Automated network analysis identifies core pathways in glioblastoma

Targeting drug resistance in glioblastoma (Review)

Glioblastoma (GBM) is the most common malignancy of the central nervous system in adults. The current standard of care includes surgery, radiation therapy, temozolomide; and tumor‑treating fields leads to dismal overall survival. There are far limited treatments upon recurrence. Therapies to date are ineffective as a result of several factors, including the presence of the blood‑brain barrier, blood tumor barrier, glioma stem‑like cells and genetic heterogeneity in GBM. In the present review, the potential mechanisms that lead to treatment resistance in GBM and the measures which have been taken so far to attempt to overcome the resistance were discussed. The complex biology of GBM and lack of comprehensive understanding of the development of therapeutic resistance in GBM demands discovery of novel antigens that are targetable and provide effective therapeutic strategies.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

Open MoA: revealing the mechanism of action (MoA) based on network topology and hierarchy

MOTIVATION

Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA).

RESULTS

We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/XinmengLiao/Open_MoA.

Cross-Platform Synaptic Network Analysis of Human Entorhinal Cortex Identifies TWF2 as a Modulator of Dendritic Spine Length

Proteomic studies using postmortem human brain tissue samples have yielded robust assessments of the aging and neurodegenerative disease(s) proteomes. While these analyses provide lists of molecular alterations in human conditions, like Alzheimer's disease (AD), identifying individual proteins that affect biological processes remains a challenge. To complicate matters, protein targets may be highly understudied and have limited information on their function. To address these hurdles, we sought to establish a blueprint to aid selection and functional validation of targets from proteomic datasets. A cross-platform pipeline was engineered to focus on synaptic processes in the entorhinal cortex (EC) of human patients, including controls, preclinical AD, and AD cases. Label-free quantification mass spectrometry (MS) data (n = 2260 proteins) was generated on synaptosome fractionated tissue from Brodmann area 28 (BA28; n = 58 samples). In parallel, dendritic spine density and morphology was measured in the same individuals. Weighted gene co-expression network analysis was used to construct a network of protein co-expression modules that were correlated with dendritic spine metrics. Module-trait correlations were used to guide unbiased selection of Twinfilin-2 (TWF2), which was the top hub protein of a module that positively correlated with thin spine length. Using CRISPR-dCas9 activation strategies, we demonstrated that boosting endogenous TWF2 protein levels in primary hippocampal neurons increased thin spine length, thus providing experimental validation for the human network analysis. Collectively, this study describes alterations in dendritic spine density and morphology as well as synaptic proteins and phosphorylated tau from the entorhinal cortex of preclinical and advanced stage AD patients.SIGNIFICANCE STATEMENT Proteomic studies can yield vast lists of molecules that are altered under various experimental or disease conditions. Here, we provide a blueprint to facilitate mechanistic validation of protein targets from human brain proteomic datasets. We conducted a proteomic analysis of human entorhinal cortex (EC) samples spanning cognitively normal and Alzheimer's disease (AD) cases with a comparison of dendritic spine morphology in the same samples. Network integration of proteomics with dendritic spine measurements allowed for unbiased discovery of Twinfilin-2 (TWF2) as a regulator of dendritic spine length. A proof-of-concept experiment in cultured neurons demonstrated that altering Twinfilin-2 protein level induced corresponding changes in dendritic spine length, thus providing experimental validation for the computational framework.

SWEET: a single-sample network inference method for deciphering individual features in disease

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

Target-Based Anticancer Indole Derivatives for the Development of Anti-Glioblastoma Agents

Glioblastoma (GBM) is the most aggressive and frequent primary brain tumor, with a poor prognosis and the highest mortality rate. Currently, GBM therapy consists of surgical resection of the tumor, radiotherapy, and adjuvant chemotherapy with temozolomide. Consistently, there are poor treatment options and only modest anticancer efficacy is achieved; therefore, there is still a need for the development of new effective therapies for GBM. Indole is considered one of the most privileged scaffolds in heterocyclic chemistry, so it may serve as an effective probe for the development of new drug candidates against challenging diseases, including GBM. This review analyzes the therapeutic benefit and clinical development of novel indole-based derivatives investigated as promising anti-GBM agents. The existing indole-based compounds which are in the pre-clinical and clinical stages of development against GBM are reported, with particular reference to the most recent advances between 2013 and 2022. The main mechanisms of action underlying their anti-GBM efficacy, such as protein kinase, tubulin and p53 pathway inhibition, are also discussed. The final goal is to pave the way for medicinal chemists in the future design and development of novel effective indole-based anti-GBM agents.

