Oncofinder, a new method for the analysis of intracellular signaling pathway activation using transcriptomic data

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

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.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

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

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

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

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

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.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

Transcriptomic Portraits and Molecular Pathway Activation Features of Adult Spinal Intramedullary Astrocytomas

In this study, we report 31 spinal intramedullary astrocytoma (SIA) RNA sequencing (RNA-seq) profiles for 25 adult patients with documented clinical annotations. To our knowledge, this is the first clinically annotated RNA-seq dataset of spinal astrocytomas derived from the intradural intramedullary compartment. We compared these tumor profiles with the previous healthy central nervous system (CNS) RNA-seq data for spinal cord and brain and identified SIA-specific gene sets and molecular pathways. Our findings suggest a trend for SIA-upregulated pathways governing interactions with the immune cells and downregulated pathways for the neuronal functioning in the context of normal CNS activity. In two patient tumor biosamples, we identified diagnostic KIAA1549-BRAF fusion oncogenes, and we also found 16 new SIA-associated fusion transcripts. In addition, we bioinformatically simulated activities of targeted cancer drugs in SIA samples and predicted that several tyrosine kinase inhibitory drugs and thalidomide analogs could be potentially effective as second-line treatment agents to aid in the prevention of SIA recurrence and progression.

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.

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.

System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation

There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Large-Scale Profiling of Signaling Pathways Reveals a Distinct Demarcation between Normal and Extended Liver Resection

Despite numerous studies addressing normal liver regeneration, we still lack comprehensive understanding of the biological processes underlying failed liver regeneration. Therefore, we analyzed the activity of 271 intracellular signaling pathways (ISPs) by genome wide profiling of differentially expressed RNAs in murine liver tissue biopsies after normal hepatectomy (nHx; 68% of liver removed) and extended hepatectomy (eHx; 86% of liver removed). Comprehensive, genome-wide transcriptome profiling using RNAseq was performed in liver tissue obtained from mice (sham, nHx, and eHx) harvested 1, 8, 16, 32, and 48 h after operation (n = 3 per group) and the OncoFinder toolkit was used for an unsupervised, unbiased identification of intracellular signaling pathways (ISP) activity. We observed that the normal regenerative process requires a transient activation and silencing of approximately two dozen of ISPs. After nHx, the Akt Pathway represented with 13 branches, the Chromatin Pathway and the DDR Pathways dominated. After eHx, the ATM main pathway and two of its branches (Cell Survival; G2_M Checkpoint Arrest) dominated, as well as the Hypoxia Pathways. Further, 14 ISPs demonstrated a strong inverse regulation, with the Hedgehog and the Brca1 Main Pathways as chief activators after nHx, and the ATM Pathway(G2_M Checkpoint Arrest) as the dominating constraining response after eHx.

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.

Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer

Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.

Quantitation of Molecular Pathway Activation Using RNA Sequencing Data

Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.

Molecular Pathway Analysis of Mutation Data for Biomarkers Discovery and Scoring of Target Cancer Drugs

DNA mutations govern cancer development. Cancer mutation profiles vary dramatically among the individuals. In some cases, they may serve as the predictors of disease progression and response to therapies. However, the biomarker potential of cancer mutations can be dramatically (several orders of magnitude) enhanced by applying molecular pathway-based approach. We developed Oncobox system for calculation of pathway instability (PI) values for the molecular pathways that are aggregated mutation frequencies of the pathway members normalized on gene lengths and on number of genes in the pathway. PI scores can be effective biomarkers in different types of comparisons, for example, as the cancer type biomarkers and as the predictors of tumor response to target therapies. The latter option is implemented using mutation drug score (MDS) values, which algorithmically rank the drugs capacity of interfering with the mutated molecular pathways. Here, describe the mathematical basis and algorithms for PI and MDS values calculation, validation and implementation. The example analysis is provided encompassing 5956 human tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project, that totally make 2,316,670 mutations in 19,872 genes and 1748 molecular pathways, thus enabling ranking of 128 clinically approved target drugs. Our results evidence that the Oncobox PI and MDS approaches are highly useful for basic and applied aspects of molecular oncology and pharmacology research.

