1
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Zhang X, Bell JT. Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison. G3 (BETHESDA, MD.) 2024; 14:jkae022. [PMID: 38289865 PMCID: PMC10989912 DOI: 10.1093/g3journal/jkae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/01/2024]
Abstract
Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL.
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Affiliation(s)
- Xiaopu Zhang
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
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2
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Alejandre C, Calle-Espinosa J, Iranzo J. Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers. PLoS Comput Biol 2024; 20:e1012081. [PMID: 38687804 PMCID: PMC11087069 DOI: 10.1371/journal.pcbi.1012081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 05/10/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.
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Affiliation(s)
- Carla Alejandre
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jaime Iranzo
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
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3
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Gökbağ B, Tang S, Fan K, Cheng L, Yu L, Zhao Y, Li L. SLKB: synthetic lethality knowledge base. Nucleic Acids Res 2024; 52:D1418-D1428. [PMID: 37889037 PMCID: PMC10767912 DOI: 10.1093/nar/gkad806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/16/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.
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Affiliation(s)
- Birkan Gökbağ
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Kunjie Fan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Lijun Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Lianbo Yu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Yue Zhao
- Department of Computational Medicine and Bioinformatics, College of Medicine, University of Michigan, Ann Arbor, MI 48104, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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4
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Tepeli YI, Seale C, Gonçalves JP. ELISL: early-late integrated synthetic lethality prediction in cancer. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btad764. [PMID: 38113447 DOI: 10.1093/bioinformatics/btad764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023]
Abstract
MOTIVATION Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality. RESULTS We propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustness to selection bias and recovery of known SL genes, as well as promising cross-cancer predictions. Co-occurring mutations in a BRCA gene and ELISL-predicted pairs from the HH, FGF, WNT, or NEIL gene families were associated with longer patient survival times, revealing therapeutic potential. AVAILABILITY AND IMPLEMENTATION Data: 10.6084/m9.figshare.23607558 & Code: github.com/joanagoncalveslab/ELISL.
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Affiliation(s)
- Yasin I Tepeli
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
| | - Colm Seale
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
- Holland Proton Therapy Center (HollandPTC), Delft, The Netherlands
| | - Joana P Gonçalves
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
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5
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Alfaro-Murillo JA, Townsend JP. Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis. Math Biosci 2023; 366:109091. [PMID: 37996064 PMCID: PMC10847963 DOI: 10.1016/j.mbs.2023.109091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/21/2023] [Accepted: 10/20/2023] [Indexed: 11/25/2023]
Abstract
Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply-but do not prove-that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.
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Affiliation(s)
- Jorge A Alfaro-Murillo
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States of America; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America.
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6
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Tang YJ, Shuldiner EG, Karmakar S, Winslow MM. High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo. Cold Spring Harb Perspect Med 2023; 13:a041382. [PMID: 37277208 PMCID: PMC10317066 DOI: 10.1101/cshperspect.a041382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.
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Affiliation(s)
- Yuning J Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Emily G Shuldiner
- Department of Biology, Stanford University, Stanford, California 94305, USA
| | - Saswati Karmakar
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
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7
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Tang S, Gökbağ B, Fan K, Shao S, Huo Y, Wu X, Cheng L, Li L. Synthetic lethal gene pairs: Experimental approaches and predictive models. Front Genet 2022; 13:961611. [PMID: 36531238 PMCID: PMC9751344 DOI: 10.3389/fgene.2022.961611] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/07/2022] [Indexed: 03/27/2024] Open
Abstract
Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.
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Affiliation(s)
- Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Birkan Gökbağ
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Kunjie Fan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Shuai Shao
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Yang Huo
- Indiana University, Bloomington, IN, United States
| | - Xue Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lijun Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
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8
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Sohail MS, Louie RHY, Hong Z, Barton JP, McKay MR. Inferring Epistasis from Genetic Time-series Data. Mol Biol Evol 2022; 39:6710201. [PMID: 36130322 PMCID: PMC9558069 DOI: 10.1093/molbev/msac199] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
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Affiliation(s)
- Muhammad Saqib Sohail
- Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, People’s Republic of China
| | - Raymond H Y Louie
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Zhenchen Hong
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
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9
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Iranzo J, Gruenhagen G, Calle-Espinosa J, Koonin EV. Pervasive conditional selection of driver mutations and modular epistasis networks in cancer. Cell Rep 2022; 40:111272. [PMID: 36001960 DOI: 10.1016/j.celrep.2022.111272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/18/2022] [Accepted: 08/05/2022] [Indexed: 11/19/2022] Open
Abstract
Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.
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Affiliation(s)
- Jaime Iranzo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
| | - George Gruenhagen
- Institute of Bioengineering and Biosciences, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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10
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Parvandeh S, Donehower LA, Katsonis P, Hsu TK, Asmussen J, Lee K, Lichtarge O. EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants. Nucleic Acids Res 2022; 50:e70. [PMID: 35412634 PMCID: PMC9262594 DOI: 10.1093/nar/gkac215] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/01/2023] Open
Abstract
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.
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Affiliation(s)
- Saeid Parvandeh
- To whom correspondence should be addressed. Tel: +1 713 798 7677;
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Houston, TX 77030, USA,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Teng-Kuei Hsu
- Department of Biochemistry & Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Jennifer K Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Correspondence may also be addressed to Olivier Lichtarge. Tel: +1 713 798 5646;
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11
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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12
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Kontio JAJ, Pyhäjärvi T, Sillanpää MJ. Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways. PLoS Comput Biol 2021; 17:e1008960. [PMID: 33939702 PMCID: PMC8118548 DOI: 10.1371/journal.pcbi.1008960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 05/13/2021] [Accepted: 04/13/2021] [Indexed: 11/19/2022] Open
Abstract
A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method's high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework's versatility and broad practical relevance.
