Search Results (1,502)

Cervical small-cell neuroendocrine carcinoma (SCNEC) is a rare cervical cancer with high metastatic potential and is frequently associated with high-risk human papillomavirus (HPV) infection. Because of its low incidence, SCNEC remains understudied and treatment options are limited, posing major therapeutic challenges. This study aimed to characterize SCNEC at the molecular and functional levels to support more informed therapeutic strategies. Organoids and spheroids were generated from a cervical SCNEC biopsy, and a matched organoid-derived xenograft was established in immunodeficient mice. Model fidelity was evaluated by histopathology and immunohistochemistry. HPV status was assessed by p16 immunostaining and HPV18 PCR, and viral–host integration sites were inferred using whole-exome sequencing (WES) junction reads. WES was also used to compare shared somatic variants and copy-number alterations across the patient tumor, organoid, and xenograft. Drug responses were assessed in organoids and spheroids following exposure to a panel of chemotherapeutic agents and a targeted inhibitor. Organoids exhibited robust growth, morphologic maturation, and efficient recovery after cryopreservation. The organoids and matched xenografts faithfully recapitulated SCNEC, with preserved neuroendocrine differentiation (CD56, synaptophysin, and NSE positivity), a high Ki-67 proliferative index (>80%), and strong p16 expression. HPV18 status was conserved across the primary tumor, organoids, and xenografts, with an integration site at chr8 (8q24.21) associated with increased MYC expression. Whole exome sequencing (WES) revealed strong cross-model concordance, including 26 shared somatic variants with a canonical PIK3CA hotspot mutation (p.E542K) and conserved oncogenic copy-number gains of PIK3CA, TERT, and MYC, as well as copy number loss of TP53. Functional assays showed dose-dependent loss of viability following exposure to conventional cytotoxic agents or an mTOR pathway inhibitor. This study presents the first integrated molecular and functional analyses of patient tumors and matched organoid and xenograft models in cervical SCNEC. These models offer robust resources for mechanistic studies and may enable precision therapeutic strategies for this rare malignancy. Full article

Tuberous Sclerosis Complex (TSC) is a genetic disorder caused by mutations that inactivate TSC1 or TSC2 genes. TSC1 or TSC2 mutations activate the mammalian target of rapamycin complex 1 (mTORC1) protein kinase pathway. Although many patients inherit a single copy of a mutant TSC gene, somatic mutations that cause loss of heterozygosity in inhibitory neuroprogenitor cells are hypothesized to be one cause of abnormal development. This may lead to cortical malformations or benign growths along the ventricular-subventricular zone (V-SVZ), cortex, olfactory tract, and olfactory bulbs (OB). This idea is supported by focal single-cell knockout experiments that induce CRE-mediated recombination following neonatal electroporation of conditional Tsc2 or Tsc1 mice. Loss of Tsc2 causes mTORC1 pathway activation and the formation of striatal hamartomas composed of ectopic clusters of abnormal cells and cytomegalic neurons, including within the OB. Neural phenotypes in this model can be partially rescued with Rapalink-1, a bisteric mTOR inhibitor, demonstrating the importance of mTOR in pathogenesis. We previously demonstrated that global V-SVZ neural stem cell (NSC) Tsc2 mutation induced by nestin-CRE-ER^T2 causes mTORC1 pathway activation, which is accompanied by transcriptional and translational errors. While we previously described cultured NSCs and OB granule cells from these mice, we did not thoroughly describe changes outside this region. Here, we provide evidence that removal of Tsc2 from neonatal V-SVZ NSCs causes subtle and rare brain malformations. This is exemplified by ectopic clusters of cytomegalic neurons and mTORC1 activation. This data supports that loss of Tsc2 in NSCs during neonatal development leads to heterotopic clusters in the adult brain. This model may be useful to study TSC, but the rarity and stochastic nature of lesions make the use challenging for identifying mechanisms and testing therapies. Full article