Exploration of the Transcriptional Landscape of ALPPS Reveals the Pathways of Accelerated Liver Regeneration

Background and Aims: ALPPS (associating liver partition and portal vein ligation for staged hepatectomy), a novel 2-staged hepatectomy, dramatically accelerates liver regeneration and thus enables extensive liver tumor resection. The signaling networks underlying the ALPPS-induced accelerated regeneration process are largely unknown.

Methods: We performed transcriptome profiling (TP) of liver tissue obtained from a mouse model of ALPPS, standard hepatectomy (68% model), and additional control surgeries (sham, PVL and Tx). We also performed TP using human liver biopsies (n = 5) taken from the occluded lobe and the future liver remnant (FLR) during the first step of ALPPS surgery (4–5 h apart). We used Oncofinder computational tools, which covers 378 ISPs, for unsupervised, unbiased quantification of ISP activity.

Results: Gene expression cluster analysis revealed an ALPPS specific signature: the IGF1R Signaling Pathway (Cell survival), the ILK Pathway (Induced cell proliferation), and the IL-10 Pathway (Stability determination) were significantly enriched, whereas the activity of the Interferon Pathway (Transcription) was reduced (p < 0.05). Further, the PAK- and ILK-associated ISPs were activated at an earlier time point, reflecting significant acceleration of liver regeneration (p < 0.001). These pathways, which were also recovered in human liver biopsies, control cell growth and proliferation, inflammatory response, and hypoxia-related processes.

Conclusions: ALPPS is not a straightforward addition of portal vein ligation (PVL) plus transection—it is more. The early stages of normal and accelerated liver regeneration are clearly discernible by a significantly increased and earlier activation of a small number of signaling pathways. Compounds mimicking these responses may help to improve the ALPPS method and further reduce the hospitalization time of the patient.

Atlas of RNA sequencing profiles for normal human tissues

Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE). Our data will be useful to all those working with the analysis of human gene expression.

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers

DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers.

Anticancer Activity of the Goat Antimicrobial Peptide ChMAP-28

Cytotoxic effect of the antimicrobial peptide ChMAP-28 from leucocytes of the goat Capra hircus was examined against five cancer and two normal human cell lines. ChMAP-28 has the amino acid sequence GRFKRFRKKLKRLWHKVGPFVGPILHY and is homologous to other α-helical mammalian antimicrobial peptides. ChMAP-28 shows considerably higher cytotoxicity against cultured tumor cells than toward normal cells at concentrations of <10 μM. Our findings suggest that ChMAP-28 can initiate necrotic death of cancer cells. Its cytotoxic effect is accomplished due to disruption of the plasma membrane integrity and is not abrogated by the addition of the caspase inhibitor Z-VAD-FMK. ChMAP-28 causes permeabilization of cytoplasmic membrane of human leukemia cells HL-60 already after 15 min of incubation. Here, we show that ChMAP-28 has one of the highest antitumor activity in vitro among all known antimicrobial peptides. We speculate that the observed specificity of ChMAP-28 cytotoxic effect against tumor cells is due to its relatively low hydrophobicity and high cationicity. In the meantime, this peptide has low hemolytic activity, which generates a potential for its use as a therapeutic agent.

Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data

Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs.

Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma

Background

Cholangiocarcinoma is an aggressive tumor with poor prognosis. Most of the cases are not available for surgery at the stage of the diagnosis and the best clinical practice chemotherapy results in about 12-month median survival. Several tyrosine kinase inhibitors (TKIs) are currently under investigation as an alternative treatment option for cholangiocarcinoma. Thus, the report of personalized selection of effective inhibitor and case outcome are of clinical interest.

Case presentation

Here we report a case of aggressive metastatic cholangiocarcinoma (MCC) in 72-year-old man, sequentially treated with two targeted chemotherapies. Initially disease quickly progressed during best clinical practice care (gemcitabine in combination with cisplatin or capecitabine), which was accompanied by significant decrease of life quality. Monotherapy with TKI sorafenib was prescribed to the patient, which resulted in stabilization of tumor growth and elimination of pain. The choice of the inhibitor was made based on high-throughput screening of gene expression in the patient’s tumor biopsy, utilized by Oncobox platform to build a personalized rating of potentially effective target therapies. However, time to progression after start of sorafenib administration did not exceed 6 months and the regimen was changed to monotherapy with Pazopanib, another TKI predicted to be effective for this patient according to the same molecular test. It resulted in disease progression according to RECIST with simultaneous elimination of sorafenib side effects such as rash and hand-foot syndrome. After 2 years from the diagnosis of MCC the patient was alive and physically active, which is substantially longer than median survival for standard therapy.

