NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes

Polyploidy Promotes Hypertranscription, Apoptosis Resistance, and Ciliogenesis in Cancer Cells and Mesenchymal Stem Cells of Various Origins: Comparative Transcriptome In Silico Study

Mesenchymal stem cells (MSC) attract an increasing amount of attention due to their unique therapeutic properties. Yet, MSC can undergo undesirable genetic and epigenetic changes during their propagation in vitro. In this study, we investigated whether polyploidy can compromise MSC oncological safety and therapeutic properties. For this purpose, we compared the impact of polyploidy on the transcriptome of cancer cells and MSC of various origins (bone marrow, placenta, and heart). First, we identified genes that are consistently ploidy-induced or ploidy-repressed through all comparisons. Then, we selected the master regulators using the protein interaction enrichment analysis (PIEA). The obtained ploidy-related gene signatures were verified using the data gained from polyploid and diploid populations of early cardiomyocytes (CARD) originating from iPSC. The multistep bioinformatic analysis applied to the cancer cells, MSC, and CARD indicated that polyploidy plays a pivotal role in driving the cell into hypertranscription. It was evident from the upregulation of gene modules implicated in housekeeping functions, stemness, unicellularity, DNA repair, and chromatin opening by means of histone acetylation operating via DNA damage associated with the NUA4/TIP60 complex. These features were complemented by the activation of the pathways implicated in centrosome maintenance and ciliogenesis and by the impairment of the pathways related to apoptosis, the circadian clock, and immunity. Overall, our findings suggest that, although polyploidy does not induce oncologic transformation of MSC, it might compromise their therapeutic properties because of global epigenetic changes and alterations in fundamental biological processes. The obtained results can contribute to the development and implementation of approaches enhancing the therapeutic properties of MSC by removing polyploid cells from the cell population.

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.

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Ovarian cancer is a major gynaecological malignant tumor associated with a high mortality rate. Identifying survival-related variants may improve treatment and survival in patients with ovarian cancer. In this work, we proposed a support vector regression (SVR)-based method called OV-SURV, which is incorporated with an inheritable bi-objective combinatorial genetic algorithm for feature selection to identify a miRNA signature associated with survival in patients with ovarian cancer. There were 209 patients with miRNA expression profiles and survival information of ovarian cancer retrieved from The Cancer Genome Atlas database. OV-SURV achieved a mean correlation coefficient of 0.77±0.01and a mean absolute error of 0.69±0.02 years using 10-fold cross-validation. Analysis of the top ranked miRNAs revealed that the miRNAs, hsa-let-7f, hsa-miR-1237, hsa-miR-98, hsa-miR-933, and hsa-miR-889, were significantly associated with the survival in patients with ovarian cancer. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that four of these miRNAs, hsa-miR-182, hsa-miR-34a, hsa-miR-342, and hsa-miR-1304, were highly enriched in fatty acid biosynthesis, and the five miRNAs, hsa-let-7f, hsa-miR-34a, hsa-miR-342, hsa-miR-1304, and hsa-miR-24, were highly enriched in fatty acid metabolism. The prediction model with the identified miRNA signature consisting of prognostic biomarkers can benefit therapeutic decision making of ovarian cancer.

Pan-cancer detection of driver genes at the single-patient resolution

BACKGROUND

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.

RESULTS

We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.

CONCLUSIONS

sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

Germline copy number variations in BRCA1/2 negative families: Role in the molecular etiology of hereditary breast cancer in Tunisia

