Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2–58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

Background: Immune checkpoint inhibitors (ICIs) treatment have shown high efficacy for about 15 cancer types. However, this therapy is only effective in 20-30% of cancer patients. Thus, the precise biomarkers of ICI response are an urgent need.Methods: We conducted a prospective observational study of the prognostic potential ofseveral existing and putative biomarkers of response to immunotherapy in acohort of 85 patients with lung cancer (LC) receiving PD-1 or PD-L1 targeted ICIs. Tumor biosamples were obtained prior to ICI treatment and profiled by whole exome and RNA sequencing. The entire 403 putative biomarkers were screened, including tumor mutation burden (TMB) and number of cancer neoantigens, 131 specific HLA alleles, homozygous state of 11 HLA alleles and their superfamilies; four gene mutation biomarkers, expression of 45 immune checkpoint genes and closely related genes, and three previously published diagnostic gene signatures; for the first time, activation levels of 188 molecular pathways containing immune checkpoint genes and activation levels of 19 pathways algorithmically generated using a human interactome model centered around immune checkpoint genes. Treatment outcomes and/or progression-free survival (PFS) times were available for 61 of 85 patients with LC, including 24 patients with adenocarcinoma and 27 patients with squamous cell LC, whose samples were further analyzed. For the rest 24 patients, both treatment outcomes and PFS data could not be collected. Of these, 54 patients were treated with PD1-specific and 7 patients with PD-L1-specific ICIs. We evaluated the potential of biomarkers based on PFS and RECIST treatment response data.Results: In our sample, 45 biomarkers were statistically significantly associated with PFS and 44 with response to treatment, of which eight were shared. Five of these (CD3G and NCAM1 gene expression levels, and levels of activation of Adrenergic signaling in cardiomyocytes, Growth hormone signaling, and Endothelin molecular pathways) were used in our signature that showed an AUC of 0.73 and HR of 0.27 (p=0.00034) on the experimental dataset. This signature was also reliable (AUC 0.76, 0.87) for the independent publicly available LC datasets GSE207422, GSE126044 annotated with ICI response data and demonstrated same survival trends on independent dataset GSE135222 annotated with PFS data. In both experimental and one independent datasets annotated with samples’ histotypes, the signature worked better for lung adenocarcinoma than for squamous cell LC. Conclusion: The high reliability of our signature to predict response and PFS after ICI treatment was demonstrated using experimental and 3 independent datasets. Additionally, annotated molecular profiles obtained in this study were made publicly accessible.

The metabolism of zinc and manganese plays a pivotal role in cancer progression by mediating cancer cell growth and metastasis. The SLC30A family proteins SLC30A3 and SLC30A10 mediate the efflux of zinc, manganese, and probably other transition element ions outside the cytoplasm to the extracellular space or into intracellular membrane compartments. The SLC39A family members SLC39A8 and SLC39A14 are their functional antagonists that transfer these ions into the cytoplasm. Recently, the SLC30A10 gene was suggested as a promising methylation biomarker of colorectal cancer. Here, we investigated whether forced overexpression or inactivation of SLC30A and SLC39A family genes has an impact on the phenotype of cancer cells and their sensitivity to cancer therapeutics. In the human colon adenocarcinoma HCT-15 and duodenal adenocarcinoma HuTu80 cell lines, we generated clones with knockouts of the SLC39A8 and SLC39A14 genes and forced overexpression of the SLC30A3, SLC30A10, and SLC39A8 genes. Gene expression in the mutant and control cells was assessed by RNA sequencing. The cell growth rate, mitochondrial activity, zinc accumulation, and sensitivity to the drugs cetuximab and cisplatin were investigated in functional tests. Overexpression or depletion of SLC30A or SLC39A family genes resulted in the deep reshaping of intracellular signaling and provoked hyperactivation of mitochondrial respiration. Variation in the expression of the SLC30A/SLC39A genes did not increase the sensitivity to cetuximab but significantly altered the sensitivity to cisplatin: overexpression of SLC30A10 resulted in an ~2.7–4 times increased IC50 of cisplatin, and overexpression of SLC30A3 resulted in an ~3.3 times decreased IC50 of cisplatin. The SLC30A/SLC39A genes should be considered as potential cancer drug resistance biomarkers and putative therapeutic targets.

Endogenous Cushing’s syndrome (CS) is a rare neuroendocrine disorder characterized by either secondary cortisol increases due to an adrenocorticotropic hormone (ACTH)-secreting pituitary tumor (Cushing’s disease (CD)), an ACTH-secreting neuroendocrine tumor (NET) of non-pituitary origin (ectopic ACTH syndrome (EAS)), or by the primarily adrenal autonomous overproduction of cortisol

Inorganic polyphosphates and respective metabolic pathways and enzymes are important factors for yeast active growth in unfavorable conditions. However, particular proteins of polyphosphate metabolism remain poorly explored in this context. Here we report biochemical and transcriptomic characterization of the CRN/PPN2 yeast strain (derived from Ppn1-lacking CRN strain) overexpressing poorly studied Ppn2 polyphosphatase. We showed that Ppn2 overexpression significantly reduced lag phase in the alkaline medium presumably due to the ability of Ppn2 to efficiently hydrolyze inorganic polyphosphates and thus neutralize hydroxide ions in the cell. With RNA-Seq, we compared the molecular phenotypes of CRN/PPN2 and its parent CRN strain grown in YPD or alkaline medium and detected transcriptomic changes induced by Ppn2 overexpression and reflecting the adaptation to alkaline conditions. The core set of upregulated genes included several genes with a previously unknown function. Respective knockout strains (∆ecm8, ∆yol160w, ∆cpp3, ∆ycr099c) exhibited defects of growth or cell morphology in the alkaline medium, proving the functional involvement of the respective proteins in sustaining growth in alkaline conditions.

Background: In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from “classic” techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3′ partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons. Methods: A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel). Results: ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy. Conclusions: RNAseq exon coverage analysis can effectively detect ALK rearrangements.