Conclusion

This case evidences that sequential personalized prescription of different TKIs may show promising efficacy in terms of survival and quality of life in MCC.

Electronic supplementary material

The online version of this article (10.1186/s40164-018-0113-x) contains supplementary material, which is available to authorized users.

Chemo brain or tumor brain - that is the question: the presence of extracranial tumors profoundly affects molecular processes in the prefrontal cortex of TumorGraft mice

Cancer chemotherapy causes numerous persistent central nervous system complications. This condition is known as chemo brain. Cognitive impairments occur even before treatment, and hence are referred to as cancer associated cognitive changes, or tumor brain. There is much yet to be learned about the mechanisms of both chemo brain and tumor brain. The frequency and timing of chemo brain and tumor brain occurrence and persistence strongly suggest they may be epigenetic in nature and associated with altered gene expression. Here we used TumorGraft^TM models wherein part of a patient's tumor is removed and grafted into immune-deficient mice and conducted global gene expression and DNA methylation analysis. We show that malignant non-central nervous system tumor growth causes profound molecular alterations in the brain. Mice harbouring triple negative or progesterone positive breast cancer TumorGrafts exhibited altered gene expression, decreased levels of DNA methylation, increased levels of DNA hydroxymethylation, and oxidative stress in the prefrontal cortex. Interestingly, chemotherapy did not have any additional synergistic effects on the analyzed processes. The molecular changes observed in this study are known signs of neurodegeneration and brain aging. This study provides an important roadmap for future large-scale analysis of the molecular and cellular mechanisms of tumor brain.

Profiling of Human Molecular Pathways Affected by Retrotransposons at the Level of Regulation by Transcription Factor Proteins

Endogenous retroviruses and retrotransposons also termed retroelements (REs) are mobile genetic elements that were active until recently in human genome evolution. REs regulate gene expression by actively reshaping chromatin structure or by directly providing transcription factor binding sites (TFBSs). We aimed to identify molecular processes most deeply impacted by the REs in human cells at the level of TFBS regulation. By using ENCODE data, we identified ~2 million TFBS overlapping with putatively regulation-competent human REs located in 5-kb gene promoter neighborhood (~17% of all TFBS in promoter neighborhoods; ~9% of all RE-linked TFBS). Most of REs hosting TFBS were highly diverged repeats, and for the evolutionary young (0–8% diverged) elements we identified only ~7% of all RE-linked TFBS. The gene-specific distributions of RE-linked TFBS generally correlated with the distributions for all TFBS. However, several groups of molecular processes were highly enriched in the RE-linked TFBS regulation. They were strongly connected with the immunity and response to pathogens, with the negative regulation of gene transcription, ubiquitination, and protein degradation, extracellular matrix organization, regulation of STAT signaling, fatty acids metabolism, regulation of GTPase activity, protein targeting to Golgi, regulation of cell division and differentiation, development and functioning of perception organs and reproductive system. By contrast, the processes most weakly affected by the REs were linked with the conservative aspects of embryo development. We also identified differences in the regulation features by the younger and older fractions of the REs. The regulation by the older fraction of the REs was linked mainly with the immunity, cell adhesion, cAMP, IGF1R, Notch, Wnt, and integrin signaling, neuronal development, chondroitin sulfate and heparin metabolism, and endocytosis. The younger REs regulate other aspects of immunity, cell cycle progression and apoptosis, PDGF, TGF beta, EGFR, and p38 signaling, transcriptional repression, structure of nuclear lumen, catabolism of phospholipids, and heterocyclic molecules, insulin and AMPK signaling, retrograde Golgi-ER transport, and estrogen signaling. The immunity-linked pathways were highly represented in both categories, but their functional roles were different and did not overlap. Our results point to the most quickly evolving molecular pathways in the recent and ancient evolution of human genome.

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency

Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.

Large-scale profiling of signalling pathways reveals an asthma specific signature in bronchial smooth muscle cells

Background

Bronchial smooth muscle (BSM) cells from asthmatic patients maintain in vitro a distinct hyper-reactive (“primed”) phenotype, characterized by increased release of pro-inflammatory factors and mediators, as well as hyperplasia and/or hypertrophy. This “primed” phenotype helps to understand pathogenesis of asthma, as changes in BSM function are essential for manifestation of allergic and inflammatory responses and airway wall remodelling.

