Pan-cancer experimental characteristic of human transcriptional patterns connected with telomerase reverse transcriptase (TERT) gene expression status.

The TERT gene encodes the reverse transcriptase subunit of telomerase and is normally transcriptionally suppressed in differentiated human cells but reactivated in cancers where its expression is frequently associated with poor survival prognosis. Here we experimentally assessed the RNA sequencing expression patterns associated with TERT transcription in 1039 human cancer samples of 27 tumor types. We observed a bimodal distribution of TERT expression where ∼27% of cancer samples did not express TERT and the rest showed a bell-shaped distribution. Expression of TERT strongly correlated with 1443 human genes including 103 encoding transcriptional factor proteins. Comparison of TERT- positive and negative cancers showed the differential activation of 496 genes and 1975 molecular pathways. Therein, 32/38 (84%) of DNA repair pathways were hyperactivated in TERT+ cancers which was also connected with accelerated replication, transcription, translation, and cell cycle progression. In contrast, the level of 40 positive cell cycle regulator proteins and a set of epithelial-to-mesenchymal transition pathways was specific for the TERT- group suggesting different proliferation strategies for both groups of cancer. Our pilot study showed that the TERT+ group had ∼13% of cancers with C228T or C250T mutated TERT promoter. However, the presence of promoter mutations was not associated with greater TERT expression compared with other TERT+ cancers, suggesting parallel mechanisms of its transcriptional activation in cancers. In addition, we detected a decreased expression of L1 retrotransposons in the TERT+ group, and further decreased L1 expression in promoter mutated TERT+ cancers. TERT expression was correlated with 17 genes encoding molecular targets of cancer therapeutics and may relate to differential survival patterns of TERT- positive and negative cancers.

Cancer fusion transcripts with human non-coding RNAs.

Cancer chimeric, or fusion, transcripts are thought to most frequently appear due to chromosomal aberrations that combine moieties of unrelated normal genes. When being expressed, this results in chimeric RNAs having upstream and downstream parts relatively to the breakpoint position for the 5'- and 3'-fusion components, respectively. As many other types of cancer mutations, fusion genes can be of either driver or passenger type. The driver fusions may have pivotal roles in malignisation by regulating survival, growth, and proliferation of tumor cells, whereas the passenger fusions most likely have no specific function in cancer. The majority of research on fusion gene formation events is concentrated on identifying fusion proteins through chimeric transcripts. However, contemporary studies evidence that fusion events involving non-coding RNA (ncRNA) genes may also have strong oncogenic potential. In this review we highlight most frequent classes of ncRNAs fusions and summarize current understanding of their functional roles. In many cases, cancer ncRNA fusion can result in altered concentration of the non-coding RNA itself, or it can promote protein expression from the protein-coding fusion moiety. Differential splicing, in turn, can enrich the repertoire of cancer chimeric transcripts, e.g. as observed for the fusions of circular RNAs and long non-coding RNAs. These and other ncRNA fusions are being increasingly recognized as cancer biomarkers and even potential therapeutic targets. Finally, we discuss the use of ncRNA fusion genes in the context of cancer detection and therapy.

Cancer Incidence Relation to Heavy Metals in Soils of Kyzylorda Region of Kazakhstan.

OBJECTIVE: The purpose of this study was to investigate the relationship of soil pollution factors such as heavy metal ions with the incidence of cancer in the Kyzylorda region of Kazakhstan. METHODS: Concentrations of heavy metal ions in the soils of different sites of Kyzylorda region, Kazakhstan, were sampled and correlated with incidence of cancer in 2021. RESULTS: Chromium content in the soil exceeded maximum permissible concentration (MPC) in the samples for all sites except Kazaly and Shieli, and the highest excess of 2.8 MPC was found in Terenozek. Content of copper, lead, and cobalt ions was also increased and varied in the range 1.9-15.4, 1.2-4, and 1.2-2.44 MPC, respectively. In addition, lung cancer incidence was statistically significantly correlated with soil concentration to MPC ratio of copper, cobalt, and lead; colorectal cancer was correlated with soil concentration of chromium. Cases of invasive cancer and mutations were recorded Terenozek and Kyzylorda areas. CONCLUSION: The higher the soil concentration correlate with higher cancer incidence in Kyzylorda region, Kazakhstan.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Human Blood Serum Counteracts EGFR/HER2-Targeted Drug Lapatinib Impact on Squamous Carcinoma SK-BR-3 Cell Growth and Gene Expression.