Hereditary breast cancer accounts for 5-10% of all breast cancer cases. So far, known genetic risk factors account for only 50% of the breast cancer genetic component and almost a quarter of hereditary cases are carriers of pathogenic mutations in BRCA1/2 genes. Hence, the genetic basis for a significant fraction of familial cases remains unsolved. This missing heritability may be explained in part by Copy Number Variations (CNVs). We herein aimed to evaluate the contribution of CNVs to hereditary breast cancer in Tunisia. Whole exome sequencing was performed for 9 BRCA negative cases with a strong family history of breast cancer and 10 matched controls. CNVs were called using the ExomeDepth R-package and investigated by pathway analysis and web-based bioinformatic tools. Overall, 483 CNVs have been identified in breast cancer patients. Rare CNVs affecting cancer genes were detected, of special interest were those disrupting APC2, POU5F1, DOCK8, KANSL1, TMTC3 and the mismatch repair gene PMS2. In addition, common CNVs known to be associated with breast cancer risk have also been identified including CNVs on APOBECA/B, UGT2B17 and GSTT1 genes. Whereas those disrupting SULT1A1 and UGT2B15 seem to correlate with good clinical response to tamoxifen. Our study revealed new insights regarding CNVs and breast cancer risk in the Tunisian population. These findings suggest that rare and common CNVs may contribute to disease susceptibility. Those affecting mismatch repair genes are of interest and require additional attention since it may help to select candidates for immunotherapy leading to better outcomes.

Rapid preliminary purity evaluation of tumor biopsies using deep learning approach

Tumor biopsy is one of the most widely used materials in cancer diagnoses and molecular studies, where the purity of the biopsies (i.e., proportion of cells that are cancerous) is crucial for both applications. However, conventional approaches for tumor biopsy purity evaluation require experienced pathologists and/or various materials/experiments therefore were time-consuming and error prone. Rapid, easy-to-perform and cost-effective methods are thus still of demand. Recent studies had demonstrated that molecular signatures were informative to this task. Previously, we had developed GeneCT, a deep learning-based cancerous status and tissue-of-origin classifier for pan-tumor/tissue biopsies. In the current work, we applied GeneCT on datasets collected from various groups, where the experimental protocols and cancer types differed from each other. We found that GeneCT showed high accuracies on most datasets; for samples with unexpected results, in-depth investigations suggested that they might suffer from imperfect purity. In silico mixture experiments further showed that GeneCT classification was highly indicative in predicting the purity of the tumor biopsies. Considering that transcriptome profiling is a common and inexpensive experiment in molecular cancer studies, our deep learning-based GeneCT could thus serve as a valuable tool for rapid, preliminary tumor biopsy purity assessment.

Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network

BACKGROUND

Cancer as a kind of genomic alteration disease each year deprives many people's life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance.

METHODS

Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN).

RESULTS

This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes.

CONCLUSION

The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Protein Domain Level Cancer Drug Targets in the Network of MAPK Pathways

Proteins in the MAPK pathways considered as potential drug targets for cancer treatment. Pathways along with the cross-talks increase their scope to view them as a network of MAPK pathways. Side effect causing targeted domains act as a proxy for drug targets due to its structural similarity and frequent reuse of their variants. We proposed to identify non-repeatable protein domains as the drug targets to disrupt the signal transduction than targeting the whole protein. Network based approach is used to understand the contribution of 52 domains in non-hub, non-essential, and intra-pathway cancerous nodes and to identify potential drug target domains. 34 distinct domains in the cancerous proteins are playing vital roles in making cancer as a complex disease and pose challenges to identify potential drug targets. Distribution of domain families follows the power law in the network. Single promiscuous domains are contributing to the formation of hubs like Pkinease, Pkinease Tyr, and Ras. Hub nodes are positively correlated with the domain coverage and targeting them would disrupt functional properties of the proteins. EIF 4EBP, alpha Kinase, Sel1, ROKNT, and KH 1 are the domains identified as potential domain targets for the disruption of the signaling mechanism involved in cancer.

The prognostic signature of the somatic mutations in Ewing sarcoma: from a network view

BACKGROUND

Ewing sarcoma is a malignant bone tumor mainly affecting teenagers and young adults. Its main driver mutation, the EWS-FLI1 fusion gene, has been identified more than 20 years ago, whereas its other somatic mutations have been just recently reported.

METHODS

In this study, we organized the somatic mutations from 216 Ewing sarcoma cases into 216 individual protein-protein interaction networks by using interactome information. These mutation networks were then classified into five different clusters based on their structural similarities. The prognostic effect of mutation genes was evaluated according to their network features.

