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Nussinov R, Tsai C, Jang H. Autoinhibition can identify rare driver mutations and advise pharmacology. FASEB J 2019; 34:16-29. [DOI: 10.1096/fj.201901341r] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 09/18/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Chung‐Jung Tsai
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Hyunbum Jang
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
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52
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Nussinov R, Tsai CJ, Jang H. Why Are Some Driver Mutations Rare? Trends Pharmacol Sci 2019; 40:919-929. [PMID: 31699406 DOI: 10.1016/j.tips.2019.10.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/13/2022]
Abstract
Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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53
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Reyna MA, Leiserson MDM, Raphael BJ. Hierarchical HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 2019; 34:i972-i980. [PMID: 30423088 PMCID: PMC6129270 DOI: 10.1093/bioinformatics/bty613] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Motivation The analysis of high-dimensional ‘omics data is often informed by the use of biological interaction networks. For example, protein–protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an ‘omics dataset and are topologically close (e.g. connected) on an interaction network. Results We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different ‘omics datasets. Availability and implementation http://github.com/raphael-group/hierarchical-hotnet. Supplementary information Supplementary material are available at Bioinformatics online.
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Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mark D M Leiserson
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
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Grant AD, Vail P, Padi M, Witkiewicz AK, Knudsen ES. Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations. Sci Rep 2019; 9:12766. [PMID: 31484939 PMCID: PMC6726654 DOI: 10.1038/s41598-019-48967-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 08/12/2019] [Indexed: 11/16/2022] Open
Abstract
Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.
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Affiliation(s)
- Adam D Grant
- University of Arizona Cancer Center, Tucson, AZ, 85719, USA
| | - Paris Vail
- University of Arizona Cancer Center, Tucson, AZ, 85719, USA
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, 85719, USA
| | | | - Erik S Knudsen
- Department of Molecular and Cellular Biology, Roswell Park Cancer Center, Buffalo, NY, 14263, USA.
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55
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Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures. Proc Natl Acad Sci U S A 2019; 116:18962-18970. [PMID: 31462496 PMCID: PMC6754584 DOI: 10.1073/pnas.1901156116] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue-residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.
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56
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Tan A, Huang H, Zhang P, Li S. Network-based cancer precision medicine: A new emerging paradigm. Cancer Lett 2019; 458:39-45. [DOI: 10.1016/j.canlet.2019.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/29/2019] [Accepted: 05/15/2019] [Indexed: 12/20/2022]
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57
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Deng Y, Luo S, Deng C, Luo T, Yin W, Zhang H, Zhang Y, Zhang X, Lan Y, Ping Y, Xiao Y, Li X. Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability. Brief Bioinform 2019; 20:254-266. [PMID: 28968730 DOI: 10.1093/bib/bbx109] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Indexed: 02/06/2023] Open
Abstract
Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.
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Affiliation(s)
- Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Shangyi Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenkang Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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58
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Wang B, Yan C, Lou S, Emani P, Li B, Xu M, Kong X, Meyerson W, Yang YT, Lee D, Gerstein M. Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions. Structure 2019; 27:1469-1481.e3. [PMID: 31279629 DOI: 10.1016/j.str.2019.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 02/14/2019] [Accepted: 06/03/2019] [Indexed: 11/17/2022]
Abstract
A key issue in drug design is how population variation affects drug efficacy by altering binding affinity (BA) in different individuals, an essential consideration for government regulators. Ideally, we would like to evaluate the BA perturbations of millions of single-nucleotide variants (SNVs). However, only hundreds of protein-drug complexes with SNVs have experimentally characterized BAs, constituting too small a gold standard for straightforward statistical model training. Thus, we take a hybrid approach: using physically based calculations to bootstrap the parameterization of a full model. In particular, we do 3D structure-based docking on ∼10,000 SNVs modifying known protein-drug complexes to construct a pseudo gold standard. Then we use this augmented set of BAs to train a statistical model combining structure, ligand and sequence features and illustrate how it can be applied to millions of SNVs. Finally, we show that our model has good cross-validated performance (97% AUROC) and can also be validated by orthogonal ligand-binding data.
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Affiliation(s)
- Bo Wang
- Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Prashant Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - William Meyerson
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Yale School of Medicine, Yale University, New Haven, CT 06520, USA
| | - Yucheng T Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Donghoon Lee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA.
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59
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Milliken LK, Motomarry SK, Kulkarni A. ARtPM: Article Retrieval for Precision Medicine. J Biomed Inform 2019; 95:103224. [DOI: 10.1016/j.jbi.2019.103224] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/13/2019] [Accepted: 06/08/2019] [Indexed: 10/26/2022]
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60
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Agajanian S, Oluyemi O, Verkhivker GM. Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations. Front Mol Biosci 2019; 6:44. [PMID: 31245384 PMCID: PMC6579812 DOI: 10.3389/fmolb.2019.00044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/23/2019] [Indexed: 12/21/2022] Open
Abstract
Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.