Non-coding RNAs (ncRNAs) are conserved in the genome of cells across the three domains of life. They comprise a diverse group that are particularly prominent in metazoans where they provide a crucial interface between genes and proteins, participating in key cellular processes at different levels: from control of DNA transcription to modulation of messenger RNA stability to modification of protein activity. The interactions of ncRNAs with one another as well as with other RNAs, DNA and proteins form the basis of a genome-wide regulatory network (GRN). Because of the mutual influence of its components on each other, the GRN is a dynamic system. Further, the GRN imposes constraints on which genes are expressed and when, leading to specific gene-expression patterns or transcriptomes. The configurations of the activities of all gene loci represent self-stabilizing cell states, referred to as “attractor” states, each of which corresponds to a distinct cell type. The cancer cell is also an attractor state that arises from a change in the topography of the epigenetic landscape caused by dysregulation of the GRN. It is proposed that the transition to a neoplastic attractor state is caused by ncRNA alterations, while subsequent somatic mutations of oncogenes and tumor suppressor genes drive cell proliferation and clonal expansion. Full article

Background/Objectives: Primary aldosteronism (PA), the leading cause of secondary hypertension, results from autonomous aldosterone hypersecretion. It is characterized by increased extracellular volume, elevated cardiac output, and greater arterial stiffness compared with essential hypertension, reflecting aldosterone-mediated hemodynamic dysregulation. The prevalence and morbidity of PA are increasingly acknowledged; however, PA continues to be underdiagnosed because of limited screening and diagnostic complexity. Methods: A narrative review was conducted using PubMed (2015–2025), with terms targeting PA epidemiology, excluding treatment-focused studies. From 971 articles, 133 relevant studies (original research studies, reviews, meta-analyses) were included, addressing prevalence, risk factors, comorbidities, genetics, and diagnostic issues. Results: PA prevalence in hypertensive populations is 5–10%, rising to 17.8% in young-onset and 20–30% in resistant hypertension. Screening indications include resistant/severe hypertension, hypokalemia, adrenal incidentaloma, young-onset disease, obstructive sleep apnea (59.8% comorbidity in hypertensive PA), and familial history, while a link may exist with papillary thyroid cancer. The aldosterone–renin ratio (ARR) is the primary screening tool, limited by assay variability and confounders (e.g., sodium intake). Confirmatory testing (such as with the saline infusion test) is often challenging to perform in routine practice. Adrenal venous sampling (AVS) is useful for subtyping unilateral (aldosterone-producing adenoma; APA; ~35–50%) vs. bilateral (idiopathic hyperaldosteronism; IHA) disease, despite technical challenges. Somatic mutations (e.g., KCNJ5, more frequent in Asians) and rare familial forms drive PA. Complications include cardiovascular events (Major Adverse Cardiovascular Events; MACE: 13.6% at 5.8 years), stroke, renal impairment (decreased eGFR, proteinuria), metabolic disorders (diabetes, obesity), and novel associations (vertebral fractures, renal stones, normal-tension glaucoma). Psychiatric comorbidities (depression/anxiety in 30–70% of patients) have been associated with central mineralocorticoid receptor effects, with sleep disturbances being prominent in females. Subclinical PA predicts hypertension and arterial stiffness. Conclusion: Improved screening protocols, standardized ARR cutoffs, and advanced imaging and genetic analyses are needed to enhance PA detection. Future research should validate cost-effective screening and clarify psychiatric-metabolic links for optimized management. Full article

Background/Objectives: Peritoneal mesothelioma is a rare malignancy characterized by limited therapeutic options and a poor prognosis. Genomic characterization can enhance the understanding of the molecular mechanisms that lead to this disease and can contribute to improved survival outcomes through therapeutic targets. Methods: Analysis was performed using a dataset from the AACR GENIE database (v17.0-public) comprising 204 samples from 192 patients. Data were analyzed to identify patterns in genomic alterations and clinical demographics. Within the GENIE cohort, histologic subtype information was incomplete and inconsistently reported across contributing institutions. Hence, histological subtype genomic analysis was not viable. Results: The most common somatic mutation was found in the BAP1 gene (25.98%). Other common mutations were found in the NF2 (15.19%), TP53 (9.3%) and SETD2 (8.3%) genes. Several pathways were found as potential treatment targets including the chromatin remodeling, Hippo, and p53 signaling pathways. Given the size of our dataset, we were unable to draw significant conclusions about certain demographics. Conclusions: This study presents data that can help draw conclusions on common mutations, mutual exclusivity patterns, and demographics at risk for peritoneal mesothelioma. Genomic analysis of peritoneal mesothelioma may inform possible intervention targets for therapeutic treatment. Full article

