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.

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.

Identification of cell type-specific correlations between ERK activity and cell viability upon treatment with ERK1/2 inhibitors.

Increased MAPK signaling is a hallmark of various cancers and is a central regulator of cell survival. Direct ERK1/2 inhibition is considered a promising approach to avoid ERK1/2 reactivation caused by upstream kinases BRAF, MEK1/2, and KRAS, as well as by receptor tyrosine kinase inhibitors, but the dynamics and selectivity of ERK1/2 inhibitors are much less studied compared with BRAF or MEK inhibitors. Using ERK1/2 and downstream kinase ELK1 reporter cell lines of lung cancer (H1299; NRASQ61K), colon cancer (HCT-116; KRASG13D), neuroblastoma (SH-SY5Y), and leukemia (U937), we examined the relationship between ERK inhibition and drug-induced toxicity for five ERK inhibitors: SCH772984, ravoxertinib, LY3214996, ulixertinib, and VX-11e, as well as one MEK inhibitor, PD0325901. Comparing cell viability and ERK inhibition revealed different ERK dependencies for these cell lines. We identify several drugs, such as SCH772984 and VX-11e, which induce excessive toxicity not directly related to ERK1/2 inhibition in specific cell lines. We also show that PD0325901, LY3214996, and ulixertinib are prone to ERK1/2 reactivation over time. We distinguished two types of ERK1/2 reactivation: the first could be reversed by adding a fresh dose of inhibitors, while the second persists even after additional treatments. We also showed that cells that became resistant to the MEK1/2 inhibitor PD0325901 due to ERK1/2 reactivation remained sensitive to ERK1/2 inhibitor ulixertinib. Our data indicate that correlation of ERK inhibition with drug-induced toxicity in multiple cell lines may help to find more selective and effective ERK1/2 inhibitors.

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.

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.

A Review of Recent Studies on the Effects of SARS-CoV-2 Infection and SARS-CoV-2 Vaccines on Male Reproductive Health.

According to world statistics, men are more susceptible to the coronavirus disease 2019 (COVID-19) than are women. Considering the interconnection between infections and male infertility, investigation of the potential impact of COVID-19 on men's reproductive health is now a particularly relevant topic. Published data indicate decreased sperm quality and orchitis development in patients with COVID-19, including reduced sperm count, decreased sperm motility, and elevated DNA fragmentation index. Although mass vaccination against COVID-19 is currently being carried out worldwide using available authorized vaccines, the effect of these vaccines on men's reproductive health has not yet been investigated. There is currently no evidence that SARS-CoV-2 can be transmitted in semen, but available data suggest that it can infect spermatogonia, spermatids, Leydig cells, and Sertoli cells. Therefore, SARS-CoV-2 orchitis and reduced male fertility may be long-term complications of COVID-19, which requires further investigation. Currently, there is also no evidence that vaccines against SARS-CoV-2 have any pathological effects on spermatogenesis or male reproductive health. Thus, further studies are needed to determine the effects of COVID-19 and COVID-19 vaccines on men's reproductive health, which will help to optimize the management and rehabilitation of these patients. This review aims to discuss recent studies on the impact of the COVID-19 and COVID-19 vaccines on men's reproductive health. The article addresses various issues such as the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on testosterone biosynthesis, semen parameters, testicular tissue, and epididymis.

Reclassification of TCGA Diffuse Glioma Profiles Linked to Transcriptomic, Epigenetic, Genomic and Clinical Data, According to the 2021 WHO CNS Tumor Classification.