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Affiliation(s)
- Steve Agajanian
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Odeyemi Oluyemi
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States.,Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, United States
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61
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Somatic Mutations in miRNA Genes in Lung Cancer-Potential Functional Consequences of Non-Coding Sequence Variants. Cancers (Basel) 2019; 11:cancers11060793. [PMID: 31181801 PMCID: PMC6627760 DOI: 10.3390/cancers11060793] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/06/2019] [Accepted: 06/06/2019] [Indexed: 02/07/2023] Open
Abstract
A growing body of evidence indicates that miRNAs may either drive or suppress oncogenesis. However, little is known about somatic mutations in miRNA genes. To determine the frequency and potential consequences of miRNA gene mutations, we analyzed whole exome sequencing datasets of 569 lung adenocarcinoma (LUAD) and 597 lung squamous cell carcinoma (LUSC) samples generated in The Cancer Genome Atlas (TCGA) project. Altogether, we identified 1091 somatic sequence variants affecting 522 different miRNA genes and showed that half of all cancers had at least one such somatic variant/mutation. These sequence variants occurred in most crucial parts of miRNA precursors, including mature miRNA and seed sequences. Due to our findings, we hypothesize that seed mutations may affect miRNA:target interactions, drastically changing the pool of predicted targets. Mutations may also affect miRNA biogenesis by changing the structure of miRNA precursors, DROSHA and DICER cleavage sites, and regulatory sequence/structure motifs. We identified 10 significantly overmutated hotspot miRNA genes, including the miR-379 gene in LUAD enriched in mutations in the mature miRNA and regulatory sequences. The occurrence of mutations in the hotspot miRNA genes was also shown experimentally. We present a comprehensive analysis of somatic variants in miRNA genes and show that some of these genes are mutational hotspots, suggesting their potential role in cancer.
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62
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Sarto Basso R, Hochbaum DS, Vandin F. Efficient algorithms to discover alterations with complementary functional association in cancer. PLoS Comput Biol 2019; 15:e1006802. [PMID: 31120875 PMCID: PMC6550413 DOI: 10.1371/journal.pcbi.1006802] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 06/05/2019] [Accepted: 01/17/2019] [Indexed: 12/20/2022] Open
Abstract
Recent large cancer studies have measured somatic alterations in an unprecedented number of tumours. These large datasets allow the identification of cancer-related sets of genetic alterations by identifying relevant combinatorial patterns. Among such patterns, mutual exclusivity has been employed by several recent methods that have shown its effectiveness in characterizing gene sets associated to cancer. Mutual exclusivity arises because of the complementarity, at the functional level, of alterations in genes which are part of a group (e.g., a pathway) performing a given function. The availability of quantitative target profiles, from genetic perturbations or from clinical phenotypes, provides additional information that can be leveraged to improve the identification of cancer related gene sets by discovering groups with complementary functional associations with such targets. In this work we study the problem of finding groups of mutually exclusive alterations associated with a quantitative (functional) target. We propose a combinatorial formulation for the problem, and prove that the associated computational problem is computationally hard. We design two algorithms to solve the problem and implement them in our tool UNCOVER. We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. In addition, our algorithms are much faster than the state-of-the-art, allowing the analysis of large datasets of thousands of target profiles from cancer cell lines. We show that on two such datasets, one from project Achilles and one from the Genomics of Drug Sensitivity in Cancer project, UNCOVER identifies several significant gene sets with complementary functional associations with targets. Software available at: https://github.com/VandinLab/UNCOVER.
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Affiliation(s)
- Rebecca Sarto Basso
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA, USA
| | - Dorit S. Hochbaum
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA, USA
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova, Italy
- Department of Computer Science, Brown University, Providence, RI, USA
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- * E-mail:
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63
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Zhang W, Wang SL. A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration. Biochem Genet 2019; 58:16-39. [PMID: 31115714 DOI: 10.1007/s10528-019-09924-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 05/02/2019] [Indexed: 12/17/2022]
Abstract
The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.
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Affiliation(s)
- Wei Zhang
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Shu-Lin Wang
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, 410082, Hunan, China.
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64
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Altieri F, Hansen TV, Vandin F. NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies. Front Genet 2019; 10:265. [PMID: 31024613 PMCID: PMC6468148 DOI: 10.3389/fgene.2019.00265] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/08/2019] [Indexed: 12/31/2022] Open
Abstract
Next-generation sequencing technologies allow to measure somatic mutations in a large number of patients from the same cancer type: one of the main goals in their analysis is the identification of mutations associated with clinical parameters. The identification of such relationships is hindered by extensive genetic heterogeneity in tumors, with different genes mutated in different patients, due, in part, to the fact that genes and mutations act in the context of pathways: it is therefore crucial to study mutations in the context of interactions among genes. In this work we study the problem of identifying subnetworks of a large gene-gene interaction network with mutations associated with survival time. We formally define the associated computational problem by using a score for subnetworks based on the log-rank statistical test to compare the survival of two given populations. We propose a novel approach, based on a new algorithm, called Network of Mutations Associated with Survival (NoMAS) to find subnetworks of a large interaction network whose mutations are associated with survival time. NoMAS is based on the color-coding technique, that has been previously employed in other applications to find the highest scoring subnetwork with high probability when the subnetwork score is additive. In our case the score is not additive, so our algorithm cannot identify the optimal solution with the same guarantees associated to additive scores. Nonetheless, we prove that, under a reasonable model for mutations in cancer, NoMAS identifies the optimal solution with high probability. We also design a holdout approach to identify subnetworks significantly associated with survival time. We test NoMAS on simulated and cancer data, comparing it to approaches based on single gene tests and to various greedy approaches. We show that our method does indeed find the optimal solution and performs better than the other approaches. Moreover, on three cancer datasets our method identifies subnetworks with significant association to survival when none of the genes has significant association with survival when considered in isolation.
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Affiliation(s)
- Federico Altieri
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Tommy V Hansen
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova, Italy
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65
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Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol 2019; 15:e1006658. [PMID: 30921324 PMCID: PMC6438456 DOI: 10.1371/journal.pcbi.1006658] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.
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66
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Gao B, Zhao Y, Li Y, Liu J, Wang L, Li G, Su Z. Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2019; 6:1801384. [PMID: 30828525 PMCID: PMC6382311 DOI: 10.1002/advs.201801384] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 10/04/2018] [Indexed: 05/07/2023]
Abstract
Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample-specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software is available at: https://sourceforge.net/projects/cancer-pathway/files/.
