ONGene: A literature-based database for human oncogenes

S100A8/S100A9 cytokine acts as a transcriptional coactivator during breast cellular transformation

Cytokines are extracellular proteins that convey messages between cells by interacting with cognate receptors at the cell surface and triggering signaling pathways that alter gene expression and other phenotypes in an autocrine or paracrine manner. Here, we show that the calcium-dependent cytokines S100A8 and S100A9 are recruited to numerous promoters and enhancers in a model of breast cellular transformation. This recruitment is associated with multiple DNA sequence motifs recognized by DNA binding transcription factors that are linked to transcriptional activation and are important for transformation. The cytokines interact with these transcription factors in nuclear extracts, and they activate transcription when artificially recruited to a target promoter. Nuclear-specific expression of S100A8/A9 promotes oncogenic transcription and leads to enhanced breast transformation phenotype. These results suggest that, in addition to its classical cytokine function, S100A8/A9 can act as a transcriptional coactivator.

Identifying Key Genes for Nasopharyngeal Carcinoma by Prioritized Consensus Differentially Expressed Genes Caused by Aberrant Methylation

Background: Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV)-associated epithelial malignancy. Large-scale genetics or epigenetics studies of NPC have been relatively scarce and sporadic, and there are no effective targeted drugs for NPC. Integrative analysis of multiple different omics profiles has been proved to be an effective approach to shed new light on cancer.

Methods: We developed a pipeline to aggregate consensus differentially expressed genes (DEGs) from multiple expression datasets from different platforms. Integrated bioinformatics analysis of DNA methylation and gene expression was used to prioritize key genes in NPC. We explored the biological and clinical importance of key genes, combining differential co-expression analysis, network analysis of protein-protein and microRNA (miRNA)-target interactions, and pan-cancer survival analysis.

Results: We obtained 668 upregulated and 594 downregulated consensus DEGs, which enriched in the PI3K-AKT, NF-κB and immune-related pathways. In NPC, 98% of 3364 differentially methylated sites were hypermethylated. Actively expressed EBV gene EBNA1 was positively correlated with over-expressed genes coding DNA methyltransferase and Polycomb group proteins, suggesting that EBV infection may have an important role in the hypermethylation of NPC. Through integrated analysis of DNA methylation and mRNA and miRNA expression profiles, we prioritized 56 hypermethylated downregulated genes, including 7 tumor suppressor genes, and constructed a miRNA-target regulation network consisting of 12 hypermethylated miRNAs and 25 upregulated oncogenes. The promoter hypermethylation of PRKCB causing its downregulation was validated by experimental results and higher PRKCB expression was associated with longer overall survival in head-neck squamous cell carcinoma, suggesting the potential of PRKCB as a promising disease biomarker for NPC.

Conclusions: Our integrative analysis provides reliable key genes for candidate biomarkers for diagnosis and prognosis in NPC. Based on the combined evidence of promoter hypermethylation, expression up-regulation, and association with overall survival, genes such as SCUBE2, PRKCB, IKZF1, MAP4K1, and GATA6 could be promising novel diagnostic biomarkers, and miRNAs including MIR150, MIR152, and MIR34 could be candidate prognosis biomarkers.

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification

High-throughput sequencing technologies have identified millions of genetic mutations in multiple human diseases. However, the interpretation of the pathogenesis of these mutations and the discovery of driver genes that dominate disease progression is still a major challenge. Combining functional features such as protein post-translational modification (PTM) with genetic mutations is an effective way to predict such alterations. Here, we present PTMsnp, a web server that implements a Bayesian hierarchical model to identify driver genetic mutations targeting PTM sites. PTMsnp accepts genetic mutations in a standard variant call format or tabular format as input and outputs several interactive charts of PTM-related mutations that potentially affect PTMs. Additional functional annotations are performed to evaluate the impact of PTM-related mutations on protein structure and function, as well as to classify variants relevant to Mendelian disease. A total of 4,11,574 modification sites from 33 different types of PTMs and 1,776,848 somatic mutations from TCGA across 33 different cancer types are integrated into the web server, enabling identification of candidate cancer driver genes based on PTM. Applications of PTMsnp to the cancer cohorts and a GWAS dataset of type 2 diabetes identified a set of potential drivers together with several known disease-related genes, indicating its reliability in distinguishing disease-related mutations and providing potential molecular targets for new therapeutic strategies. PTMsnp is freely available at: http://ptmsnp.renlab.org.

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Supplemental Digital Content is available for this article.

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival.

Longitudinal analysis of healthy colon establishes aspirin as a suppressor of cancer-related epigenetic aging

BACKGROUND

Colon cancer (CC) is the third most common cancer worldwide, highlighting the importance of developing effective prevention strategies. Accumulating evidence supports that aspirin use reduces CC incidence. We reported previously that aspirin suppresses age-associated and CC-relevant DNA methylation (DNAm) in healthy colon. Here we addressed the aspirin's effectiveness in longitudinal cohort.

METHODS

We measured genome-wide DNAm in 124 healthy normal mucosa samples taken at baseline (time point 1, t1) and after 10-years follow-up (time point 2, t2) from a longitudinal female screening cohort. We investigated the time-dependent methylation drift in aspirin users and nonusers using multivariable regression and related the modulatory effect of aspirin to colonic epigenome-aging and CC.

RESULTS

Over time, compared to nonusers, long-term (≥ 2 years) aspirin users showed less hypermethylated CpGs (proximal: 17% vs. 87%; distal: 16% vs. 70%) and more hypomethylated CpGs (proximal: 83% vs. 13%; distal: 84% vs. 30%). Overall, users showed 2% (P = 0.02) less mean methylation levels than nonusers in proximal colon and displayed repressed methylation age (mAge). Methylation loss in users occurred at several CC-specific tumor suppressors that gained methylation in nonusers. Methylation loss in users effected genes involved in immune system and inflammation, while methylation gain in nonusers effected genes involved in metabolism.