In 2021, the fifth edition of the WHO classification of tumors of the central nervous system (WHO CNS5) was published. Molecular features of tumors were directly incorporated into the diagnostic decision tree, thus affecting both the typing and staging of the tumor. It has changed the traditional approach, based solely on histopathological classification. The Cancer Genome Atlas project (TCGA) is one of the main sources of molecular information about gliomas, including clinically annotated transcriptomic and genomic profiles. Although TCGA itself has played a pivotal role in developing the WHO CNS5 classification, its proprietary databases still retain outdated diagnoses which frequently appear incorrect and misleading according to the WHO CNS5 standards. We aimed to define the up-to-date annotations for gliomas from TCGA's database that other scientists can use in their research. Based on WHO CNS5 guidelines, we developed an algorithm for the reclassification of TCGA glioma samples by molecular features. We updated tumor type and diagnosis for 828 out of a total of 1122 TCGA glioma cases, after which available transcriptomic and methylation data showed clustering features more consistent with the updated grouping. We also observed better stratification by overall survival for the updated diagnoses, yet WHO grade 3 IDH-mutant oligodendrogliomas and astrocytomas are still indistinguishable. We also detected altered performance in the previous diagnostic transcriptomic molecular biomarkers (expression of SPRY1, CRNDE and FREM2 genes and FREM2 molecular pathway) and prognostic gene signature (FN1, ITGA5, OSMR, and NGFR) after reclassification. Thus, we conclude that further efforts are needed to reconsider glioma molecular biomarkers.

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level.

Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.

Subtype of Neuroblastoma Cells with High KIT Expression Are Dependent on KIT and Its Knockdown Induces Compensatory Activation of Pro-Survival Signaling.

Neuroblastoma (NB) is a pediatric cancer with high clinical and molecular heterogeneity, and patients with high-risk tumors have limited treatment options. Receptor tyrosine kinase KIT has been identified as a potential marker of high-risk NB and a promising target for NB treatment. We investigated 19,145 tumor RNA expression and molecular pathway activation profiles for 20 cancer types and detected relatively high levels of KIT expression in NB. Increased KIT expression was associated with activation of cell survival pathways, downregulated apoptosis induction, and cell cycle checkpoint control pathways. KIT knockdown with shRNA encoded by lentiviral vectors in SH-SY5Y cells led to reduced cell proliferation and apoptosis induction up to 50%. Our data suggest that apoptosis induction was caused by mitotic catastrophe, and there was a 2-fold decrease in percentage of G2-M cell cycle phase after KIT knockdown. We found that KIT knockdown in NB cells leads to strong upregulation of other pro-survival growth factor signaling cascades such as EPO, NGF, IL-6, and IGF-1 pathways. NGF, IGF-1 and EPO were able to increase cell proliferation in KIT-depleted cells in an ERK1/2-dependent manner. Overall, we show that KIT is a promising therapeutic target in NB, although such therapy efficiency could be impeded by growth factor signaling activation.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways.

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

RNA sequencing for research and diagnostics in clinical oncology.

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

EGFR Pathway-Based Gene Signatures of Druggable Gene Mutations in Melanoma, Breast, Lung, and Thyroid Cancers.

EGFR, BRAF, PIK3CA, and KRAS genes play major roles in EGFR pathway, and accommodate activating mutations that predict response to many targeted therapeutics. However, connections between these mutations and EGFR pathway expression patterns remain unexplored. Here, we investigated transcriptomic associations with these activating mutations in three ways. First, we compared expressions of these genes in the mutant and wild type tumors, respectively, using RNA sequencing profiles from The Cancer Genome Atlas project database (n = 3660). Second, mutations were associated with the activation level of EGFR pathway. Third, they were associated with the gene signatures of differentially expressed genes from these pathways between the mutant and wild type tumors. We found that the upregulated EGFR pathway was linked with mutations in the BRAF (thyroid cancer, melanoma) and PIK3CA (breast cancer) genes. Gene signatures were associated with BRAF (thyroid cancer, melanoma), EGFR (squamous cell lung cancer), KRAS (colorectal cancer), and PIK3CA (breast cancer) mutations. However, only for the BRAF gene signature in the thyroid cancer we observed strong biomarker diagnostic capacity with AUC > 0.7 (0.809). Next, we validated this signature on the independent literature-based dataset (n = 127, fresh-frozen tissue samples, AUC 0.912), and on the experimental dataset (n = 42, formalin fixed, paraffin embedded tissue samples, AUC 0.822). Our results suggest that the RNA sequencing profiles can be used for robust identification of the replacement of Valine at position 600 with Glutamic acid in the BRAF gene in the papillary subtype of thyroid cancer, and evidence that the specific gene expression levels could provide information about the driver carcinogenic mutations.