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Affiliation(s)
- Bo Gao
- School of MathematicsShandong UniversityJinan250100China
- State Key Laboratory of Microbial TechnologyShandong UniversityJinan250100China
| | - Yue Zhao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Yang Li
- School of MathematicsShandong UniversityJinan250100China
- State Key Laboratory of Microbial TechnologyShandong UniversityJinan250100China
| | - Juntao Liu
- School of MathematicsShandong UniversityJinan250100China
| | - Lushan Wang
- State Key Laboratory of Microbial TechnologyShandong UniversityJinan250100China
| | - Guojun Li
- School of MathematicsShandong UniversityJinan250100China
- State Key Laboratory of Microbial TechnologyShandong UniversityJinan250100China
| | - Zhengchang Su
- Department of Bioinformatics and GenomicsCollege of Computing and InformaticsThe University of North Carolina at Charlotte9201 University City BlvdCharlotteNC28223USA
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Liu X, Luo X, Jiang C, Zhao H. Difficulties and challenges in the development of precision medicine. Clin Genet 2019; 95:569-574. [PMID: 30653655 DOI: 10.1111/cge.13511] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/09/2019] [Accepted: 01/11/2019] [Indexed: 12/25/2022]
Abstract
The rapid development of precision medicine is introducing a new era of significance in medicine. However, attaining precision medicine is an ambitious goal that is bound to encounter some challenges. Here, we have put forward some difficulties or questions that should be addressed by the progress in this field. The proposed issues include the long road to precision medicine for all types of diseases as the unknown domains of the human genome hinder the development of precision medicine. The challenges in the acquisition and analysis of large amounts of omics data, including difficulties in the establishment of a library of biological samples and large-scale data analysis, as well as the challenges of informed consent and medical ethics in precision medicine, must be overcome to attain the goals of precision medicine. To date, precision medicine programs have accomplished many preliminary achievements and will help to drive a dramatic revolution in clinical practices for the medical community. Through these advances, the diagnosis and treatment of many diseases will achieve many breakthroughs. This project is just beginning and requires a great deal of time and money. Precision medicine also requires extensive collaboration. Ultimately, these difficulties can be overcome. We should realize that precision medicine is good for patients, but there is still a long way to go.
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Affiliation(s)
- Xiaoqin Liu
- Department of Nephrology, Hongqi Hospital, Mudanjiang Medical University, Mudanjiang, People's Republic of China
| | - Xin Luo
- Department of Radiotherapy, The Second Hospital of PingLiang City, Second Affiliated Hospital of Gansu Medical College, Pingliang, People's Republic of China
| | - Chunyang Jiang
- Department of Thoracic Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China
| | - Hui Zhao
- Department of Thoracic Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China
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Precision medicine review: rare driver mutations and their biophysical classification. Biophys Rev 2019; 11:5-19. [PMID: 30610579 PMCID: PMC6381362 DOI: 10.1007/s12551-018-0496-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023] Open
Abstract
How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.
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Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1168:9-30. [DOI: 10.1007/978-3-030-24100-1_2] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Cario CL, Witte JS. Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations. Bioinformatics 2018; 34:936-942. [PMID: 29106441 DOI: 10.1093/bioinformatics/btx709] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 10/31/2017] [Indexed: 12/31/2022] Open
Abstract
Motivation As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Availability and implementation Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. Contact JWitte@ucsf.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Clinton L Cario
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
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Complexity of genome sequencing and reporting: Next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol 2018; 133:171-182. [PMID: 30661654 DOI: 10.1016/j.critrevonc.2018.11.008] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/23/2018] [Indexed: 12/17/2022] Open
Abstract
The finalization of the Human Genome Project in 2003 paved the way for a deeper understanding of cancer, favouring a faster progression towards "personalized" medicine. Research in oncology has progressively focused on the sequencing of cancer genomes, to better understand the genetic basis of tumorigenesis and identify actionable alterations to guide cancer therapy. Thanks to the development of next-generation-sequencing (NGS) techniques, sequencing of tumoral DNA is today technically easier, faster and cheaper. Commercially available NGS panels enable the detection of single or global genomic alterations, namely gene mutation and mutagenic burden, both on germline and somatic DNA, potentially predicting the response or resistance to cancer treatments. Profiling of tumor DNA is nowadays a standard in cancer research and treatment. In this review we discuss the history, techniques and applications of NGS in cancer care, under a "personalized tailored therapy" perspective.
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72
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Liu Y, He Q, Sun W. Association analysis using somatic mutations. PLoS Genet 2018; 14:e1007746. [PMID: 30388102 PMCID: PMC6235399 DOI: 10.1371/journal.pgen.1007746] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 11/14/2018] [Accepted: 10/07/2018] [Indexed: 11/18/2022] Open
Abstract
Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively. Cancer is a genetic disease that is driven by the accumulation of somatic mutations. Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features. One challenge for such tasks is the non-ignorable somatic mutation calling errors. We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types. Our results show that some somatic mutations affect gene expression in several cancer types. In particular, we show that the associations between gene expression traits and TP53 gene level mutation reveal some similarities across a few cancer types.
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Affiliation(s)
- Yang Liu
- Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, United States of America
| | - Qianchan He
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Wei Sun
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail:
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Agajanian S, Odeyemi O, Bischoff N, Ratra S, Verkhivker GM. Machine Learning Classification and Structure–Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes. J Chem Inf Model 2018; 58:2131-2150. [DOI: 10.1021/acs.jcim.8b00414] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Steve Agajanian
- Graduate Program in Computational and Data Sciences, Department of Computational Sciences, Schmid College of Science and Technology, Chapman University, One University
Drive, Orange, California 92866, United States
| | - Oluyemi Odeyemi
- Graduate Program in Computational and Data Sciences, Department of Computational Sciences, Schmid College of Science and Technology, Chapman University, One University
Drive, Orange, California 92866, United States
| | - Nathaniel Bischoff
- Graduate Program in Computational and Data Sciences, Department of Computational Sciences, Schmid College of Science and Technology, Chapman University, One University
Drive, Orange, California 92866, United States
| | - Simrath Ratra
- Graduate Program in Computational and Data Sciences, Department of Computational Sciences, Schmid College of Science and Technology, Chapman University, One University
Drive, Orange, California 92866, United States
| | - Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Department of Computational Sciences, Schmid College of Science and Technology, Chapman University, One University
Drive, Orange, California 92866, United States
- Chapman University, School of Pharmacy, Irvine, California 92618, United States
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74
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Ozturk K, Dow M, Carlin DE, Bejar R, Carter H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 2018; 430:2875-2899. [PMID: 29908887 PMCID: PMC6097914 DOI: 10.1016/j.jmb.2018.06.016] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 12/19/2022]
Abstract
Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.