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Affiliation(s)
- Juho A. J. Kontio
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Tanja Pyhäjärvi
- Department of Ecology and Genetics, University of Oulu, Oulu, Finland
- Department of Forest Sciences, University of Helsinki, Helsinki, Finland
| | - Mikko J. Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- * E-mail:
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13
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Drainas AP, Lambuta RA, Ivanova I, Serçin Ö, Sarropoulos I, Smith ML, Efthymiopoulos T, Raeder B, Stütz AM, Waszak SM, Mardin BR, Korbel JO. Genome-wide Screens Implicate Loss of Cullin Ring Ligase 3 in Persistent Proliferation and Genome Instability in TP53-Deficient Cells. Cell Rep 2021; 31:107465. [PMID: 32268084 PMCID: PMC7166082 DOI: 10.1016/j.celrep.2020.03.029] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 11/07/2019] [Accepted: 03/10/2020] [Indexed: 12/22/2022] Open
Abstract
TP53 deficiency is the most common alteration in cancer; however, this alone is typically insufficient to drive tumorigenesis. To identify genes promoting tumorigenesis in combination with TP53 deficiency, we perform genome-wide CRISPR-Cas9 knockout screens coupled with proliferation and transformation assays in isogenic cell lines. Loss of several known tumor suppressors enhances cellular proliferation and transformation. Loss of neddylation pathway genes promotes uncontrolled proliferation exclusively in TP53-deficient cells. Combined loss of CUL3 and TP53 activates an oncogenic transcriptional program governed by the nuclear factor κB (NF-κB), AP-1, and transforming growth factor β (TGF-β) pathways. This program maintains persistent cellular proliferation, induces partial epithelial to mesenchymal transition, and increases DNA damage, genomic instability, and chromosomal rearrangements. Our findings reveal CUL3 loss as a key event stimulating persistent proliferation in TP53-deficient cells. These findings may be clinically relevant, since TP53-CUL3-deficient cells are highly sensitive to ataxia telangiectasia mutated (ATM) inhibition, exposing a vulnerability that could be exploited for cancer treatment. Mixed-effect models with MEMcrispR applied to CRISPR screen analyses Knockout of neddylation genes increases persistent proliferation in TP53−/− cells TP53−/−,CUL3−/− cells exhibit persistent proliferation and partial EMT phenotype TP53−/−,CUL3−/− cells show increased DNA damage and display sensitivity to ATM inhibition
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Affiliation(s)
- Alexandros P Drainas
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Ruxandra A Lambuta
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Irina Ivanova
- BioMed X Innovation Center, 69120 Heidelberg, Germany
| | | | - Ioannis Sarropoulos
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Mike L Smith
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Theocharis Efthymiopoulos
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Benjamin Raeder
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Adrian M Stütz
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Sebastian M Waszak
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | | | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.
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14
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Wang H, Bennett DA, De Jager PL, Zhang QY, Zhang HY. Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction. Alzheimers Res Ther 2021; 13:55. [PMID: 33663605 PMCID: PMC7934265 DOI: 10.1186/s13195-021-00794-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/22/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD. METHODS We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS We identified 2 candidate genetic interactions with PFDR < 0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR < 0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.
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Affiliation(s)
- Hui Wang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - David A Bennett
- Rush University Medical Center, Rush Alzheimer's Disease Center, Chicago, IL, USA
- Rush University Medical Center, Department of Neurological Sciences, Chicago, IL, USA
| | - Philip L De Jager
- Columbia University Medical Center, Center for Translational and Computational Neuroimmunology, New York, NY, USA
- Broad Institute, Cell Circuits Program, Cambridge, MA, USA
| | - Qing-Ye Zhang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Hong-Yu Zhang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China.
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15
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Sinnott-Armstrong N, Naqvi S, Rivas M, Pritchard JK. GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background. eLife 2021; 10:e58615. [PMID: 33587031 PMCID: PMC7884075 DOI: 10.7554/elife.58615] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/18/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.
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Affiliation(s)
| | - Sahin Naqvi
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Chemical and Systems Biology, Stanford UniversityStanfordUnited States
| | - Manuel Rivas
- Department of Biomedical Data Sciences, Stanford UniversityStanfordUnited States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
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16
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Rauscher R, Bampi GB, Guevara-Ferrer M, Santos LA, Joshi D, Mark D, Strug LJ, Rommens JM, Ballmann M, Sorscher EJ, Oliver KE, Ignatova Z. Positive epistasis between disease-causing missense mutations and silent polymorphism with effect on mRNA translation velocity. Proc Natl Acad Sci U S A 2021; 118:e2010612118. [PMID: 33468668 PMCID: PMC7848603 DOI: 10.1073/pnas.2010612118] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Epistasis refers to the dependence of a mutation on other mutation(s) and the genetic context in general. In the context of human disorders, epistasis complicates the spectrum of disease symptoms and has been proposed as a major contributor to variations in disease outcome. The nonadditive relationship between mutations and the lack of complete understanding of the underlying physiological effects limit our ability to predict phenotypic outcome. Here, we report positive epistasis between intragenic mutations in the cystic fibrosis transmembrane conductance regulator (CFTR)-the gene responsible for cystic fibrosis (CF) pathology. We identified a synonymous single-nucleotide polymorphism (sSNP) that is invariant for the CFTR amino acid sequence but inverts translation speed at the affected codon. This sSNP in cis exhibits positive epistatic effects on some CF disease-causing missense mutations. Individually, both mutations alter CFTR structure and function, yet when combined, they lead to enhanced protein expression and activity. The most robust effect was observed when the sSNP was present in combination with missense mutations that, along with the primary amino acid change, also alter the speed of translation at the affected codon. Functional studies revealed that synergistic alteration in ribosomal velocity is the underlying mechanism; alteration of translation speed likely increases the time window for establishing crucial domain-domain interactions that are otherwise perturbed by each individual mutation.