Background/Objectives: Head and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surgery, radiation, and immunotherapy, have shown limited improvement in outcomes. Drug repurposing offers a cost-effective strategy to identify new therapeutic options by leveraging existing medications with known safety profiles. Within this study, we developed the GARD pipeline (Genomic Alteration-based Repurposing for Drugs), designed to uncover repurposing candidates for HNC using genomic and network-based approaches. Methods: GARD integrates multi-omics data from The Cancer Genome Atlas (TCGA), including copy number variation (CNV) and somatic mutations (SOM). The cohort was stratified by human papillomavirus (HPV) status. Risk-associated genes were identified and then expanded via high-confidence protein–protein interaction (PPI) networks. Top candidate genes were filtered through comprehensive analysis of publicly available literature data in PubMed using LLMs to validate the relationship between the identified genes and HNC. The top risk genes and their network-expanded neighbors were mapped against DrugBank, and through statistical significance testing and literature validation, established significant drug–gene associations. Results: Significant genes associated with HNC, inferred by genomics alteration, were identified across HPV-positive and HPV-negative subgroups, such as PIK3CA, SOX2, TP53, EIF4G1, TLR7, CLDN1, PRKCI, and EPHA2. Further expansion through the PPI network identified other targetable genes such as EGFR, ERBB2, and the FGFRs. Literature-based validation efforts ensured confidence in the gene–disease association. Drug–gene mapping revealed candidates spanning those already in clinical trials for HNC (e.g., Afatinib, Cabozantinib, Dasatinib, Brigatinib, Lenvatinib, Capivasertib, and Erdafitinib) and emerging or repurposing candidates (Amuvatinib, XL765 (Voxtalisib), Golotimod, Artenimol, Quercetin, and Acetylsalicylic Acid), offering opportunities for precision repurposing. Conclusions: The GARD pipeline demonstrates a genomics-driven, network-informed framework for systematic drug repurposing in HNC. HPV stratification enhances precision, literature-based validation strengthens confidence, and integrated drug mapping enables refinement of existing therapies and discovery of novel candidates for personalized treatment strategies. Code Availability: The full implementation of the GARD pipeline, including preprocessing scripts, statistical analysis modules, and visualization tools, is publicly available on GitHub. Full article

Background/Objectives: Breast cancer (BCa) is a heterogeneous disease with molecular and genetic characteristics that significantly influence prognosis and treatment strategies. Age-related differences in tumor biology may impact therapeutic decisions; however, data on somatic mutation profiles in geriatric patients are limited. Methods: This retrospective study included 371 BCa patients (53 geriatric ≥ 65 years, 318 non-geriatric) whose clinicopathological and next-generation sequencing (NGS) data were analyzed. Immunohistochemical markers and molecular subtypes were assessed according to ASCO/CAP guidelines. Mutational profiles were obtained using the QIAseq Human BCa Panel (93 genes). Results: Among all patients, 1669 somatic mutations were detected, and 93.3% of patients harbored at least one mutation. Mutation prevalence was similar between geriatric (96.2%) and non-geriatric (92.8%) groups (p = 0.526), indicating that age did not significantly affect overall mutational burden. The most frequently mutated genes were ATR, TP53, PIK3CA, PTEN, RAD50, BLM, NF1, AR, BRCA2, and KMT2C. Notably, PIK3CA mutations were significantly more frequent in geriatric patients (28.3% vs. 23.2%, p = 0.0418). TP53 mutations correlated with higher Ki-67 proliferation indices (p = 0.035), while ATR mutations were more common in HER2-enriched subtypes (p = 0.002). Conclusions: Our findings indicate that while the overall somatic mutational load in BCa does not differ significantly with age, specific molecular alterations—particularly the enrichment of PIK3CA mutations in elderly patients—underscore the importance of integrating genomic profiling into personalized treatment planning. This study represents the first comprehensive molecular characterization of geriatric BCa patients in Türkiye, providing valuable insights for age-specific genetic profiling, treatment optimization, and future multicenter translational studies. Full article