RESULTS

The cases in cluster two exhibited remarkably high metastasis and mortality rates, and STAG2, TP53 and TTN were the three most significantly mutated genes in this cluster. Microarray data demonstrate that the expression of STAG2, TP53 and TTN are down-regulated in the EWS-FLI1-knockdown Ewing sarcoma cells. However, the mutation effect analysis shows that the somatic mutations in TTN are less damaging than those in STAG2 and TP53. The analyses of functional network modules further revealed that STAG2, TP53 and their interacting gene partners participate in the oncogenic-related biological modules such as cell cycle and regulation of transcription from RNA polymerase II promoter while TTN, TP53 and their interacting gene partners constitute the modules less relevant to oncogenesis. The results of Gene Ontology analyses demonstrated that the uniquely mutated genes associated with poor prognosis in Clusters 1, 4 and 5 were distinctively enriched in epidermal growth factor-related functions and phosphoproteins.

CONCLUSIONS

Our study identified the highly lethal mutation combination cases and characterized the possible prognostic genes in Ewing sarcoma from a network perceptive.

A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph

BACKGROUND

Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.

RESULTS

Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.

CONCLUSIONS

The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment.

AVAILABILITY

The source code can be obtained at https://github.com/weiba/Subdyquency .

Nuclear localization of LDL receptor-related protein 1B in mammary gland carcinogenesis

LRP1B intracellular domain is released and transported to the nucleus; however, pathological consequences of this nuclear transport are largely unclear. We aimed to unravel the pathobiological significance of nuclear localization of LRP1B intracellular domain in mammary gland carcinogenesis. Immunohistochemical staining using antibodies for LRP1B intracellular domain was performed to determine LRP1B expression in 92 invasive ductal breast carcinomas. LRP1B immunoreactivity was detected in the surface membrane and cytoplasm of 60 of 92 invasive ductal carcinomas and in the nucleus of 15 of 92 carcinomas. Nuclear LRP1B was significantly associated with poor patient prognosis, particularly luminal A type breast cancer, where it was significantly related to nodal metastasis. Doxycycline-dependent nuclear expression of LRP1B intracellular domain was established in cultured breast cancer cells. Enforced nuclear expression significantly increased Matrigel invasion activity in MCF-7 and T47D luminal A breast cancer cells. Moreover, enforced nuclear expression of LRP1B intracellular domain facilitated MCF-7 cells growth in mammary fat pad of nude mice, which was supplemented with estrogen. Comprehensive microarray-based analysis demonstrated that nuclear expression of LRP1B intracellular domain significantly increased long non-coding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) expression, which facilitates breast cancer invasion with poor prognosis. Nuclear-localized LRP1B intracellular domain promoted breast cancer progression with poor prognosis, possibly through the NEAT1 pathway. Nuclear transport of LRP1B intracellular domain could be a therapeutic target for breast cancer patients. KEY MESSAGES: Nuclear LRP1B was significantly associated with poor patient prognosis. Nuclear LRP1B increased Matrigel invasion activity of breast cancer cells. Nuclear expression of LRP1B intracellular domain increased NEAT1 expression.

The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

The Network of Cancer Genes (NCG) is a manually curated repository of 2372 genes whose somatic modifications have known or predicted cancer driver roles. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more than 100 cancer types from 34,905 cancer donors and multiple primary sites. This represents a more than 1.5-fold content increase compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, and protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/ .

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

ConsensusDriver Improves upon Individual Algorithms for Predicting Driver Alterations in Different Cancer Types and Individual Patients

Existing cancer driver prediction methods are based on very different assumptions and each of them can detect only a particular subset of driver genes. Here we perform a comprehensive assessment of 18 driver prediction methods on more than 3,400 tumor samples from 15 cancer types, all to determine their suitability in guiding precision medicine efforts. We categorized these methods into five groups: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC), and methods that use gene interaction network-based analysis (INA). The performance of driver prediction methods varied considerably, with concordance with a gold standard varying from 9% to 68%. FI methods showed relatively poor performance (concordance <22%), while CBA methods provided conservative results but required large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provided the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of driver genes, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity) in patient subgroups or even individual patients. Consensus-based methods like ConsensusDriver promise to harness the strengths of different driver prediction paradigms.Significance: These findings assess state-of-the-art cancer driver prediction methods and develop a new and improved consensus-based approach for use in precision oncology. Cancer Res; 78(1); 290-301. ©2017 AACR.

Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer

Efforts to identify driver mutations in cancer have largely focused on genes, whereas non-coding sequences remain relatively unexplored. Here we develop a statistical method based on characteristics known to influence local mutation rate and a series of enrichment filters in order to identify distal regulatory elements harboring putative driver mutations in breast cancer. We identify ten DNase I hypersensitive sites that are significantly mutated in breast cancers and associated with the aberrant expression of neighboring genes. A pan-cancer analysis shows that three of these elements are significantly mutated across multiple cancer types and have mutation densities similar to protein-coding driver genes. Functional characterization of the most highly mutated DNase I hypersensitive sites in breast cancer (using in silico and experimental approaches) confirms that they are regulatory elements and affect the expression of cancer genes. Our study suggests that mutations of regulatory elements in tumors likely play an important role in cancer development.

Cancer driver mutations can occur within noncoding genomic sequences. Here, the authors develop a statistical approach to identify candidate noncoding driver mutations in DNase I hypersensitive sites in breast cancer and experimentally demonstrate they are regulatory elements of known cancer genes.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

A single-copy Sleeping Beauty transposon mutagenesis screen identifies new PTEN-cooperating tumor suppressor genes

The overwhelming number of genetic alterations identified through cancer genome sequencing requires complementary approaches to interpret their significance and interactions. Here we developed a novel whole-body insertional mutagenesis screen in mice, which was designed for the discovery of Pten-cooperating tumor suppressors. Toward this aim, we coupled mobilization of a single-copy inactivating Sleeping Beauty transposon to Pten disruption within the same genome. The analysis of 278 transposition-induced prostate, breast and skin tumors detected tissue-specific and shared data sets of known and candidate genes involved in cancer. We validated ZBTB20, CELF2, PARD3, AKAP13 and WAC, which were identified by our screens in multiple cancer types, as new tumor suppressor genes in prostate cancer. We demonstrated their synergy with PTEN in preventing invasion in vitro and confirmed their clinical relevance. Further characterization of Wac in vivo showed obligate haploinsufficiency for this gene (which encodes an autophagy-regulating factor) in a Pten-deficient context. Our study identified complex PTEN-cooperating tumor suppressor networks in different cancer types, with potential clinical implications.

Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information

An integrated approach to identify driver genes based on information of somatic mutations, the interaction network and Gene Ontology similarity.

Global DNA methylation profiling reveals new insights into epigenetically deregulated protein coding and long noncoding RNAs in CLL

Background

Methyl-CpG-binding domain protein enriched genome-wide sequencing (MBD-Seq) is a robust and powerful method for analyzing methylated CpG-rich regions with complete genome-wide coverage. In chronic lymphocytic leukemia (CLL), the role of CpG methylated regions associated with transcribed long noncoding RNAs (lncRNA) and repetitive genomic elements are poorly understood. Based on MBD-Seq, we characterized the global methylation profile of high CpG-rich regions in different CLL prognostic subgroups based on IGHV mutational status.

Results

Our study identified 5800 hypermethylated and 12,570 hypomethylated CLL-specific differentially methylated genes (cllDMGs) compared to normal controls. From cllDMGs, 40 % of hypermethylated and 60 % of hypomethylated genes were mapped to noncoding RNAs. In addition, we found that the major repetitive elements such as short interspersed elements (SINE) and long interspersed elements (LINE) have a high percentage of cllDMRs (differentially methylated regions) in IGHV subgroups compared to normal controls. Finally, two novel lncRNAs (hypermethylated CRNDE and hypomethylated AC012065.7) were validated in an independent CLL sample cohort (48 samples) compared with 6 normal sorted B cell samples using quantitative pyrosequencing analysis. The methylation levels showed an inverse correlation to gene expression levels analyzed by real-time quantitative PCR. Notably, survival analysis revealed that hypermethylation of CRNDE and hypomethylation of AC012065.7 correlated with an inferior outcome.

Conclusions

Thus, our comprehensive methylation analysis by MBD-Seq provided novel hyper and hypomethylated long noncoding RNAs, repetitive elements, along with protein coding genes as potential epigenetic-based CLL-signature genes involved in disease pathogenesis and prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0274-6) contains supplementary material, which is available to authorized users.