CONCLUSIONS

This is the first longitudinal study demonstrating effectiveness of aspirin-use in suppression of age-related and CC-relevant hypermethylation in the normal colon. These findings provide a rationale for future studies to evaluate loci that may serve as markers to identify individuals that will benefit most from aspirin and hence increase its efficiency in CC prevention and therapy.

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

Characterization of BRCA1-deficient premalignant tissues and cancers identifies Plekha5 as a tumor metastasis suppressor

Single-cell whole-exome sequencing (scWES) is a powerful approach for deciphering intratumor heterogeneity and identifying cancer drivers. So far, however, simultaneous analysis of single nucleotide variants (SNVs) and copy number variations (CNVs) of a single cell has been challenging. By analyzing SNVs and CNVs simultaneously in bulk and single cells of premalignant tissues and tumors from mouse and human BRCA1-associated breast cancers, we discover an evolution process through which the tumors initiate from cells with SNVs affecting driver genes in the premalignant stage and malignantly progress later via CNVs acquired in chromosome regions with cancer driver genes. These events occur randomly and hit many putative cancer drivers besides p53 to generate unique genetic and pathological features for each tumor. Upon this, we finally identify a tumor metastasis suppressor Plekha5, whose deficiency promotes cancer metastasis to the liver and/or lung.

Large-Scale Topological Changes Restrain Malignant Progression in Colorectal Cancer

Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic.

Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data

DNA methylation plays an important role in many biological processes and diseases. With the rise of the whole genome bisulfite sequencing technique, aberrant methylation patterns can now be detected by comparing paired normal and disease samples at the single nucleotide level. We develop a novel Bayesian method for detecting differentially methylated regions from paired bisulfite sequencing data, and implement it as a R package called BSDMR. Based on a non-homogeneous hidden Markov model, BSDMR provides a better modeling strategy for the spatial correlation between CpG sites and takes into consideration the relationship between methylation signals from normal and disease samples. Simulations show that BSDMR performs well even under low read depth and has a smaller false discovery rates than existing methods. We also apply BSDMR to the colon cancer data from Gene Expression Omnibus. The detected DMRs are well supported by existing biomedical literatures.

Mutation landscape of TSC1/TSC2 in Chinese patients with tuberous sclerosis complex

Genetic testing of TSC1 and TSC2 is important for the diagnosis of tuberous sclerosis complex (TSC), an autosomal dominant neurocutaneous disease. This study retrospectively reviewed 347 samples from patients with clinically suspected TSC being tested for mutations in TSC1 and TSC2 genes using next-generation sequencing and multiplex ligation-dependent probe amplification. Two hundred eighty-one patients (80.98%) were classified as definite/possible/uncertain diagnosis of TSC and the mutational spectrum of TSC1/TSC2 was described. Two hundred eighteen unique nonsynonymous SNVs/Indels (64 in TSC1, 154 in TSC2) and 13 copy number variants (CNVs) were identified in 241 samples (85.77%), including 82 novel variants. CNVs involving 12 large deletions and one duplication were detected exclusively in TSC2. Both TSC1 and TSC2 mutations were nearly uniformly distributed in their protein-coding regions. Furthermore, a string of non-TSC1/TSC2 deleterious variants in 12 genes was identified in the patients, especially overwhelmingly present in the patients with no mutation identified (NMI) in TSC1/TSC2. Our study provides a comprehensive TSC1/TSC2 mutation landscape and reveal some potential risk non-TSCs variants present in patients with NMI.

NEDD4 Plays Roles in the Maintenance of Breast Cancer Stem Cell Characteristics

Triple-negative breast cancer (TNBC) is the most aggressive type with poor prognosis among the breast cancers and has a high population of cancer stem cells (CSCs), which are the main target to cure and inhibit TNBC. In this study, we examined the role of neural precursor cell expressed developmentally downregulated protein 4 (NEDD4) in the proliferation, migration, and CSC characteristics of MDA-MB-231, a TNBC cell line. Interestingly, the Kaplan–Meier plotter showed that the survival rate of patients with a higher expression level of NEDD4 was significantly shorter than those of patients with a lower expression only in relatively aggressive and higher stage (grade 3) breast cancer patients. The knockdown of NEDD4 drastically decreased the proliferation, migration, and mammosphere formation in MDA-MB-231 cells. A proteomic analysis revealed the alteration of CSC-related proteins; notably, Myc targets stem cell-like signatures in siNEDD4-treated MDA-MB-231. An immunoassay also showed that the expression and the activity of breast CSC markers are decreased in NEDD4-deleted MDA-MB-231. Taken together, these results indicate that NEDD4 is involved in the maintenance of populations and characteristics of breast CSCs.

Multiple Chromoanasynthesis in a Rare Case of Sporadic Renal Leiomyosarcoma: A Case Report

We present the genetic profile of kidney giant leiomyosarcoma characterized by sequencing of 409 cancer related genes and chromosomal microarray analysis. Renal leiomyosarcomas are extremely rare neoplasms with aggressive behavior and poor survival prognosis. Most frequent somatic events in leiomyosarcomas are mutations in the TP53, RB1, ATRX, and PTEN genes, chromosomal instability (CIN) and chromoanagenesis. 67-year-old woman presented with a right kidney completely replaced by tumor. Immunohistochemical reaction on surgical material was positive to desmin and smooth muscle actin. Molecular genetic analysis revealed that tumor harbored monosomy of chromosomes 3 and 11, gain of Xp (ATRX) arm and three chromoanasynthesis regions (6q21-q27, 7p22.3-p12.1, and 12q13.11-q21.2), with MDM2 and CDK4 oncogenes copy number gains, whereas no copy number variations (CNVs) or tumor specific single nucleotide variants (SNVs) in TP53, RB1, and PTEN genes were present. We hypothesize that chromoanasynthesis in 12q13.11-q21.2 could be a trigger of observed CIN in this tumor.

