A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

Clonal Hematopoiesis of Indeterminate Potential and Kidney Function Decline in the General Population

RATIONALE & OBJECTIVE

Clonal hematopoiesis of indeterminate potential (CHIP), defined by the age-related ontogenesis of expanded leukemogenic variants indicative of a genetically distinct clonal leukocyte population, is associated with risk of hematologic malignancy and cardiovascular disease. In experimental models, recapitulation of CHIP promotes kidney interstitial fibrosis with direct tissue infiltration of donor macrophages. We tested the hypothesis that CHIP is associated with kidney function decline in the general population.

STUDY DESIGN

Cohort study.

SETTING & PARTICIPANTS

12,004 individuals from 3 community-based cohorts in the TOPMed Consortium.

EXPOSURE

CHIP status from whole-genome sequences obtained from DNA extracted from peripheral blood.

OUTCOME

Risk of 30% decline in estimated glomerular filtration rate (eGFR) and percent eGFR decline per year during the follow-up period.

ANALYTICAL APPROACH

Cox proportional hazards models for 30% eGFR decline end point and generalized estimating equations for annualized relative change in eGFR with meta-analysis. Study-specific estimates were combined using fixed-effect meta-analysis.

RESULTS

The median baseline eGFR was 84mL/min/1.73m2. The prevalence of CHIP was 6.6%, 9.0%, and 12.2% in persons aged 50-60, 60-70, and>70 years, respectively. Over a median follow-up period of 8 years, for the 30% eGFR outcome 205 events occurred among 1,002 CHIP carriers (2.1 events per 100 person-years) and 2,041 events in persons without CHIP (1.7 events per 100 person-years). In meta-analysis, CHIP was associated with greater risk of a 30% eGFR decline (17% [95% CI, 1%-36%] higher; P=0.04). Differences were not observed between those with baseline eGFR above or below 60mL/min/1.73m2, of age above or below 60 years, or with or without diabetes.

LIMITATIONS

Small number of participants with moderate-to-advanced kidney disease and restricted set of CHIP driver variants.

CONCLUSIONS

We report an association between CHIP and eGFR decline in 3 general population cohorts without known kidney disease. Further studies are needed to investigate this novel condition and its potential impact among individuals with overt kidney disease.

MiRNA-gene network embedding for predicting cancer driver genes

The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene-gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene-miRNA network. First, we constructed a gene-miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.

BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration

The discovery of cancer drivers and drug targets are often limited to the biological systems - from cancer model systems to patients. While multiomic patient databases have sparse drug response data, cancer model systems databases, despite covering a broad range of pharmacogenomic platforms, provide lower lineage-specific sample sizes, resulting in reduced statistical power to detect both functional driver genes and their associations with drug sensitivity profiles. Hence, integrating evidence across model systems, taking into account the pros and cons of each system, in addition to multiomic integration, can more efficiently deconvolve cellular mechanisms of cancer as well as learn therapeutic associations. To this end, we propose BaySyn - a hierarchical Bayesian evidence synthesis framework for multi-system multiomic integration. BaySyn detects functionally relevant driver genes based on their associations with upstream regulators using additive Gaussian process models and uses this evidence to calibrate Bayesian variable selection models in the (drug) outcome layer. We apply BaySyn to multiomic cancer cell line and patient datasets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas, respectively, across pan-gynecological cancers. Our mechanistic models implicate several relevant functional genes across cancers such as PTPN6 and ERBB2 in the KEGG adherens junction gene set. Furthermore, our outcome model is able to make higher number of discoveries in drug response models than its uncalibrated counterparts under the same thresholds of Type I error control, including detection of known lineage-specific biomarker associations such as BCL11A in breast and FGFRL1 in ovarian cancers. All our results and implementation codes are freely available via an interactive R Shiny dashboard at tinyurl.com/BaySynApp. The supplementary materials are available online at tinyurl.com/BaySynSup.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Pan-cancer analysis of non-coding transcripts reveals the prognostic onco-lncRNA HOXA10-AS in gliomas