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Affiliation(s)
- Kivilcim Ozturk
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel E Carlin
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Rafael Bejar
- Moores Cancer Center, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center and Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA; CIFAR, MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada.
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75
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Coudray A, Battenhouse AM, Bucher P, Iyer VR. Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data. PeerJ 2018; 6:e5362. [PMID: 30083469 PMCID: PMC6074801 DOI: 10.7717/peerj.5362] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 07/09/2018] [Indexed: 02/06/2023] Open
Abstract
To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.
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Affiliation(s)
- Alexandre Coudray
- School of Life Sciences, École Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Anna M Battenhouse
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Philipp Bucher
- School of Life Sciences, École Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Vishwanath R Iyer
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
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76
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Larson C, Oronsky B, Varner G, Caroen S, Burbano E, Insel E, Hedjran F, Carter CA, Reid TR. A practical guide to the handling and administration of personalized transcriptionally attenuated oncolytic adenoviruses (PTAVs). Oncoimmunology 2018; 7:e1478648. [PMID: 30228948 DOI: 10.1080/2162402x.2018.1478648] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 05/14/2018] [Accepted: 05/15/2018] [Indexed: 02/05/2023] Open
Abstract
The aim of this review is to provide practical information on the handling, storage, and administration procedures for personalized oncolytic adenoviruses (PTAVs), which have recently entered clinical trials. As described herein, personalized oncolytic viruses refer to transcriptionally attenuated (TA) type 5 adenoviruses that are engineered to carry one or more neoantigenic transgenes derived from patient tumors. Vials of personalized viruses should be stored at -60°C without refreezing after thawing to maintain infectivity. To prevent accidental exposure and transmission, full implementation of universal precautions for preparation, administration, and handling is required. Contaminated materials that come into contact with personalized viruses should be properly disposed of in accordance with local institutional procedures. Severely immunocompromised or pregnant healthcare workers should not prepare or administer personalized viruses or directly contact injection sites. Personalized viruses are administered subcutaneously and intratumorally; however, only subcutaneous injection will be considered in this review. The specific storage, handling, administration, and safety requirements for personalized viruses are easily managed in the context of a clinical trial following the directives from the study protocol.
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Affiliation(s)
| | - Bryan Oronsky
- Clinical Research Department, EpicentRx, La Jolla, USA
| | - Gina Varner
- Clinical Research Department, EpicentRx, La Jolla, USA
| | - Scott Caroen
- Clinical Research Department, EpicentRx, La Jolla, USA
| | - Erica Burbano
- Clinical Research Department, EpicentRx, La Jolla, USA
| | - Elisa Insel
- Clinical Research Department, EpicentRx, La Jolla, USA
| | - Farah Hedjran
- Clinical Research Department, EpicentRx, La Jolla, USA
| | | | - Tony R Reid
- Clinical Research Department, EpicentRx, La Jolla, USA
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77
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Xi J, Wang M, Li A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinformatics 2018; 19:214. [PMID: 29871594 PMCID: PMC5989443 DOI: 10.1186/s12859-018-2218-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 05/24/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. RESULTS To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. CONCLUSIONS In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.
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Affiliation(s)
- Jianing Xi
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Minghui Wang
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
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78
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Xi J, Li A, Wang M. A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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79
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Colombo I, Kurnit KC, Westin SN, Oza AM. Moving From Mutation to Actionability. Am Soc Clin Oncol Educ Book 2018; 38:495-503. [PMID: 30231353 DOI: 10.1200/edbk_199665] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The diffusion of high-throughput next-generation sequencing technologies has sustained massive parallel sequencing of tumor tissue providing a deep insight into tumor biology and advancement of personalized medicine. A substantial number of targeted agents have been investigated in gynecologic cancer and some have received U.S. Food and Drug Administration approval, like PARP inhibitors in ovarian cancer, bevacizumab in ovarian and cervical cancers, and pembrolizumab in microsatellite-unstable or mismatch repair-deficient endometrial cancer. To improve effectiveness of targeted therapy, identification of predictive biomarkers able to guide the selection of the correct drug for the correct patient is crucial. Different limitations must be addressed to favor a more rapid implementation of a genotyping approach in treatment selection, such as the possibility to easily assess tumor heterogeneity and clonal evolution along the disease trajectory and the need for innovative trial designs like adaptive or basket trials incorporating molecular features as selection criteria. A deep dive into the genomic features of exceptional responders may also favor better understanding of tumor biology, mechanism of action of a specific target agent, and identification or predictive biomarkers for subsequent tailored studies.