Tumor Mutational Burden (TMB) is a critical biomarker used to determine patient eligibility for immunotherapy with immune checkpoint inhibitors. However, its gold-standard assessment via whole exome sequencing is limited by high costs, technical complexity, and lengthy processing times. To address these challenges, we investigated whether Ultra-High-Frequency (UHF) electromagnetic wave sensing could serve as an alternative method for evaluating TMB. We analyzed the dielectrophoresis crossover frequency spectrum and corresponding electromagnetic signature (EMS) of cancer cells using a lab-on-a-chip biosensor that integrates microfluidics with dielectrophoresis-based electro-manipulation. Across seven solid tumor cell lines exhibiting diverse TMB levels, EMS exhibited an upward shift correlated with higher TMB, suggesting a relationship between mutational load and electromagnetic behavior. To further explore this connection, we artificially increased the somatic variant burden by exposing cells to the mutagen N-ethyl-N-nitrosourea (ENU). EMS measurements reliably detected the induced increase in variant load in ENU-treated cells. Overall, these findings demonstrate that EMS can detect both intrinsic TMB differences and experimentally induced increases in mutational burden, enabling refined categorization of cancer cells. Although further validation is required, this work lays the foundation for developing complementary, rapid, and accessible tools to support cancer cell stratification and guide immunotherapy decision-making. Full article

Background/Objectives: Colorectal cancer (CRC) liver metastases present a significant clinical challenge due to high recurrence risks post-resection. Traditional diagnostics often fail to detect early-stage minimal residual disease (MRD). This preliminary pilot study evaluated ctDNA dynamics in 10 patients with liver metastases using a personalized multistep approach. Methods: Following primary tumor Next-Generation Sequencing (NGS) to identify somatic mutations in KRAS, NRAS, TP53, RET, APC, and WRN, custom TaqMan assays were designed for longitudinal plasma analysis. Four methodologies were compared: HRM-PCR, PNA-enhanced qPCR, and two digital platforms (dPCR and ddPCR). Results: While HRM-PCR sensitivity was limited in plasma, digital platforms demonstrated 100% qualitative concordance. MRD-negative status (VAF 0.00%) was identified in 70% of cases (P01, P03, P06, P07, P08, P09, P10), while detectable ctDNA in patients P02, P04, and P05 strongly correlated with aggressive progression. Digital PCR enabled the ultra-low detection of Variant Allele Frequencies (VAFs), identifying high molecular burdens (e.g., P05, VAF 49%) correlating with rapid decline, and capturing early molecular residue in P04 (VAF 0.62%). Conclusions: Our preliminary findings confirm that personalized longitudinal VAF tracking via digital PCR provides superior prognostic value, serving as a robust tool for recurrence monitoring in personalized CRC therapy. Full article

Background: Gastric-type adenocarcinoma (GAS) of the uterine cervix is a rare malignancy with poor clinical outcomes. However, the carcinogenic processes involved remain unclear. Methods: Normal cervical glands, lobular endocervical glandular hyperplasia (LEGH), and GAS from the same patients were collected using laser microdissection for whole-exome sequencing. Single nucleotide variants (SNVs) and copy number alterations (CNAs) were analyzed. Phylogenetic trees were constructed based on the SNV and CNA profiles. Results: Analysis of seven matched samples demonstrated higher frequency of somatic mutations in the exonic regions in GAS than in LEGH. CNAs were prevalent in GAS but rare in LEGH. The phylogenetic analyses revealed various branching patterns. However, in three cases, the data suggested a sequential transition from LEGH to GAS, potentially associated with mutations in receptor-type protein tyrosine phosphatases such as PTPRF and PTPRT. STK11 and ARID1A mutations were present in LEGH, with an increased variant allele frequency observed in GAS. In contrast, SMAD4 and SMAD2 showed frequent loss-of-function–type alterations in GAS, including copy-number loss, but were not detected in LEGH. Conclusions: These findings provide insights into the genomic landscapes of LEGH and GAS and suggest potential molecular markers for this transition, which may inform future diagnostic and therapeutic research. Full article