Graph algorithms for predicting subcellular localization at the pathway level

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.

A network-based method for identifying cancer driver genes based on node control centrality

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

Use of multimodality imaging, histology, and treatment feasibility to characterize a transgenic Rag2-null rat model of glioblastoma

Many drugs that show potential in animal models of glioblastoma (GBM) fail to translate to the clinic, contributing to a paucity of new therapeutic options. In addition, animal model development often includes histologic assessment, but multiparametric/multimodality imaging is rarely included despite increasing utilization in patient cancer management. This study developed an intracranial recurrent, drug-resistant, human-derived glioblastoma tumor in Sprague-Dawley Rag2-Rag2 ^tm1Hera knockout rat and was characterized both histologically and using multiparametric/multimodality neuroimaging. Hybrid ¹⁸F-fluoroethyltyrosine positron emission tomography and magnetic resonance imaging, including chemical exchange saturation transfer (¹⁸F-FET PET/CEST MRI), was performed for full tumor viability determination and characterization. Histological analysis demonstrated human-like GBM features of the intracranially implanted tumor, with rapid tumor cell proliferation (Ki67 positivity: 30.5 ± 7.8%) and neovascular heterogeneity (von Willebrand factor VIII:1.8 to 5.0% positivity). Early serial MRI followed by simultaneous ¹⁸F-FET PET/CEST MRI demonstrated consistent, predictable tumor growth, with exponential tumor growth most evident between days 35 and 49 post-implantation. In a second, larger cohort of rats, ¹⁸F-FET PET/CEST MRI was performed in mature tumors (day 49 post-implantation) for biomarker determination, followed by evaluation of single and combination therapy as part of the model development and validation. The mean percentage of the injected dose per mL of ¹⁸F-FET PET correlated with the mean %CEST (r = 0.67, P < 0.05), but there was also a qualitative difference in hot spot location within the tumor, indicating complementary information regarding the tumor cell demand for amino acids and tumor intracellular mobile phase protein levels. Finally, the use of this glioblastoma animal model for therapy assessment was validated by its increased overall survival after treatment with combination therapy (temozolomide and idasanutlin) (P < 0.001). Our findings hold promise for a more accurate tumor viability determination and novel therapy assessment in vivo in a recently developed, reproducible, intracranial, PDX GBM.

Probabilistic edge inference of gene networks with markov random field-based bayesian learning

Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model’s performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights.

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Background

Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer.

Methods

We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution.

Results

DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11-NRAS/HRAS/KRAS-BRAF pathway, AKT1-MTOR pathway, and TCEB1-VHL-HIF1A pathway.

Conclusion

Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

Novel Protein-Protein Interactions Highlighting the Crosstalk between Hypoplastic Left Heart Syndrome, Ciliopathies and Neurodevelopmental Delays

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease (CHD) affecting 1 in 5000 newborns. We constructed the interactome of 74 HLHS-associated genes identified from a large-scale mouse mutagenesis screen, augmenting it with 408 novel protein-protein interactions (PPIs) using our High-Precision Protein-Protein Interaction Prediction (HiPPIP) model. The interactome is available on a webserver with advanced search capabilities. A total of 364 genes including 73 novel interactors were differentially regulated in tissue/iPSC-derived cardiomyocytes of HLHS patients. Novel PPIs facilitated the identification of TOR signaling and endoplasmic reticulum stress modules. We found that 60.5% of the interactome consisted of housekeeping genes that may harbor large-effect mutations and drive HLHS etiology but show limited transmission. Network proximity of diabetes, Alzheimer's disease, and liver carcinoma-associated genes to HLHS genes suggested a mechanistic basis for their comorbidity with HLHS. Interactome genes showed tissue-specificity for sites of extracardiac anomalies (placenta, liver and brain). The HLHS interactome shared significant overlaps with the interactomes of ciliopathy- and microcephaly-associated genes, with the shared genes enriched for genes involved in intellectual disability and/or developmental delay, and neuronal death pathways, respectively. This supported the increased burden of ciliopathy variants and prevalence of neurological abnormalities observed among HLHS patients with developmental delay and microcephaly, respectively.