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Affiliation(s)
- Ilaria Colombo
- From the Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX; University of Toronto, Department of Medicine, Toronto, ON, Canada
| | - Katherine C Kurnit
- From the Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX; University of Toronto, Department of Medicine, Toronto, ON, Canada
| | - Shannon N Westin
- From the Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX; University of Toronto, Department of Medicine, Toronto, ON, Canada
| | - Amit M Oza
- From the Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX; University of Toronto, Department of Medicine, Toronto, ON, Canada
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80
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Dissecting the Mutational Landscape of Cutaneous Melanoma: An Omic Analysis Based on Patients from Greece. Cancers (Basel) 2018; 10:cancers10040096. [PMID: 29596374 PMCID: PMC5923351 DOI: 10.3390/cancers10040096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 03/20/2018] [Accepted: 03/27/2018] [Indexed: 12/21/2022] Open
Abstract
Melanoma is a lethal type of skin cancer, unless it is diagnosed early. Formalin-fixed, paraffin-embedded (FFPE) tissue is a valuable source for molecular assays after diagnostic examination, but isolated nucleic acids often suffer from degradation. Here, for the first time, we examine primary melanomas from Greek patients, using whole exome sequencing, so as to derive their mutational profile. Application of a bioinformatic framework revealed a total of 10,030 somatic mutations. Regarding the genes containing putative protein-altering mutations, 73 were common in at least three patients. Sixty-five of these 73 top common genes have been previously identified in melanoma cases. Biological processes related to melanoma were affected by varied genes in each patient, suggesting differences in the components of a pathway possibly contributing to pathogenesis. We performed a multi-level analysis highlighting a short list of candidate genes with a probable causative role in melanoma.
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81
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Hu W, Li X, Wang T, Zheng S. Association mining of mutated cancer genes in different clinical stages across 11 cancer types. Oncotarget 2018; 7:68270-68277. [PMID: 27556693 PMCID: PMC5356553 DOI: 10.18632/oncotarget.11392] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 08/08/2016] [Indexed: 01/14/2023] Open
Abstract
Many studies have demonstrated that some genes (e.g. APC, BRAF, KRAS, PTEN, TP53) are frequently mutated in cancer, however, underlying mechanism that contributes to their high mutation frequency remains unclear. Here we used Apriori algorithm to find the frequent mutational gene sets (FMGSs) from 4,904 tumors across 11 cancer types as part of the TCGA Pan-Cancer effort and then mined the hidden association rules (ARs) within these FMGSs. Intriguingly, we found that well-known cancer driver genes such as BRAF, KRAS, PTEN, and TP53 were often co-occurred with other driver genes and FMGSs size peaked at an itemset size of 3∼4 genes. Besides, the number and constitution of FMGS and ARs differed greatly among different cancers and stages. In addition, FMGS and ARs were rare in endocrine-related cancers such as breast carcinoma, ovarian cystadenocarcinoma, and thyroid carcinoma, but abundant in cancers contact directly with external environments such as skin melanoma and stomach adenocarcinoma. Furthermore, we observed more rules in stage IV than in other stages, indicating that distant metastasis needed more sophisticated gene regulatory network.
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Affiliation(s)
- Wangxiong Hu
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Xiaofen Li
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Tingzhang Wang
- Zhejiang Institute of Microbiology, Hangzhou, Zhejiang 310012, China
| | - Shu Zheng
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
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82
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Delhalle S, Bode SFN, Balling R, Ollert M, He FQ. A roadmap towards personalized immunology. NPJ Syst Biol Appl 2018; 4:9. [PMID: 29423275 PMCID: PMC5802799 DOI: 10.1038/s41540-017-0045-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/29/2017] [Accepted: 12/19/2017] [Indexed: 12/30/2022] Open
Abstract
Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.
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Affiliation(s)
- Sylvie Delhalle
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
| | - Sebastian F N Bode
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg.,2Center for Pediatrics-Department of General Pediatrics, Adolescent Medicine, and Neonatology, Medical Center, Faculty of Medicine, University of Freiburg, Mathildenstrasse 1, 79106 Freiburg, Germany
| | - Rudi Balling
- 3Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Markus Ollert
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg.,4Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, 5000 Odense C, Denmark
| | - Feng Q He
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
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83
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Affiliation(s)
- Srivatsan Raman
- Department of Biochemistry
and Department of Bacteriology, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
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84
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Jia P, Zhao Z. Impacts of somatic mutations on gene expression: an association perspective. Brief Bioinform 2017; 18:413-425. [PMID: 27127206 DOI: 10.1093/bib/bbw037] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Indexed: 12/28/2022] Open
Abstract
Assessing the functional impacts of somatic mutations in cancer genomes is critical for both identifying driver mutations and developing molecular targeted therapies. Currently, it remains a fundamental challenge to distinguish the patterns through which mutations execute their biological effects and to infer biological mechanisms underlying these patterns. To this end, we systematically studied the association between somatic mutations in protein-coding regions and expression profiles, which represents an indirect measurement of impacts. We defined mutation features (mutation type, cluster and status) and built linear regression models to assess mutation associations with mRNA expression and protein expression. Our results presented a comprehensive landscape of the associations between mutation features and expression profile in multiple cancer types, including 62 genes showing mutation type associated expression changes, 21 genes showing mutation cluster associations and 51 genes showing mutation status associations. We revealed four characteristics of the patterns that mutations impact on expression. First, we showed that mutation type (truncation versus amino acid-altering mutations) was the most important determinant of expression levels. Second, we detected mutation clusters in well-studied oncogenes that were associated with gene expression. Third, we found both similarities and differences in association patterns existed within and across cancer types. Fourth, although many of the observed associations stay stable at both mRNA and protein expression levels, there are also novel associations uniquely observed at the protein level, which warrant future investigation. Taken together, our findings provided implications for cancer driver gene prioritization and insights into the functional consequences of somatic mutations.
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85
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Peng X, Wang J, Peng W, Wu FX, Pan Y. Protein-protein interactions: detection, reliability assessment and applications. Brief Bioinform 2017; 18:798-819. [PMID: 27444371 DOI: 10.1093/bib/bbw066] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Indexed: 01/06/2023] Open
Abstract
Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.