Objective

To identify signalling pathways in cultured primary BSMs of asthma patients and non-asthmatic subjects by genome wide profiling of differentially expressed mRNAs and activated intracellular signalling pathways (ISPs).

Methods

Transcriptome profiling by cap-analysis-of-gene-expression (CAGE), which permits selection of preferentially capped mRNAs most likely to be translated into proteins, was performed in human BSM cells from asthmatic (n=8) and non-asthmatic (n=6) subjects and OncoFinder tool were then exploited for identification of ISP deregulations.

Results

CAGE revealed >600 RNAs differentially expressed in asthma vs control cells (p≤0.005), with asthma samples showing a high degree of similarity among them. Comprehensive ISP activation analysis revealed that among 269 pathways analysed, 145 (p<0.05) or 103 (p<0.01) are differentially active in asthma, with profiles that clearly characterize BSM cells of asthmatic individuals. Notably, we identified 7 clusters of coherently acting pathways functionally related to the disease, with ISPs down-regulated in asthma mostly targeting cell death-promoting pathways and up-regulated ones affecting cell growth and proliferation, inflammatory response, control of smooth muscle contraction and hypoxia-related signalization.

Conclusions

These first-time results can now be exploited toward development of novel therapeutic strategies targeting ISP signatures linked to asthma pathophysiology.

The anti-tumorigenic activity of A2M-A lesson from the naked mole-rat

Cancer resistance is a major cause for longevity of the naked mole-rat. Recent liver transcriptome analysis in this animal compared to wild-derived mice revealed higher expression of alpha2-macroglobulin (A2M) and cell adhesion molecules, which contribute to the naked mole-rat’s cancer resistance. Notably, A2M is known to dramatically decrease with age in humans. We hypothesize that this might facilitate tumour development. Here we found that A2M modulates tumour cell adhesion, migration and growth by inhibition of tumour promoting signalling pathways, e.g. PI3K / AKT, SMAD and up-regulated PTEN via down-regulation of miR-21, in vitro and in tumour xenografts. A2M increases the expression of CD29 and CD44 but did not evoke EMT. Transcriptome analysis of A2M-treated tumour cells, xenografts and mouse liver demonstrated a multifaceted regulation of tumour promoting signalling pathways indicating a less tumorigenic environment mediated by A2M. By virtue of these multiple actions the naturally occurring A2M has strong potential as a novel therapeutic agent.

Acquired resistance to tyrosine kinase inhibitors may be linked with the decreased sensitivity to X-ray irradiation

Acquired resistance to chemotherapy and radiation therapy is one of the major obstacles decreasing efficiency of treatment of the oncologic diseases. In this study, on the two cell lines (ovarian carcinoma SKOV-3 and neuroblastoma NGP-127), we modeled acquired resistance to five target anticancer drugs. The cells were grown on gradually increasing concentrations of the clinically relevant tyrosine kinase inhibitors (TKIs) Sorafenib, Pazopanib and Sunitinib, and rapalogs Everolimus and Temsirolimus, for 20 weeks. After 20 weeks of culturing, the half-inhibitory concentrations (IC₅₀) increased by 25 – 186% for the particular combinations of the drugs and cell types. We next subjected cells to 10 Gy irradiation, a dose frequently used in clinical radiation therapy. For the SKOV-3, but not NGP-127 cells, for the TKIs Sorafenib, Pazopanib and Sunitinib, we noticed statistically significant increase in capacity to repair radiation-induced DNA double strand breaks compared to naïve control cells not previously treated with TKIs. These peculiarities were linked with the increased activation of ATM DNA repair pathway in the TKI-treated SKOV-3, but not NGP-127 cells. Our results provide a new cell culture model for studying anti-cancer therapy efficiency and evidence that there may be a tissue-specific radioresistance emerging as a side effect of treatment with TKIs.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state

Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.