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer and powerful biomarkers; however, most remain uncharacterized. Here, we analyze 5,592 prognostic lncRNAs in 9,446 cancers of 30 types using machine learning. We identify 166 lncRNAs whose expression correlates with survival and improves the accuracy of common clinical variables, molecular features, and cancer subtypes. Prognostic lncRNAs are often characterized by switch-like expression patterns. In low-grade gliomas, HOXA10-AS activation is a robust marker of poor prognosis that complements IDH1/2 mutations, as validated in another retrospective cohort, and correlates with developmental pathways in tumor transcriptomes. Loss- and gain-of-function studies in patient-derived glioma cells, organoids, and xenograft models identify HOXA10-AS as a potent onco-lncRNA that regulates cell proliferation, contact inhibition, invasion, Hippo signaling, and mitotic and neuro-developmental pathways. Our study underscores the pan-cancer potential of the non-coding transcriptome for identifying biomarkers and regulators of cancer progression.

Improving cancer driver gene identification using multi-task learning on graph convolutional network

Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.

A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.

Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.

Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.

Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis

Cancer genome sequencing studies have identified driver genes for a variety of different cancers and helped to understand the genetic landscape of human cancer. It is still challenging, however, to identify cancer driver genes with confidence simply from genetic data alone. In vivo forward genetic screens using Sleeping Beauty (SB) transposon mutagenesis provides another powerful genetic tool for identifying candidate cancer driver genes in wild-type and sensitized mouse tumors. By comparing cancer driver genes identified in human and mouse tumors, cancer driver genes can be identified with additional confidence based upon comparative oncogenomics. This review describes how SB mutagenesis works in mice and focuses on studies that have identified cancer driver genes in the mouse gastrointestinal tract.

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes

Exitron splicing (EIS) creates a cryptic intron (called an exitron) within a protein-coding exon to increase proteome diversity. EIS is poorly characterized, but emerging evidence suggests a role for EIS in cancer. Through a systematic investigation of EIS across 33 cancers from 9,599 tumor transcriptomes, we discovered that EIS affected 63% of human coding genes and that 95% of those events were tumor specific. Notably, we observed a mutually exclusive pattern between EIS and somatic mutations in their affected genes. Functionally, we discovered that EIS altered known and novel cancer driver genes for causing gain- or loss-of-function, which promotes tumor progression. Importantly, we identified EIS-derived neoepitopes that bind to major histocompatibility complex (MHC) class I or II. Analysis of clinical data from a clear cell renal cell carcinoma cohort revealed an association between EIS-derived neoantigen load and checkpoint inhibitor response. Our findings establish the importance of considering EIS alterations when nominating cancer driver events and neoantigens.

Genomics-Guided Drawing of Molecular and Pathophysiological Components of Malignant Regulatory Signatures Reveals a Pivotal Role in Human Diseases of Stem Cell-Associated Retroviral Sequences and Functionally-Active hESC Enhancers

Repetitive DNA sequences (repeats) colonized two-third of human genome and a majority of repeats comprised of transposable genetic elements (TE). Evolutionary distinct categories of TE represent nucleic acid sequences that are repeatedly copied from and pasted into chromosomes at multiple genomic locations and acquired a multitude of regulatory functions. Here, genomics-guided maps of stemness regulatory signatures were drawn to dissect the contribution of TE to clinical manifestations of malignant phenotypes of human cancers. From patients’ and physicians’ perspectives, the clinical definition of a tumor’s malignant phenotype could be restricted to the early diagnosis of sub-types of malignancies with the increased risk of existing therapy failure and high likelihood of death from cancer. It is the viewpoint from which the understanding of stemness and malignant regulatory signatures is considered in this contribution. Genomics-guided analyses of experimental and clinical observations revealed the pivotal role of human stem cell-associated retroviral sequences (SCARS) in the origin and pathophysiology of clinically-lethal malignancies. SCARS were defined as the evolutionary- and biologically-related family of genomic regulatory sequences, the principal physiological function of which is to create and maintain the stemness phenotype during human preimplantation embryogenesis. For cell differentiation to occur, SCARS expression must be silenced and SCARS activity remains repressed in most terminally-differentiated human cells which are destined to perform specialized functions in the human body. Epigenetic reprogramming, de-repression, and sustained activity of SCARS results in various differentiation-defective phenotypes. One of the most prominent tissue- and organ-specific clinical manifestations of sustained SCARS activities is diagnosed as a pathological condition defined by a consensus of morphological, molecular, and genetic examinations as the malignant growth. Here, contemporary evidence are acquired, analyzed, and reported defining both novel diagnostic tools and druggable molecular targets readily amenable for diagnosis and efficient therapeutic management of clinically-lethal malignancies. These diagnostic and therapeutic approaches are based on monitoring of high-fidelity molecular signals of continuing SCARS activities in conjunction with genomic regulatory networks of thousands’ functionally-active embryonic enhancers affecting down-stream phenotype-altering genetic loci. Collectively, reported herein observations support a model of SCARS-activation triggered singular source code facilitating the intracellular propagation and intercellular (systemic) dissemination of disease states in the human body.