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86
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Belikov AV. The number of key carcinogenic events can be predicted from cancer incidence. Sci Rep 2017; 7:12170. [PMID: 28939880 PMCID: PMC5610194 DOI: 10.1038/s41598-017-12448-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 09/06/2017] [Indexed: 12/02/2022] Open
Abstract
The widely accepted multiple-hit hypothesis of carcinogenesis states that cancers arise after several successive events. However, no consensus has been reached on the quantity and nature of these events, although “driver” mutations or epimutations are considered the most probable candidates. By using the largest publicly available cancer incidence statistics (20 million cases), I show that incidence of 20 most prevalent cancer types in relation to patients’ age closely follows the Erlang probability distribution (R2 = 0.9734–0.9999). The Erlang distribution describes the probability y of k independent random events occurring by the time x, but not earlier or later, with events happening on average every b time intervals. This fits well with the multiple-hit hypothesis and potentially allows to predict the number k of key carcinogenic events and the average time interval b between them, for each cancer type. Moreover, the amplitude parameter A likely predicts the maximal populational susceptibility to a given type of cancer. These parameters are estimated for 20 most common cancer types and provide numerical reference points for experimental research on cancer development.
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Affiliation(s)
- Aleksey V Belikov
- School of Biological and Medical Physics, Laboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Institutsky per., 9, 141701 Dolgoprudny, Moscow Region, Russia.
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87
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Kato M, Vasco DA, Sugino R, Narushima D, Krasnitz A. Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer. ROYAL SOCIETY OPEN SCIENCE 2017; 4:171060. [PMID: 28989791 PMCID: PMC5627131 DOI: 10.1098/rsos.171060] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 08/31/2017] [Indexed: 06/07/2023]
Abstract
Single-cell sequencing is a promising technology that can address cancer cell evolution by identifying genetic alterations in individual cells. In a recent study, genome-wide DNA copy numbers of single cells were accurately quantified by single-cell sequencing in breast cancers. Phylogenetic-tree analysis revealed genetically distinct populations, each consisting of homogeneous cells. Bioinformatics methods based on population genetics should be further developed to quantitatively analyse the single-cell sequencing data. We developed a bioinformatics framework that was combined with molecular-evolution theories to analyse copy-number losses. This analysis revealed that most deletions in the breast cancers at the single-cell level were generated by simple stochastic processes. A non-standard type of coalescent theory, the multiple-merger coalescent model, aided by approximate Bayesian computation fit well with the data, allowing us to estimate the population-genetic parameters in addition to false-positive and false-negative rates. The estimated parameters suggest that the cancer cells underwent sweepstake evolution, where only one or very few parental cells produced a descendent cell population. We conclude that breast cancer cells successively substitute in a tumour mass, and the high reproduction of only a portion of cancer cells may confer high adaptability to this cancer.
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Affiliation(s)
- Mamoru Kato
- Department of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuuoo-ku, Tokyo 104-0045, Japan
| | - Daniel A. Vasco
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
| | - Ryuichi Sugino
- School of Advanced Sciences, The Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan
| | - Daichi Narushima
- Department of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuuoo-ku, Tokyo 104-0045, Japan
| | - Alexander Krasnitz
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
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88
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Tanaka H, Ishikawa K, Mizuno M, Toyokuni S, Kajiyama H, Kikkawa F, Metelmann HR, Hori M. State of the art in medical applications using non-thermal atmospheric pressure plasma. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s41614-017-0004-3] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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89
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Amar D, Izraeli S, Shamir R. Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications. Oncogene 2017; 36:3375-3383. [PMID: 28092680 PMCID: PMC5485176 DOI: 10.1038/onc.2016.489] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 11/20/2016] [Accepted: 11/22/2016] [Indexed: 02/07/2023]
Abstract
Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a catalog of recurring mutations in cancer. Many methods analyze these data jointly with auxiliary information with the aim of identifying subtype-specific results. Here, we show that somatic gene mutations alone can reliably and specifically predict cancer subtypes. Interpretation of the classifiers provides useful insights for several biomedical applications. We analyze the COSMIC database, which collects somatic mutations from The Cancer Genome Atlas (TCGA) as well as from many smaller scale studies. We use multi-label classification techniques and the Disease Ontology hierarchy in order to identify cancer subtype-specific biomarkers. Cancer subtype classifiers based on TCGA and the smaller studies have comparable performance, and the smaller studies add a substantial value in terms of validation, coverage of additional subtypes, and improved classification. The gene sets of the classifiers are used for threefold contribution. First, we refine the associations of genes to cancer subtypes and identify novel compelling candidate driver genes. Second, using our classifiers we successfully predict the primary site of metastatic samples. Third, we provide novel hypotheses regarding detection of subtype-specific synthetic lethality interactions. From the cancer research community perspective, our results suggest that curation efforts, such as COSMIC, have great added and complementary value even in the era of large international cancer projects.
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Affiliation(s)
- D Amar
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - S Izraeli
- Department of Pediatric Hematology-Oncology, Safra Children’s Hospital, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - R Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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90
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Vandin F. Computational Methods for Characterizing Cancer Mutational Heterogeneity. Front Genet 2017; 8:83. [PMID: 28659971 PMCID: PMC5469877 DOI: 10.3389/fgene.2017.00083] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 05/30/2017] [Indexed: 12/21/2022] Open
Abstract
Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.
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Affiliation(s)
- Fabio Vandin
- Department of Information Engineering, University of PadovaPadova, Italy
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91
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A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Sci Rep 2017; 7:2855. [PMID: 28588243 PMCID: PMC5460199 DOI: 10.1038/s41598-017-03141-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 04/20/2017] [Indexed: 01/01/2023] Open
Abstract
Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their interaction network context. However, recent studies show that some cancer genes in different perturbed pathways are mutated in different subsets of samples. Subsequently, these genes may not display significant mutational recurrence and thus remain undiscovered even in consideration of network information. We develop a novel method called mCGfinder to efficiently detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Based on matrix decomposition framework incorporated with gene interaction network information, mCGfinder can successfully measure the significance of mutational recurrence of genes in a subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of five types of cancers, we find that the genes detected by mCGfinder are significantly enriched for known cancer genes, and yield substantially smaller p-values than other existing methods. All the results demonstrate that mCGfinder is an efficient method in detecting mutated cancer genes.