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Affiliation(s)
- Robert Rauscher
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Giovana B Bampi
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Marta Guevara-Ferrer
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Leonardo A Santos
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Disha Joshi
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - David Mark
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Lisa J Strug
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
- Department of Statistical Sciences, Computer Science and Division of Biostatistics, University of Toronto, Toronto M5G 0A4, Canada
| | - Johanna M Rommens
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
| | | | - Eric J Sorscher
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Kathryn E Oliver
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Zoya Ignatova
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany;
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17
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Marderstein AR, Davenport ER, Kulm S, Van Hout CV, Elemento O, Clark AG. Leveraging phenotypic variability to identify genetic interactions in human phenotypes. Am J Hum Genet 2021; 108:49-67. [PMID: 33326753 DOI: 10.1016/j.ajhg.2020.11.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.
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Affiliation(s)
- Andrew R Marderstein
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Emily R Davenport
- Department of Biology, Huck Institutes of the Life Sciences, Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Scott Kulm
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Olivier Elemento
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Andrew G Clark
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.
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18
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Rochman ND, Wolf YI, Koonin EV. Deep phylogeny of cancer drivers and compensatory mutations. Commun Biol 2020; 3:551. [PMID: 33009502 PMCID: PMC7532533 DOI: 10.1038/s42003-020-01276-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 09/03/2020] [Indexed: 12/14/2022] Open
Abstract
Driver mutations (DM) are the genetic impetus for most cancers. The DM are assumed to be deleterious in species evolution, being eliminated by purifying selection unless compensated by other mutations. We present deep phylogenies for 84 cancer driver genes and investigate the prevalence of 434 DM across gene-species trees. The DM are rare in species evolution, and 181 are completely absent, validating their negative fitness effect. The DM are more common in unicellular than in multicellular eukaryotes, suggesting a link between these mutations and cell proliferation control. 18 DM appear as the ancestral state in one or more major clades, including 3 among mammals. We identify within-gene, compensatory mutations for 98 DM and infer likely interactions between the DM and compensatory sites in protein structures. These findings elucidate the evolutionary status of DM and are expected to advance the understanding of the functions and evolution of oncogenes and tumor suppressors. Rochman et al. present deep phylogenies for 84 cancer driver genes and examine the prevalence of driver mutations across gene-species trees. Their results show that driver mutations are rare in species evolution and give insight into the evolution of driver mutations and oncogenes.
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Affiliation(s)
- Nash D Rochman
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, 20894, USA
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, 20894, USA
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, 20894, USA.
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19
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Wang H, Yang J, Schneider JA, De Jager PL, Bennett DA, Zhang HY. Genome-wide interaction analysis of pathological hallmarks in Alzheimer's disease. Neurobiol Aging 2020; 93:61-68. [PMID: 32450446 PMCID: PMC9795865 DOI: 10.1016/j.neurobiolaging.2020.04.025] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 12/31/2022]
Abstract
Genome-wide association studies have identified many loci associated with Alzheimer's dementia. However, these variants only explain part of the heritability of Alzheimer's disease (AD). As genetic epistasis can be a major contributor to the "missing heritability" of AD, we conducted genome-wide epistasis screening for AD pathologies in 2 independent cohorts. First, we performed a genome-wide epistasis study of AD-related brain pathologies (Nmax = 1318) in ROS/MAP. Candidate interactions were validated using cerebrospinal fluid biomarkers of AD in ADNI (Nmax = 1128). Further functional analysis tested the association of candidate interactions with neuroimaging phenotypes. For tau and amyloid-β pathology, we identified 2803 and 464 candidate SNP-SNP interactions, respectively. Associations of candidate SNP-SNP interactions with brain volume and white matter changes from neuroimages provides additional insights into their molecular functions. Transcriptional analysis supported possible gene-gene interactions identified by statistical screening through their co-expression in the brain. In summary, we outlined an exhaustive epistasis analysis to identify novel genetic interactions with potential roles in AD pathologies. We further delved into the functional relevance of candidate interactions by association with neuroimaging phenotypes and analysis of co-expression between corresponding gene pairs.
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Affiliation(s)
- Hui Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jingyun Yang
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA,Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA,Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA,Department of Pathology, Rush University Medical Center, Chicago, Illinois, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, New York, USA,Cell Circuits Program, Broad Institute, Cambridge, Massachusetts, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA,Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA,Corresponding to Hong-Yu Zhang, Huazhong Agricultural University, No.1 Shizishan Street, Hongshan District, Wuhan, Hubei 430070, China, Tel: +86-27-87285085, , David A. Bennett, Rush Medical College, 600 S Paulina St, Chicago, IL 60612, USA, Tel: +1-312-942-4463,
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China,Corresponding to Hong-Yu Zhang, Huazhong Agricultural University, No.1 Shizishan Street, Hongshan District, Wuhan, Hubei 430070, China, Tel: +86-27-87285085, , David A. Bennett, Rush Medical College, 600 S Paulina St, Chicago, IL 60612, USA, Tel: +1-312-942-4463,
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20
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Grossmann P, Cristea S, Beerenwinkel N. Clonal evolution driven by superdriver mutations. BMC Evol Biol 2020; 20:89. [PMID: 32689942 PMCID: PMC7370525 DOI: 10.1186/s12862-020-01647-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 06/29/2020] [Indexed: 11/10/2022] Open
Abstract
Background Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers. Results In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times. Conclusions Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.
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Affiliation(s)
- Patrick Grossmann
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Simona Cristea
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Harvard Department of Stem Cell and Regenerative Biology, Cambridge, MA, USA
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland.
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21
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Wang R, Han Y, Zhao Z, Yang F, Chen T, Zhou W, Wang X, Qi L, Zhao W, Guo Z, Gu Y. Link synthetic lethality to drug sensitivity of cancer cells. Brief Bioinform 2020; 20:1295-1307. [PMID: 29300844 DOI: 10.1093/bib/bbx172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 11/22/2017] [Indexed: 12/16/2022] Open
Abstract
Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.