Towards natural mimetics of metformin and rapamycin

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

Gene expression and molecular pathway activation signatures of MYCN-amplified neuroblastomas

Neuroblastoma is a pediatric cancer arising from sympathetic nervous system. Remarkable heterogeneity in outcomes is one of its widely known features. One of the traits strongly associated with the unfavorable subtype is the amplification of oncogene MYCN. Here, we performed cross-platform biomarker detection by comparing gene expression and pathway activation patterns from the two literature reports and from our experimental dataset, combining profiles for the 761 neuroblastoma patients with known MYCN amplification status. We identified 109 / 25 gene expression / pathway activation biomarkers strongly linked with the MYCN amplification. The marker genes/pathways are involved in the processes of purine nucleotide biosynthesis, ATP-binding, tetrahydrofolate metabolism, building mitochondrial matrix, biosynthesis of amino acids, tRNA aminoacylation and NADP-linked oxidation-reduction processes, as well as in the tyrosine phosphatase activity, p53 signaling, cell cycle progression and the G1/S and G2/M checkpoints. To connect molecular functions of the genes involved in MYCN-amplified phenotype, we built a new molecular pathway using known intracellular protein interaction networks. The activation of this pathway was highly selective in discriminating MYCN-amplified neuroblastomas in all three datasets. Our data also suggest that the phosphoinositide 3-kinase (PI3K) inhibitors may provide new opportunities for the treatment of the MYCN-amplified neuroblastoma subtype.

Differentially expressed proteins in glioblastoma multiforme identified with a nanobody-based anti-proteome approach and confirmed by OncoFinder as possible tumor-class predictive biomarker candidates

Glioblastoma multiforme is the most frequent primary malignancy of the central nervous system. Despite remarkable progress towards an understanding of tumor biology, there is no efficient treatment and patient outcome remains poor. Here, we present a unique anti-proteomic approach for selection of nanobodies specific for overexpressed glioblastoma proteins. A phage-displayed nanobody library was enriched in protein extracts from NCH644 and NCH421K glioblastoma cell lines. Differential ELISA screenings revealed seven nanobodies that target the following antigens: the ACTB/NUCL complex, VIM, NAP1L1, TUFM, DPYSL2, CRMP1, and ALYREF. Western blots showed highest protein up-regulation for ALYREF, CRMP1, and VIM. Moreover, bioinformatic analysis with the OncoFinder software against the complete "Cancer Genome Atlas" brain tumor gene expression dataset suggests the involvement of different proteins in the WNT and ATM pathways, and in Aurora B, Sem3A, and E-cadherin signaling. We demonstrate the potential use of NAP1L1, NUCL, CRMP1, ACTB, and VIM for differentiation between glioblastoma and lower grade gliomas, with DPYSL2 as a promising "glioma versus reference" biomarker. A small scale validation study confirmed significant changes in mRNA expression levels of VIM, DPYSL2, ACTB and TRIM28. This work helps to fill the information gap in this field by defining novel differences in biochemical profiles between gliomas and reference samples. Thus, selected genes can be used to distinguish glioblastoma from lower grade gliomas, and from reference samples. These findings should be valuable for glioblastoma patients once they are validated on a larger sample size.

Chemo brain: From discerning mechanisms to lifting the brain fog-An aging connection

Mounting evidence indicates that cancer treatments cause numerous deleterious effects, including central nervous system (CNS) toxicity. Chemotherapy-caused CNS side effects encompass changes in cognitive function, memory, and attention, to name a few. Although chemotherapy treatment-induced side effects occur in 16-75% of all patients, the mechanisms of these effects are not well understood. We have recently proposed a new epigenetic theory of chemo brain and, in a pioneer study, determined that cytotoxic chemotherapy agents induce oxidative DNA damage and affect molecular and epigenetic processes in the brain, and may be associated with brain aging processes. In this paper, we discuss the implications of chemo brain epigenetic effects and future perspectives, as well as outline potential links with brain aging and future translational research opportunities.

