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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas.

Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.

Case of multifocal glioblastoma with four fusion transcripts of ALK, FGFR2, NTRK2, and NTRK3 genes stresses the need for tumor tissue multisampling for transcriptomic analysis.

Glioblastoma multiforme (GBM) is the most malignant brain tumor with patient mortality rate close to 100%, 5-yr survival rate of ∼5%, and a median survival of 14 mo. GBMs have notorious histomorphologic and molecular heterogeneities thus giving hope for development of future personalized therapies. We describe here a case of a 48-yr-old male patient with three-nodular GBM. To address the question of intratumoral molecular heterogeneity, a comparative analysis of gene expression was performed by using multiple samples collected from different tumor sites with the aid of intraoperative magnetic resonance imaging (MRI). Sixteen GBM biosamples from parietal, temporal, and temporo-polar localizations were collected from primary, recurrent, and second recurrent tumors and were obtained and investigated by RNA sequencing. Our investigations revealed that biosamples derived from different tumor sites differ in their gene expression profiles with classical or mesenchymal signatures associated with clinically distinct molecular subtypes of GBM found within the same tumor. The results also showed significant differences in the expression of genes specific for targeted therapeutics. Our investigations have enabled the identification of four novel fusion transcripts-KIF5C-NTRK3, AC016907.2-ALK, CNTNAP3-NTRK2, and ZNF135-FGFR2-each present in only one sample. We found no differences between untreated and recurrent stages in the expression levels of genes involved in fusion transcripts, suggesting the lack of association between fusion transcript and treatment response. In contrast, longitudinal changes in the expression of VEGF and MGMT genes were concordant with the tumor response to bevacizumab and temozolomide. Our study underscores the importance of integrating a multisampling approach and RNA sequencing and demonstrates the predictive merit of an integrated approach for differentiating genomic aberrations associated with untreated or post-treatment recurrent GBMs.

COVID-19-associated inhibition of energy accumulation pathways in human semen samples.

OBJECTIVE: To investigate transcriptional alterations in human semen samples associated with COVID-19 infection. DESIGN: Retrospective observational cohort study. SETTING: City hospital. PATIENTS: Ten patients who had recovered from mild COVID-19 infection. Eight of these patients had different sperm abnormalities that were diagnosed before infection. The control group consisted of 5 healthy donors without known abnormalities and no history of COVID-19 infection. INTERVENTIONS: We used RNA sequencing to determine gene expression profiles in all studied biosamples. Original standard bioinformatic instruments were used to analyze activation of intracellular molecular pathways. MAIN OUTCOME MEASURES: Routine semen analysis, gene expression levels, and molecular pathway activation levels in semen samples. RESULTS: We found statistically significant inhibition of genes associated with energy production pathways in the mitochondria, including genes involved in the electron transfer chain and genes involved in toll-like receptor signaling. All protein-coding genes encoded by the mitochondrial genome were significantly down-regulated in semen samples collected from patients after recovery from COVID-19. CONCLUSIONS: Our results may provide a molecular basis for the previously observed phenomenon of decreased sperm motility associated with COVID-19 infection. Moreover, the data will be beneficial for the optimization of preconception care for men who have recently recovered from COVID-19 infection.

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

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.

DNA repair pathway activation features in follicular and papillary thyroid tumors, interrogated using 95 experimental RNA sequencing profiles.

DNA repair can prevent mutations and cancer development, but it can also restore damaged tumor cells after chemo and radiation therapy. We performed RNA sequencing on 95 human pathological thyroid biosamples including 17 follicular adenomas, 23 follicular cancers, 3 medullar cancers, 51 papillary cancers and 1 poorly differentiated cancer. The gene expression profiles are annotated here with the clinical and histological diagnoses and, for papillary cancers, with BRAF gene V600E mutation status. DNA repair molecular pathway analysis showed strongly upregulated pathway activation levels for most of the differential pathways in the papillary cancer and moderately upregulated pattern in the follicular cancer, when compared to the follicular adenomas. This was observed for the BRCA1, ATM, p53, excision repair, and mismatch repair pathways. This finding was validated using independent thyroid tumor expression dataset PRJEB11591. We also analyzed gene expression patterns linked with the radioiodine resistant thyroid tumors (n = 13) and identified 871 differential genes that according to Gene Ontology analysis formed two functional groups: (i) response to topologically incorrect protein and (ii) aldo-keto reductase (NADP) activity. We also found RNA sequencing reads for two hybrid transcripts: one in-frame fusion for well-known NCOA4-RET translocation, and another frameshift fusion of ALK oncogene with a new partner ARHGAP12. The latter could probably support increased expression of truncated ALK downstream from 4th exon out of 28. Both fusions were found in papillary thyroid cancers of follicular histologic subtype with node metastases, one of them (NCOA4-RET) for the radioactive iodine resistant tumor. The differences in DNA repair activation patterns may help to improve therapy of different thyroid cancer types under investigation and the data communicated may serve for finding additional markers of radioiodine resistance.

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.

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.

BACKGROUND: Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn't allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. METHODS: We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. RESULTS: We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. CONCLUSIONS: We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others - microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens.

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.