Higher expression of cell division cycle-associated protein 5 predicts poorer survival outcomes in hepatocellular carcinoma

The upregulation of cell division cycle associated protein 5 (CDCA5) has been observed in various cancer types. However, the prognostic value of CDCA5 and its underlying mechanism contributing to tumorigenesis in hepatocellular carcinoma (HCC) remain poorly understood. We used tissue microarray (TMA) to evaluate the prognosis of 304 HCC samples based on their CDCA5 expression, and analyzed the genomic features correlated with CDCA5 by using dataset from The Cancer Genome Atlas (TCGA). Compared with adjacent normal tissues, increased expression of CDCA5 was found in HCC tissues. Moreover, higher expression of CDCA5 was associated with inferior OS and DFS outcomes in HCC patients. The enrichment plots showed that the gene signatures in cell cycle, DNA replication and p53 pathways were enriched in patients with higher CDCA5 expression. Meanwhile, statistically higher mutations burdens in TP53 could also be observed in CDCA5-high patients. Integrative analysis based on miRNAseq and methylation data demonstrated a potential association between CDCA5 expression and epigenetic changes. In conclusion, our study provided the evidence of CDCA5 as an oncogenic promoter in HCC and the potential function of CDCA5 in affecting tumor microenvironment.

Genomic and Transcriptomic Characterization of Sporadic Medullary Thyroid Carcinoma

Background: Sporadic medullary thyroid carcinoma (MTC) is a relatively uncommon neuroendocrine malignancy and the molecular tumorigenesis of its sporadic type (sMTC) is only partially understood. In this study, we performed a study focusing on the genomic and transcriptomic characterization of sMTC. Methods: Twenty-nine sMTC patients were included. Whole-exome sequencing (WES) was carried out in 18 patients, including both tumor samples and matched noncancerous tissues. Whole transcriptome sequencing (RNA-Seq) was performed in all 29 tumors. WES, RNA-Seq, and copy number alteration (CNA) data were analyzed. A Cell Counting Kit-8 (CCK-8) assay was used to evaluate cell proliferation. Results: Among the somatic mutations, RET was the only recurrently cancer-related mutated gene (5/18, 27.8%). In the germline, FAT1 and FAT4, two members of the FAT gene family, were identified as the two most common mutated genes. CNA analysis found that FAT1 and FAT4, both located on chromosome 4q, were also two of the genes most commonly affected by somatic chromosomal deletions (4/18, 22.2%). Using TT and MZ-CRC-1 cell lines, the CCK-8 assay showed that FAT1 and FAT4 knockdown could promote MTC cell proliferation. Based on the gene expression profile, patients were clustered into two molecular subtypes: the mesenchymal-like subtype is characterized by epithelial-mesenchymal transition, while the proliferative-like subtype is associated with enrichment of cell cycle pathways. Most events of structural recurrence (80%) occurred in the proliferative-like subtype. Conclusion: In addition to RET, these findings demonstrate that FAT1/FAT4 genomic alterations appear to be frequent in sMTC. Two molecular subtypes of sMTC with distinct biological behavior could be identified. However, these results need to be validated by larger samples and more comprehensive experiments in the future, especially for the frequency and function of FAT1/FAT4 germline variants.

Rare Tumor-Normal Matched Whole Exome Sequencing Identifies Novel Genomic Pathogenic Germline and Somatic Aberrations

Whole exome sequencing (WES) of matched tumor-normal pairs in rare tumors has the potential to identify genome-wide mutations and copy number alterations (CNAs). We evaluated 27 rare cancer patients with tumor-normal matching by WES and tumor-only next generation sequencing (NGS) as a comparator. Our goal was to: 1) identify known and novel variants and CNAs in rare cancers with comparison to common cancers; 2) examine differences between germline and somatic variants and how that functionally impacts rare tumors; 3) detect and characterize alleles in biologically relevant genes-pathways that may be of clinical importance but not represented in classical cancer genes. We identified 3343 germline single nucleotide variants (SNVs) and small indel variants-1670 in oncogenes and 1673 in tumor suppressor genes-generating an average of 124 germline variants/case. The number of somatic SNVs and small indels detected in all cases was 523:306 in oncogenes and 217 in tumor suppressor genes. Of the germline variants, six were identified to be pathogenic or likely pathogenic. In the 27 analyzed rare cancer cases, CNAs are variable depending on tumor type, germline pathogenic variants are more common. Cell fate pathway mutations (e.g., Hippo, Notch, Wnt) dominate pathogenesis and double hit (mutation + CNV) represent ~18% cases.

VISDB: a manually curated database of viral integration sites in the human genome

Virus integration into the human genome occurs frequently and represents a key driving event in human disease. Many studies have reported viral integration sites (VISs) proximal to structural or functional regions of the human genome. Here, we systematically collected and manually curated all VISs reported in the literature and publicly available data resources to construct the Viral Integration Site DataBase (VISDB, https://bioinfo.uth.edu/VISDB). Genomic information including target genes, nearby genes, nearest transcription start site, chromosome fragile sites, CpG islands, viral sequences and target sequences were integrated to annotate VISs. We further curated VIS-involved oncogenes and tumor suppressor genes, virus–host interactions involved in non-coding RNA (ncRNA), target gene and microRNA expression in five cancers, among others. Moreover, we developed tools to visualize single integration events, VIS clusters, DNA elements proximal to VISs and virus–host interactions involved in ncRNA. The current version of VISDB contains a total of 77 632 integration sites of five DNA viruses and four RNA retroviruses. VISDB is currently the only active comprehensive VIS database, which provides broad usability for the study of disease, virus related pathophysiology, virus biology, host–pathogen interactions, sequence motif discovery and pattern recognition, molecular evolution and adaption, among others.