Synergistic suppression of t(8;21)-positive leukemia cell growth by combining oridonin and MAPK1/ERK2 inhibitors

One of the most common chromosomal translocations in acute myeloid leukemia is t(8;21)(q22;q22), which results in the appearance of abnormal transcripts encoding for the fusion protein RUNX1-ETO. Therefore, this oncoprotein is considered to be a pertinent and promising target for treating t(8;21) leukemia. Previously, we have shown that downregulation of RUNX1-ETO leads to activation of intracellular signaling pathways enhancing cell survival and determined that the protein ERK2 can mediate activation of most of these pathways. Here we used a combination of oridonin (natural tetracycline diterpenoid), which has been shown to exhibit anti-RUNX1-ETO activity, and ERK2 kinase inhibitors. We found that treatment of leukemic t(8;21)-positive Kasumi-1 cells with oridonin cause decrease of phosphorylated ERK1/2. Treatment of these cells with ERK2 inhibitors makes them more sensitive to RUNX1-ETO inhibition with oridonin. Therefore we postulate that simultaneous inhibition of RUNX1-ETO and ERK2 cause synergistic effect on survival of leukemic cells.

In silico analysis of pathways activation landscape in oral squamous cell carcinoma and oral leukoplakia

A subset of patients with oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSCC), harbor dysplastic lesions (often visually identified as leukoplakia) prior to cancer diagnosis. Although evidence suggest that leukoplakia represents an initial step in the progression to cancer, signaling networks driving this progression are poorly understood. Here, we applied in silico Pathway Activation Network Decomposition Analysis (iPANDA), a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in OSCC and pre-neoplastic oral lesions. In tumor samples, our analysis detected major conserved mitogenic and survival signaling pathways strongly associated with HNSCC, suggesting that some of the pathways identified by our algorithm, but not yet validated as HNSCC related, may be attractive targets for future research. While pathways activation landscape in the majority of leukoplakias was different from that seen in OSCC, a subset of pre-neoplastic lesions has demonstrated some degree of similarity to the signaling profile seen in tumors, including dysregulation of the cancer-driving pathways related to survival and apoptosis. These results suggest that dysregulation of these signaling networks may be the driving force behind the early stages of OSCC tumorigenesis. While future studies with larger leukoplakia data sets are warranted to further estimate the values of this approach for capturing signaling features that characterize relevant lesions that actually progress to cancers, our platform proposes a promising new approach for detecting cancer-promoting pathways and tailoring the right therapy to prevent tumorigenesis.

SMAD4 Loss Is Associated with Cetuximab Resistance and Induction of MAPK/JNK Activation in Head and Neck Cancer Cells

Purpose: We previously demonstrated an association between decreased SMAD4 expression and cetuximab resistance in head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to further elucidate the clinical relevance of SMAD4 loss in HNSCC.Experimental Design: SMAD4 expression was assessed by IHC in 130 newly diagnosed and 43 patients with recurrent HNSCC. Correlative statistical analysis with clinicopathologic data was also performed. OncoFinder, a bioinformatics tool, was used to analyze molecular signaling in TCGA tumors with low or high SMAD4 mRNA levels. The role of SMAD4 was investigated by shRNA knockdown and gene reconstitution of HPV-negative HNSCC cell lines in vitro and in vivoResults: Our analysis revealed that SMAD4 loss was associated with an aggressive, HPV-negative, cetuximab-resistant phenotype. We found a signature of prosurvival and antiapoptotic pathways that were commonly dysregulated in SMAD4-low cases derived from TCGA-HNSCC dataset and an independent oral cavity squamous cell carcinoma (OSCC) cohort obtained from GEO. We show that SMAD4 depletion in an HNSCC cell line induces cetuximab resistance and results in worse survival in an orthotopic mouse model in vivo We implicate JNK and MAPK activation as mediators of cetuximab resistance and provide the foundation for the concomitant EGFR and JNK/MAPK inhibition as a potential strategy for overcoming cetuximab resistance in HNSCCs with SMAD4 loss.Conclusions: Our study demonstrates that loss of SMAD4 expression is a signature characterizing the cetuximab-resistant phenotype and suggests that SMAD4 expression may be a determinant of sensitivity/resistance to EGFR/MAPK or EGFR/JNK inhibition in HPV-negative HNSCC tumors. Clin Cancer Res; 23(17); 5162-75. ©2017 AACR.