Aberrations of Chromosomes 1 and 16 in Breast Cancer: A Framework for Cooperation of Transcriptionally Dysregulated Genes

Derivative chromosome der(1;16), isochromosome 1q, and deleted 16q-producing arm-level 1q-gain and/or 16q-loss-are recurrent cytogenetic abnormalities in breast cancer, but their exact role in determining the malignant phenotype is still largely unknown. We exploited The Cancer Genome Atlas (TCGA) data to generate and analyze groups of breast invasive carcinomas, called 1,16-chromogroups, that are characterized by a pattern of arm-level somatic copy number aberrations congruent with known cytogenetic aberrations of chromosome 1 and 16. Substantial differences were found among 1,16-chromogroups in terms of other chromosomal aberrations, aneuploidy scores, transcriptomic data, single-point mutations, histotypes, and molecular subtypes. Breast cancers with a co-occurrence of 1q-gain and 16q-loss can be distinguished in a "low aneuploidy score" group, congruent to der(1;16), and a "high aneuploidy score" group, congruent to the co-occurrence of isochromosome 1q and deleted 16q. Another three groups are formed by cancers showing separately 1q-gain or 16q-loss or no aberrations of 1q and 16q. Transcriptome comparisons among the 1,16-chromogroups, integrated with functional pathway analysis, suggested the cooperation of overexpressed 1q genes and underexpressed 16q genes in the genesis of both ductal and lobular carcinomas, thus highlighting the putative role of genes encoding gamma-secretase subunits (APH1A, PSEN2, and NCSTN) and Wnt enhanceosome components (BCL9 and PYGO2) in 1q, and the glycoprotein E-cadherin (CDH1), the E3 ubiquitin-protein ligase WWP2, the deubiquitinating enzyme CYLD, and the transcription factor CBFB in 16q. The analysis of 1,16-chromogroups is a strategy with far-reaching implications for the selection of cancer cell models and novel experimental therapies.

Genomic analysis of low-grade serous ovarian carcinoma to identify key drivers and therapeutic vulnerabilities

Low-grade serous ovarian carcinoma (LGSOC) is associated with a poor response to existing chemotherapy, highlighting the need to perform comprehensive genomic analysis and identify new therapeutic vulnerabilities. The data presented here represent the largest genetic study of LGSOCs to date (n = 71), analysing 127 candidate genes derived from whole exome sequencing cohorts to generate mutation and copy-number variation data. Additionally, immunohistochemistry was performed on our LGSOC cohort assessing oestrogen receptor, progesterone receptor, TP53, and CDKN2A status. Targeted sequencing identified 47% of cases with mutations in key RAS/RAF pathway genes (KRAS, BRAF, and NRAS), as well as mutations in putative novel driver genes including USP9X (27%), MACF1 (11%), ARID1A (9%), NF2 (4%), DOT1L (6%), and ASH1L (4%). Immunohistochemistry evaluation revealed frequent oestrogen/progesterone receptor positivity (85%), along with CDKN2A protein loss (10%) and CDKN2A protein overexpression (6%), which were linked to shorter disease outcomes. Indeed, 90% of LGSOC samples harboured at least one potentially actionable alteration, which in 19/71 (27%) cases were predictive of clinical benefit from a standard treatment, either in another cancer's indication or in LGSOC specifically. In addition, we validated ubiquitin-specific protease 9X (USP9X), which is a chromosome X-linked substrate-specific deubiquitinase and tumour suppressor, as a relevant therapeutic target for LGSOC. Our comprehensive genomic study highlighted that there is an addiction to a limited number of unique 'driver' aberrations that could be translated into improved therapeutic paths. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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

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

Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers

Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer-colorectal, lung, ovary, prostate, pancreas, and melanoma-by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations.