ChimerDB 4.0: an updated and expanded database of fusion genes

Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http://www.kobic.re.kr/chimerdb/.

Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer

Cervical cancer (CC) remains a most prevalent female cancer worldwide, but there are few biomarkers used in diagnosis and prognosis of CC. The aim of this study is to find reliable and effective biomarkers regarding CC development. Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to search potential miRNA-mRNA in CC. The gene ontology term enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were conducted to reveal the underlying functions and pathways of differently expressed genes (DEGs). Univariate Cox, multivariate Cox, and risk scoring methods were performed to identify a prognostic model. A total of 209 DEGs of CC were identified. In the protein-protein interaction network, hub module, and hub genes were recognized. Based on DEGs, three small molecules (thioguanosine, apigenin, and trichostatin A) were screened out as potential drugs. Two miRNAs (hsa-mir-101-3p and hsa-mir-6507-5p) and some transcription factors were found to be associated with prognosis of CC. A five-candidate gene signature (APOBEC3B, DSG2, CXCL8, ABCA8, and PLAGL1) was constructed to stratify risk subgroups for patients with CC. The risk score of the prognostic model was also found to be associated with immune cells infiltration, including mast cell activation, natural killer cells resting, dendritic cells resting, T cells regulatory (Tregs), and T cells follicular helper. The miRNA-mRNA regulatory network and the prognostic model are of great clinical significance in promoting prognosis prediction and treatment of CC.

A multidimensional systems biology analysis of cellular senescence in aging and disease

BACKGROUND

Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases. Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking.

RESULTS

We develop CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build cellular senescence protein-protein interaction and co-expression networks. Clusters in the networks are enriched for cell cycle and immunological processes. Network topological parameters also reveal novel potential cellular senescence regulators. Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.

CONCLUSIONS

Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

TOPORS, a tumor suppressor protein, contributes to the maintenance of higher-order chromatin architecture

In the nucleus, chromosomes are hierarchically folded into active (A) and inactive (B) compartments composed of topologically associating domains (TADs). Genomic regions interact with nuclear lamina, termed lamina-associated domains (LADs). However, the molecular mechanisms underlying these 3D chromatin architectures remain incompletely understood. Here, we investigated the role of a potential tumor suppressor, TOP1 Binding Arginine/Serine Rich Protein (TOPORS), in genome organization. In mouse hepatocytes, chromatin interactions between A and B compartments increase and compartmentalization strength is reduced significantly upon Topors knockdown. Correspondingly, strength of TAD boundaries located at A/B borders is weakened. In the absence of TOPORS, chromatin-lamina interactions decrease and the coverage of LADs reduces from 53.31% to 46.52%. Interestingly, these changes in 3D genome are associated with PML nuclear bodies and PML-associated domains (PADs). Moreover, chromatin accessibility is altered predominantly at intergenic regions upon Topors knockdown, including a subset of enhancers. These alterations of chromatin are concordant with transcriptome changes, which are associated with carcinogenesis. Collectively, our findings demonstrate that TOPORS functions as a regulator in chromatin structure, providing novel insight into the architectural roles of tumor suppressors in higher-order genome organization.

Effects of ordered mutations on dynamics in signaling networks

BACKGROUND

Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations.

METHODS

To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model.

RESULTS

Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter.

CONCLUSION

Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

Integrated characterisation of cancer genes identifies key molecular biomarkers in stomach adenocarcinoma

Aims

Gastric cancer is one of the leading causes for cancer mortality. Recent studies have defined the landscape of genomic alterations of gastric cancer and their association with clinical outcomes. However, the pathogenesis of gastric cancer has not been completely characterised.

Methods

Driver genes were detected by five computational tools, MutSigCV, OncodriveCLUST, OncodriveFM, dendrix and edriver, using mutation data of stomach adenocarcinoma (STAD) from the cancer genome altas database, followed by an integrative investigation.

Results

TTN, TP53, LRP1B, CSMD3, OBSCN, ARID1A, FAT4, FLG, PCLO and CSMD1 were the 10 most frequently mutated genes. PIK3CD, NLRC3, FMNL1, TRAF3IP3 and CR1 were the top five hub genes of the blue coexpression module positively correlated with pathological tumour stage and lymph node stage (p values <0.05 for all cases). Hierarchical clustering analysis of copy number variations of driver genes revealed three subgroups of STAD patients, and cluster 2 tumours were significantly associated with lower lymph node stage, less number of positive lymph nodes and higher microsatellite instability and better overall survival than cluster 1 and cluster 3 tumours (p values <0.05 for all cases, Wilcoxon rank-sum test or log rank test). High expression in one or more of DNER, LHCGR, NLRP14, OR4N2, PSG6, TTC29 and ZNF568 genes was associated with increased mortality (p values <0.05 for all cases, log rank test).

Conclusions

The driver genes shed insights into the tumourigenesis of gastric cancer and the genes DNER, LHCGR, NLRP14, OR4N2, PSG6, TTC29 and ZNF568 pave the way for developing prognostic biomarkers for the disease.