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92
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Hu Z, Sun R, Curtis C. A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochim Biophys Acta Rev Cancer 2017; 1867:109-126. [PMID: 28274726 DOI: 10.1016/j.bbcan.2017.03.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 03/01/2017] [Accepted: 03/02/2017] [Indexed: 12/17/2022]
Abstract
Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Zheng Hu
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruping Sun
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Christina Curtis
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
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93
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Hofmann AL, Behr J, Singer J, Kuipers J, Beisel C, Schraml P, Moch H, Beerenwinkel N. Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics 2017; 18:8. [PMID: 28049408 PMCID: PMC5209852 DOI: 10.1186/s12859-016-1417-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 12/10/2016] [Indexed: 12/30/2022] Open
Abstract
Background Next-generation sequencing of matched tumor and normal biopsy pairs has become a technology of paramount importance for precision cancer treatment. Sequencing costs have dropped tremendously, allowing the sequencing of the whole exome of tumors for just a fraction of the total treatment costs. However, clinicians and scientists cannot take full advantage of the generated data because the accuracy of analysis pipelines is limited. This particularly concerns the reliable identification of subclonal mutations in a cancer tissue sample with very low frequencies, which may be clinically relevant. Results Using simulations based on kidney tumor data, we compared the performance of nine state-of-the-art variant callers, namely deepSNV, GATK HaplotypeCaller, GATK UnifiedGenotyper, JointSNVMix2, MuTect, SAMtools, SiNVICT, SomaticSniper, and VarScan2. The comparison was done as a function of variant allele frequencies and coverage. Our analysis revealed that deepSNV and JointSNVMix2 perform very well, especially in the low-frequency range. We attributed false positive and false negative calls of the nine tools to specific error sources and assigned them to processing steps of the pipeline. All of these errors can be expected to occur in real data sets. We found that modifying certain steps of the pipeline or parameters of the tools can lead to substantial improvements in performance. Furthermore, a novel integration strategy that combines the ranks of the variants yielded the best performance. More precisely, the rank-combination of deepSNV, JointSNVMix2, MuTect, SiNVICT and VarScan2 reached a sensitivity of 78% when fixing the precision at 90%, and outperformed all individual tools, where the maximum sensitivity was 71% with the same precision. Conclusions The choice of well-performing tools for alignment and variant calling is crucial for the correct interpretation of exome sequencing data obtained from mixed samples, and common pipelines are suboptimal. We were able to relate observed substantial differences in performance to the underlying statistical models of the tools, and to pinpoint the error sources of false positive and false negative calls. These findings might inspire new software developments that improve exome sequencing pipelines and further the field of precision cancer treatment. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1417-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ariane L Hofmann
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jonas Behr
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jochen Singer
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland
| | - Peter Schraml
- Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland
| | - Holger Moch
- Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland. .,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland.
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94
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Xi J, Wang M, Li A. Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information. MOLECULAR BIOSYSTEMS 2017; 13:2135-2144. [DOI: 10.1039/c7mb00303j] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
An integrated approach to identify driver genes based on information of somatic mutations, the interaction network and Gene Ontology similarity.
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Affiliation(s)
- Jianing Xi
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
| | - Minghui Wang
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
- Centers for Biomedical Engineering
| | - Ao Li
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
- Centers for Biomedical Engineering
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95
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Dimitrakopoulos CM, Beerenwinkel N. Computational approaches for the identification of cancer genes and pathways. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 9. [PMID: 27863091 PMCID: PMC5215607 DOI: 10.1002/wsbm.1364] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 07/26/2016] [Accepted: 08/23/2016] [Indexed: 12/27/2022]
Abstract
High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Christos M Dimitrakopoulos
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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96
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The challenges of tumor genetic diversity. Cancer 2016; 123:917-927. [PMID: 27861749 DOI: 10.1002/cncr.30430] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 09/27/2016] [Accepted: 09/29/2016] [Indexed: 12/14/2022]
Abstract
The authors review and discuss the implications of genomic analyses documenting the diversity of tumors, both among patients and within individual tumors. Genetic diversity among solid tumors limits targeted therapies, because few mutations that drive tumors are both targetable and at high prevalence. Many more driver mutations and how they affect cellular signaling pathways must be identified if targeted therapy is to become widely useful. Genetic diversity within a tumor-intratumor genetic heterogeneity-makes the tumor a collection of subclones: related yet distinct cancers. Selection for pre-existing, resistant subclones by conventional or targeted therapies may explain many treatment failures. Immune therapy faces the same fundamental challenges. Nevertheless, the processes that generate and maintain heterogeneity might provide novel therapeutic targets. Addressing both types of diversity requires genomic tumor analyses linked to detailed clinical data. The trend toward sequencing restricted cancer gene panels, however, limits the ability to discover new driver mutations and assess intratumor heterogeneity. Clinical data currently collected with genomic analyses often lack critical information, substantially limiting their use in understanding tumor diversity. Now that diversity among and within tumors can no longer be ignored, research and clinical practice must adapt to take diversity into account. Cancer 2017;123:917-27. © 2016 American Cancer Society.