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22
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Biane C, Delaplace F. Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1574-1585. [PMID: 30582550 DOI: 10.1109/tcbb.2018.2889102] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of disease-induced molecular perturbations and subsequent development of efficient therapies are challenged by the complexity of the genotype-phenotype relationship. Accordingly, a key issue is to develop frameworks relating molecular perturbations and drug effects to their consequences on cellular phenotypes. Such framework would aim at identifying the sets of causal molecular factors leading to phenotypic reprogramming. In this article, we propose a theoretical framework, called Boolean Control Networks, where disease-induced molecular perturbations and drug actions are seen as topological perturbations/actions on molecular networks leading to cell phenotype reprogramming. We present a new method using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. Then, we compare different implementations of the algorithm and finally, show a proof-of-concept of the approach on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and their synthetic lethal drug target partner.
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23
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Kumar A, Hosseinnia A, Gagarinova A, Phanse S, Kim S, Aly KA, Zilles S, Babu M. A Gaussian process-based definition reveals new and bona fide genetic interactions compared to a multiplicative model in the Gram-negative Escherichia coli. Bioinformatics 2019; 36:880-889. [PMID: 31504172 PMCID: PMC9883677 DOI: 10.1093/bioinformatics/btz673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/24/2019] [Accepted: 08/23/2019] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E.coli. RESULTS Using our published E.coli GI datasets, we show computationally that a machine learning Gaussian process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E.coli, and, potentially, other microbes. AVAILABILITY AND IMPLEMENTATION The source code and parameters used to generate the machine learning models in WEKA software were provided in the Supplementary information. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Ali Hosseinnia
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Alla Gagarinova
- Department of Biochemistry, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sunyoung Kim
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Khaled A Aly
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | | | - Mohan Babu
- To whom correspondence should be addressed. or
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Codenotti S, Mansoury W, Pinardi L, Monti E, Marampon F, Fanzani A. Animal models of well-differentiated/dedifferentiated liposarcoma: utility and limitations. Onco Targets Ther 2019; 12:5257-5268. [PMID: 31308696 PMCID: PMC6613351 DOI: 10.2147/ott.s175710] [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: 02/16/2019] [Accepted: 06/04/2019] [Indexed: 12/31/2022] Open
Abstract
Liposarcoma is a malignant neoplasm of fat tissue. Well-differentiated and dedifferentiated liposarcoma (WDL/DDL) represent the two most clinically observed histotypes occurring in middle-aged to older adults, particularly within the retroperitoneum or extremities. WDL/DDL are thought to represent the broad spectrum of one disease, as they are both associated with the amplification in the chromosomal 12q13-15 region that causes MDM2 and CDK4 overexpression, the most useful predictor for liposarcoma diagnosis. In comparison to WDL, DDL contains additional genetic abnormalities, principally coamplifications of 1p32 and 6q23, that increase recurrence and metastatic rate. In this review, we discuss the xenograft and transgenic animal models generated for studying progression of WDL/DDL, highlighting utilities and pitfalls in such approaches that can facilitate or impede the development of new therapies.
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Affiliation(s)
- Silvia Codenotti
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Walaa Mansoury
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Luca Pinardi
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Eugenio Monti
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Francesco Marampon
- Department of Radiotherapy, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy
| | - Alessandro Fanzani
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
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Li Y, Xiao X, Bossé Y, Gorlova O, Gorlov I, Han Y, Byun J, Leighl N, Johansen JS, Barnett M, Chen C, Goodman G, Cox A, Taylor F, Woll P, Wichmann HE, Manz J, Muley T, Risch A, Rosenberger A, Han J, Siminovitch K, Arnold SM, Haura EB, Bolca C, Holcatova I, Janout V, Kontic M, Lissowska J, Mukeria A, Ognjanovic S, Orlowski TM, Scelo G, Swiatkowska B, Zaridze D, Bakke P, Skaug V, Zienolddiny S, Duell EJ, Butler LM, Houlston R, Artigas MS, Grankvist K, Johansson M, Shepherd FA, Marcus MW, Brunnström H, Manjer J, Melander O, Muller DC, Overvad K, Trichopoulou A, Tumino R, Liu G, Bojesen SE, Wu X, Le Marchand L, Albanes D, Bickeböller H, Aldrich MC, Bush WS, Tardon A, Rennert G, Teare MD, Field JK, Kiemeney LA, Lazarus P, Haugen A, Lam S, Schabath MB, Andrew AS, Bertazzi PA, Pesatori AC, Christiani DC, Caporaso N, Johansson M, McKay JD, Brennan P, Hung RJ, Amos CI. Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development. Oncotarget 2019; 10:1760-1774. [PMID: 30956756 PMCID: PMC6442994 DOI: 10.18632/oncotarget.26678] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 01/22/2019] [Indexed: 12/31/2022] Open
Abstract
The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.