Identification of ritanserin analogs that display DGK isoform specificity

The diacylglycerol kinase (DGK) family of lipid enzymes catalyzes the conversion of diacylglycerol (DAG) to phosphatidic acid (PA). Both DAG and PA are lipid signaling molecules that are of notable importance in regulating cell processes such as proliferation, apoptosis, and migration. There are ten mammalian DGK enzymes that appear to have distinct biological functions. DGKα has emerged as a promising therapeutic target in numerous cancers including glioblastoma (GBM) and melanoma as treatment with small molecule DGKα inhibitors results in reduced tumor sizes and prolonged survival. Importantly, DGKα has also been identified as an immune checkpoint due to its promotion of T cell anergy, and its inhibition has been shown to improve T cell activation. There are few small molecule DGKα inhibitors currently available, and the application of existing compounds to clinical settings is hindered by species-dependent variability in potency, as well as concerns regarding isotype specificity particularly amongst other type I DGKs. In order to resolve these issues, we have screened a library of compounds structurally analogous to the DGKα inhibitor, ritanserin, in an effort to identify more potent and specific alternatives. We identified two compounds that more potently and selectively inhibit DGKα, one of which (JNJ-3790339) demonstrates similar cytotoxicity in GBM and melanoma cells as ritanserin. Consistent with its inhibitor profile towards DGKα, JNJ-3790339 also demonstrated improved activation of T cells compared with ritanserin. Together our data support efforts to identify DGK isoform-selective inhibitors as a mechanism to produce pharmacologically relevant cancer therapies.

The DOMINO web-server for active module identification analysis

MOTIVATION

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ('active modules'). Such modules can point out key molecular processes in the analyzed biological conditions.

RESULTS

We recently introduced a novel AMI algorithm called DOMINO and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms.

AVAILABILITY AND IMPLEMENTATION

The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

A network-based method for detecting cancer driver gene in transcriptional regulatory networks using the structure analysis of weighted regulatory interactions

Background:

The identification of genes that instigate cell anomalies and cause cancer in humans is an important field in oncology research. Abnormalities in these genes are transferred to other genes in the cell, disrupting its normal functionality. Such genes are known as cancer driver genes (CDGs). Various methods have been proposed for predicting CDGs, most of which are based on genomic data and computational methods. Some novel bioinformatic approaches have been developed.

Objective:

In this article, we propose a network-based algorithm, SalsaDriver (Stochastic approach for link-structure analysis to driver detection), which can calculate the receiving and influencing power of each gene using the stochastic analysis of regulatory interaction structures in gene regulatory networks.

Method:

First, regulatory networks related to breast, colon, and lung cancers were constructed using gene expression data and a list of regulatory interactions, the weights of which were then calculated using biological and topological features of the network. After that, the weighted regulatory interactions were used in the structure analysis of interactions achieved using two separate Markov chains on the bipartite graph taken from the main graph of the gene network and implementing the stochastic approach for link-structure analysis. The proposed algorithm categorizes higher-ranked genes as driver genes.

Results:

The proposed algorithm was compared with 24 other computational and network tools based on the F-measure value and the number of detected CDGs. The results were validated using four valid databases. The findings of this study show that SalsaDriver outperforms other methods and can identify a significant number of driver genes not identified using other methods.

Conclusion:

The SalsaDriver network-based approach is suitable for predicting CDGs and can be used as a complementary method along with other computational tools.

