integrating hepatocyte-directed metabolic therapies, immune-cell modulation, hepatic stellate cell-targeted antifibrotic strategies, niche-directed approaches involving liver sinusoidal endothelial cells and cholangiocytes, systemic multi-cell modulators, and precision-delivery technologies. We further compare how these interventions reshape pathogenic communication among hepatic and extrahepatic compartments, while emphasizing unresolved challenges in drug target selection, cellular specificity, disease-stage dependency, safety, and patient stratification. This perspective emphasizes the need to move from isolated pathway targeting toward cell- and network-informed therapeutic strategies supported by spatial multi-omics, human-relevant models, and precision delivery.

PMID:42719321 | PMC:PMC13557022 | DOI:10.2147/DDDT.S543657

Open Res Eur. 2026 Apr 3;6:87. doi: 10.12688/openreseurope.23403.1. eCollection 2026.

ABSTRACT

BACKGROUND: Pancreatic cancer and ovarian cancer are very challenging to diagnose at early stages. The endoscopic retrieval of biopsy tissue from a suspected benign or malignant lesion is challenging due to the tissue's nature. Therefore, within the Instand-NGS4P framework, we developed Measurable Residual Disease (MRD) prototypes to analyze blood plasma samples, with the aim of cost-effectively supporting the differential diagnosis of suspected pancreatic or ovarian neoplasms.

METHODS: Our MRD prototypes examine blood plasma for mutations in cell-free DNA in specific genes associated with pancreatic neoplasms or ovarian neoplasms, respectively. Unique molecular identifiers (UMIs) are used to enable bioinformatic error correction. Ultra-deep sequencing is demonstrated on sequencing platforms from two different vendors (Illumina and MGI). We provide detailed information on bioinformatic processing of sequencing data to perform error-correction.

RESULTS: Using commercially available reference standards, we demonstrate stable mutation detection down to a variant allele frequency (VAF) of 0.1%. At a coverage of 4,000x duplex consensus reads, only two false positives were observed, which can be efficiently mitigated using an appropriate filtering strategy.

CONCLUSIONS: The technical usability of our MRD prototype has been clearly demonstrated for stable low-level VAF detection in commercial reference samples.

PMID:42728989 | PMC:PMC13560845 | DOI:10.12688/openreseurope.23403.1

J Mol Diagn. 2026 Sep 11:S1525-1578(26)00156-X. doi: 10.1016/j.jmoldx.2026.08.002. Online ahead of print.

ABSTRACT

Analysis of circulating tumor-DNA (ctDNA) provides a molecular assessment that can complement routine imaging in childhood cancer management. Detailed monitoring of ctDNA levels may provide information on treatment efficacy and resistance, minimal residual disease and allows for early detection of relapse. Here, tumor-informed ctDNA analysis was applied to 90 blood plasma samples collected from eight children with malignant tumors. Four to ten tumor-specific mutations per patient were assessed using SiMSen-Seq, a digital sequencing approach utilizing unique molecular identifiers (UMIs). The effects of individual SiMSen-Seq assays and plasma samples were evaluated in relation to their impact on background error rate, number of detected target molecules and mutant calling using different UMI family size cutoff settings. The use of at least two sequencing reads per UMI provided the best overall performance by generating the highest number of detected target molecules and hence the optimal chance to detect low-frequent mutations. Data were consistent between SiMSen-Seq assays and plasma samples, providing robust ctDNA profiling over time for all patients. In conclusion, the results show that optimal use of UMIs in tumor-informed ctDNA analysis enables sensitive molecular readout that can assist in management of childhood cancers.

PMID:42727690 | DOI:10.1016/j.jmoldx.2026.08.002

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279