Molecular Oncology最新文献

Potassium channels in brain tissue orchestrate essential cellular processes, including the regulation of membrane potential and neuronal excitability. Among them, large-conductance calcium-activated potassium (BK_Ca) channels play a pivotal role in both normal brain physiology and the pathogenesis of glioblastoma multiforme, a highly aggressive primary brain tumor. Within the central nervous system, BK_Ca channels are widely expressed in neurons, astrocytes, and oligodendrocytes, contributing to ion homeostasis and synaptic transmission. In glioblastoma cells, overexpression of BK_Ca channels, particularly the glioma-specific gBK_Ca variant, facilitates tumor progression by enhancing cell migration, invasion, and therapeutic resistance. Recent evidence highlights the significance of the mitochondrial isoform of the BK_Ca channel (mitoBK_Ca) in modulating oxidative phosphorylation and reactive oxygen species generation, thereby promoting tumor cell survival under hypoxic and cytotoxic stress. This review summarizes current insights into the role of BK_Ca and mitoBK_Ca channels in glioblastoma biology, their potential classification as oncochannels, and the emerging pharmacological strategies targeting these channels, emphasizing the translational challenges in developing BK_Ca-directed therapies for glioblastoma treatment.

The emergence of resistance to mutant BRAF-specific inhibitors (BRAFi) requires novel strategies for melanoma treatment. The progression of these tumors involves metabolic adaptations, which also affect the cellular redox status. Previous studies have linked RAF kinase signaling, a key component of the MAPK/ERK pathway involved in cell division and survival, to the suppression of mitochondrial reactive oxygen species (ROS) production, resulting in protection against cell death. In BRAF-transformed cells, we have identified impaired JNK1/2-dependent activation of the mitochondrial prooxidant protein p66Shc as a potential cause. In the present study, we dissected signaling and mitochondrial alterations that characterize the transition from BRAFi responsiveness to resistance in A375 melanoma cells. Insensitivity to BRAFi dabrafenib exposure was associated with reactivation of ERK1/2 phosphorylation, increased JNK1/2 kinase activity, p66ShcS36 phosphorylation, and elevated ROS production. Utilizing high-resolution respirometry (HRR) and transmission electron microscopy (TEM), we show that dabrafenib-resistant cells displayed mitochondrial damage, compensated by increased respiration, leading to high ROS levels. Moreover, dabrafenib-resistant cells (A375D) have more efficient antioxidant systems, which may explain why despite ongoing cell death, net cell growth was observed. Treatment of both parental and resistant cells with phenethyl isothiocyanate (PEITC) increased ROS production but caused substantial cell death only in A375D melanoma cells. This PEITC effect could be demonstrated in two further dabrafenib-resistant cell lines, WM164D and 451LuP. These results suggest that the altered redox status is linked to compromised mitochondria and is associated with the development of BRAFi resistance, rendering cells exquisitely sensitive to the actions of selective ROS-inducing therapeutics.

High-grade serous ovarian carcinoma (HGSOC) associates with the worst patient outcome. Understanding the tumor environment in terms of quantifying endogenous retroviruses (ERVs) and LINE-1 expression and their correlations with inflammation genes, checkpoint inhibitors and patient survival is needed. Analysis of 102 treatment-naïve HGSOC and control tissues for ERVs, LINE-1, inflammation and immune checkpoints identified five clusters with diverse patient recurrence-free survivals. One cluster termed Triple-I with the best patient survival showed the highest number of tumor infiltrating lymphocytes along with 22 overexpressed genes, including CXCL9 and AIM2. However, Triple-I associated with the lowest ERV/LINE-1 expression. The tumor cluster with the second-best patient survival had both high ERV/LINE-1 expression and inflammation. Multiplex-immunohistochemistry revealed CD28 protein solely on immune cells, without co-expression of the inhibitory CTLA4 receptor. The largest tumor cluster with high ERV/LINE-1 expression but low inflammation showed a significant low gene expression of the dsRNA sensors MDA5 and RIG-I supporting an aberrant block in IFN signaling. Our study represents an intrinsic 'molecular and immunological snapshot' of the HGSOC tumor environment important for understanding retroelements and inflammation for clinical relevance.