MicroRNA-detargeting proves more effective than leader gene deletion for improving safety of oncolytic Mengovirus in a nude mouse model

A dual microRNA-detargeted oncolytic Mengovirus, vMC24NC, proved highly effective against a murine plasmacytoma in an immunocompetent syngeneic mouse model; however, there remains the concern of escape mutant development and the potential for toxicity in severely immunocompromised cancer patients when it is used as an oncolytic virus. Therefore, we sought to compare the safety and efficacy profiles of an attenuated Mengovirus containing a virulence gene deletion versus vMC24NC in an immunodeficient xenograft mouse model of human glioblastoma. A Mengovirus construct, vMC24ΔL, wherein the gene coding for the leader protein, a virulence factor, was deleted, was used for comparison. The vMC24ΔL induced significant levels of toxicity following treatment of subcutaneous human glioblastoma (U87-MG) xenografts as well as when injected intracranially in athymic nude mice, reducing the overall survival. The in vivo toxicity of vMC24ΔL was associated with viral replication in nervous and cardiac tissue. In contrast, microRNA-detargeted vMC24NC demonstrated excellent efficacy against U87-MG subcutaneous xenografts and improved overall survival significantly compared to that of control mice without toxicity. These results reinforce microRNA-detargeting as an effective strategy for ameliorating unwanted toxicities of oncolytic picornaviruses and substantiate vMC24NC as an ideal candidate for clinical development against certain cancers in both immunocompetent and immunodeficient hosts.

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

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Disease-associated gene modules are conserved across multiple NAFLD cohorts

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The central genes in disease-associated modules are key enzymes in cholesterol synthesis

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YY1 and KLF13 are potential key transcriptional regulators of NAFLD development

Delineating infection strategies of Leishmania donovani secretory proteins in Human through host-pathogen protein Interactome prediction

Interactions of Leishmania donovani secretory virulence factors with the host proteins and their interplay during the infection process in humans is poorly studied in Visceral Leishmaniasis. Lack of a holistic study of pathway level de-regulations caused due to these virulence factors leads to a poor understanding of the parasite strategies to subvert the host immune responses, secure its survival inside the host and further the spread of infection to the visceral organs. In this study, we propose a computational workflow to predict host-pathogen protein interactome of L.donovani secretory virulence factors with human proteins combining sequence-based Interolog mapping and structure-based Domain Interaction mapping techniques. We further employ graph theoretical approaches and shortest path methods to analyze the interactome. Our study deciphers the infection paths involving some unique and understudied disease-associated signaling pathways influencing the cellular phenotypic responses in the host. Our statistical analysis based in silico knockout study unveils for the first time UBC, 1433Z and HS90A mediator proteins as potential immunomodulatory candidates through which the virulence factors employ the infection paths. These identified pathways and novel mediator proteins can be effectively used as possible targets to control and modulate the infection process further aiding in the treatment of Visceral Leishmaniasis.

Sequential Defects in Cardiac Lineage Commitment and Maturation Cause Hypoplastic Left Heart Syndrome

BACKGROUND

Complex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role.

METHODS

To identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed whole-exome sequencing of 87 HLHS parent-offspring trios, nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, single cell RNA sequencing, and 3D modeling in induced pluripotent stem cells from 3 patients with HLHS and 3 controls.

RESULTS

Gene set enrichment and protein network analyses of damaging de novo mutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell cycle and cardiomyocyte maturation. Single-cell and 3D modeling with induced pluripotent stem cells demonstrated intrinsic defects in the cell cycle/unfolded protein response/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte subtype differentiation/maturation in HLHS. Premature cell cycle exit of ventricular cardiomyocytes from patients with HLHS prevented normal tissue responses to developmental signals for growth, leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues.

CONCLUSIONS

Our results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.

Molecular response to PARP1 inhibition in ovarian cancer cells as determined by mass spectrometry based proteomics

BACKGROUND

Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood.

METHODS

Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment.

RESULTS

We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and β-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-κB signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest.

CONCLUSION

Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.

A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity

[Figure: see text].

Tripartite Separation of Glomerular Cell Types and Proteomes from Reporter-Free Mice

BACKGROUND

The glomerulus comprises podocytes, mesangial cells, and endothelial cells, which jointly determine glomerular filtration. Understanding this intricate functional unit beyond the transcriptome requires bulk isolation of these cell types for biochemical investigations. We developed a globally applicable tripartite isolation method for murine mesangial and endothelial cells and podocytes (timMEP).