Lapatinib is a targeted therapeutic inhibiting HER2 and EGFR proteins. It is used for the therapy of HER2-positive breast cancer, although not all the patients respond to it. Using human blood serum samples from 14 female donors (separately taken or combined), we found that human blood serum dramatically abolishes the lapatinib-mediated inhibition of growth of the human breast squamous carcinoma SK-BR-3 cell line. This antagonism between lapatinib and human serum was associated with cancelation of the drug induced G1/S cell cycle transition arrest. RNA sequencing revealed 308 differentially expressed genes in the presence of lapatinib. Remarkably, when combined with lapatinib, human blood serum showed the capacity of restoring both the rate of cell growth, and the expression of 96.1% of the genes expression of which were altered by the lapatinib treatment alone. Co-administration of EGF with lapatinib also restores the cell growth and cancels alteration of expression of 95.8% of the genes specific to lapatinib treatment of SK-BR-3 cells. Differential gene expression analysis also showed that in the presence of human serum or EGF, lapatinib was unable to inhibit the Toll-Like Receptor signaling pathway and alter expression of genes linked to the Gene Ontology term of Focal adhesion.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers.

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.

Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers.

Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived "healthy" normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact "differential" expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.

Human Blood Serum Can Diminish EGFR-Targeted Inhibition of Squamous Carcinoma Cell Growth through Reactivation of MAPK and EGFR Pathways.

Regardless of the presence or absence of specific diagnostic mutations, many cancer patients fail to respond to EGFR-targeted therapeutics, and a personalized approach is needed to identify putative (non)responders. We found previously that human peripheral blood and EGF can modulate the activities of EGFR-specific drugs on inhibiting clonogenity in model EGFR-positive A431 squamous carcinoma cells. Here, we report that human serum can dramatically abolish the cell growth rate inhibition by EGFR-specific drugs cetuximab and erlotinib. We show that this phenomenon is linked with derepression of drug-induced G1S cell cycle transition arrest. Furthermore, A431 cell growth inhibition by cetuximab, erlotinib, and EGF correlates with a decreased activity of ERK1/2 proteins. In turn, the EGF- and human serum-mediated rescue of drug-treated A431 cells restores ERK1/2 activity in functional tests. RNA sequencing revealed 1271 and 1566 differentially expressed genes (DEGs) in the presence of cetuximab and erlotinib, respectively. Erlotinib- and cetuximab-specific DEGs significantly overlapped. Interestingly, the expression of 100% and 75% of these DEGs restores to the no-drug level when EGF or a mixed human serum sample, respectively, is added along with cetuximab. In the case of erlotinib, EGF and human serum restore the expression of 39% and 83% of DEGs, respectively. We further assessed differential molecular pathway activation levels and propose that EGF/human serum-mediated A431 resistance to EGFR drugs can be largely explained by reactivation of the MAPK signaling cascade.

Host nasopharyngeal transcriptome dataset of a SARS-CoV-2 positive Italian cohort.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has affected millions of people worldwide and has significant implications for public health. Host transcriptomics profiling provides comprehensive understanding of how the virus interacts with host cells and how the host responds to the virus. COVID-19 disease alters the host transcriptome, affecting cellular pathways and key molecular functions. To contribute to the global effort to understand the virus's effect on host cell transcriptome, we have generated a dataset from nasopharyngeal swabs of 35 individuals infected with SARS-CoV-2 from the Campania region in Italy during the three outbreaks, with different clinical conditions. This dataset will help to elucidate the complex interactions among genes and can be useful in the development of effective therapeutic pathways.

Spatial transcriptomics: Technologies, applications and experimental considerations.

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Distinct Traits of Structural and Regulatory Evolutional Conservation of Human Genes with Specific Focus on Major Cancer Molecular Pathways.

The evolution of protein-coding genes has both structural and regulatory components. The first can be assessed by measuring the ratio of non-synonymous to synonymous nucleotide substitutions. The second component can be measured as the normalized proportion of transposable elements that are used as regulatory elements. For the first time, we characterized in parallel the regulatory and structural evolutionary profiles for 10,890 human genes and 2972 molecular pathways. We observed a ~0.1 correlation between the structural and regulatory metrics at the gene level, which appeared much higher (~0.4) at the pathway level. We deposited the data in the publicly available database RetroSpect. We also analyzed the evolutionary dynamics of six cancer pathways of two major axes: Notch/WNT/Hedgehog and AKT/mTOR/EGFR. The Hedgehog pathway had both components slower, whereas the Akt pathway had clearly accelerated structural evolution. In particular, the major hub nodes Akt and beta-catenin showed both components strongly decreased, whereas two major regulators of Akt TCL1 and CTMP had outstandingly high evolutionary rates. We also noticed structural conservation of serine/threonine kinases and the genes related to guanosine metabolism in cancer signaling: GPCRs, G proteins, and small regulatory GTPases (Src, Rac, Ras); however, this was compensated by the accelerated regulatory evolution.

Molecules promoting circulating clusters of cancer cells suggest novel therapeutic targets for treatment of metastatic cancers.