The accumulation of somatic mutations contributes to clonal evolution and biological properties of cancers. Acquired mutations in mitochondrial (mt)DNA have been studied, but with the exception of those in isocitrate dehydrogenase genes, no comprehensive assessment of mutations in nuclear mitochondrial genes has been reported in sequential glioblastoma (GBM). We obtained ten pairs of GBM samples at diagnosis (GBM-P) and at recurrence (GBM-R). Extracted DNA was subjected to whole exome and mtDNA sequencing. After filtering out germline variants, bioinformatics analysis was performed using a mitochondrial gene panel of 483 nuclear-encoded, and 37 mtDNA-encoded genes. Variant classification was performed using established clinical- and molecular criteria, integrating population-frequency data, bioinformatic predictions, functional evidence, segregation information, and curated entries from the Mitomap and ClinVar databases. Benign single nucleotide variants in mtDNA-encoded genes of RNR1, RNR2, ATP6, CYB, CO2, TV, ATP8, and ND2 were detected, which changed little over time. However, three variants in TI, ND5 and ND1 with possible or likely pathogenic significance were found in the GBM-R samples. In contrast, pathogenic or likely pathogenic variants in 29 nuclear genes were found in GBM-P and GBM-R samples. Not only the overall number, but also the number of protein-truncating variants in nuclear genes increased over time. Conclusions: This study sheds light on the accumulation of mutations in nuclear genes of mitochondrial proteins in sequential GBM samples. As such variants may influence metabolic, proliferative and invasive properties as well as the necrotic propensity of the tumor, a comprehensive analysis of these genes merits further studies. Full article

Somatic copy-number alterations (CNAs) are pervasive in cancer, but routine targeted panels yield sparse CNA readouts unsuited for CNA signature analysis. We built a consensus framework that integrates four deconvolution algorithms to extract CNA signatures from panel data. Analysis of 24,870 tumors sequenced using MSK-IMPACT identified five reproducible signatures (CON1–CON5). CON5 mirrored near-diploid profiles, whereas the others captured distinct aneuploid patterns. Technical fidelity was confirmed by internal cross-validation and external validation in sarcoma and hepatocellular carcinoma cohorts. Clinically, these signatures were associated with overall survival across tumor types (hazard ratio 1.3–2.5; FDR < 0.01) and provided additive prognostic information beyond Fraction of Genome Altered. Associations with driver mutations (GATA3 in CON1, KRAS in CON5) supported biological specificity, and the signatures delineated resistance landscapes for chemotherapy, hormonal, targeted, and immunotherapy. By converting routine panel data into biologically interpretable prognostic features, our framework enables risk stratification and therapeutic guidance in precision oncology. Full article

Background: Molecular subtyping of breast cancer usually relies on transcriptomic profiles, a method constrained by limitations in robustness and clinical applicability. While somatic point mutations represent a stable genomic alternative, their predictive utility is hindered by high dimensionality, extreme sparsity, and weak single-gene associations. Methods: Here, we present deepGene-BC, a deep learning framework that synergizes a pathway-informed feature selection strategy with a hybrid neural network tailored for sparse binary data. To distill sparse genome-wide mutations into a compact and interpretable feature set, deepGene-BC integrates mutation recurrence filtering, curated pathway priors, and mutual information-based gene prioritization. These refined features are subsequently modeled using a specialized hybrid architecture designed to capture complex linear effects, feature interactions, and higher-order nonlinear patterns. Results: When benchmarked against an independent test set (n = 273) from the TCGA breast cancer cohort, deepGene-BC achieved an overall accuracy of 77.3% and an average sensitivity of 75.2%, accompanied by a strong overall discriminative performance (macro-averaged AU-ROC = 0.94, 95% CI: 0.92–0.96). Conclusions: By effectively combining biologically informed feature engineering with deep learning, deepGene-BC holds significant promise for non-invasive molecular stratification and precision oncology. Full article