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms

Background

High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc.

Results

In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies.

Conclusions

GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1250-z) contains supplementary material, which is available to authorized users.

A comparison of foamy and lentiviral vector genotoxicity in SCID-repopulating cells shows foamy vectors are less prone to clonal dominance

Hematopoietic stem cell (HSC) gene therapy using retroviral vectors has immense potential, but vector-mediated genotoxicity limits use in the clinic. Lentiviral vectors are less genotoxic than gammaretroviral vectors and have become the vector of choice in clinical trials. Foamy retroviral vectors have a promising integration profile and are less prone to read-through transcription than gammaretroviral or lentiviral vectors. Here, we directly compared the safety and efficacy of foamy vectors to lentiviral vectors in human CD34(+) repopulating cells in immunodeficient mice. To increase their genotoxic potential, foamy and lentiviral vectors with identical transgene cassettes with a known genotoxic spleen focus forming virus promoter were used. Both vectors resulted in efficient marking in vivo and a total of 825 foamy and 460 lentiviral vector unique integration sites were recovered in repopulating cells 19 weeks after transplantation. Foamy vector proviruses were observed less often near RefSeq gene and proto-oncogene transcription start sites than lentiviral vectors. The foamy vector group were also more polyclonal with fewer dominant clones (two out of six mice) than the lentiviral vector group (eight out of eight mice), and only lentiviral vectors had integrants near known proto-oncogenes in dominant clones. Our data further support the relative safety of foamy vectors for HSC gene therapy.

Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles

MOTIVATION

microRNAs (miRNAs) play crucial roles in post-transcriptional gene regulation of both plants and mammals, and dysfunctions of miRNAs are often associated with tumorigenesis and development through the effects on their target messenger RNAs (mRNAs). Identifying miRNA functions is critical for understanding cancer mechanisms and determining the efficacy of drugs. Computational methods analyzing high-throughput data offer great assistance in understanding the diverse and complex relationships between miRNAs and mRNAs. However, most of the existing methods do not fully utilise the available knowledge in biology to reduce the uncertainty in the modeling process. Therefore it is desirable to develop a method that can seamlessly integrate existing biological knowledge and high-throughput data into the process of discovering miRNA regulation mechanisms.

RESULTS

In this article we present an integrative framework, CIDER (Causal miRNA target Discovery with Expression profile and Regulatory knowledge), to predict miRNA targets. CIDER is able to utilise a variety of gene regulation knowledge, including transcriptional and post-transcriptional knowledge, and to exploit gene expression data for the discovery of miRNA-mRNA regulatory relationships. The benefits of our framework is demonstrated by both simulation study and the analysis of the epithelial-to-mesenchymal transition (EMT) and the breast cancer (BRCA) datasets. Our results reveal that even a limited amount of either Transcription Factor (TF)-miRNA or miRNA-mRNA regulatory knowledge improves the performance of miRNA target prediction, and the combination of the two types of knowledge enhances the improvement further. Another useful property of the framework is that its performance increases monotonically with the increase of regulatory knowledge.

HER2 Signaling Drives DNA Anabolism and Proliferation through SRC-3 Phosphorylation and E2F1-Regulated Genes

Approximately 20% of early-stage breast cancers display amplification or overexpression of the ErbB2/HER2 oncogene, conferring poor prognosis and resistance to endocrine therapy. Targeting HER2(+) tumors with trastuzumab or the receptor tyrosine kinase (RTK) inhibitor lapatinib significantly improves survival, yet tumor resistance and progression of metastatic disease still develop over time. Although the mechanisms of cytosolic HER2 signaling are well studied, nuclear signaling components and gene regulatory networks that bestow therapeutic resistance and limitless proliferative potential are incompletely understood. Here, we use biochemical and bioinformatic approaches to identify effectors and targets of HER2 transcriptional signaling in human breast cancer. Phosphorylation and activity of the Steroid Receptor Coactivator-3 (SRC-3) is reduced upon HER2 inhibition, and recruitment of SRC-3 to regulatory elements of endogenous genes is impaired. Transcripts regulated by HER2 signaling are highly enriched with E2F1 binding sites and define a gene signature associated with proliferative breast tumor subtypes, cell-cycle progression, and DNA replication. We show that HER2 signaling promotes breast cancer cell proliferation through regulation of E2F1-driven DNA metabolism and replication genes together with phosphorylation and activity of the transcriptional coactivator SRC-3. Furthermore, our analyses identified a cyclin-dependent kinase (CDK) signaling node that, when targeted using the CDK4/6 inhibitor palbociclib, defines overlap and divergence of adjuvant pharmacologic targeting. Importantly, lapatinib and palbociclib strictly block de novo synthesis of DNA, mostly through disruption of E2F1 and its target genes. These results have implications for rational discovery of pharmacologic combinations in preclinical models of adjuvant treatment and therapeutic resistance.

Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer

Accumulating evidence has underscored the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in cancer initiation and progression. In this study, we used an integrative computational method to identify miRNA-mediated ceRNA crosstalk between lncRNAs and mRNAs, and constructed global and progression-related lncRNA-associated ceRNA networks (LCeNETs) in ovarian cancer (OvCa) based on "ceRNA hypothesis". The constructed LCeNETs exhibited small world, modular architecture and high functional specificity for OvCa. Known OvCa-related genes tended to be hubs and occurred preferentially in the functional modules. Ten lncRNA ceRNAs were identified as potential candidates associated with stage progression in OvCa using ceRNA-network driven method. Finally, we developed a ten-lncRNA signature which classified patients into high- and low-risk subgroups with significantly different survival outcomes. Our study will provide novel insight for better understanding of ceRNA-mediated gene regulation in progression of OvCa and facilitate the identification of novel diagnostic and therapeutic lncRNA ceRNAs for OvCa.

Discovering potential cancer driver genes by an integrated network-based approach

An integrated network-based approach is proposed to nominate driver genes. It is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation.

ccmGDB: a database for cancer cell metabolism genes

Accumulating evidence has demonstrated that rewiring of metabolism in cells is an important hallmark of cancer. The percentage of patients killed by metabolic disorder has been estimated to be 30% of the advanced-stage cancer patients. Thus, a systematic annotation of cancer cell metabolism genes is imperative. Here, we present ccmGDB (Cancer Cell Metabolism Gene DataBase), a comprehensive annotation database for cell metabolism genes in cancer, available at http://bioinfo.mc.vanderbilt.edu/ccmGDB. We assembled, curated, and integrated genetic, genomic, transcriptomic, proteomic, biological network and functional information for over 2000 cell metabolism genes in more than 30 cancer types. In total, we integrated over 260 000 somatic alterations including non-synonymous mutations, copy number variants and structural variants. We also integrated RNA-Seq data in various primary tumors, gene expression microarray data in over 1000 cancer cell lines and protein expression data. Furthermore, we constructed cancer or tissue type-specific, gene co-expression based protein interaction networks and drug-target interaction networks. Using these systematic annotations, the ccmGDB portal site provides 6 categories: gene summary, phenotypic information, somatic mutations, gene and protein expression, gene co-expression network and drug pharmacological information with a user-friendly interface for browsing and searching. ccmGDB is developed and maintained as a useful resource for the cancer research community.

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

Colorectal cancer atlas: An integrative resource for genomic and proteomic annotations from colorectal cancer cell lines and tissues

In order to advance our understanding of colorectal cancer (CRC) development and progression, biomedical researchers have generated large amounts of OMICS data from CRC patient samples and representative cell lines. However, these data are deposited in various repositories or in supplementary tables. A database which integrates data from heterogeneous resources and enables analysis of the multidimensional data sets, specifically pertaining to CRC is currently lacking. Here, we have developed Colorectal Cancer Atlas (http://www.colonatlas.org), an integrated web-based resource that catalogues the genomic and proteomic annotations identified in CRC tissues and cell lines. The data catalogued to-date include sequence variations as well as quantitative and non-quantitative protein expression data. The database enables the analysis of these data in the context of signaling pathways, protein–protein interactions, Gene Ontology terms, protein domains and post-translational modifications. Currently, Colorectal Cancer Atlas contains data for >13 711 CRC tissues, >165 CRC cell lines, 62 251 protein identifications, >8.3 million MS/MS spectra, >18 410 genes with sequence variations (404 278 entries) and 351 pathways with sequence variants. Overall, Colorectal Cancer Atlas has been designed to serve as a central resource to facilitate research in CRC.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches.

