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.

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.

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.

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.

Shambhala: a platform-agnostic data harmonizer for gene expression data.

BACKGROUND: Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing. RESULTS: Unlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Shambhala harmonization is based on the calibration of gene expression profiles using the auxiliary standardization dataset. Each profile is transformed to make it similar to the output of microarray hybridization platform Affymetrix Human Gene. This platform was chosen because it has the biggest number of human gene expression profiles deposited in public databases. We evaluated Shambhala ability to retain biologically important features after harmonization. The same four biological samples taken in multiple replicates were profiled independently using three and four different experimental platforms, respectively, then Shambhala-harmonized and investigated by hierarchical clustering. CONCLUSION: Our results showed that unlike other frequently used methods: quantile normalization and DESeq/DESeq2 normalization, Shambhala harmonization was the only method supporting sample-specific and platform-independent biologically meaningful clustering for the data obtained from multiple experimental platforms.

Molecular functions of human endogenous retroviruses in health and disease.

Human endogenous retroviruses (HERVs) and related genetic elements form 504 distinct families and occupy ~8% of human genome. Recent success of high-throughput experimental technologies facilitated understanding functional impact of HERVs for molecular machinery of human cells. HERVs encode active retroviral proteins, which may exert important physiological functions in the body, but also may be involved in the progression of cancer and numerous human autoimmune, neurological and infectious diseases. The spectrum of related malignancies includes, but not limits to, multiple sclerosis, psoriasis, lupus, schizophrenia, multiple cancer types and HIV. In addition, HERVs regulate expression of the neighboring host genes and modify genomic regulatory landscape, e.g., by providing regulatory modules like transcription factor binding sites (TFBS). Indeed, recent bioinformatic profiling identified ~110,000 regulatory active HERV elements, which formed at least ~320,000 human TFBS. These and other peculiarities of HERVs might have played an important role in human evolution and speciation. In this paper, we focus on the current progress in understanding of normal and pathological molecular niches of HERVs, on their implications in human evolution, normal physiology and disease. We also review the available databases dealing with various aspects of HERV genetics.

Human-specific endogenous retroviral insert serves as an enhancer for the schizophrenia-linked gene PRODH.

Using a systematic, whole-genome analysis of enhancer activity of human-specific endogenous retroviral inserts (hsERVs), we identified an element, hsERVPRODH, that acts as a tissue-specific enhancer for the PRODH gene, which is required for proper CNS functioning. PRODH is one of the candidate genes for susceptibility to schizophrenia and other neurological disorders. It codes for a proline dehydrogenase enzyme, which catalyses the first step of proline catabolism and most likely is involved in neuromediator synthesis in the CNS. We investigated the mechanisms that regulate hsERVPRODH enhancer activity. We showed that the hsERVPRODH enhancer and the internal CpG island of PRODH synergistically activate its promoter. The enhancer activity of hsERVPRODH is regulated by methylation, and in an undermethylated state it can up-regulate PRODH expression in the hippocampus. The mechanism of hsERVPRODH enhancer activity involves the binding of the transcription factor SOX2, whch is preferentially expressed in hippocampus. We propose that the interaction of hsERVPRODH and PRODH may have contributed to human CNS evolution.

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.

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.

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.

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.

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.

Gene Expression-Based Signature Can Predict Sorafenib Response in Kidney Cancer.

Sorafenib is a tyrosine kinase inhibitory drug with multiple molecular specificities that is approved for clinical use in second-line treatments of metastatic and advanced renal cell carcinomas (RCCs). However, only 10-40% of RCC patients respond on sorafenib-containing therapies, and personalization of its prescription may help in finding an adequate balance of clinical efficiency, cost-effectiveness, and side effects. We investigated whether expression levels of known molecular targets of sorafenib in RCC can serve as prognostic biomarker of treatment response. We used Illumina microarrays to profile RNA expression in pre-treatment formalin-fixed paraffin-embedded (FFPE) samples of 22 metastatic or advanced RCC cases with known responses on next-line sorafenib monotherapy. Among them, nine patients showed partial response (PR), three patients-stable disease (SD), and 10 patients-progressive disease (PD) according to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. We then classified PR + SD patients as "responders" and PD patients as "poor responders". We found that gene signature including eight sorafenib target genes was congruent with the drug response characteristics and enabled high-quality separation of the responders and poor responders [area under a receiver operating characteristic curve (AUC) 0.89]. We validated these findings on another set of 13 experimental annotated FFPE RCC samples (for 2 PR, 1 SD, and 10 PD patients) that were profiled by RNA sequencing and observed AUC 0.97 for 8-gene signature as the response classifier. We further validated these results in a series of qRT-PCR experiments on the third experimental set of 12 annotated RCC biosamples (for 4 PR, 3 SD, and 5 PD patients), where 8-gene signature showed AUC 0.83.

OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization.

OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein-protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.

Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways.

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways.

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.