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Affiliation(s)
- Yafang Li
- Baylor College of Medicine, Houston, TX, USA
| | | | | | - Olga Gorlova
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA
| | - Ivan Gorlov
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA
| | | | | | - Natasha Leighl
- University Health Network, The Princess Margaret Cancer Centre, Toronto, CA, USA
| | - Jakob S. Johansen
- Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Matt Barnett
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chu Chen
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Angela Cox
- Department of Oncology, University of Sheffield, Sheffield, UK
| | - Fiona Taylor
- Department of Oncology, University of Sheffield, Sheffield, UK
| | - Penella Woll
- Department of Oncology, University of Sheffield, Sheffield, UK
| | - H. Erich Wichmann
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Judith Manz
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Muley
- Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
| | - Angela Risch
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
- German Center for Lung Research (DKFZ), Heidelberg, Germany
- University of Salzburg and Cancer Cluster, Salzburg, Austria
| | - Albert Rosenberger
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | - Jiali Han
- Indiana University, Bloomington, IN, USA
| | | | | | - Eric B. Haura
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ciprian Bolca
- Institute of Pneumology “Marius Nasta”, Bucharest, Romania
| | - Ivana Holcatova
- Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Vladimir Janout
- Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Milica Kontic
- Clinical Center of Serbia, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jolanta Lissowska
- M. Sklodowska-Curie Cancer Center, Institute of Oncology, Warsaw, Poland
| | - Anush Mukeria
- Department of Epidemiology and Prevention, N.N. Blokhin Russian Cancer Research Center, Moscow, Russian Federation
| | - Simona Ognjanovic
- International Organization for Cancer Prevention and Research, Belgrade, Serbia
| | - Tadeusz M. Orlowski
- Department of Surgery, National Tuberculosis and Lung Diseases Research Institute, Warsaw, Poland
| | - Ghislaine Scelo
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Beata Swiatkowska
- Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland
| | - David Zaridze
- Department of Epidemiology and Prevention, N.N. Blokhin Russian Cancer Research Center, Moscow, Russian Federation
| | - Per Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Vidar Skaug
- National Institute of Occupational Health, Oslo, Norway
| | | | - Eric J. Duell
- Unit of Nutrition and Cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain
| | | | | | - María Soler Artigas
- Department of Health Sciences, Genetic Epidemiology Group, University of Leicester, Leicester, UK
- National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | | | | | - Michael W. Marcus
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | | | - Jonas Manjer
- Faculty of Medicine, Lund University, Lund, Sweden
| | | | - David C. Muller
- School of Public Health, St. Mary’s Campus, Imperial College London, London, UK
| | - Kim Overvad
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Rosario Tumino
- Molecular and Nutritional Epidemiology Unit CSPO (Cancer Research and Prevention Centre), Scientific Institute of Tuscany, Florence, Italy
| | - Geoffrey Liu
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William S. Bush
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Adonina Tardon
- IUOPA, University of Oviedo and CIBERESP, Faculty of Medicine, Campus del Cristo s/n, Oviedo, Spain
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - M. Dawn Teare
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - John K. Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | | | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA
| | - Aage Haugen
- National Institute of Occupational Health, Oslo, Norway
| | - Stephen Lam
- British Columbia Cancer Agency, Vancouver, Canada
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | - Pier Alberto Bertazzi
- Department of Preventive Medicine, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Angela C. Pesatori
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - David C. Christiani
- Department of Epidemiology, Program in Molecular and Genetic Epidemiology Harvard School of Public Health, Boston, MA, USA
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mattias Johansson
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - James D. McKay
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - Paul Brennan
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - Rayjean J. Hung
- International Agency for Research on Cancer, World Health Organization, Lyon, France
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The structure and evolution of eukaryotic chaperonin-containing TCP-1 and its mechanism that folds actin into a protein spring. Biochem J 2018; 475:3009-3034. [DOI: 10.1042/bcj20170378] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 08/16/2018] [Accepted: 08/28/2018] [Indexed: 12/15/2022]
Abstract
Actin is folded to its native state in eukaryotic cytosol by the sequential allosteric mechanism of the chaperonin-containing TCP-1 (CCT). The CCT machine is a double-ring ATPase built from eight related subunits, CCT1–CCT8. Non-native actin interacts with specific subunits and is annealed slowly through sequential binding and hydrolysis of ATP around and across the ring system. CCT releases a folded but soft ATP-G-actin monomer which is trapped 80 kJ/mol uphill on the folding energy surface by its ATP-Mg2+/Ca2+ clasp. The energy landscape can be re-explored in the actin filament, F-actin, because ATP hydrolysis produces dehydrated and more compact ADP-actin monomers which, upon application of force and strain, are opened and closed like the elements of a spring. Actin-based myosin motor systems underpin a multitude of force generation processes in cells and muscles. We propose that the water surface of F-actin acts as a low-binding energy, directional waveguide which is recognized specifically by the myosin lever-arm domain before the system engages to form the tight-binding actomyosin complex. Such a water-mediated recognition process between actin and myosin would enable symmetry breaking through fast, low energy initial binding events. The origin of chaperonins and the subsequent emergence of the CCT–actin system in LECA (last eukaryotic common ancestor) point to the critical role of CCT in facilitating phagocytosis during early eukaryotic evolution and the transition from the bacterial world. The coupling of CCT-folding fluxes to the cell cycle, cell size control networks and cancer are discussed together with directions for further research.
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Petan T, Jarc E, Jusović M. Lipid Droplets in Cancer: Guardians of Fat in a Stressful World. Molecules 2018; 23:molecules23081941. [PMID: 30081476 PMCID: PMC6222695 DOI: 10.3390/molecules23081941] [Citation(s) in RCA: 217] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 07/31/2018] [Accepted: 08/01/2018] [Indexed: 12/12/2022] Open
Abstract
Cancer cells possess remarkable abilities to adapt to adverse environmental conditions. Their survival during severe nutrient and oxidative stress depends on their capacity to acquire extracellular lipids and the plasticity of their mechanisms for intracellular lipid synthesis, mobilisation, and recycling. Lipid droplets, cytosolic fat storage organelles present in most cells from yeast to men, are emerging as major regulators of lipid metabolism, trafficking, and signalling in various cells and tissues exposed to stress. Their biogenesis is induced by nutrient and oxidative stress and they accumulate in various cancers. Lipid droplets act as switches that coordinate lipid trafficking and consumption for different purposes in the cell, such as energy production, protection against oxidative stress or membrane biogenesis during rapid cell growth. They sequester toxic lipids, such as fatty acids, cholesterol and ceramides, thereby preventing lipotoxic cell damage and engage in a complex relationship with autophagy. Here, we focus on the emerging mechanisms of stress-induced lipid droplet biogenesis; their roles during nutrient, lipotoxic, and oxidative stress; and the relationship between lipid droplets and autophagy. The recently discovered principles of lipid droplet biology can improve our understanding of the mechanisms that govern cancer cell adaptability and resilience to stress.
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Affiliation(s)
- Toni Petan
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana SI-1000, Slovenia.
| | - Eva Jarc
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana SI-1000, Slovenia.