Database URL:http://ppi.bioinfo.asia.edu.tw/FARE-CAFE

Identification and validation of potential prognostic lncRNA biomarkers for predicting survival in patients with multiple myeloma

Background

Dysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated.

Methods

LncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis.

Results

Four lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR = 2.718, 95 % CI = 1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (Sβ2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM.

Conclusions

Our results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-015-0219-5) contains supplementary material, which is available to authorized users.

Network analysis reveals a stress-affected common gene module among seven stress-related diseases/systems which provides potential targets for mechanism research

Chronic stress (CS) was reported to associate with many complex diseases and stress-related diseases show strong comorbidity; however, molecular analyses have not been performed to date to evaluate common stress-induced biological processes across these diseases. We utilized networks constructed by genes from seven genetic databases of stress-related diseases or systems to explore the common mechanisms. Genes were connected based on the interaction information of proteins they encode. A common sub-network constructed by 561 overlapping genes and 8863 overlapping edges among seven networks was identified and it provides a common gene module among seven stress-related diseases/systems. This module is significantly overlapped with network that constructed by genes from the CS gene database. 36 genes with high connectivity (hub genes) were identified from seven networks as potential key genes in those diseases/systems, 33 of hub genes were included in the common module. Genes in the common module were enriched in 190 interactive gene ontology (GO) functional clusters which provide potential disease mechanism. In conclusion, by analyzing gene networks we revealed a stress-affected common gene module among seven stress-related diseases/systems which provides insight into the process of stress induction of disease and suggests potential gene and pathway candidates for further research.

Personalized targeted therapy for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma continues to heavily burden clinicians worldwide. Researchers have discovered the genomic landscape of esophageal squamous cell carcinoma, which holds promise for an era of personalized oncology care. One of the most pressing problems facing this issue is to improve the understanding of the newly available genomic data, and identify the driver-gene mutations, pathways, and networks. The emergence of a legion of novel targeted agents has generated much hope and hype regarding more potent treatment regimens, but the accuracy of drug selection is still arguable. Other problems, such as cancer heterogeneity, drug resistance, exceptional responders, and side effects, have to be surmounted. Evolving topics in personalized oncology, such as interpretation of genomics data, issues in targeted therapy, research approaches for targeted therapy, and future perspectives, will be discussed in this editorial.

Computational methods and resources for the interpretation of genomic variants in cancer

The recent improvement of the high-throughput sequencing technologies is having a strong impact on the detection of genetic variations associated with cancer. Several institutions worldwide have been sequencing the whole exomes and or genomes of cancer patients in the thousands, thereby providing an invaluable collection of new somatic mutations in different cancer types. These initiatives promoted the development of methods and tools for the analysis of cancer genomes that are aimed at studying the relationship between genotype and phenotype in cancer. In this article we review the online resources and computational tools for the analysis of cancer genome. First, we describe the available repositories of cancer genome data. Next, we provide an overview of the methods for the detection of genetic variation and computational tools for the prioritization of cancer related genes and causative somatic variations. Finally, we discuss the future perspectives in cancer genomics focusing on the impact of computational methods and quantitative approaches for defining personalized strategies to improve the diagnosis and treatment of cancer.

Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers

The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1) Using both standard and non-standard metrics, EC almost always outperforms single method classifiers, often by wide margins. (2) Of the 50 highest ranked genes for breast (ovarian) cancer, 34 (30) are associated with other cancers in either the OMIM, CGC or NCG database (P < 10⁻²²). (3) Another 10, for both breast and ovarian cancer, have been identified by GWAS studies. (4) Several of the remaining genes--including a protein kinase that regulates the Fra-1 transcription factor which is overexpressed in ER negative breast cancer cells; and Fyn, which is overexpressed in pancreatic and prostate cancer, among others--are biologically plausible. Biological implications are briefly discussed. Source codes and detailed results are available at http://www.visantnet.org/misi/driver_integration.zip.