Gene Expression Signature of Endometrial Samples from Women with and without Endometriosis.

STUDY OBJECTIVE: To develop a prototype of a complex gene expression biomarker for the diagnosis of endometriosis on the basis of differences between the molecular signatures of the endometrium from women with and without endometriosis. DESIGN: Prospective observational cohort study. Evidence obtained from a well-designed, controlled trial without randomization. SETTING: Department of reproductive medicine and surgery, A.I. Evdokimov Moscow State University of Medicine and Dentistry. PATIENTS: A total of 33 women (aged 32-38 years) were included in this study. Patients with and without endometriosis were divided into 2 separate groups. The group composed of patients with endometriosis included 19 living patients with endometriosis who underwent laparoscopic excision of endometriosis. The control group included 6 living patients who underwent laparoscopic excision of incompetent uterine scar after cesarean section, with both surgically and histologically confirmed absence of endometriosis and adenomyosis. An additional control/verification group included various previously RNA-sequencing-profiled tissue samples (endocervix, ovarian surface epithelium) of 8 randomly selected healthy female cadaveric donors aged 32 to 38 years. The exclusion criteria for all patients were hormone therapy and any intrauterine device use for more than 1 year preceding surgery, as well as absence of other diseases of the uterus, fallopian tubes, and ovaries. INTERVENTIONS: Laparoscopic excision of endometriotic foci and hysteroscopy with endometrial sampling were performed. The cadaveric tissue samples included endocervix and ovarian surface epithelium. Endometrial sampling was obtained from the women in the control group. RNA sequencing was performed using Illumina HiSeq 3000 equipment (Illumina, Inc., San Diego, CA) for single-end sequencing. Unique bioinformatics algorithms were developed and validated using experimental and public gene expression datasets. MEASUREMENTS AND MAIN RESULTS: We generated a characteristic signature of 5 genes downregulated in the endometrium and endometriotic tissue of the patients with endometriosis, selected after comparison with the endometrium of the women without endometriosis. This gene signature showed a capacity for nearly perfect separation of all 52 analyzed tissue samples of the patients with endometriosis (endometrial as well as endometriotic samples) from the 14 tissue samples of both living and cadaveric donors without endometriosis (area under the curve = 0.982, Matthews correlation coefficient = 0.832). CONCLUSION: The gene signature of the endometrium identified in this study may potentially serve as a nonsurgical diagnostic method for endometriosis detection. Our data also suggest that the statistical method of 5-fold cross-validation of differential gene expression analysis can be used to generate robust gene signatures using real-world clinical data.

Differences between human and chimpanzee genomes and their implications in gene expression, protein functions and biochemical properties of the two species.

Chimpanzees are the closest living relatives of humans. The divergence between human and chimpanzee ancestors dates to approximately 6,5-7,5 million years ago. Genetic features distinguishing us from chimpanzees and making us humans are still of a great interest. After divergence of their ancestor lineages, human and chimpanzee genomes underwent multiple changes including single nucleotide substitutions, deletions and duplications of DNA fragments of different size, insertion of transposable elements and chromosomal rearrangements. Human-specific single nucleotide alterations constituted 1.23% of human DNA, whereas more extended deletions and insertions cover ~ 3% of our genome. Moreover, much higher proportion is made by differential chromosomal inversions and translocations comprising several megabase-long regions or even whole chromosomes. However, despite of extensive knowledge of structural genomic changes accompanying human evolution we still cannot identify with certainty the causative genes of human identity. Most structural gene-influential changes happened at the level of expression regulation, which in turn provoked larger alterations of interactome gene regulation networks. In this review, we summarized the available information about genetic differences between humans and chimpanzees and their potential functional impacts on differential molecular, anatomical, physiological and cognitive peculiarities of these species.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41-235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61-0.88 range to 0.70-0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

Reciprocal Dysregulation of MiR-146b and MiR-451 Contributes in Malignant Phenotype of Follicular Thyroid Tumor.