The long noncoding RNA linc-NeD125 controls the expression of medulloblastoma driver genes by microRNA sponge activity

Long noncoding RNAs (lncRNAs) are major regulators of physiological and disease-related gene expression, particularly in the central nervous system. Dysregulated lncRNA expression has been documented in several human cancers, and their tissue-specificity makes them attractive candidates as diagnostic/prognostic biomarkers and/or therapeutic agents. Here we show that linc-NeD125, which we previously characterized as a neuronal-induced lncRNA, is significantly overexpressed in Group 4 medulloblastomas (G4 MBs), the largest and least well characterized molecular MB subgroup. Mechanistically, linc-NeD125 is able to recruit the miRNA-induced silencing complex (miRISC) and to directly bind the microRNAs miR-19a-3p, miR-19b-3p and miR-106a-5p. Functionally, linc-NeD125 acts as a competing endogenous RNA (ceRNA) that, sequestering the three miRNAs, leads to de-repression of their targets CDK6, MYCN, SNCAIP, and KDM6A, which are major driver genes of G4 MB. Accordingly, linc-NeD125 downregulation reduces G4 cell proliferation. Moreover, we also provide evidence that linc-NeD125 ectopic expression in the aggressive Group 3 MB cells attenuates their proliferation, migration and invasion.This study unveils the first lncRNA-based ceRNA network in central nervous system tumours and provides a novel molecular circuit underlying the enigmatic Group 4 medulloblastoma.

Interaction analysis between germline susceptibility loci and somatic alterations in lung cancer

Emerging evidence indicates that germline variations may interact with somatic events in carcinogenesis. However, the germline-somatic interaction in lung cancer remains largely unknown. We investigated whether lung cancer driver genes (CDGs) were more likely to locate within cancer susceptibility regions. Pathway analysis was performed to identify common pathways underlying CDGs and cancer susceptibility genes (CSGs). Next, we analyzed the associations between lung cancer risk SNPs and somatic alterations, including mutations and copy number alterations, in the level of genes, pathways, and overall burden of alterations. Enrichment analysis showed that lung CDGs are more likely to locate within cancer susceptibility regions (p = 8.40 × 10^-3 ). Both of lung CSGs and CDGs showed significant enrichment in pathways such as cell cycle and p53 signaling pathway. Gene-based analysis showed that rs36600 (22q12.2) was associated with somatic mutations within ARID1A (OR = 2.45, 95%CI: 1.47-4.08, p = 5.78 × 10^-4 ). Pathway-based analysis of somatic truncation mutations identified rs2395185 and rs3817963 at 6p22.1 was associated with cell cycle pathway (OR = 1.56, p = 3.61 × 10^-4 for rs2395185; OR = 1.58, p = 4.15 × 10^-4 for rs3817963), and rs3817963 was also associated with MAPK signaling pathway (OR = 1.54, p = 8.58 × 10^-4 ). Further analysis associated rs2395185 at 6p22.1 (HLA class II genes) with increased APOBEC3A expression (p = 9.50 × 10^-3 ) and elevated APOBEC mutagenesis (p = 3.58 × 10^-3 ). These results indicate germline-somatic interactions in lung tumorigenesis, and help to uncover the molecular mechanisms underlying lung cancer risk SNPs.

Exome and deep sequencing of clinically aggressive neuroblastoma reveal somatic mutations that affect key pathways involved in cancer progression

The spectrum of somatic mutation of the most aggressive forms of neuroblastoma is not completely determined. We sought to identify potential cancer drivers in clinically aggressive neuroblastoma.

Whole exome sequencing was conducted on 17 germline and tumor DNA samples from high-risk patients with adverse events within 36 months from diagnosis (HR-Event3) to identify somatic mutations and deep targeted sequencing of 134 genes selected from the initial screening in additional 48 germline and tumor pairs (62.5% HR-Event3 and high-risk patients), 17 HR-Event3 tumors and 17 human-derived neuroblastoma cell lines.

