Nucleic Acids Res. 2025 Oct 14;53(19):gkaf990. doi: 10.1093/nar/gkaf990.

ABSTRACT

BRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PTC) as a cause of resistance to poly(ADP-ribose) polymerase inhibitors (PARPis) and cisplatin in cells derived from the BRCA2-mutated ovarian cancer line PEO1 by PARPi selection. Despite persistence of the signature 4965C > G (p.Y1655X) BRCA2 mutation, low-level expression of full-length BRCA2 protein was detectable in these cells by immunoblotting and tandem mass spectrometry. Either BRCA2 knockdown or gene interruption 5' or 3' to the PTC restored treatment sensitivity, implicating BRCA2 in the resistance. Reporter assays demonstrated UAG-selective readthrough in the resistant clones but not parental cells. Moreover, custom searching of global proteomic data indicated readthrough of stop codons, particularly UAGs, in additional proteins in the resistant clones. Finally, multi-omic analysis identified multiple changes in the nonsense-mediated decay and termination machineries that favor readthrough. Accordingly, the present results identify PTC readthrough as a potential mechanism of drug resistance in cells with BRCA2 nonsense mutations.

PMID:41099700 | PMC:PMC12526053 | DOI:10.1093/nar/gkaf990

Diagnostics (Basel). 2025 Sep 23;15(19):2425. doi: 10.3390/diagnostics15192425.

ABSTRACT

Background/Objectives: Next-Generation Sequencing (NGS) has transformed cancer diagnostics and treatment by enabling comprehensive genomic profiling of tumors. This review aims to summarize the current applications of NGS in oncology, highlighting its role in early detection, precision therapy, and disease monitoring. Methods: We conducted a comprehensive review of the recent literature, focusing on the application of NGS in cancer care. Results: NGS enables high-resolution genomic profiling, identifying actionable mutations (e.g., EGFR, KRAS, and ALK) and immunotherapy biomarkers (e.g., PD-L1, TMB, and MSI), guiding personalized treatment selection and improving outcomes in advanced malignancies. Liquid biopsy enhances diagnostic accessibility and enables real-time monitoring of minimal residual disease and treatment resistance. Despite these advances, widespread clinical adoption remains constrained by technical limitations (e.g., coverage uniformity and sample quality), economic challenges (high costs and complex reimbursement), and interpretative issues, including the management of variants of uncertain significance (VUSs). Conclusions: NGS is central to precision oncology, enabling molecularly driven cancer care. Integration with artificial intelligence, single-cell sequencing, spatial transcriptomics, multi-omics, and nanotechnology promises to overcome current limitations, advancing personalized treatment strategies. Standardization of workflows, cost reduction, and improved bioinformatics expertise are critical for its full clinical integration.

PMID:41095644 | PMC:PMC12523276 | DOI:10.3390/diagnostics15192425

Cancers (Basel). 2025 Sep 29;17(19):3165. doi: 10.3390/cancers17193165.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential-yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap.

OBJECTIVE: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques-including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)-that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records.

DISCUSSION: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain-including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption.

CONCLUSION: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.

PMID:41097693 | PMC:PMC12524159 | DOI:10.3390/cancers17193165

Ann Surg Oncol. 2025 Oct 14. doi: 10.1245/s10434-025-18556-3. Online ahead of print.

ABSTRACT

BACKGROUND: Familial pancreatic cancer (FPC) will be enriched for germline mutations (GLMs), particularly in homologous recombination repair (HRR) genes, but its distinction from sporadic pancreatic cancer (PC) remains unclear.

METHODS: We retrospectively analyzed 111 resected PCs, including 13 patients with FPC (11.8%) and 98 with non-FPC (88.2%). Whole-exome sequencing targeted 151 cancer-related genes, with parallel gene expression profiling. GLMs were assessed by ClinVar and in silico tools. Homologous recombination deficiency (HRD) scores, COSMIC signatures, immune deconvolution, and survival were compared.

RESULTS: Patients with FPC and non-FPC were comparable in age, sex, tumor stage, and receipt of adjuvant chemotherapy. ClinVar-annotated GLMs were found in 2/13 patients with FPC (15.4%) and 4/98 patients with non-FPC (4.1%). FPC cases more often carried pancreatitis-associated variants (SPINK1, CFTR), whereas non-FPC included HRR-related variants (PALB2, FANCG). When potentially pathogenic HRR-related variants were considered together, prevalence was similar (23.1% vs. 12.2%, p = 0.380). HRD scores did not differ (median 22 vs. 19, p = 0.591), and high HRD scores (≥ 42) were observed only in two non-FPC cases, including one with PALB2. Differential expression analysis revealed no significant differences after false discovery rate correction. Multivariate analysis indicated that FPC status was not an independent prognostic factor (hazard ratio 1.73, p = 0.084).

CONCLUSIONS: Transcriptomic profiles and HRD status were similar between patients with FPC and patients with non-FPC. A spectrum of GLMs was observed in both groups, suggesting that hereditary risk variants are not exclusive to FPC and underscoring the importance of germline testing in all patients with PC.

PMID:41085800 | DOI:10.1245/s10434-025-18556-3

Front Med (Lausanne). 2025 Sep 26;12:1648612. doi: 10.3389/fmed.2025.1648612. eCollection 2025.

ABSTRACT

Small airways-defined as bronchioles <2 mm in internal diameter that lack cartilaginous support-are frequently involved in the earliest stages of chronic obstructive pulmonary disease (COPD). While COPD is defined per GOLD by persistent post-bronchodilator airflow limitation, small-airway dysfunction can precede spirometric abnormality, motivating earlier, imaging- and physiology-based detection (Agustí et al., 2023). Pathological progression typically begins with loss and stenosis of terminal bronchioles, followed by mucus retention/plugging, fibrotic remodeling, chronic inflammation, microvascular abnormalities, and cellular senescence, ultimately resulting in irreversible impairment of gas exchange. Early diagnosis remains difficult, but a suite of advanced non-invasive modalities-including impulse oscillometry system/forced oscillation techniques (IOS/FOT), single- and multiple-breath washout tests, high-resolution CT with parametric response mapping (PRM), nuclear medicine approaches (e.g., SPECT), dynamic measurements of lung compliance, and Fluorine-19 (¹⁹F) MRI-combined with artificial intelligence markedly improve the sensitivity and specificity for detecting small-airway disease. Therapeutic strategies that target cellular senescence and fibrotic pathways-such as senolytics and antifibrotic interventions-are showing promise, particularly approaches that clear senescent cells or block pro-fibrotic signaling. The integration of single-cell omics, high-resolution microvascular imaging, and molecularly targeted therapies is expected to accelerate precision diagnostics and enable personalized early interventions. This review summarizes recent insights into small-airway physiology, key pathophysiological and molecular mechanisms, and current pharmacological strategies, and emphasizes the clinical principle of "early detection, early diagnosis, early intervention" for managing COPD-related small-airway disease.

PMID:41080967 | PMC:PMC12510933 | DOI:10.3389/fmed.2025.1648612