Large-scale Cancer Genomics Projects Research Articles

Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism

BackgroundCorrectly identifying the driver genes that promote cell growth can significantly assist drug design, cancer diagnosis and treatment. The recent large-scale cancer genomics projects have revealed multi-omics data from thousands of cancer patients, which requires to design effective models to unlock the hidden knowledge within the valuable data and discover cancer drivers contributing to tumorigenesis.ResultsIn this work, we propose a graph convolution network-based method called MRNGCN that integrates multiple gene relationship networks to identify cancer driver genes. First, we constructed three gene relationship networks, including the gene–gene, gene–outlying gene and gene–miRNA networks. Then, genes learnt feature presentations from the three networks through three sharing-parameter heterogeneous graph convolution network (HGCN) models with the self-attention mechanism. After that, these gene features pass a convolution layer to generate fused features. Finally, we utilized the fused features and the original feature to optimize the model by minimizing the node and link prediction losses. Meanwhile, we combined the fused features, the original features and the three features learned from every network through a logistic regression model to predict cancer driver genes.ConclusionsWe applied the MRNGCN to predict pan-cancer and cancer type-specific driver genes. Experimental results show that our model performs well in terms of the area under the ROC curve (AUC) and the area under the precision–recall curve (AUPRC) compared to state-of-the-art methods. Ablation experimental results show that our model successfully improved the cancer driver identification by integrating multiple gene relationship networks.

Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation.

Large-scale cancer genome projects have generated genomic, transcriptomic, epigenomic and clinicopathological data from thousands of samples in almost every human tumor site. Although most omics data and their associated resources are publicly available, its full integration and interpretation to dissect the sources of gene expression modulation require specialized knowledge and software. We present Multiomix, an interactive cloud-based platform that allows biologists to identify genetic and epigenetic events associated with the transcriptional modulation of cancer-related genes through the analysis of multi-omics data available on public functional genomic databases or user-uploaded datasets. Multiomix consists of an integrated set of functions, pipelines and a graphical user interface that allows retrieval, aggregation, analysis and visualization of different omics data sources. After the user provides the data to be analyzed, Multiomix identifies all significant correlations between mRNAs and non-mRNA genomics features (e.g. miRNA, DNA methylation and CNV) across the genome, the predicted sequence-based interactions (e.g. miRNA-mRNA) and their associated prognostic values. Multiomix is available at https://www.multiomix.org. The source code is freely available at https://github.com/omics-datascience/multiomix. Supplementary data are available at Bioinformatics online.

Functional impact of cancer patient-associated Bcl-xL mutations.

Bcl‐xL, an antiapoptotic protein, is frequently overexpressed in cancer to promote survival of tumor cells. However, we have previously shown that Bcl‐xL promotes migration, invasion, and metastasis independent of its antiapoptotic function in mitochondria. The pro‐metastatic function of Bcl‐xL may require its translocation into the nucleus. Besides overexpression, patient‐associated mutations of Bcl‐xL have been identified in large‐scale cancer genomics projects. Understanding the functions of these mutations will guide the development of precision medicine. Here, we selected four patient‐associated Bcl‐xL mutations, R132W, N136K, R165W, and A201T, to investigate their impacts on antiapoptosis, migration, and nuclear translocation. We found that all four mutation proteins could be detected in both the nucleus and cytosol. Although all four mutations disrupted the antiapoptosis function, one of these mutants, N136K, significantly improved the ability to promote cell migration. These data suggest the importance of developing novel Bcl‐xL inhibitors to ablate both antiapoptotic and pro‐metastatic functions of Bcl‐xL in cancer.

Computational approaches in cancer genomics

Large-scale cancer genome projects provide an extraordinary mine of molecular information on a vast range of cancer types and samples and offer the exciting potential of understanding the molecular mechanisms of cancer. Much knowledge is however still hidden in the data and this significantly reduces the effective contribution of cancer molecular profiling to the personalised medicine agenda. In my lecture I will review some of the technical, analytical and scientific challenges in cancer genomic data analysis. I will also provide examples of how this type of analysis can contribute effectively to unravel cancer driver mechanisms and potential targets for anti-cancer therapy.

Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data.

Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in evaluating the cancer progression. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of mutations across many patients. To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive property of driver mutations within a pathway and the property of linear progression between pathways. To assess the robustness of NetInf, we apply it on simulated data with the addition of different levels of noise. To verify the performance of NetInf, we apply it to analyze somatic mutation data from three real cancer studies with large number of samples. Experimental results reveal that the pathways detected by NetInf show significant enrichment. Our method reduces computational complexity by constructing gene networks without assigning the number of pathways, which also provides new insights on the temporal order of somatic mutations at the pathway level rather than at the gene level.

Genomics of Cancer and a New Era for Cancer Prevention.

A primary justification for dedicating substantial amounts of research funding to large-scale cancer genomics projects of both somatic and germline DNA is that the biological insights will lead to new treatment targets and strategies for cancer therapy. While it is too early to judge the success of these projects in terms of clinical breakthroughs, an alternative rationale is that new genomics techniques can be used to reduce the overall burden of cancer by prevention of new cases occurring and also by detecting them earlier. In particular, it is now becoming apparent that studying the genomic profile of tumors can help to identify new carcinogens and may subsequently result in implementing strategies that limit exposure. In parallel, it may be feasible to utilize genomic biomarkers to identify cancers at an earlier and more treatable stage using screening or other early detection approaches based on prediagnostic biospecimens. While the potential for these techniques is large, their successful outcome will depend on international collaboration and planning similar to that of recent sequencing initiatives.

Inference of cancer progression from somatic mutation data

Large-scale cancer genomics projects are providing a wealth of somatic mutation data. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of somatic mutations. In the paper, we present a network-based method (NetInf) to infer cancer progression at the pathway level. We apply it to analyze somatic mutation data from real cancer studies. Experimental results show that these detected pathways overlap with known pathways, including RB, P53 signaling pathways. Our method reduces computational complexity and also provides new insights on the temporalorder of somatic mutations at the pathway level.

Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools.

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.

Trans-ancestry mutational landscape of hepatocellular carcinoma genomes

Diverse epidemiological factors are associated with hepatocellular carcinoma (HCC) prevalence in different populations. However, the global landscape of the genetic changes in HCC genomes underpinning different epidemiological and ancestral backgrounds still remains uncharted. Here a collection of data from 503 liver cancer genomes from different populations uncovered 30 candidate driver genes and 11 core pathway modules. Furthermore, a collaboration of two large-scale cancer genome projects comparatively analyzed the trans-ancestry substitution signatures in 608 liver cancer cases and identified unique mutational signatures that predominantly contribute to Asian cases. This work elucidates previously unexplored ancestry-associated mutational processes in HCC development. A combination of hotspot TERT promoter mutation, TERT focal amplification and viral genome integration occurs in more than 68% of cases, implicating TERT as a central and ancestry-independent node of hepatocarcinogenesis. Newly identified alterations in genes encoding metabolic enzymes, chromatin remodelers and a high proportion of mTOR pathway activations offer potential therapeutic and diagnostic opportunities.

Simulated annealing based algorithm for identifying mutated driver pathways in cancer.

With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random “passenger mutations.” In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.

Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data

MotivationUnderstanding the molecular mechanisms underlying cancer is an important step for the effective diagnosis and treatment of cancer patients. With the huge volume of data from the large-scale cancer genomics projects, an open challenge is to distinguish driver mutations, pathways, and gene sets (or core modules) that contribute to cancer formation and progression from random passengers which accumulate in somatic cells but do not contribute to tumorigenesis. Due to mutational heterogeneity, current analyses are often restricted to known pathways and functional modules for enrichment of somatic mutations. Therefore, discovery of new pathways and functional modules is a pressing need.ResultsIn this study, we propose a novel method to identify Mutated Core Modules in Cancer (iMCMC) without any prior information other than cancer genomic data from patients with tumors. This is a network-based approach in which three kinds of data are integrated: somatic mutations, copy number variations (CNVs), and gene expressions. Firstly, the first two datasets are merged to obtain a mutation matrix, based on which a weighted mutation network is constructed where the vertex weight corresponds to gene coverage and the edge weight corresponds to the mutual exclusivity between gene pairs. Similarly, a weighted expression network is generated from the expression matrix where the vertex and edge weights correspond to the influence of a gene mutation on other genes and the Pearson correlation of gene mutation-correlated expressions, respectively. Then an integrative network is obtained by further combining these two networks, and the most coherent subnetworks are identified by using an optimization model. Finally, we obtained the core modules for tumors by filtering with significance and exclusivity tests. We applied iMCMC to the Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and ovarian carcinoma data, and identified several mutated core modules, some of which are involved in known pathways. Most of the implicated genes are oncogenes or tumor suppressors previously reported to be related to carcinogenesis. As a comparison, we also performed iMCMC on two of the three kinds of data, i.e., the datasets combining somatic mutations with CNVs and secondly the datasets combining somatic mutations with gene expressions. The results indicate that gene expressions or CNVs indeed provide extra useful information to the original data for the identification of core modules in cancer.ConclusionsThis study demonstrates the utility of our iMCMC by integrating multiple data sources to identify mutated core modules in cancer. In addition to presenting a generally applicable methodology, our findings provide several candidate pathways or core modules recurrently perturbed in GBM or ovarian carcinoma for further studies.