Background: Pheochromocytomas (PCCs) and paragangliomas (PGLs), collectively known as PPGLs, are rare neuroendocrine tumors that produce catecholamines. The majority of PPGL cases are caused by germline and/or somatic mutations in over 20 different genes. A study of post-surgical PCC patients revealed a high risk of new tumor recurrence in both hereditary and apparently sporadic cases, suggesting that some germline mutations remain undetected. Since transcript levels can indicate gene dysfunction, our study focuses on the transcriptional profiling of PCC-associated genes in post-surgical patients. Methods: RT-PCR was performed on blood samples from patients and a control group. The t-SNE algorithm was applied to the transcriptional data. Sanger sequencing was used to identify mutations in the coding sequences of the VHL, SDHB, RET, and NF1 genes. Results: We obtained transcriptional profiles for 11 genes involved in the Krebs cycle and for 21 genes involved in the hypoxia, PI3K/AKT/mTOR, and RAS/RAF/ERK signaling pathways. We identified a minimal set of 16 genes with stable transcription levels that can be used to differentiate PCC patients from controls. Germline mutations in the VHL, SDHB, RET and NF1 genes, which correlated with an altered transcriptional profile, were detected in three patients. Conclusions: Our pilot data suggest that transcript levels of the genes involved in Krebs cycle, hypoxia, PI3K/AKT/mTOR, and RAS/RAF/ERK signaling pathways indicate their potential suitability as a candidate diagnostic marker. The results from this pilot study form the basis for a larger project to investigate gene transcription in an expanded cohort of patients who have undergone surgery for PCC. Full article

Background: Cutaneous squamous cell carcinoma (cSCC) represents one of the most common keratinocyte-derived malignancies encountered in clinical practice; however, its genomic landscape remains far less comprehensively characterized than that of other cutaneous cancers. This study aims to identify key molecular drivers and potential therapeutic targets by comprehensively characterizing the genomic landscape of cSCC using data from the American Association for Cancer Research (AACR) Project Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) consortium. Methods: A retrospective cohort analysis of cSCC samples was performed utilizing AACR Project GENIE data accessed via the cBioPortal platform (v18.0-public) on 22 November 2025. Analyses included identification of recurrent somatic and copy-number alterations, pairwise gene–gene co-occurrence testing using Fisher’s exact tests with Benjamini–Hochberg False Discovery Rate (FDR) correction, and exploratory subgroup comparisons by sex and race, with statistical significance defined as p < 0.05. Results: Recurrent mutations were identified in TP53 (83.5%), NOTCH1 (56.3%), KMT2D (47.0%), CDKN2A (44.4%), TERT (41.4%), ROS1 (34.3%), FAT1 (33.3%), NOTCH2 (31.2%), ERBB4 (28.4%), and KMT2A (24.3%), reflecting disruption of the p53 pathway, cell-cycle control, Notch signaling, epigenetic regulation, telomere maintenance, RTK/MAPK pathways, and Wnt signaling. Statistically significant co-occurrence patterns were observed, and exploratory subgroup analyses evaluated mutation frequency differences by sex and race. Conclusions: This large, multi-institutional genomic analysis defines recurrent mutational and structural alterations in cSCC and highlights an integrated pattern of pathway disruption involving genomic integrity, differentiation, epigenetic control, and proliferative signaling. These findings enhance current understandings of the molecular architecture underlying this common yet genomically understudied malignancy and provide a foundation for future mechanistic studies and development of targeted diagnostic and therapeutic strategies. Full article