High CYP3A4 expression sensitizes tumor cells to certain antitumor agents while for others it can lower their therapeutic efficacy. We have elucidated the influence of CYP3A4 overexpression on the cellular response induced by antitumor acridine derivatives, C-1305 and C-1748, in two hepatocellular carcinoma (HepG2) cell lines, Hep3A4 stably transfected with CYP3A4 isoenzyme, and HepC34 expressing empty vector. The compounds were selected considering their different chemical structures and different metabolic pathways seen earlier in human and rat liver microsomes C-1748 was transformed to several metabolites at a higher rate in Hep3A4 than in HepC34 cells. In contrast, C-1305 metabolism in Hep3A4 cells was unchanged compared to HepC34 cells, with each cell line producing a single metabolite of comparable concentration. C-1748 resulted in a progressive appearance of sub-G1 population to its high level in both cell lines. In turn, the sub-G1 fraction was dominated in CYP3A4-overexpressing cells following C-1305 exposure. Both compounds induced necrosis and to a lesser extent apoptosis, which were more pronounced in Hep3A4 than in wild-type cells. In conclusion, CYP3A4-overexpressing cells produce higher levels of C-1748 metabolites, but they do not affect the cellular responses to the drug. Conversely, cellular response was modulated following C-1305 treatment in CYP3A4-overexpressing cells, although metabolism of this drug was unaltered.
Abstract The majority of supratentorial ependymomas in children contain oncogenic fusions, such as ZFTA–RELA or YAP1‐MAMLD1 . In contrast, posterior fossa (PF) ependymomas lack recurrent somatic mutations and are classified based on gene expression or methylation profiling into group A (PFA) and group B (PFB). We have applied a novel method, NanoString nCounter Technology, to identify four molecular groups among 16 supratentorial and 50 PF paediatric ependymomas, using 4–5 group‐specific signature genes. Clustering analysis of 16 supratentorial ependymomas revealed 9 tumours with a RELA fusion‐positive signature (RELA+), 1 tumour with a YAP1 fusion‐positive signature (YAP1+), and 6 not‐classified tumours. Additionally, we identified one RELA+ tumour among historically diagnosed CNS primitive neuroectodermal tumour samples. Overall, 9 of 10 tumours with the RELA+ signature possessed the ZFTA‐RELA fusion as detected by next‐generation sequencing ( p = 0.005). Similarly, the only tumour with a YAP1+ signature exhibited the YAP1‐MAMLD1 fusion. Among the remaining unclassified ependymomas, which did not exhibit the ZFTA‐RELA fusion, the ZFTA‐MAML2 fusion was detected in one case. Notably, among nine ependymoma patients with the RELA+ signature, eight survived at least 5 years after diagnosis. Clustering analysis of PF tumours revealed 42 samples with PFA signatures and 7 samples with PFB signatures. Clinical characteristics of patients with PFA and PFB ependymomas corroborated the previous findings. In conclusion, we confirm here that the NanoString method is a useful single tool for the diagnosis of all four main molecular groups of ependymoma. The differences in reported survival rates warrant further clinical investigation of patients with the ZFTA‐RELA fusion.
Additional file 1: Supplementary Table S1. Taxa differentiating patients with active Crohn’s disease from healthy controls. sd - standard deviation, zeroes - number of zeroes in the group, IQR - interquartile range, log2FoldChange - base 2 logarithm from fold-change, stat - Wald’s test statistic, pvalue - unadjusted p-value, padj - FDR adjusted p-value for most abundant taxa, FC - fold-change, Taxonomy - taxonomic assignment, FisherTest - p-value in Fisher’s exact test for taxa prevalence, adjustFisher- FDR adjusted p-value for Fihser’s exact test. Supplementary Table S2. Taxa differentiating patients with inactive Crohn’s disease from healthy controls. sd - standard deviation, zeroes - number of zeroes in the group, IQR - interquartile range, log2FoldChange - base 2 logarithm from fold-change, stat - Wald’s test statistic, pvalue - unadjusted p-value, padj - FDR adjusted p-value for most abundant taxa, FC - fold-change, Taxonomy - taxonomic assignment, FisherTest - p-value in Fisher’s exact test for taxa prevalence, adjustFisher- FDR adjusted p-value for Fihser’s exact test. Supplementary Table S3. Taxa differentiating patients with inactive Crohn’s disease from patients with active disease. sd - standard deviation, zeroes - number of zeroes in the group, IQR - interquartile range, log2FoldChange - base 2 logarithm from fold-change, stat - Wald’s test statistic, pvalue - unadjusted p-value, padj - FDR adjusted p-value for most abundant taxa, FC - fold-change, Taxonomy - taxonomic assignment, FisherTest - p-value in Fisher’s exact test for taxa prevalence, adjustFisher- FDR adjusted p-value for Fihser’s exact test. Supplementary Table S4. SCFA log10 conetrations [ppm] for patients with active, inactive CD and control group. Supplementary Table S4. Error rates for PLS-DA models after M fold model vaildation. Supplementary Table S6. Correlation coefficients’ values for the relevant variables in the whole dataset, rho - Spearman’s correlation coefficient, pvalue - pvalue for the coefficient, padjusted - FDR adjusted pvalue, Taxonomy - taxonomic assignment (if applicable). Supplementary Table SA. GC/MS data.