Efficient methods for identifying mutated driver pathways in cancer

The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones. In this study, we propose two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer. The first one is an exact method that can be helpful for assessing other approximate or/and heuristic algorithms. The second one is a stochastic and flexible method that can be employed to incorporate other types of information to improve the first method. Particularly, we propose an integrative model to combine mutation and expression data. We first apply our methods onto simulated data to show their efficiency. We further apply the proposed methods onto several real biological datasets, such as the mutation profiles of 74 head and neck squamous cell carcinomas samples, 90 glioblastoma tumor samples and 313 ovarian carcinoma samples. The gene expression profiles were also considered for the later two data. The results show that our integrative model can identify more biologically relevant gene sets. We have implemented all these methods and made a package called mutated driver pathway finder, which can be easily used for other researchers. A MATLAB package of MDPFinder is available at http://zhangroup.aporc.org/ShiHuaZhang. zsh@amss.ac.cn. Supplementary data are available at Bioinformatics online.

The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data

The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.

Integrative Subtype Discovery in Glioblastoma Using iCluster

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

A DNA transposon-based approach to validate oncogenic mutations in the mouse

Large-scale cancer genome projects will soon be able to sequence many cancer genomes to comprehensively identify genetic changes in human cancer. Genome-wide association studies have also identified putative cancer associated loci. Functional validation of these genetic mutations in vivo is becoming a challenge. We describe here a DNA transposon-based platform that permits us to explore the oncogenic potential of genetic mutations in the mouse. Briefly, promoter-less human cancer gene cDNAs were first cloned into Sleeping Beauty (SB) transposons. DNA transposition in the mouse that carried both the transposons and the SB transposase made it possible for the cDNAs to be expressed from an appropriate endogenous genomic locus and in the relevant cell types for tumor development. Consequently, these mice developed a broad spectrum of tumors at very early postnatal stages. This technology thus complements the large-scale cancer genome projects.

Computational Approaches for Predicting Causal Missense Mutations in Cancer Genome Projects

A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. Although missense variants are commonly identified in genomic sequence, only a small fraction directly contributes to oncogenesis. The ability to distinguish those somatic missense changes that contribute to cancer progression from those that do not is a difficult problem usually accomplished through functional in vivo analyses. With the advent of several largescale cancer genome projects geared toward identifying mutations that are causally implicated in cancer, it is becoming increasingly important to develop methods for distinguishing functionally relevant mutations from those passenger mutations and other innocuous polymorphisms. Here we review two general strategies that are based on either mutation frequency data or the nature of amino acid substitutions. Frequency-based methods are commonly used for estimating the enrichment of causal mutations and for identifying specific mutations under positive selection pressure. The statistical power of these methods is dependent on the number of cancer samples being surveyed. The potential functional consequences of missense mutations can also be examined by bioinformatics approaches since multiple computational methods have been developed to estimate the deleterious effect of amino acid substitutions. It is likely that many of the existing methods can potentially be applied to large-scale cancer genome data to detect relevant causal mutations regardless of their prevalence. Future data analysis of missense somatic mutations will likely benefit from continual development of integrated and automated methods for combining all available information to predict whether a particular mutation is causally implicated.