Targeted molecular profiling of genetic alterations in colorectal cancer using next-generation sequencing

Colorectal cancer (CRC) is a major contributor to cancer-associated mortality in China and remains a vast challenge worldwide. Although the genetic basis of CRC has been investigated, the uncommonly mutated genes in CRC remain unknown, in particular in the Asian population. In the present study, targeted region sequencing on 22 CRC and 10 paired non-cancerous tissues was performed to determine the genetic pattern of CRC samples in the Chinese population. Driver genes were detected by three distinct softwares, including MutSigCV, oncodriveFM and iCAGES. A total of 1,335 reliable somatic mutations were identified in tumour samples compared with normal samples. Furthermore, mismatch repair (MMR) mutant patients presented significantly higher mutation density compared with MMR wild-type patients. The results from MutSigCV, oncodriveFM and iCAGES analyses simultaneously detected 29 unique driver genes. In addition, the genes APC regulator of WNT signaling pathway, SMAD family member 4, neurofibromin 1, AT-rich interaction domain 5B and nuclear receptor corepressor 1 were the top five most frequently mutated genes in CRC samples, with mutation rates of 68, 36, 36, 32 and 27%, respectively. The findings from the present study may therefore serve as a basis for future investigation on the diagnosis and oncogenesis of CRC.

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics

Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To address this challenge, we used targeted proteomics and other molecular data compiled by The Cancer Genome Atlas to reveal that pancreatic tumours can be broadly segregated into two distinct subtypes. Besides being associated with substantially different clinical outcomes, tumours belonging to each of these subtypes also display notable differences in diverse signalling pathways and biological processes. At the proteome level, we show that tumours belonging to the less severe subtype are characterised by aberrant mTOR signalling, whereas those belonging to the more severe subtype are characterised by disruptions in SMAD and cell cycle-related processes. We use machine learning algorithms to define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Lastly, we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to accurately infer the drug sensitivity of pancreatic cancer cell lines. Our study shows that integrative profiling of multiple data types enables a biological and clinical representation of pancreatic cancer that is comprehensive enough to provide a foundation for future therapeutic strategies.

NPInter v4.0: an integrated database of ncRNA interactions

Noncoding RNAs (ncRNAs) play crucial regulatory roles in a variety of biological circuits. To document regulatory interactions between ncRNAs and biomolecules, we previously created the NPInter database (http://bigdata.ibp.ac.cn/npinter). Since the last version of NPInter was issued, a rapidly growing number of studies have reported novel interactions and accumulated numerous high-throughput interactome data. We have therefore updated NPInter to its fourth edition in which are integrated 600 000 new experimentally identified ncRNA interactions. ncRNA-DNA interactions derived from ChIRP-seq data and circular RNA interactions have been included in the database. Additionally, disease associations were annotated to the interacting molecules. The database website has also been redesigned with a more user-friendly interface and several additional functional modules. Overall, NPInter v4.0 now provides more comprehensive data and services for researchers working on ncRNAs and their interactions with other biomolecules.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.

GWAS studies reveal a possible genetic link between cancer and suicide attempt

Inuit is the population with the highest incidence of suicide attempt and cancer in the world. Previous studies reported that people attempted suicide have a higher future risk for cancer. In view of these data, the largest available genome wide association studies (GWAS) for four major mental disorder groups were screened here for any common genes with all known cancer associated genes and oncogenes/tumor suppressor genes. A common genetic background came out only between suicide attempt and cancer (cancer associated genes analysis: RR = 1.64, p = 7.83 × 10⁻⁵; oncogenes/tumor suppressor genes analysis: RR = 2.55, p = 2.82 × 10⁻²²), this supporting existing epidemiological data. Incidence/prevalence of both conditions was found to correlate with extreme cold geographical regions (adjusted R² = 0.135, p = 3.00 × 10⁻⁴); this is not the case for other mental disorders. Our results show a possible genetic link between suicide attempt and cancer and a possible evolutionary connection of both diseases with extreme cold environments. These data are useful for future molecular studies or even for investigation of possible therapeutic protocols.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

Distinct signatures of lung cancer types: aberrant mucin O-glycosylation and compromised immune response

BACKGROUND

Genomic initiatives such as The Cancer Genome Atlas (TCGA) contain data from -omics profiling of thousands of tumor samples, which may be used to decipher cancer signaling, and related alterations. Managing and analyzing data from large-scale projects, such as TCGA, is a demanding task. It is difficult to dissect the high complexity hidden in genomic data and to account for inter-tumor heterogeneity adequately.

METHODS

In this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patients from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC).

RESULTS

We used the gene expression data to identify co-expression modules and differentially expressed genes to discriminate between LUAD and LUSC. We identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, along with two dual role genes. Our results have been validated against other transcriptomics data of lung cancer patients.

CONCLUSIONS

Our integrative approach allowed us to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC might be linked to the activation of three oncogenic pathways, which promote the evasion of the antitumor immune response. Collectively, our results provide new future directions for the design of target therapies in lung cancer.

CR2Cancer: a database for chromatin regulators in human cancer

Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Somatic alterations or misexpression of CRs might reprogram the epigenomic landscape of chromatin, which in turn lead to a wide range of common diseases, notably cancer. Here, we present CR2Cancer, a comprehensive annotation and visualization database for CRs in human cancer constructed by high throughput data analysis and literature mining. We collected and integrated genomic, transcriptomic, proteomic, clinical and functional information for over 400 CRs across multiple cancer types. We also built diverse types of CR-associated relations, including cancer type dependent (CR-target and miRNA-CR) and independent (protein-protein interaction and drug-target) ones. Furthermore, we manually curated around 6000 items of aberrant molecular alterations and interactions of CRs in cancer development from 5007 publications. CR2Cancer provides a user-friendly web interface to conveniently browse, search and download data of interest. We believe that this database would become a valuable resource for cancer epigenetics investigation and potential clinical application. CR2Cancer is freely available at http://cis.hku.hk/CR2Cancer.

