Edwin Wang | National Research Council Canada (original) (raw)

Papers by Edwin Wang

Mutated genes are rarely common even in the same pathological type between cancer patients and as... more Mutated genes are rarely common even in the same pathological type between cancer patients and as such, it has been very challenging to interpret genome sequencing data and difficult to predict clinical outcomes. PIK3CA is one of a few genes whose mutations are relatively popular in tumors. For example, more than 46.6% of luminal-A breast cancer samples have PIK3CA mutated, whereas only 35.5% of all breast cancer samples contain PIK3CA mutations. To understand the function of PIK3CA mutations in luminal A breast cancer, we applied our recently-proposed Cancer Hallmark Network Framework to investigate the network motifs in the PIK3CA-mutated luminal A tumors. We found that more than 70% of the PIK3CA-mutated

Network operational signature Drug resistance Personalized medicine a b s t r a c t Tumor genome ... more Network operational signature Drug resistance Personalized medicine a b s t r a c t Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents.

Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy nu... more Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations), making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival) onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.

Nature Communications, 2010

Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. ... more Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifies prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identified prognostic gene signature sets for both estrogen receptor (ER) negative (−) and positive (+) subtypes. A combinatorial use of the signatures allowed the stratification of patients into low-, intermediate-and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87-100%. Integrative network analysis identified modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis.

We have developed a Multiple Survival Screening algorithm (MSS) for identifying high-quality canc... more We have developed a Multiple Survival Screening algorithm (MSS) for identifying high-quality cancer prognostic markers from the gene expression profiles of cancer samples. By applying the MSS algorithm to breast cancer samples, we have identified several marker sets which showed~90% predicting accuracy across 8 independent breast cancer cohorts. We realized that the algorithm could be used for finding other biomarkers including drug response markers. We are describing the protocol with some comments based on our experience in using the algorithm.

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often t... more A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopula... more Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones.

Molecular Systems Biology, 2007

We conducted a comprehensive analysis of a manually curated human signaling network containing 16... more We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancersignaling architectural and functional organization. From the network, we extracted an oncogenesignaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogenesignaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancerassociated genes and biomarkers.

Mutations or overexpression of signalling genes can result in cancer development and metastasis. ... more Mutations or overexpression of signalling genes can result in cancer development and metastasis. In this study, we manually assembled a human cellular signalling network and developed a robust bioinformatics strategy for extracting cancer-associated single nucleotide polymorphisms (SNPs) using expressed sequence tags (ESTs). We then investigated the relationships of cancer-associated genes [cancer-associated SNP genes, known as cancer genes (CG) and cell mobility genes (CMGs)] in a signalling network context. Through a graph-theory-based analysis, we found that CGs are significantly enriched in network hub proteins and cancer-associated genes are significantly enriched or depleted in some particular network motif types. Furthermore, we identified a substantial number of hotspots, the three-and four-node network motifs in which all nodes are either CGs or CMGs. More importantly, we uncovered that CGs are enriched in the convergent target nodes of most network motifs, although CMGs are enriched in the source nodes of most motifs. These results have implications for the foundations of the regulatory mechanisms of cancer development and metastasis.

When an accident occurs on a busy road during rush hour in a big city, such as Montreal or New Yo... more When an accident occurs on a busy road during rush hour in a big city, such as Montreal or New York, traffic is blocked for a short time. Soon, however, drivers begin to turn around and use alternative roads to reach their destinations. A road map of a city is a web, a collection of intertwined roads that allows for identification of alternative routes. Increasing

and sharing with colleagues.

Genomic alterations lead to cancer complexity and form a major hurdle for a comprehensive underst... more Genomic alterations lead to cancer complexity and form a major hurdle for a comprehensive understanding of the molecular mechanisms underlying oncogenesis. In this review, we describe the recent advances in studying cancer-associated genes from a systems biological point of view. The integration of known cancer genes onto protein and signaling networks reveals the characteristics of cancer genes within networks. This approach shows that cancer genes often function as network hub proteins which are involved in many cellular processes and form focal nodes in the information exchange between many signaling pathways. Literature mining allows constructing gene-gene networks, in which new cancer genes can be identified. The gene expression profiles of cancer cells are used for reconstructing gene regulatory networks. By doing so, the genes, which are involved in the regulation of cancer progression, can be picked up from these networks after which their functions can be further confirmed in the laboratory.