- Jožef Stefan International Postgraduate School, Ljubljana SI-1000, Slovenia.
| | - Maida Jusović
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana SI-1000, Slovenia.
- Jožef Stefan International Postgraduate School, Ljubljana SI-1000, Slovenia.
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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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29
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Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N, Davidson M, McGarry L, James D, Amzallag A, Park SG, Cheng K, Robinson W, Atias D, Stossel C, Buzhor E, Stein G, Waterfall JJ, Meltzer PS, Golan T, Hannenhalli S, Gottlieb E, Benes CH, Samuels Y, Shanks E, Ruppin E. Harnessing synthetic lethality to predict the response to cancer treatment. Nat Commun 2018; 9:2546. [PMID: 29959327 PMCID: PMC6026173 DOI: 10.1038/s41467-018-04647-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 05/15/2018] [Indexed: 12/21/2022] Open
Abstract
While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.
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Affiliation(s)
- Joo Sang Lee
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Avinash Das
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Livnat Jerby-Arnon
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Rand Arafeh
- Department of Molecular Cell Biology, Weizmann Institute, Rehovot, 7610001, Israel
| | - Noam Auslander
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Matthew Davidson
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Lynn McGarry
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Daniel James
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Arnaud Amzallag
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02114, USA
- PatientsLikeMe, 160 Second Street, Cambridge, MA, 02142, USA
| | - Seung Gu Park
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Kuoyuan Cheng
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Welles Robinson
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Dikla Atias
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Chani Stossel
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Ella Buzhor
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Gidi Stein
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Joshua J Waterfall
- Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Paul S Meltzer
- Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Talia Golan
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Eyal Gottlieb
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Cyril H Benes
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02114, USA
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute, Rehovot, 7610001, Israel
| | - Emma Shanks
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Eytan Ruppin
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel.
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
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Abstract
BACKGROUND Previously, using linkage analysis and substitution mapping, two closely-linked interactive blood pressure quantitative trait loci (QTLs), BP QTL1 and BP QTL2, were located within a 13.96 Mb region from 117894038 to 131853815 bp (RGSC 3.4 version) on rat chromosome 5 (RNO5). This was done by using a series of congenic strains consisting of genomic segments of the Dahl salt-sensitive (S) rat substituted with that of the normotensive Lewis (LEW) rat. The interactive nature of the two loci was further confirmed by the construction and characterization of a panel of S.LEW bicongenic strains and corresponding S.LEW monocongenic strains, which provided definitive evidence of epistasis (genetic interaction) between BP QTL1 (7.77 Mb) and BP QTL2 (4.18 Mb). The purpose of this work was to further map these interacting QTLs. METHOD A new panel of seven new S.LEW bicongenic strains was constructed and characterized for BP. RESULTS The data obtained from these new strains further resolved BP QTL1 from 7.77 to 2.93 Mb. Further, BP QTL2 was traceable as not being a single QTL, but a composite of at least three QTLs, LEW alleles at two of which located within 2.26 Mb and 175 kb lowered BP but the third one located within 1.31 Mb increased BP. CONCLUSION Lack of coding variation within any of the regions further mapped within the previous QTL2 suggests noncoding variation as likely responsible for the observed epistasis.
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De S, Ganesan S. Looking beyond drivers and passengers in cancer genome sequencing data. Ann Oncol 2018; 28:938-945. [PMID: 27998972 DOI: 10.1093/annonc/mdw677] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Cancer arises as a result of acquired changes in the DNA sequence of the genome of somatic cells. A subset of the genetic changes, dubbed driver mutations, propels tumor growth, and the remaining changes are passengers, apparently inconsequential for neoplastic transformation. Massive genome sequencing of thousands of tumors from all major cancer types has enabled cataloging of the so-called driver and passenger mutations, and facilitated molecular classification of cancer, guiding precision medicine approach for the patients. Nonetheless, innovative analyses of cancer genomics data has led to novel, sometimes serendipitous findings that have aided to our understanding of other aspects of the biology of the disease and opened up new frontiers. For instance, emerging findings show that mutational patterns in cancer genomes can help detect signatures of known and novel DNA damage and repair processes, provide a likely chronological account of genomic changes in cancer genomes, and allow revisiting the models of cancer evolution. These findings have stimulated original approaches to identify disease etiology, stratify patients, target the disease, and monitor patient responses, complementing driver-mutation centric approaches. In this review, we discuss these emerging approaches and unexpected breakthroughs, and their implications for basic cancer research and clinical practices.
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Affiliation(s)
- S De
- Center for Cancer Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, New Brunswick, USA
| | - S Ganesan
- Center for Cancer Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, New Brunswick, USA
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Jarc E, Kump A, Malavašič P, Eichmann TO, Zimmermann R, Petan T. Lipid droplets induced by secreted phospholipase A2 and unsaturated fatty acids protect breast cancer cells from nutrient and lipotoxic stress. Biochim Biophys Acta Mol Cell Biol Lipids 2018; 1863:247-265. [DOI: 10.1016/j.bbalip.2017.12.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 11/13/2017] [Accepted: 12/07/2017] [Indexed: 12/12/2022]
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Kim YA, Madan S, Przytycka TM. WeSME: uncovering mutual exclusivity of cancer drivers and beyond. Bioinformatics 2017; 33:814-821. [PMID: 27153670 DOI: 10.1093/bioinformatics/btw242] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 04/22/2016] [Indexed: 12/18/2022] Open
Abstract
Motivation Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute P -values. Results To reduce the computing cost and perform less restricted mutual exclusivity analysis, we developed an efficient method to estimate P -values while controlling the mutation rates of individual patients and genes similar to the permutation test. A comprehensive mutual exclusivity analysis allowed us to uncover mutually exclusive pairs, some of which may have relatively low mutation rates. These pairs often included likely cancer drivers that have been missed in previous analyses. More importantly, our results demonstrated that mutual exclusivity can also provide information that goes beyond the interactions between cancer drivers and can, for example, elucidate different mutagenic processes in different cancer groups. In particular, including frequently mutated, long genes such as TTN in our analysis allowed us to observe interesting patterns of APOBEC activity in breast cancer and identify a set of related driver genes that are highly predictive of patient survival. In addition, we utilized our mutual exclusivity analysis in support of a previously proposed model where APOBEC activity is the underlying process that causes TP53 mutations in a subset of breast cancer cases. Availability and Implementation http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme. Contact przytyck@ncbi.nlm.nih.gov. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yoo-Ah Kim
- NCBI, NLM, NIH, Bethesda, MD, 20894, USA
| | - Sanna Madan
- Poolesville High School, Poolesville, 20837 MD, USA
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Senft D, Leiserson MDM, Ruppin E, Ronai ZA. Precision Oncology: The Road Ahead. Trends Mol Med 2017; 23:874-898. [PMID: 28887051 PMCID: PMC5718207 DOI: 10.1016/j.molmed.2017.08.003] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 08/06/2017] [Accepted: 08/08/2017] [Indexed: 02/06/2023]
Abstract
Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.