Impact of chronically alternating light-dark cycles on circadian clock mediated expression of cancer (glioma)-related genes in the brain

Disruption of the circadian rhythm is a risk factor for cancer, while glioma is a leading contributor to mortality worldwide. Substantial efforts are being undertaken to decrypt underlying molecular pathways. Our understanding of the mechanisms through which disrupted circadian rhythm induces glioma development and progression is incomplete. We, therefore, examined changes in the expression of glioma-related genes in the mouse brain after chronic jetlag (CJL) exposure. A total of 22 candidate tumor suppressor (n= 14) and oncogenes (n= 8) were identified and analyzed for their interaction with clock genes. Both the control and CJL groups were investigated for the expression of candidate genes in the nucleus accumbens, hippocampus, prefrontal cortex, hypothalamus, and striatum of wild type, Bmal1-/- and Cry1/2 double knockout male mice. We found significant variations in the expression of candidate tumor suppressor and oncogenes in the brain tissues after CJL treatment in the wild type, Bmal1-/- and Cry1/2 double knockout mice. In response to CJL treatment, some of the genes were regulated in the wild type, Bmal1-/- and Cry1/2 similarly. However, the expression of some of the genes indicated their association with the functional clock. Overall, our result suggests a link between CJL and gliomas risk at least partially dependent on the circadian clock. However, further studies are needed to investigate the molecular mechanism associated with CJL and gliomas.

Antifibrotic Potential of MiR-335-3p in Hereditary Gingival Fibromatosis

Hereditary gingival fibromatosis (HGF) is a highly genetically heterogeneous disease, and current therapeutic method is limited to surgical resection with a high recurrence rate. MicroRNAs (miRNAs) are able to fine-tune large-scale target genes. Here we established a simple but effective computational strategy based on available miRNA target prediction algorithms to pinpoint the most potent miRNA that could negatively regulate a group of functional genes. Based on this rationale, miR-335-3p was top ranked by putatively targeting 85 verified profibrotic genes and 79 upregulated genes in HGF patients. Experimentally, downregulation of miR-355-3p was demonstrated in HGF-derived gingival fibroblasts as well as in transforming growth factor β–stimulated normal human gingival fibroblasts (NHGFs) compared to normal control. Ectopic miR-335-3p attenuated, whereas knockdown of miR-335-3p promoted, the fibrogenic activity of human gingival fibroblasts. Mechanically, miR-335-3p directly targeted SOS1, SMAD2/3, and CTNNB1 by canonical and noncanonical base paring. In particular, different portfolios of fibrotic markers were suppressed by silencing SOS1, SMAD2/3, or CTNNB1, respectively. Thus, our study first proposes a novel miRNA screening approach targeting a functionally related gene set and identifies miR-335-3p as a novel target for HGF treatment. Mechanically, miR-335-3p suppresses the fibrogenic activity of human gingival fibroblasts by repressing multiple core molecules in profibrotic networks. Our strategy provides a new paradigm in the treatment for HGF as well as other diseases.

DNA hypermethylation of aurora kinase A in hepatitis C virus‑positive hepatocellular carcinoma

Changes in the methylation levels of tumor suppressor genes or proto-oncogenes are involved in the pathogenesis of hepatitis C virus (HCV) infection-induced hepatocellular carcinoma (HCC). The aim of the present study was to identify novel aberrantly methylated differentially expressed genes by integrating mRNA expression profile (GSE19665 and GSE62232) and methylation profile (GSE60753) microarrays downloaded from the Gene Expression Omnibus database. Functional enrichment analysis of screened genes was performed using the DAVID software and BinGO database. Protein-protein interaction (PPI) networks were constructed using the STRING database, followed by module analysis with MCODE software. The transcriptional and translational expression levels of crucial genes were confirmed using The Cancer Genome Atlas (TCGA) datasets and Human Protein Atlas database (HPA). A total of 122 downregulated/hypermethylated genes and 63 upregulated/hypomethylated genes were identified. These genes were enriched in the Gene Ontology biological processes terms of ‘inflammatory response’ [Fos proto-oncogene, AP-1 transcription factor subunit (FOS)] and ‘cell cycle process’ [aurora kinase A (AURKA), cyclin dependent kinase inhibitor 3 (CDKN3) and ubiquitin conjugating enzyme E2 C (UBE2C)]. PPI network and module analysis indicated that human oncogenes FOS, AURKA, CDKN3 and UBE2C may be hub genes. mRNA, protein expression and methylation levels of AURKA and FOS were validated by TCGA and HPA data. In conclusion, aberrantly methylated AURKA and FOS may be potential therapeutic targets for HCV-positive HCC.

Transcriptome analysis identifies metallothionein as biomarkers to predict recurrence in hepatocellular cacinoma

BACKGROUND

Liver cancer is the fifth most common cancer, and hepatocellular carcinoma (HCC) is the major liver tumor type seen in adults. HCC is usually caused by chronic liver disease such as hepatitis B virus or hepatitis C virus infection. One of the promising treatments for HCC is liver transplantation, in which a diseased liver is replaced with a healthy liver from another person. However, recurrence of HCC after surgery is a significant problem. Therefore, it is important to discover reliable cellular biomarkers that can predict recurrence in HCC.

METHODS

We analyzed previously published HCC RNA-Seq data that includes 21 paired tumor and normal samples, in which nine tumors were recurrent after orthotopic liver transplantation and 12 were nonrecurrent tumors with their paired normal samples. We used both the reference genome and de novo transcriptome assembly based analyses to identify differentially expressed genes (DEG) and used RandomForest to discover biomarkers.