We revealed 22 significantly mutated genes, many of which implicated in cancer progression. Fifteen genes (68.2%) were highly expressed in neuroblastoma supporting their involvement in the disease. CHD9, a cancer driver gene, was the most significantly altered (4.0% of cases) after ALK.

Other genes (PTK2, NAV3, NAV1, FZD1 and ATRX), expressed in neuroblastoma and involved in cell invasion and migration were mutated at frequency ranged from 4% to 2%.

Focal adhesion and regulation of actin cytoskeleton pathways, were frequently disrupted (14.1% of cases) thus suggesting potential novel therapeutic strategies to prevent disease progression.

Notably BARD1, CHEK2 and AXIN2 were enriched in rare, potentially pathogenic, germline variants.

In summary, whole exome and deep targeted sequencing identified novel cancer genes of clinically aggressive neuroblastoma. Our analyses show pathway-level implications of infrequently mutated genes in leading neuroblastoma progression.

Network-Based Coverage of Mutational Profiles Reveals Cancer Genes

A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., "cover") a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers.

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types.

Translating the molecular diversity of hepatocellular carcinoma into clinical practice

Deciphering genomic diversity could improve clinical care for patients with hepatocellular carcinoma. Recently, our study group identified 161 putative driver genes and 2 new mutational signatures, and demonstrated that 28% of patients harbor targetable alterations. This could be the first promising step in the development of genome-based clinical trials.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Using common variants to indicate cancer genes

The catalogue of tumour-specific somatic mutations (SMs) is growing rapidly owing to the advent of next-generation sequencing. Identifying those mutations responsible for the development and progression of the disease, so-called driver mutations, will increase our understanding of carcinogenesis and provide candidates for targeted therapeutics. The phenotypic consequence(s) of driver mutations cause them to be selected for within the tumour environment, such that many approaches aimed at distinguishing drivers are based on finding significantly somatically mutated genes. Currently, these methods are designed to analyse, or be specifically applied to, nonsynonymous mutations: those that alter an encoded protein. However, growing evidence suggests the involvement of noncoding transcripts in carcinogenesis, mutations in which may also be disease-driving. We wished to test the hypothesis that common DNA variation rates within humans can be used as a baseline from which to score the rate of SMs, irrespective of coding capacity. We preliminarily tested this by applying it to a dataset of 159,498 SMs and using the results to rank genes. This resulted in significant enrichment of known cancer genes, indicating that the approach has merit. As additional data from cancer sequencing studies are made publicly available, this approach can be refined and applied to specific cancer subtypes. We named this preliminary version of our approach PRISMAD (polymorphism rates indicate somatic mutations as drivers) and have made it publicly accessible, with scripts, via a link at www.precancer.leeds.ac.uk/software-and-datasets.

Integrative genomic identification of genes on 8p associated with hepatocellular carcinoma progression and patient survival

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is an aggressive malignancy; its mechanisms of development and progression are poorly understood. We used an integrative approach to identify HCC driver genes, defined as genes whose copy numbers associate with gene expression and cancer progression.

METHODS

We combined data from high-resolution, array-based comparative genomic hybridization and transcriptome analysis of HCC samples from 76 patients with hepatitis B virus infection with data on patient survival times. Candidate genes were functionally validated using in vitro and in vivo models.

RESULTS

Unsupervised analyses of array comparative genomic hybridization data associated loss of chromosome 8p with poor outcome (reduced survival time); somatic copy number alterations correlated with expression of 27.3% of genes analyzed. We associated expression levels of 10 of these genes with patient survival times in 2 independent cohorts (comprising 319 cases of HCC with mixed etiology) and 3 breast cancer cohorts (637 cases). Among the 10-gene signature, a cluster of 6 genes on 8p, (DLC1, CCDC25, ELP3, PROSC, SH2D4A, and SORBS3) were deleted in HCCs from patients with poor outcomes. In vitro and in vivo analyses indicated that the products of PROSC, SH2D4A, and SORBS3 have tumor-suppressive activities, along with the known tumor suppressor gene DLC1.

CONCLUSIONS

We used an unbiased approach to identify 10 genes associated with HCC progression. These might be used in assisting diagnosis and to stage tumors based on gene expression patterns.