We investigated whether DNA methylation and cell-free DNA (cfDNA) fragmentation patterns can improve circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer. In a cohort of 33 patients, ctDNA detection was performed in a tumor-agnostic fashion using DNA methylation, cfDNA fragment lengths, and 4-mer 5' end motifs. Machine learning models estimating ctDNA levels were built for each individual detection method and their combination. All models significantly differentiated ctDNA levels in patients from healthy individuals (P < 0.001). Using the highest estimated levels in healthy volunteers as cutoffs, ctDNA was detected in 79%, 67%, 67%, and 55% of patients using methylation, fragment length, end motifs, and the combined model, respectively. Univariable Cox regression showed that all ctDNA level estimates were associated with increased hazard ratios (HR, all P < 0.001) for progression-free survival (PFS) and overall survival (OS). Multivariable Cox regression confirmed ctDNA levels as an independent predictor of PFS (HR = 1.9, P < 0.001) and OS (HR = 2.7, P < 0.001). Our findings suggest that machine learning models based on DNA methylation, cfDNA fragment lengths, and cfDNA end motifs can estimate ctDNA levels and predict clinical outcomes in advanced pancreatic cancer.

Prostate cancer is a prevalent tumor type that, despite being highly curable, progresses to metastatic disease in a fraction of patients, thus accounting for more than 350 000 annual deaths worldwide. In turn, uncovering the molecular insights of metastatic disease is instrumental in improving the survival rate of prostate cancer patients. By means of gene expression meta-analysis in multiple prostate cancer patient cohorts, we identified a set of genes that are differentially expressed in aggressive prostate cancer. Transcription factor 19 (TCF19) stood out as an unprecedented epithelial gene upregulated in metastatic disease, with prognostic potential and negatively associated with the activity of the androgen receptor. By combining computational and empirical approaches, our data revealed that TCF19 is required for full metastatic capacity, and its depletion influences core cancer-related processes, such as tumor growth and vascular permeability, supporting the role of this gene in the dissemination of prostate tumor cells.

Homologous recombination deficiency (HRD) leads to genomic instability, and patients with HRD can benefit from HRD-targeting therapies. Previous studies have primarily focused on identifying HRD biomarkers using data from a single technology. Here we integrated features from different genomic data types, including total copy number (CN), allele-specific copy number (ASCN) and single nucleotide variants (SNV). Using a semi-supervised method, we developed HRD classifiers from 1404 breast tumours across two datasets based on their BRCA1/2 status, demonstrating improved HRD identification when aggregating different data types. Notably, HRD-positive tumours in ER-negative disease showed improved survival post-adjuvant chemotherapy, while HRD status strongly correlated with neoadjuvant treatment response. Furthermore, our analysis of cell lines highlighted a sensitivity to PARP inhibitors, particularly rucaparib, among predicted HRD-positive lines. Exploring somatic mutations outside BRCA1/2, we confirmed variants in several genes associated with HRD. Our method for HRD classification can adapt to different data types or resolutions and can be used in various scenarios to help refine patient selection for HRD-targeting therapies that might lead to better clinical outcomes.