RESULTS

We obtained 398 DEG using the Reference approach and 412 DEG using de novo assembly approach. Among these DEG, 258 genes were identified by both approaches. We further identified 30 biomarkers that could predict the recurrence. We used another independent HCC study that includes 50 patients normal and tumor samples. By using these 30 biomarkers, the prediction accuracy was 100% for normal condition and 98% for tumor condition. A group of Metallothionein was specifically discovered as biomarkers in both reference and de novo assembly approaches.

CONCLUSION

We identified a group of Metallothionein genes as biomarkers to predict recurrence. The metallothionein genes were all down-regulated in tumor samples, suggesting that low metallothionein expression may be a promoter of tumor growth. In addition, using de novo assembly identified some unique biomarkers, further confirmed the necessity of conducting a de novo assembly in human cancer study.

The pan-cancer analysis of gain-of-functional mutations to identify the common oncogenic signatures in multiple cancers

Oncogenes can potentially cause uncontrolled cell growth, leading to cancer development, and these genes are normally mutated and over-expressed in tumor cells. Genomic alteration of oncogenes might result in oncogenesis and promotion of cancer progression. To date, researchers have focused mainly on the roles of oncogenes in particular cancers, but investigation of oncogenes with gain-of-function mutations in multiple cancer types are less represented in the literature. Furthermore, the effect of those gain-of-function are not validated in gene expression level. To meet this demand, we performed a systematic analysis of gene expression in oncogenes to identify the occurrence of gain-of-function mutations in pan-cancer. We identified 33,551 oncogenic mutations in 5000 samples. From our analysis, we identified three tissues with the highest frequency of gain-of-functional oncogenic mutations in hundreds of samples: breast (739 samples), central nervous system (646 samples) and large intestine (498 samples). By further counting the number of occurrences of oncogenes across cancer types, we identified a list cross-cancer mutational signatures of 99 oncogenes highly mutated in >400 samples in breast, central nervous system and large intestine samples. By further overlapping with gene expression data in the matched tumor samples, we further identified 1875 gain-of-functional mutations/events with consistent gene up-regulation in 1031 samples from multiple cancers. This result may offer additional insight into the relationship between gene dosage and oncogenesis and maybe useful in targeted cancer therapy. In summary, this study provides the first globally examining on the genetic alteration of oncogenes across cancer types. Clinical association analysis has shown that these 99 genes have a significant effect on survival.

Elucidating the transcriptional program of feline injection-site sarcoma using a cross-species mRNA-sequencing approach

BACKGROUND

Feline injection-site sarcoma (FISS), an aggressive iatrogenic subcutaneous malignancy, is challenging to manage clinically and little is known about the molecular basis of its pathogenesis. Tumor transcriptome profiling has proved valuable for gaining insights into the molecular basis of cancers and for identifying new therapeutic targets. Here, we report the first study of the FISS transcriptome and the first cross-species comparison of the FISS transcriptome with those of anatomically similar soft-tissue sarcomas in dogs and humans.

METHODS

Using high-throughput short-read paired-end sequencing, we comparatively profiled FISS tumors vs. normal tissue samples as well as cultured FISS-derived cell lines vs. skin-derived fibroblasts. We analyzed the mRNA-seq data to compare cancer/normal gene expression level, identify biological processes and molecular pathways that are associated with the pathogenesis of FISS, and identify multimegabase genomic regions of potential somatic copy number alteration (SCNA) in FISS. We additionally conducted cross-species analyses to compare the transcriptome of FISS to those of soft-tissue sarcomas in dogs and humans, at the level of cancer/normal gene expression ratios.

RESULTS

We found: (1) substantial differential expression biases in feline orthologs of human oncogenes and tumor suppressor genes suggesting conserved functions in FISS; (2) a genomic region with recurrent SCNA in human sarcomas that is syntenic to a feline genomic region of probable SCNA in FISS; and (3) significant overlap of the pattern of transcriptional alterations in FISS with the patterns of transcriptional alterations in soft-tissue sarcomas in humans and in dogs. We demonstrated that a protein, BarH-like homeobox 1 (BARX1), has increased expression in FISS cells at the protein level. We identified 11 drugs and four target proteins as potential new therapies for FISS, and validated that one of them (GSK-1059615) inhibits growth of FISS-derived cells in vitro.

CONCLUSIONS

(1) Window-based analysis of mRNA-seq data can uncover SCNAs. (2) The transcriptome of FISS-derived cells is highly consistent with that of FISS tumors. (3) FISS is highly similar to soft-tissue sarcomas in dogs and humans, at the level of gene expression. This work underscores the potential utility of comparative oncology in improving understanding and treatment of FISS.

Transposable elements drive widespread expression of oncogenes in human cancers

Transposable elements (TEs) are an abundant and rich genetic resource of regulatory sequences1-3. Cryptic regulatory elements within TEs can be epigenetically reactivated in cancer to influence oncogenesis in a process termed onco-exaptation4. However, the prevalence and impact of TE onco-exaptation events across cancer types are poorly characterized. Here, we analyzed 7,769 tumors and 625 normal datasets from 15 cancer types, identifying 129 TE cryptic promoter-activation events involving 106 oncogenes across 3,864 tumors. Furthermore, we interrogated the AluJb-LIN28B candidate: the genetic deletion of the TE eliminated oncogene expression, while dynamic DNA methylation modulated promoter activity, illustrating the necessity and sufficiency of a TE for oncogene activation. Collectively, our results characterize the global profile of TE onco-exaptation and highlight this prevalent phenomenon as an important mechanism for promiscuous oncogene activation and ultimately tumorigenesis.