With the advent of high-throughput biology, we now routinely scan cells and organisms at practica... more With the advent of high-throughput biology, we now routinely scan cells and organisms at practically all levels, from genome to protein, metabolism, signaling and other cellular functions. This methodology allowed biological studies to move from a reductionist approach, such as isolation of specific pathways and mechanisms, to a more integrative approach, where biological systems are seen as a network of interconnected components that provide specific outputs and functions in response to stimuli. Recent literature on biological networks demonstrates two important concepts that we will consider in this review: (i) cellular pathways are highly interconnected and should not be studied separately, but as a network; (ii) simple, recurrent feedback motifs within the network can produce very specific functions that favor their modular use. The first theme differs from the traditional approach in biology because it provides a framework (i.e., the network view) in which large datasets are analyzed with an unbiased view. The second theme (feedback motifs) shows the importance of locally analyzing the dynamic properties of biological networks in order to better understand their functionality. We will review these themes with examples from cell signaling networks, gene regulatory networks and metabolic pathways. The deregulation of cellular networks (metabolism, signaling etc.) is involved in cancer, but the size of the networks and resulting non-linear behavior do not allow for intuitive reasoning. In that context, we argue that the qualitative classification of the 'building blocs' of biological networks (i.e. the motifs) in terms of dynamics and functionality will be critical to improve our understanding of cancer biology and rationalize the wealth of information from high-throughput experiments. From the examples highlighted in this review, it is clear that dynamic feedback motifs can be used to provide a unified view of various cellular processes involved in cancer and this will be critical for future research on personalized and predictive cancer therapies.

Molecular Biosystems, 2009

We performed a comprehensive analysis of a literature-mined human signaling network by integratin... more We performed a comprehensive analysis of a literature-mined human signaling network by integrating data on ubiquitin-mediated protein half-lives. We found that proteins with very long half-lives are connected to form a network backbone, while proteins with short and medium half-lives preferentially attach to the network backbone and scatter throughout the network. Furthermore, proteins with short and medium half-lives are mutually exclusive in network neighbors. Short half-life proteins are enriched in the upstream portion of the network, suggesting that ubiquitination might help initiate signal processing and specificity. We also discovered that ubiquitination preferentially occurs in positive regulatory loops. Furthermore, these loops predominately induce or positively regulate apoptosis, a major component in cancer signaling. These results lead us to discover that the highly expressed genes involved in the common machinery of ubiquitination, the 26S proteasome genes, are significantly correlated with tumor progression and metastasis. Furthermore, expression of the 26S proteasome gene set predicts the clinical outcome of breast cancer patients. Our findings have implications for the development of cancer treatments and prognostic markers focused on the ubiquitination machinery.

The androgen receptor (AR) is a ligand-inducible transcription factor, a member of the nuclear re... more The androgen receptor (AR) is a ligand-inducible transcription factor, a member of the nuclear receptor superfamily, which plays an important role in the development and progression of prostate cancer (CaP). The transformation to CaP has been linked to several somatic AR gene mutations and changes in AR protein complex formation, which in turn increase the potential activity of the receptor. Thus, to address the mechanism of AR-mediated neoplastic transformation, we developed in vitro methodology to isolate and characterize, via mass spectrometry, AR complexes of three AR genetic variants: wild type-AR, and two somatic gain-of-function AR prostatic mutants (T877A-AR and 0CAG-AR isoforms). To fully characterize the significance of our large raw data set, we employed a sophisticated systems biology approach to create an integrative protein-interaction network profile for each AR isoform. Our comparative analysis identified subnetwork cluster profiles for AR isoforms (WT, T877A, and 0CAG) that segregated AR isoforms on the basis of androgen stimulation conditions and mutant aggressiveness. Furthermore, results from additional correlative gene microarray analysis studies of all three AR isoform (WT, T877A, 0CAG) subnetwork clusters were assessed and found to be significantly enriched in tumor versus normal prostate tissues. We also identified two AR-interaction clusters, containing 21 and 30 proteins, respectively, that showed unfavourable prognosis outcome of recurrent cancers, on the basis of PSA, Gleason score and combined PSA/Gleason score. In conclusion, we have characterized a large panel of novel AR-interacting proteins, through a combined proteomics/systems biology screen, that are of clinical relevance and could potentially serve as novel markers for diagnosis and prognosis of CaP.