Treatment of metastatic disease remains among the most challenging tasks in oncology. One of the early events that predicts a poor prognosis and precedes the development of metastasis is the occurrence of clusters of cancer cells in the blood flow. Moreover, the presence of heterogeneous clusters of cancerous and noncancerous cells in the circulation is even more dangerous. Review of pathological mechanisms and biological molecules directly involved in the formation and pathogenesis of the heterotypic circulating tumor cell (CTC) clusters revealed their common properties, which include increased adhesiveness, combined epithelial-mesenchymal phenotype, CTC-white blood cell interaction, and polyploidy. Several molecules involved in the heterotypic CTC interactions and their metastatic properties, including IL6R, CXCR4 and EPCAM, are targets of approved or experimental anticancer drugs. Accordingly, analysis of patient survival data from the published literature and public datasets revealed that the expression of several molecules affecting the formation of CTC clusters predicts patient survival in multiple cancer types. Thus, targeting of molecules involved in CTC heterotypic interactions might be a valuable strategy for the treatment of metastatic cancers.

Transcriptome-Based Traits of Radioresistant Sublines of Non-Small Cell Lung Cancer Cells.

Radioresistance is a major obstacle for the successful therapy of many cancers, including non-small cell lung cancer (NSCLC). To elucidate the mechanism of radioresistance of NSCLC cells and to identify key molecules conferring radioresistance, the radioresistant subclones of p53 wild-type A549 and p53-deficient H1299 cell cultures were established. The transcriptional changes between parental and radioresistant NSCLC cells were investigated by RNA-seq. In total, expression levels of 36,596 genes were measured. Changes in the activation of intracellular molecular pathways of cells surviving irradiation relative to parental cells were quantified using the Oncobox bioinformatics platform. Following 30 rounds of 2 Gy irradiation, a total of 322 genes were differentially expressed between p53 wild-type radioresistant A549IR and parental A549 cells. For the p53-deficient (H1299) NSCLC cells, the parental and irradiated populations differed in the expression of 1628 genes and 1616 pathways. The expression of genes associated with radioresistance reflects the complex biological processes involved in clinical cancer cell eradication and might serve as a potential biomarker and therapeutic target for NSCLC treatment.

Pan-cancer antagonistic inhibition pattern of ATM-driven G2/M checkpoint pathway vs other DNA repair pathways.

DNA repair mechanisms keep genome integrity and limit tumor-associated alterations and heterogeneity, but on the other hand they promote tumor survival after radiation and genotoxic chemotherapies. We screened pathway activation levels of 38 DNA repair pathways in nine human cancer types (gliomas, breast, colorectal, lung, thyroid, cervical, kidney, gastric, and pancreatic cancers). We took RNAseq profiles of the experimental 51 normal and 408 tumor samples, and from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases - of 500/407 normal and 5752/646 tumor samples, and also 573 normal and 984 tumor proteomic profiles from Proteomic Data Commons portal. For all the samplings we observed a congruent trend that all cancer types showed inhibition of G2/M arrest checkpoint pathway compared to the normal samples, and relatively low activities of p53-mediated pathways. In contrast, other DNA repair pathways were upregulated in most of the cancer types. The G2/M checkpoint pathway was statistically significantly downregulated compared to the other DNA repair pathways, and this inhibition was strongly impacted by antagonistic regulation of (i) promitotic genes CCNB and CDK1, and (ii) GADD45 genes promoting G2/M arrest. At the DNA level, we found that ATM, TP53, and CDKN1A genes accumulated loss of function mutations, and cyclin B complex genes - transforming mutations. These findings suggest importance of activation for most of DNA repair pathways in cancer progression, with remarkable exceptions of G2/M checkpoint and p53-related pathways which are downregulated and neutrally activated, respectively.

Targeted Inhibitors of EGFR: Structure, Biology, Biomarkers, and Clinical Applications.

Members of the EGFR family of tyrosine kinase receptors are major regulators of cellular proliferation, differentiation, and survival. In humans, abnormal activation of EGFR is associated with the development and progression of many cancer types, which makes it an attractive target for molecular-guided therapy. Two classes of EGFR-targeted cancer therapeutics include monoclonal antibodies (mAbs), which bind to the extracellular domain of EGFR, and tyrosine kinase inhibitors (TKIs), which mostly target the intracellular part of EGFR and inhibit its activity in molecular signaling. While EGFR-specific mAbs and three generations of TKIs have demonstrated clinical efficacy in various settings, molecular evolution of tumors leads to apparent and sometimes inevitable resistance to current therapeutics, which highlights the need for deeper research in this field. Here, we tried to provide a comprehensive and systematic overview of the rationale, molecular mechanisms, and clinical significance of the current EGFR-targeting drugs, highlighting potential candidate molecules in development. We summarized the underlying mechanisms of resistance and available personalized predictive approaches that may lead to improved efficacy of EGFR-targeted therapies. We also discuss recent developments and the use of specific therapeutic strategies, such as multi-targeting agents and combination therapies, for overcoming cancer resistance to EGFR-specific drugs.

Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data.

BACKGROUND: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. METHODS: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. RESULTS: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84-0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. CONCLUSION: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect.

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.