METHODS

We separated glomerular cell types from wild-type or mT/mG mice via a novel FACS approach, and validated their purity. Cell type proteomes were compared between strains, ages, and sex. We applied timMEP to the podocyte-targeting, immunologic, THSD7A-associated, model of membranous nephropathy.

RESULTS

timMEP enabled protein-biochemical analyses of podocytes, mesangial cells, and endothelial cells derived from reporter-free mice, and allowed for the characterization of podocyte, endothelial, and mesangial proteomes of individual mice. We identified marker proteins for mesangial and endothelial proteins, and outlined protein-based, potential communication networks and phosphorylation patterns. The analysis detected cell type-specific proteome differences between mouse strains and alterations depending on sex, age, and transgene. After exposure to anti-THSD7A antibodies, timMEP resolved a fine-tuned initial stress response, chiefly in podocytes, that could not be detected by bulk glomerular analyses. The combination of proteomics with super-resolution imaging revealed a specific loss of slit diaphragm, but not of other foot process proteins, unraveling a protein-based mechanism of podocyte injury in this animal model.

CONCLUSION

timMEP enables glomerular cell type-resolved investigations at the transcriptional and protein-biochemical level in health and disease, while avoiding reporter-based artifacts, paving the way toward the comprehensive and systematic characterization of glomerular cell biology.

Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma

Background

A plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using machine learning algorithms.

Methods

Biomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to recurrence or prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of identified molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival.

Results

A total of 48 clinically proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131,071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts.

Conclusion

The occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10^-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10^-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

MDM2/X Inhibitors as Radiosensitizers for Glioblastoma Targeted Therapy

Inhibition of the MDM2/X-p53 interaction is recognized as a potential anti-cancer strategy, including the treatment of glioblastoma (GB). In response to cellular stressors, such as DNA damage, the tumor suppression protein p53 is activated and responds by mediating cellular damage through DNA repair, cell cycle arrest and apoptosis. Hence, p53 activation plays a central role in cell survival and the effectiveness of cancer therapies. Alterations and reduced activity of p53 occur in 25-30% of primary GB tumors, but this number increases drastically to 60-70% in secondary GB. As a result, reactivating p53 is suggested as a treatment strategy, either by using targeted molecules to convert the mutant p53 back to its wild type form or by using MDM2 and MDMX (also known as MDM4) inhibitors. MDM2 down regulates p53 activity via ubiquitin-dependent degradation and is amplified or overexpressed in 14% of GB cases. Thus, suppression of MDM2 offers an opportunity for urgently needed new therapeutic interventions for GB. Numerous small molecule MDM2 inhibitors are currently undergoing clinical evaluation, either as monotherapy or in combination with chemotherapy and/or other targeted agents. In addition, considering the major role of both p53 and MDM2 in the downstream signaling response to radiation-induced DNA damage, the combination of MDM2 inhibitors with radiation may offer a valuable therapeutic radiosensitizing approach for GB therapy. This review covers the role of MDM2/X in cancer and more specifically in GB, followed by the rationale for the potential radiosensitizing effect of MDM2 inhibition. Finally, the current status of MDM2/X inhibition and p53 activation for the treatment of GB is given.

Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Simple Summary

Cancer is caused by the accumulation of somatic mutations, some of which are responsible for the disease’s progression (drivers) while others are functionally neutral (passengers). Although several methods have been developed to distinguish between the two classes of mutations, very few have concentrated on using the neighborhood nucleotide sequences as potential discrimination features. In this study, we show that driver mutations’ neighborhood is significantly different from that of passengers. We further develop a novel machine learning tool, NBDriver, which is highly efficient at identifying pathogenic variants from multiple independent test datasets. Efficient and accurate identification of novel pathogenic variants from sequenced cancer genomes would help facilitate more effective therapies tailored to patients’ mutational profiles.

Abstract

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

Cancer-Associated circRNA-miRNA-mRNA Regulatory Networks: A Meta-Analysis

Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA-miRNA-mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer's disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA-miRNA-mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA-miRNA-mRNA regulatory mechanisms in cancers.

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

BACKGROUND

Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.

METHODS

In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI.

RESULTS

This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR < 0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.