Nature Communications, 2010

Nature Communications, 2012

Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. ... more Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifi es prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identifi ed prognostic gene signature sets for both estrogen receptor (ER) negative ( − ) and positive ( + ) subtypes. A combinatorial use of the signatures allowed the stratifi cation of patients into low-, intermediate-and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87 -100 % . Integrative network analysis identifi ed modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis. P c-2-random > = P c-2-NRC1 and P c-3-random > = P c-3-NRC1 , by performing 5,000 randomization tests and calculating the P -v a l u e .

BMC Systems Biology, 2010

Background microRNAs (miRNAs) are important cellular components. The understanding of their evolu... more Background microRNAs (miRNAs) are important cellular components. The understanding of their evolution is of critical importance for the understanding of their function. Although some specific evolutionary rules of miRNAs have been revealed, the rules of miRNA evolution in cellular networks remain largely unexplored. According to knowledge from protein-coding genes, the investigations of gene evolution in the context of biological networks often generate valuable observations that cannot be obtained by traditional approaches. Results Here, we conducted the first systems-level analysis of miRNA evolution in a human transcription factor (TF)-miRNA regulatory network that describes the regulatory relations among TFs, miRNAs, and target genes. We found that the architectural structure of the network provides constraints and functional innovations for miRNA evolution and that miRNAs showed different and even opposite evolutionary patterns from TFs and other protein-coding genes. For example, miRNAs preferentially coevolved with their activators but not with their inhibitors. During transcription, rapidly evolving TFs frequently activated but rarely repressed miRNAs. In addition, conserved miRNAs tended to regulate rapidly evolving targets, and upstream miRNAs evolved more rapidly than downstream miRNAs. Conclusions In this study, we performed the first systems level analysis of miRNA evolution. The findings suggest that miRNAs have a unique evolution process and thus may have unique functions and roles in various biological processes and diseases. Additionally, the network presented here is the first TF-miRNA regulatory network, which will be a valuable platform of systems biology.

Recently, microRNAs (miRNAs) have emerged as central posttranscriptional regulators of gene expre... more Recently, microRNAs (miRNAs) have emerged as central posttranscriptional regulators of gene expression. miRNAs regulate many key biological processes, including cell growth, death, development and differentiation. This discovery is challenging the central dogma of molecular biology. Genes are working together by forming cellular networks. It has become an emerging concept that miRNAs could intertwine with cellular networks to exert their function. Thus, it is essential to understand how miRNAs take part in cellular processes at a systems-level. In this review, I will first introduce basic knowledge of miRNAs and their relations to heart disaeses and cancer, highlight recently dicovered functions such as filtering out gene expression noise by miRNAs. I will aslo introduce basic concepts of cellular networks and interpret their biological meaning in such a way that the network concepts are digested in a biological context and are understandable for biologists. Finally, I will summarize the most recent progress in understanding of miRNA biology at a systems-level: the principles of miRNA regulation of the major cellular networks including signaling, metabolic, protein interaction and gene regulatory networks. A common miRNA regulatory principle is emerging: miRNAs preferentially regulated the genes that have high regulation complexity. In addition, miRNAs preferentially regulate positive regulatory motifs, highly connected scaffolds and the most network downstream components of cellular signaling networks, while miRNAs selectively regulate the genes which have specific network structural features on metabolic networks.

Nature Communications, 2010

Molecular Systems Biology, 2007

and sharing with colleagues.

Molecular Biosystems, 2009

Nature Communications, 2010

Nature Communications, 2012

Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. ... more Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifi es prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identifi ed prognostic gene signature sets for both estrogen receptor (ER) negative ( − ) and positive ( + ) subtypes. A combinatorial use of the signatures allowed the stratifi cation of patients into low-, intermediate-and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87 -100 % . Integrative network analysis identifi ed modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis. P c-2-random > = P c-2-NRC1 and P c-3-random > = P c-3-NRC1 , by performing 5,000 randomization tests and calculating the P -v a l u e .

BMC Systems Biology, 2010