Abstract Background This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. Methods Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype–phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. Results Five deleterious pharmacovariants in the RYR1 , POLG , ANXA11 , CCNH , and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). Conclusion While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.
Abstract Four molecular types of rare central nervous system (CNS) tumors have been recently identified by gene methylation profiling: CNS Neuroblastoma with FOXR2 activation (CNS NB-FOXR2), CNS Ewing Sarcoma Family Tumor with CIC alteration (CNS EFT-CIC), CNS high grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1) and CNS high grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR). Although they are not represented in 2016 updated WHO classification of CNS tumors, their diagnostic recognition is important because of clinical consequences. We have introduced a diagnostic method based on transcription profiling of tumor specific signature genes from formalin-fixed, paraffin-embedded tumor blocks using NanoString nCounter Technology. Altogether, 14 out of 187 (7.4%) high grade pediatric brain tumors were diagnosed with either of four new CNS categories. Histopathological examination of the tumors confirmed, that they demonstrate a spectrum of morphology mimicking other CNS high grade tumors. However, they also exhibit some suggestive histopathological and immunohistochemical features that allow for a presumptive diagnosis prior to molecular assessment. Clinical characteristics of patients corroborated with the previous findings for CNS EFT-CIC, CNS NB-FOXR2 and CNS HGNET-MN1 patients, with a favorable survival rate for the latter two groups. Among six CNS HGNET-BCOR patients, three patients are long term survivors, suggesting possible heterogeneity within this molecular category of tumors. In summary, we confirmed the effectiveness of NanoString method using a single, multi-gene tumor specific signature and recommend this novel approach for identification of either one of the four newly described CNS tumor entities.
Abstract Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p=5.41×10 −7 ). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights. Author Summary COVID-19 clinical outcomes vary immensely, but a patient’s genetic make-up is an important determinant of how they will fare against the virus. While many genetic variants commonly found in the populations were previously found to be contributing to more severe disease by the COVID-19 Host Genetics Initiative, it isn’t clear if more rare variants found in less individuals could also play a role. This is important because genetic variants with the largest impact on COVID-19 severity are expected to be rarely found in the population, and these rare variants require different technologies to be studies (usually whole-exome or whole-genome sequencing). Here, we combined sequencing results from 21 cohorts across 12 countries to perform a rare variant association study. In an analysis comprising 5,085 participants with severe COVID-19 and 571,737 controls, we found that the gene for toll-like receptor 7 ( TLR7 ) on chromosome X was an important determinant of severe COVID-19. Importantly, despite being found on a sex chromosome, this observation was consistent across both sexes.
According to the fifth edition of the WHO Classification of Tumours of the Central Nervous System (CNS) published in 2021, grade 4 gliomas classification includes IDH-mutant astrocytomas and wild-type IDH glioblastomas. Unfortunately, despite precision oncology development, the prognosis for patients with grade 4 glioma remains poor, indicating an urgent need for better diagnostic and therapeutic strategies. Circulating miRNAs besides being important regulators of cancer development could serve as promising diagnostic biomarkers for patients with grade 4 glioma. Here, we propose a two-miRNA miR-362-3p and miR-6721-5p screening signature for serum for non-invasive classification of identified glioma cases into the highest-grade 4 and lower-grade gliomas. A total of 102 samples were included in this study, comprising 78 grade 4 glioma cases and 24 grade 2–3 glioma subjects. Using the NanoString platform, seven miRNAs were identified as differentially expressed (DE), which was subsequently confirmed via RT-qPCR analysis. Next, numerous combinations of DE miRNAs were employed to develop classification models. The dual panel of miR-362-3p and miR-6721-5p displayed the highest diagnostic value to differentiate grade 4 patients and lower grade cases with an AUC of 0.867. Additionally, this signature also had a high AUC = 0.854 in the verification cohorts by RT-qPCR and an AUC = 0.842 using external data from the GEO public database. The functional annotation analyses of predicted DE miRNA target genes showed their primary involvement in the STAT3 and HIF-1 signalling pathways and the signalling pathway of pluripotency of stem cells and glioblastoma-related pathways. For additional exploration of miRNA expression patterns correlated with glioma, we performed the Weighted Gene-Co Expression Network Analysis (WGCNA). We showed that the modules most associated with glioma grade contained as many as six DE miRNAs. In conclusion, this study presents the first evidence of serum miRNA expression profiling in adult-type diffuse glioma using a classification based on the WHO 2021 guidelines. We expect that the discovered dual miR-362-3p and miR-6721-5p signatures have the potential to be utilised for grading gliomas in clinical applications.
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