CONCLUSIONS

This research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphic abstract.

Computational methods for cancer driver discovery: A survey

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.

Stepwise crosstalk between aberrant Nf1, Tp53 and Rb signalling pathways induces gliomagenesis in zebrafish

The molecular pathogenesis of glioblastoma indicates that RTK/Ras/PI3K, RB and TP53 pathways are critical for human gliomagenesis. Here, several transgenic zebrafish lines with single or multiple deletions of nf1, tp53 and rb1 in astrocytes, were established to genetically induce gliomagenesis in zebrafish. In the mutant with a single deletion, we found only the nf1 mutation low-efficiently induced tumour incidence, suggesting that the Nf1 pathway is critical for the initiation of gliomagenesis in zebrafish. Combination of mutations, nf1;tp53 and rb1;tp53 combined knockout fish, showed much higher tumour incidences, high-grade histology, increased invasiveness, and shortened survival time. Further bioinformatics analyses demonstrated the alterations in RTK/Ras/PI3K, cell cycle, and focal adhesion pathways, induced by abrogated nf1, tp53, or rb1, were probably the critical stepwise biological events for the initiation and development of gliomagenesis in zebrafish. Gene expression profiling and histological analyses showed the tumours derived from zebrafish have significant similarities to the subgroups of human gliomas. Furthermore, temozolomide treatment effectively suppressed gliomagenesis in these glioma zebrafish models, and the histological responses in temozolomide-treated zebrafish were similar to those observed in clinically treated glioma patients. Thus, our findings will offer a potential tool for genetically investigating gliomagenesis and screening potential targeted anti-tumour compounds for glioma treatment.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Identification of significantly mutated subnetworks in the breast cancer genome

Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.

DOMINO: a network-based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir-Lab.

Integrin alpha5 in human breast cancer is a mediator of bone metastasis and a therapeutic target for the treatment of osteolytic lesions

Bone metastasis remains a major cause of mortality and morbidity in breast cancer. Therefore, there is an urgent need to better select high-risk patients in order to adapt patient's treatment and prevent bone recurrence. Here, we found that integrin alpha5 (ITGA5) was highly expressed in bone metastases, compared to lung, liver, or brain metastases. High ITGA5 expression in primary tumors correlated with the presence of disseminated tumor cells in bone marrow aspirates from early stage breast cancer patients (n = 268; p = 0.039). ITGA5 was also predictive of poor bone metastasis-free survival in two separate clinical data sets (n = 855, HR = 1.36, p = 0.018 and n = 427, HR = 1.62, p = 0.024). This prognostic value remained significant in multivariate analysis (p = 0.028). Experimentally, ITGA5 silencing impaired tumor cell adhesion to fibronectin, migration, and survival. ITGA5 silencing also reduced tumor cell colonization of the bone marrow and formation of osteolytic lesions in vivo. Conversely, ITGA5 overexpression promoted bone metastasis. Pharmacological inhibition of ITGA5 with humanized monoclonal antibody M200 (volociximab) recapitulated inhibitory effects of ITGA5 silencing on tumor cell functions in vitro and tumor cell colonization of the bone marrow in vivo. M200 also markedly reduced tumor outgrowth in experimental models of bone metastasis or tumorigenesis, and blunted cancer-associated bone destruction. ITGA5 was not only expressed by tumor cells but also osteoclasts. In this respect, M200 decreased human osteoclast-mediated bone resorption in vitro. Overall, this study identifies ITGA5 as a mediator of breast-to-bone metastasis and raises the possibility that volociximab/M200 could be repurposed for the treatment of ITGA5-positive breast cancer patients with bone metastases.

GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation

Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diffusion concept. Due to the type of these networks and the processes of abnormality progression in cells and the formation of cancerous tumors, high-influence genes can be the most likely considered as the driver genes. Therefore, we can use the influence diffusion concept as an acceptable theory to identify these genes. Recently, a method has been proposed to detect CDGs with the concept of the influence maximization. One of the challenges in these types of networks is finding the power of regulatory interaction between genes. Moreover, we have proposed a novel method to calculate the weight of regulatory interactions, based on the concept of diffusion. The performance of the proposed method was compared with other seventeen computational and network tools. Correspondingly, three cancer types were used as benchmarks as follows: breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). In addition, to determine the accuracy of the detected drivers using each method, CGC (Cancer Gene Census) and Mut-driver gene lists were utilized as gold standard. The results show that GenHITS performs better compared to the most of the other computational and network methods. Besides, it is also able to identify genes that have been identified by none of the other methods yet.

KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network

One of the important problems in oncology is finding the genes that perturb the cell functionality and cause cancer. These genes, namely cancer driver genes (CDGs), when mutated, lead to the activation of the abnormal proteins. This abnormality is passed on to other genes by protein-protein interactions, which can cause cells to uncontrollably multiply and become cancerous. So, many methods have been introduced to predict this group of genes. Most of these methods are computational-based, which identify the CDGs based on mutations and genomic data. In this study, we proposed KatzDriver, as a network-based approach, in order to detect CDGs. This method is able to calculate the relative impact of each gene in the spread of abnormality in the gene regulatory network. In this approach, we firstly create the studied networks using gene expression and regulatory interaction data. Then by combining the topological and biological data, the weights of edges (regulatory interactions) and nodes (genes) are calculated. Afterward, based on the KATZ approach, the receiving and broadcasting powers of each gene were calculated to find the relative impact of each gene. At the end, the top genes with the highest relative impact ranks were selected as potential cancer drivers. The result of the proposed approach was compared with 18 existing computational and network-based methods in terms of F-measure, and the number of the predicted cancer driver genes. The result shows that our proposed algorithm is better than most of the other methods. KatzDriver is also able to detect a significant number of unique driver genes compared to other computational and network-based methods.

FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network

Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.

netboxr: Automated discovery of biological process modules by network analysis in R

SUMMARY

Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology.

AVAILABILITY AND IMPLEMENTATION

The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.

Analysis, identification and visualization of subgroups in genomics

Motivation

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients’ samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

Results

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar (‘analysis and visualization of alteration data’) that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

Availability

AVAtar is available at: https://github.com/sysbio-bioinf/avatar

Contact

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes

Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.

Disease characterization using a partial correlation-based sample-specific network

A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with 'virus' and the interactors with 'pulmonary'".

Subtype-specific signaling pathways and genomic aberrations associated with prognosis of glioblastoma

Background

A high heterogeneity and activation of multiple oncogenic pathways have been implicated in failure of targeted therapies in glioblastoma (GBM).

Methods

Using The Cancer Genome Atlas data, we identified subtype-specific prognostic core genes by a combined approach of genome-wide Cox regression and Gene Set Enrichment Analysis. The results were validated with 8 combined public datasets containing 608 GBMs. We further examined prognostic chromosome aberrations and mutations.

Results

In classical and mesenchymal subtypes, 2 receptor tyrosine kinases (RTKs) (MET and IGF1R), and the genes in RTK downstream pathways such as phosphatidylinositol-3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), and nuclear factor-kappaB (NF-kB), were commonly detected as prognostic core genes. Classical subtype-specific prognostic core genes included those in cell cycle, DNA repair, and the Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathway. Immune-related genes were enriched in the prognostic genes showing negative promoter cytosine-phosphate-guanine (CpG) methylation/expression correlations. Mesenchymal subtype-specific prognostic genes were those related to mesenchymal cell movement, PI3K/Akt, mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK), Wnt/β-catenin, and Wnt/Ca2+ pathways. In copy number alterations and mutations, 6p loss and TP53 mutation were associated with poor and good survival, respectively, in the classical subtype. In the mesenchymal subtype, patients with PIK3R1 or PCLO mutations showed poor prognosis. In the glioma CpG island methylator phenotype (G-CIMP) subtype, patients harboring 10q loss, 12p gain, or 14q loss exhibited poor survival. Furthermore, 10q loss was significantly associated with the recently recognized G-CIMP subclass showing relatively low CpG methylation and poor prognosis.

Conclusion

These subtype-specific alterations have promising potentials as new prognostic biomarkers and therapeutic targets combined with surrogate markers of GBM subtypes. However, considering the small number of events, the results of copy number alterations and mutations require further validations.

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.