Foamy viral vector integration sites in SCID-repopulating cells after MGMTP140K-mediated in vivo selection

Foamy virus (FV) vectors are promising for hematopoietic stem cell (HSC) gene therapy but preclinical data on the clonal composition of FV vector-transduced human repopulating cells is needed. Human CD34(+) human cord blood cells were transduced with an FV vector encoding a methylguanine methyltransferase (MGMT)P140K transgene, transplanted into immunodeficient NOD/SCID IL2Rγ(null) mice, and selected in vivo for gene-modified cells. The retroviral insertion site profile of repopulating clones was examined using modified genomic sequencing PCR. We observed polyclonal repopulation with no evidence of clonal dominance even with the use of a strong internal spleen focus forming virus promoter known to be genotoxic. Our data supports the use of FV vectors with MGMTP140K for HSC gene therapy but also suggests additional safety features should be developed and evaluated.

Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation

Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases. Several computational algorithms have recently been developed for this purpose by using transcriptome and network data. However, it remains largely unclear which algorithm performs better under a specific condition. Such knowledge is important for both appropriate application and future enhancement of these algorithms. Here, we systematically evaluated seven main algorithms (TED, TDD, TFactS, RIF1, RIF2, dCSA_t2t, and dCSA_r2t), using both simulated and real datasets. In our simulation evaluation, we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators. We found that all these algorithms could effectively discern signals arising from regulatory network differences, indicating the validity of our simulation schema. Among the seven tested algorithms, TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered. When applied to two independent lung cancer datasets, both TED and TFactS replicated a substantial fraction of their respective differential regulators. Since TED and TFactS rely on two distinct features of transcriptome data, namely differential co-expression and differential expression, both may be applied as mutual references during practical application.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Deep sequencing of the X chromosome reveals the proliferation history of colorectal adenomas

BACKGROUND

Mismatch repair deficient colorectal adenomas are composed of transformed cells that descend from a common founder and progressively accumulate genomic alterations. The proliferation history of these tumors is still largely unknown. Here we present a novel approach to rebuild the proliferation trees that recapitulate the history of individual colorectal adenomas by mapping the progressive acquisition of somatic point mutations during tumor growth.

RESULTS

Using our approach, we called high and low frequency mutations acquired in the X chromosome of four mismatch repair deficient colorectal adenomas deriving from male individuals. We clustered these mutations according to their frequencies and rebuilt the proliferation trees directly from the mutation clusters using a recursive algorithm. The trees of all four lesions were formed of a dominant subclone that co-existed with other genetically heterogeneous subpopulations of cells. However, despite this similar hierarchical organization, the growth dynamics varied among and within tumors, likely depending on a combination of tumor-specific genetic and environmental factors.

CONCLUSIONS

Our study provides insights into the biological properties of individual mismatch repair deficient colorectal adenomas that may influence their growth and also the response to therapy. Extended to other solid tumors, our novel approach could inform on the mechanisms of cancer progression and on the best treatment choice.

Massive gene amplification drives paediatric hepatocellular carcinoma caused by bile salt export pump deficiency

Hepatocellular carcinoma (HCC) is almost invariably associated with an underlying inflammatory state, whose direct contribution to the acquisition of critical genomic changes is unclear. Here we map acquired genomic alterations in human and mouse HCCs induced by defects in hepatocyte biliary transporters, which expose hepatocytes to bile salts and cause chronic inflammation that develops into cancer. In both human and mouse cancer genomes, we find few somatic point mutations with no impairment of cancer genes, but massive gene amplification and rearrangements. This genomic landscape differs from that of virus- and alcohol-associated liver cancer. Copy-number gains preferentially occur at late stages of cancer development and frequently target the MAPK signalling pathway, and in particular direct regulators of JNK. The pharmacological inhibition of JNK retards cancer progression in the mouse. Our study demonstrates that intrahepatic cholestasis leading to hepatocyte exposure to bile acids and inflammation promotes cancer through genomic modifications that can be distinguished from those determined by other aetiological factors.