Combination of 247 Genome-Wide Association Studies Reveals High Cancer Risk as a Result of Evolutionary Adaptation

Analysis of GLOBOCAN-2012 data shows clearly here that cancer incidence worldwide is highly related with low average annual temperatures and extreme low temperatures. This applies for all cancers together or separately for many frequent or rare cancer types (all cancers P = 9.49×10-18). Supporting fact is that Inuit people, living at extreme low temperatures, have the highest cancer rates today. Hypothesizing an evolutionary explanation, 240 cancer genome-wide association studies, and seven genome-wide association studies for cold and high-altitude adaptation were combined. A list of 1,377 cancer-associated genes was created to initially investigate whether cold selected genes are enriched with cancer-associated genes. Among Native Americans, Inuit and Eskimos, the highest association was observed for Native Americans (P = 6.7×10-5). An overall or a meta-analysis approach confirmed further this result. Similar approach for three populations living at extreme high altitude, revealed high association for Andeans-Tibetans (P = 1.3×10-11). Overall analysis or a meta-analysis was also significant. A separate analysis showed special selection for tumor suppressor genes. These results can be viewed along with those of previous functional studies that showed that reduced apoptosis potential due to specific p53 variants (the most important tumor suppressor gene) is beneficial in high-altitude and cold environments. In conclusion, this study shows that genetic variants selected for adaptation at extreme environmental conditions can increase cancer risk later on age. This is in accordance with antagonistic pleiotropy hypothesis.

Integrative analysis of copy number and gene expression data identifies potential oncogenic drivers that promote mammary tumor recurrence

Tumor recurrence represents a significant clinical challenge in the treatment and management of breast cancer. To investigate whether copy number aberrations (CNAs) facilitate the re-emergence of tumor growth from residual disease, we performed array comparative genomic hybridization on primary and recurrent mammary tumors from an inducible mouse model of type-I insulin-like growth factor receptor driven breast cancer. This genome-wide analysis revealed primary and recurrent tumors harbored distinct CNAs with relapsed tumors containing an increased number of gene-level gains and losses. Remarkably, high-level CNAs detected in primary tumors were largely devoid of annotated cancer genes while the vast majority of recurrent tumors harbored at least one CNA containing a known oncogene or tumor suppressor. Specifically, 38% of recurrent tumors carried gains at 6qA2 and 9qA2 which encode the Met and Yap1 oncogenes, respectively. The most frequent CNA, occurring in 63% of recurrent tumors, was a focal deletion at 4qC5 involving the Cdkn2a/b tumor suppressor genes. Integrative analysis revealed positive correlations between gene copy number and mRNA expression suggesting Met, Yap1, and Cdkn2a/b may serve as potential drivers that promote tumor recurrence. Accordingly, cross-species analysis revealed gene-level murine CNAs were present in a subset of human breast cancers with high MET and YAP1 mRNA predictive of decreased relapse-free survival in basal-like breast cancers. Together, these findings indicate that tumor recurrence is facilitated by the acquisition of CNAs with oncogenic potential and provide a framework to dissect the molecular mechanisms that mediate tumor escape from dormancy.

Integrative analysis of cancer driver genes in prostate adenocarcinoma

Large-scale genomics studies have identified recurrently mutated genes in the ETS gene family, including fusions and copy number variations (CNVs), which are involved in the development of prostate adenocarcinoma (PRAD). However, the aetiology of PRAD remains to be fully elucidated. In the present study, 333 driver genes were identified using four computational tools: OncodriveFM, OncodriveCLUST, iCAGES and DrGaP. In addition, 32 driver pathways were identified using DrGaP. SPOP, TP53, SPTA1, AHNAK, HMCN1, ATM, FOXA1, CSMD3, LRP1B and FREM2 were the 10 most recurrently mutated genes in PRAD. ITGAL, TAGAP, SIGLEC10, RAC2 and ITGA4 were the five hub genes in the yellow module that were associated with the number of positive lymph nodes. Hierarchical clustering analysis of the 20 driver genes with the most frequent CNVs revealed three clusters of patients with PRAD. Cluster 3 tumours exhibited significantly higher numbers of positive lymph nodes, higher Gleason scores, more advanced cancer stages and poorer prognosis than cluster 1 and 2 tumours. A total of 48 genes were significantly associated with the number of positive lymph nodes, Gleason scores and pathologic stage in patients with PRAD. The identified set of cancer genes and pathways sheds light on the tumorigenesis of PRAD and creates avenues for the development of prognostic biomarkers and driver gene-targeted therapies in PRAD.

Oncogenic Regulation of Extracellular Vesicle Proteome and Heterogeneity

Mutational and epigenetic driver events profoundly alter intercellular communication pathways in cancer. This effect includes deregulated release, molecular composition, and biological activity of extracellular vesicles (EVs), membranous cellular fragments ranging from a few microns to less than 100 nm in diameter and filled with bioactive molecular cargo (proteins, lipids, and nucleic acids). While EVs are usually classified on the basis of their physical properties and biogenetic mechanisms, recent analyses of their proteome suggest a larger than expected molecular diversity, a notion that is also supported by multicolour nano-flow cytometry and other emerging technology platforms designed to analyze single EVs. Both protein composition and EV diversity are markedly altered by oncogenic transformation, epithelial to mesenchymal transition, and differentiation of cancer stem cells. Interestingly, only a subset of EVs released from mutant cells may carry oncogenic proteins (e.g., EGFRvIII), hence, these EVs are often referred to as "oncosomes". Indeed, oncogenic transformation alters the repertoire of EV-associated proteins, increases the presence of pro-invasive cargo, and alters the composition of distinct EV populations. Molecular profiling of single EVs may reveal a more intricate effect of transforming events on the architecture of EV populations in cancer and shed new light on their biological role and diagnostic utility.