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97
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Lu S, Cai C, Yan G, Zhou Z, Wan Y, Chen V, Chen L, Cooper GF, Obeid LM, Hannun YA, Lee AV, Lu X. Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Res 2016; 76:6785-6794. [PMID: 27758891 DOI: 10.1158/0008-5472.can-16-1740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 08/31/2016] [Accepted: 09/18/2016] [Indexed: 11/16/2022]
Abstract
Defining processes that are synthetic lethal with p53 mutations in cancer cells may reveal possible therapeutic strategies. In this study, we report the development of a signal-oriented computational framework for cancer pathway discovery in this context. We applied our bipartite graph-based functional module discovery algorithm to identify transcriptomic modules abnormally expressed in multiple tumors, such that the genes in a module were likely regulated by a common, perturbed signal. For each transcriptomic module, we applied our weighted k-path merge algorithm to search for a set of somatic genome alterations (SGA) that likely perturbed the signal, that is, the candidate members of the pathway that regulate the transcriptomic module. Computational evaluations indicated that our methods-identified pathways were perturbed by SGA. In particular, our analyses revealed that SGA affecting TP53, PTK2, YWHAZ, and MED1 perturbed a set of signals that promote cell proliferation, anchor-free colony formation, and epithelial-mesenchymal transition (EMT). These proteins formed a signaling complex that mediates these oncogenic processes in a coordinated fashion. Disruption of this signaling complex by knocking down PTK2, YWHAZ, or MED1 attenuated and reversed oncogenic phenotypes caused by mutant p53 in a synthetic lethal manner. This signal-oriented framework for searching pathways and therapeutic targets is applicable to all cancer types, thus potentially impacting precision medicine in cancer. Cancer Res; 76(23); 6785-94. ©2016 AACR.
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Affiliation(s)
- Songjian Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chunhui Cai
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gonghong Yan
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.,Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Zhuan Zhou
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yong Wan
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vicky Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lujia Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lina M Obeid
- Department of Medicine, the State University of New York at Stony Brook, Stony Brook, New York
| | - Yusuf A Hannun
- Department of Medicine, the State University of New York at Stony Brook, Stony Brook, New York
| | - Adrian V Lee
- Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania. .,University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.,Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania. .,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
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98
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Choi S, Go JH, Kim EK, Lee H, Lee WM, Cho CS, Han K. Mutational Analysis of Extranodal NK/T-Cell Lymphoma Using Targeted Sequencing with a Comprehensive Cancer Panel. Genomics Inform 2016; 14:78-84. [PMID: 27729836 PMCID: PMC5056900 DOI: 10.5808/gi.2016.14.3.78] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 08/10/2016] [Accepted: 08/11/2016] [Indexed: 02/06/2023] Open
Abstract
Extranodal natural killer (NK)/T-cell lymphoma, nasal type (NKTCL), is a malignant disorder of cytotoxic lymphocytes of NK or T cells. It is an aggressive neoplasm with a very poor prognosis. Although extranodal NKTCL reportedly has a strong association with Epstein-Barr virus, the molecular pathogenesis of NKTCL has been unexplored. The recent technological advancements in next-generation sequencing (NGS) have made DNA sequencing cost- and time-effective, with more reliable results. Using the Ion Proton Comprehensive Cancer Panel, we sequenced 409 cancer-related genes to identify somatic mutations in five NKTCL tissue samples. The sequencing analysis detected 25 mutations in 21 genes. Among them, KMT2D, a histone modification-related gene, was the most frequently mutated gene (four of the five cases). This result was consistent with recent NGS studies that have suggested KMT2D as a novel driver gene in NKTCL. Mutations were also found in ARID1A, a chromatin remodeling gene, and TP53, which also recurred in recent NGS studies. We also found mutations in 18 novel candidate genes, with molecular functions that were potentially implicated in cancer development. We suggest that these genes may result in multiple oncogenic events and may be used as potential bio-markers of NKTCL in the future.
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Affiliation(s)
- Seungkyu Choi
- Department of Pathology, Dankook University College of Medicine, Cheonan 31116, Korea
| | - Jai Hyang Go
- Department of Pathology, Dankook University College of Medicine, Cheonan 31116, Korea
| | - Eun Kyung Kim
- Department of Pathology, Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Korea
| | - Hojung Lee
- Department of Pathology, Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Korea
| | - Won Mi Lee
- Department of Pathology, Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Korea
| | - Chun-Sung Cho
- Department of Neurosurgery, Dankook University College of Medicine, Cheonan 31116, Korea
| | - Kyudong Han
- Department of Nanobiomedical Science, BK21 PLUS NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea
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99
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Willis RE. Targeted Cancer Therapy: Vital Oncogenes and a New Molecular Genetic Paradigm for Cancer Initiation Progression and Treatment. Int J Mol Sci 2016; 17:ijms17091552. [PMID: 27649156 PMCID: PMC5037825 DOI: 10.3390/ijms17091552] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 09/05/2016] [Accepted: 09/07/2016] [Indexed: 12/18/2022] Open
Abstract
It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.
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Affiliation(s)
- Rudolph E Willis
- OncoStem Biotherapeutics LLC, 423 W 127th St., New York, NY 10027, USA.
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100
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Macintyre G, Ylstra B, Brenton JD. Sequencing Structural Variants in Cancer for Precision Therapeutics. Trends Genet 2016; 32:530-542. [PMID: 27478068 DOI: 10.1016/j.tig.2016.07.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 12/18/2022]
Abstract
The identification of mutations that guide therapy selection for patients with cancer is now routine in many clinical centres. The majority of assays used for solid tumour profiling use DNA sequencing to interrogate somatic point mutations because they are relatively easy to identify and interpret. Many cancers, however, including high-grade serous ovarian, oesophageal, and small-cell lung cancer, are driven by somatic structural variants that are not measured by these assays. Therefore, there is currently an unmet need for clinical assays that can cheaply and rapidly profile structural variants in solid tumours. In this review we survey the landscape of 'actionable' structural variants in cancer and identify promising detection strategies based on massively-parallel sequencing.
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Affiliation(s)
- Geoff Macintyre
- Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Bauke Ylstra
- Department of Pathology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, UK.
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