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Affiliation(s)
- Daniela Senft
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Mark D M Leiserson
- Microsoft Research New England, Cambridge, MA 02142, USA; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Eytan Ruppin
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Ze'ev A Ronai
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA; Technion Integrated Cancer Center, Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, 31096, Israel.
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Epistatic interaction between the lipase-encoding genes Pnpla2 and Lipe causes liposarcoma in mice. PLoS Genet 2017; 13:e1006716. [PMID: 28459858 PMCID: PMC5432192 DOI: 10.1371/journal.pgen.1006716] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 05/15/2017] [Accepted: 03/25/2017] [Indexed: 11/19/2022] Open
Abstract
Liposarcoma is an often fatal cancer of fat cells. Mechanisms of liposarcoma development are incompletely understood. The cleavage of fatty acids from acylglycerols (lipolysis) has been implicated in cancer. We generated mice with adipose tissue deficiency of two major enzymes of lipolysis, adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL), encoded respectively by Pnpla2 and Lipe. Adipocytes from double adipose knockout (DAKO) mice, deficient in both ATGL and HSL, showed near-complete deficiency of lipolysis. All DAKO mice developed liposarcoma between 11 and 14 months of age. No tumors occurred in single knockout or control mice. The transcriptome of DAKO adipose tissue showed marked differences from single knockout and normal controls as early as 3 months. Gpnmb and G0s2 were among the most highly dysregulated genes in premalignant and malignant DAKO adipose tissue, suggesting a potential utility as early markers of the disease. Similar changes of GPNMB and G0S2 expression were present in a human liposarcoma database. These results show that a previously-unknown, fully penetrant epistatic interaction between Pnpla2 and Lipe can cause liposarcoma in mice. DAKO mice provide a promising model for studying early premalignant changes that lead to late-onset malignant disease. Liposarcoma is an often fatal adult-onset tumor of fat tissue. Lipolysis, the central pathway of fat tissue metabolism, has been implicated in cancer. We generated mice that were deficient in two key enzymes of lipolysis, adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL). Strikingly, all mice with combined ATGL and HSL deficiency developed liposarcoma by 11–14 months of age. No liposarcoma occurred in single knockout or normal controls. Transcriptome analysis revealed that a subset of genes is dysregulated by 3 months of age. Our study reveals a novel epistatic interaction in fat cells between these two lipase genes and that causes a unique form of liposarcoma in mice. The double knockout mice provide a novel tool to study the early stages of liposarcoma development, prognostic markers and preventive treatments.
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Abstract
How can we treat cancer more effectively? Traditionally, tumours from the same anatomical site are treated as one tumour entity. This concept has been challenged by recent breakthroughs in cancer genomics and translational research that have enabled molecular tumour profiling. The identification and validation of cancer drivers that are shared between different tumour types, spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use, or within so-called basket or umbrella trials which are designed to test whether molecular alterations in one tumour entity can be extrapolated to all others. However, recent clinical and preclinical studies suggest that there are tissue- and cell type-specific differences in tumorigenesis and the organization of oncogenic signalling pathways. In this Opinion article, we focus on the molecular, cellular, systemic and environmental determinants of organ-specific tumorigenesis and the mechanisms of context-specific oncogenic signalling outputs. Investigation, recognition and in-depth biological understanding of these differences will be vital for the design of next-generation clinical trials and the implementation of molecularly guided cancer therapies in the future.
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Affiliation(s)
- Günter Schneider
- Department of Medicine II, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675 München, Germany
- German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Marc Schmidt-Supprian
- German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Department of Medicine III, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675 München, Germany
| | - Roland Rad
- Department of Medicine II, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675 München, Germany
- German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Dieter Saur
- Department of Medicine II, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675 München, Germany
- German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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Poelwijk FJ, Krishna V, Ranganathan R. The Context-Dependence of Mutations: A Linkage of Formalisms. PLoS Comput Biol 2016; 12:e1004771. [PMID: 27337695 PMCID: PMC4919011 DOI: 10.1371/journal.pcbi.1004771] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Frank J. Poelwijk
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail: (FJP); (RR)
| | - Vinod Krishna
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Rama Ranganathan
- Green Center for Systems Biology and Departments of Biophysics and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail: (FJP); (RR)
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Abstract
Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.
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Affiliation(s)
- Michael Boutros
- Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Department of Cell and Molecular Biology, Heidelberg University, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany.
| | - Florian Heigwer
- Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Department of Cell and Molecular Biology, Heidelberg University, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
| | - Christina Laufer
- Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Department of Cell and Molecular Biology, Heidelberg University, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
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Billmann M, Horn T, Fischer B, Sandmann T, Huber W, Boutros M. A genetic interaction map of cell cycle regulators. Mol Biol Cell 2016; 27:1397-407. [PMID: 26912791 PMCID: PMC4831891 DOI: 10.1091/mbc.e15-07-0467] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 02/10/2016] [Indexed: 12/20/2022] Open
Abstract
A combination of genome-scale RNA interference screening and genetic interaction analysis using process-directed phenotypes is used to assign components to specific pathways and complexes for modulators of mitosis and cytokinesis in Drosophila S2 cells. Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle–relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes.