Over the last few years, incidental thyroid nodules are being diagnosed with increasing frequency with the use of highly sensitive imaging techniques. The ultrasound thyroid gland examination, followed by the fine-needle aspiration cytology is the standard diagnostic approach. However, in cases of the follicular nature of nodules, cytological diagnosis is not enough. Analysis of miRNAs in the biopsy presents a promising approach. Increasing our knowledge of miRNA's role in follicular carcinogenesis, and development of the appropriate the miRNA analytical technologies are required to implement miRNA-based tests in clinical practice. We used material from follicular thyroid nodes (n.84), grouped in accordance with their invasive properties. The invasion-associated miRNAs expression alterations were assayed. Expression data were confirmed by highly sensitive two-tailed RT-qPCR. Reciprocally dysregulated miRNAs pair concentration ratios were explored as a diagnostic parameter using receiver operation curve (ROC) analysis. A new bioinformatics method (MiRImpact) was applied to evaluate the biological significance of the observed expression alterations. Coupled experimental and computational approaches identified reciprocal dysregulation of miR-146b and miR-451 as important attributes of follicular cell malignant transformation and follicular thyroid cancer progression. Thus, evaluation of combined dysregulation of miRNAs relevant to invasion and metastasis can help to distinguish truly malignant follicular thyroid cancer from indolent follicular adenoma.

Analysis of miR-9-5p, miR-124-3p, miR-21-5p, miR-138-5p, and miR-1-3p in Glioblastoma Cell Lines and Extracellular Vesicles.

Glioblastoma (GBM), the most common primary brain tumor, is a complex and extremely aggressive disease. Despite recent advances in molecular biology, there is a lack of biomarkers, which would improve GBM's diagnosis, prognosis, and therapy. Here, we analyzed by qPCR the expression levels of a set of miRNAs in GBM and lower-grade glioma human tissue samples and performed a survival analysis in silico. We then determined the expression of same miRNAs and their selected target mRNAs in small extracellular vesicles (sEVs) of GBM cell lines. We showed that the expression of miR-21-5p was significantly increased in GBM tissue compared to lower-grade glioma and reference brain tissue, while miR-124-3p and miR-138-5p were overexpressed in reference brain tissue compared to GBM. We also demonstrated that miR-9-5p and miR-124-3p were overexpressed in the sEVs of GBM stem cell lines (NCH421k or NCH644, respectively) compared to the sEVs of all other GBM cell lines and astrocytes. VIM mRNA, a target of miR-124-3p and miR-138-5p, was overexpressed in the sEVs of U251 and U87 GBM cell lines compared to the sEVs of GBM stem cell line and also astrocytes. Our results suggest VIM mRNA, miR-9-5p miRNA, and miR-124-3p miRNA could serve as biomarkers of the sEVs of GBM cells.

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology.

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

Molecular pathway activation - New type of biomarkers for tumor morphology and personalized selection of target drugs.

Anticancer target drugs (ATDs) specifically bind and inhibit molecular targets that play important roles in cancer development and progression, being deeply implicated in intracellular signaling pathways. To date, hundreds of different ATDs were approved for clinical use in the different countries. Compared to previous chemotherapy treatments, ATDs often demonstrate reduced side effects and increased efficiency, but also have higher costs. However, the efficiency of ATDs for the advanced stage tumors is still insufficient. Different ATDs have different mechanisms of action and are effective in different cohorts of patients. Personalized approaches are therefore needed to select the best ATD candidates for the individual patients. In this review, we focus on a new generation of biomarkers - molecular pathway activation - and on their applications for predicting individual tumor response to ATDs. The success in high throughput gene expression profiling and emergence of novel bioinformatic tools reinforced quick development of pathway related field of molecular biomedicine. The ability to quantitatively measure degree of a pathway activation using gene expression data has revolutionized this field and made the corresponding analysis quick, robust and inexpensive. This success was further enhanced by using machine learning algorithms for selection of the best biomarkers. We review here the current progress in translating these studies to clinical oncology and patient-oriented adjustment of cancer therapy.