Liver and pancreatic cancers are difficult to detect early, leading to high mortality rates. Blood-based diagnostics present a viable alternative for earlier detection, potentially improving survival rates. The comprehensive serum glycopeptide spectra analysis (CSGSA) method combines enriched glycopeptides (EGPs) with conventional tumor markers through machine learning to accurately identify early stage cancers. Here, we analyzed nine tumor markers (CA19-9, AFP, PSA, CEA, CA125, CYFRA, CA15-3, SCC antigen, and NCC-ST439) in 119 patients with pancreatic cancer and 49 with hepatocellular carcinoma, alongside 590 healthy controls. We also analyzed EGPs using liquid chromatography-mass spectrometry. We found that α1-antitrypsin with a fully sialylated biantennary glycan at asparagine 271 and α2-macroglobulin with a fully sialylated biantennary glycan at asparagine 70 effectively distinguished liver and pancreatic cancers. The integration of these two glycopeptides, along with the nine tumor markers and 1688 EGPs using a machine learning model enhanced diagnostic accuracy, achieving a receiver operating characteristic-area under curve (ROC-AUC) score of 0.996. CSGSA has the potential to minimize the need for invasive diagnostic procedures and serves as a promising tool for widespread screening.

Acute lymphoblastic leukemia (ALL), the most common cancer in children, is overall divided into two subtypes, B-cell precursor ALL (B-ALL) and T-cell ALL (T-ALL), which have different molecular characteristics. Despite massive progress in understanding the disease trajectories of ALL, ALL remains a major cause of death in children. Thus, further research exploring the biological foundations of ALL is essential. Here, we examined the diagnostic, prognostic, and therapeutic potential of gene expression data in pediatric patients with ALL. We discovered a subset of expression markers differentiating B- and T-ALL: CCN2, VPREB3, NDST3, EBF1, RN7SKP185, RN7SKP291, SNORA73B, RN7SKP255, SNORA74A, RN7SKP48, RN7SKP80, LINC00114, a novel gene (ENSG00000227706), and 7SK. The expression level of these markers all demonstrated significant effects on patient survival, comparing the two subtypes. We also discovered four expression subgroups in the expression data with eight genes driving separation between two of these predicted subgroups. A subset of the 14 markers could distinguish B- and T-ALL in an independent cohort of patients with ALL. This study can enhance our knowledge of the transcriptomic profile of different ALL subtypes.

While the incidence of endometrial cancer is increasing among all US women, Black women face higher mortality rates. The reasons for this remain unclear. In this study, whole genome differential methylation analysis, along with state-of-the-art computational methods such as the recursive feature elimination technique and supervised/unsupervised machine learning models, was used to identify 38 epigenetic signature genes (ESGs) and four core-ESGs (cg19933311: TRPC5; cg09651654: APOBEC1; cg27299712: PLEKHG5; cg03150409: WHSC1) in endometrial tumors from Black and White women, incorporating genetic ancestry estimation. Methylation at two Core-ESGs, namely APOBEC1 and PLEKHG5, showed statistically significant overall survival differences between the two ancestral groups (Likelihood ratio test; P value = 0.006). Moreover, our comprehensive ancestry-based analysis revealed that tumors from women with high African ancestry exhibited increased hypomethylation compared to those with low African ancestry. These hypomethylated genes were enriched in drug metabolism pathways, indicating a potential link between genetic ancestry, epigenetic modifications, and pharmacogenomic responses. Combining ancestry, race, and disease type may help identify which patient groups will benefit most from these biomarkers for targeted treatments.

Monitoring levels of circulating tumour-derived DNA (ctDNA) provides both a noninvasive snapshot of tumour burden and also potentially clonal evolution. Here, we describe how applying a novel statistical model to serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive predictive assessment of treatment response and progression-free survival. A cohort of 149 patients had DNA extracted from serial plasma samples (total 1013, mean samples per patient = 6.80). Plasma DNA was assessed using sWGS and the tumour fraction in total cell-free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA15-3 measurements. We identified a transition point of 7% estimated tumour fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox Proportional Hazards model and validated it across different breast cancer subtypes and treatments, outperforming the alternative methods. We used the longitudinal ichorCNA values to develop a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. In patients with metastatic breast cancer, a strategy of sWGS of ctDNA with longitudinal tracking of tumour fraction provides real-time information on treatment response. These results encourage a prospective large-scale clinical trial to evaluate the clinical benefit of early treatment changes based on ctDNA levels.