Pro-fibrotic pathway activation in trabecular meshwork and lamina cribrosa is the main driving force of glaucoma

While primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide, it still does not have a clear mechanism that can explain all clinical cases of the disease. Elevated IOP is associated with increased accumulation of extracellular matrix (ECM) proteins in the trabecular meshwork (TM) that prevents normal outflow of aqueous humor (AH) and has damaging effects on the fine mesh-like lamina cribrosa (LC) through which the optic nerve fibers pass. Applying a pathway analysis algorithm, we discovered that an elevated level of TGFβ observed in glaucoma-affected tissues could lead to pro-fibrotic pathway activation in TM and in LC. In turn, activated pro-fibrotic pathways lead to ECM remodeling in TM and LC, making TM less efficient in AH drainage and making LC more susceptible to damage from elevated IOP via ECM transformation in LC. We propose pathway targets for potential therapeutic interventions to delay or avoid fibrosis initiation in TM and LC tissues.

Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data

We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.

Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS)

Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.Here we show two principal points: 1. First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified. 2. Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.

Early stage of cytomegalovirus infection suppresses host microRNA expression regulation in human fibroblasts

Responses to human cytomegalovirus (HCMV) infection are largely individual and cell type specific. We investigated molecular profiles in 2 primary cell cultures of human fibroblasts, which are highly or marginally sensitive to HCMV infection, respectively. We screened expression of genes and microRNAs (miRs) at the early (3 hours) stage of infection. To assess molecular pathway activation profiles, we applied bioinformatic algorithms OncoFinder and MiRImpact. In both cell types, pathway regulation properties at mRNA and miR levels were markedly different. Surprisingly, in the infected highly sensitive cells, we observed a "freeze" of miR expression profiles compared to uninfected controls. Our results evidence that in the sensitive cells, HCMV blocks intracellular regulation of microRNA expression already at the earliest stage of infection. These data suggest somewhat new functions for HCMV products and demonstrate dependence of miR expression arrest on the host-encoded factors.

MiRImpact, a new bioinformatic method using complete microRNA expression profiles to assess their overall influence on the activity of intracellular molecular pathways

MicroRNAs (miRs) are short noncoding RNA molecules that regulate expression of target mRNAs. Many published sources provide information about miRs and their targets. However, bioinformatic tools elucidating higher level impact of the established total miR profiles, are still largely missing. Recently, we developed a method termed OncoFinder enabling quantification of the activities of intracellular molecular pathways basing on gene expression data. Here we propose a new technique, MiRImpact, which enables to link miR expression data with its estimated outcome on the regulation of molecular pathways, like signaling, metabolic, cytoskeleton rearrangement, and DNA repair pathways. MiRImpact uses OncoFinder rationale for pathway activity calculations, with the major distinctions that (i) it deals with the concentrations of miRs - known regulators of gene products participating in molecular pathways, and (ii) miRs are considered as negative regulators of target molecules, if other is not specified. MiRImpact operates with 2 types of databases: for molecular targets of miRs and for gene products participating in molecular pathways. We applied MiRImpact to compare regulation of human bladder cancer-specific signaling pathways at the levels of mRNA and miR expression. We took 2 most complete alternative databases of experimentally validated miR targets – miRTarBase and DianaTarBase, and an OncoFinder database featuring 2725 gene products and 271 signaling pathways. We showed that the impact of miRs is orthogonal to pathway regulation at the mRNA level, which stresses the importance of studying posttranscriptional regulation of gene expression. We also report characteristic set of miR and mRNA regulation features linked with bladder cancer.

Molecular pathway activation features of pediatric acute myeloid leukemia (AML) and acute lymphoblast leukemia (ALL) cells

Acute lymphoblast leukemia (ALL) is characterized by overproduction of immature white blood cells in the bone marrow. ALL is most common in the childhood and has high (>80%) cure rate. In contrast, acute myeloid leukemia (AML) has far greater mortality rate than the ALL and is most commonly affecting older adults. However, AML is a leading cause of childhood cancer mortality. In this study, we compare gene expression and molecular pathway activation patterns in three normal blood, seven pediatric ALL and seven pediatric AML bone marrow samples. We identified 172/94 and 148/31 characteristic gene expression/pathway activation signatures, clearly distinguishing pediatric ALL and AML cells, respectively, from the normal blood. The pediatric AML and ALL cells differed by 139/34 gene expression/pathway activation biomarkers. For the adult 30 AML and 17 normal blood samples, we found 132/33 gene expression/pathway AML-specific features, of which only 7/2 were common for the adult and pediatric AML and, therefore, age-independent. At the pathway level, we found more differences than similarities between the adult and pediatric forms. These findings suggest that the adult and pediatric AMLs may require different treatment strategies.