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Affiliation(s)
- Maximilian Billmann
- Division of Signaling and Functional Genomics, German Cancer Research Center, and Department of Cell and Molecular Biology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Horn
- Division of Signaling and Functional Genomics, German Cancer Research Center, and Department of Cell and Molecular Biology, Heidelberg University, 69120 Heidelberg, Germany
| | - Bernd Fischer
- Genome Biology Unit, EMBL, 69118 Heidelberg, Germany Computational Genome Biology, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Thomas Sandmann
- Division of Signaling and Functional Genomics, German Cancer Research Center, and Department of Cell and Molecular Biology, Heidelberg University, 69120 Heidelberg, Germany
| | | | - Michael Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center, and Department of Cell and Molecular Biology, Heidelberg University, 69120 Heidelberg, Germany
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Abstract
Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies. Availability and implementation: Source code is at https://github.com/marinkaz/fusenet. Contact:blaz.zupan@fri.uni-lj.si Supplementary information:Supplementary information is available at Bioinformatics online.
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Affiliation(s)
- Marinka Žitnik
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Blaž Zupan
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
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41
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Jamshidi M, Fagerholm R, Khan S, Aittomäki K, Czene K, Darabi H, Li J, Andrulis IL, Chang-Claude J, Devilee P, Fasching PA, Michailidou K, Bolla MK, Dennis J, Wang Q, Guo Q, Rhenius V, Cornelissen S, Rudolph A, Knight JA, Loehberg CR, Burwinkel B, Marme F, Hopper JL, Southey MC, Bojesen SE, Flyger H, Brenner H, Holleczek B, Margolin S, Mannermaa A, Kosma VM, Dyck LV, Nevelsteen I, Couch FJ, Olson JE, Giles GG, McLean C, Haiman CA, Henderson BE, Winqvist R, Pylkäs K, Tollenaar RA, García-Closas M, Figueroa J, Hooning MJ, Martens JW, Cox A, Cross SS, Simard J, Dunning AM, Easton DF, Pharoah PD, Hall P, Blomqvist C, Schmidt MK, Nevanlinna H. SNP-SNP interaction analysis of NF-κB signaling pathway on breast cancer survival. Oncotarget 2015; 6:37979-94. [PMID: 26317411 PMCID: PMC4741978 DOI: 10.18632/oncotarget.4991] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 07/16/2015] [Indexed: 12/03/2022] Open
Abstract
In breast cancer, constitutive activation of NF-κB has been reported, however, the impact of genetic variation of the pathway on patient prognosis has been little studied. Furthermore, a combination of genetic variants, rather than single polymorphisms, may affect disease prognosis. Here, in an extensive dataset (n = 30,431) from the Breast Cancer Association Consortium, we investigated the association of 917 SNPs in 75 genes in the NF-κB pathway with breast cancer prognosis. We explored SNP-SNP interactions on survival using the likelihood-ratio test comparing multivariate Cox' regression models of SNP pairs without and with an interaction term. We found two interacting pairs associating with prognosis: patients simultaneously homozygous for the rare alleles of rs5996080 and rs7973914 had worse survival (HRinteraction 6.98, 95% CI=3.3-14.4, P=1.42E-07), and patients carrying at least one rare allele for rs17243893 and rs57890595 had better survival (HRinteraction 0.51, 95% CI=0.3-0.6, P = 2.19E-05). Based on in silico functional analyses and literature, we speculate that the rs5996080 and rs7973914 loci may affect the BAFFR and TNFR1/TNFR3 receptors and breast cancer survival, possibly by disturbing both the canonical and non-canonical NF-κB pathways or their dynamics, whereas, rs17243893-rs57890595 interaction on survival may be mediated through TRAF2-TRAIL-R4 interplay. These results warrant further validation and functional analyses.
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Affiliation(s)
- Maral Jamshidi
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029 HUS, Finland
| | - Rainer Fagerholm
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029 HUS, Finland
| | - Sofia Khan
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029 HUS, Finland
| | - Kristiina Aittomäki
- Department of Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029 HUS, Finland
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jenny Chang-Claude
- Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Department of Medicine, Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qi Guo
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Valerie Rhenius
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sten Cornelissen
- Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julia A. Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Christian R. Loehberg
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, German Cancer Research Center, Heidelberg, Germany
- Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany
| | - Frederik Marme
- Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Department of Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Stig E. Bojesen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Henrik Flyger
- Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Sara Margolin
- Department of Oncology - Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Arto Mannermaa
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Veli-Matti Kosma
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
| | | | - Laurien Van Dyck
- Vesalius Research Center (VRC), VIB, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Leuven, Belgium
| | - Ines Nevelsteen
- Multidisciplinary Breast Center, Medical Oncology, University Hospital Leuven, Leuven, Belgium
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Janet E. Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, School of Population and Global health, The University of Melbourne, Melbourne, Australia
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, Australia
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian E. Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer Research and Translational Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre NordLab, Oulu, Finland
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Cancer Research and Translational Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre NordLab, Oulu, Finland
| | - Rob A.E.M. Tollenaar
- Department of Surgical Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Montserrat García-Closas
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, SM2 5NG, UK
- Breakthrough Breast Cancer Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, SW3 6JB, UK
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, AE Rotterdam, The Netherlands
| | - John W.M. Martens
- Department of Medical Oncology, Erasmus MC Cancer Institute, AE Rotterdam, The Netherlands
| | - Angela Cox
- Sheffield Cancer Research, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Simon S. Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jacques Simard
- Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Canada
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul D.P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Carl Blomqvist
- Department of Oncology, University of Helsinki and Helsinki University Central Hospital, Helsinki, HUS, Finland
| | - Marjanka K. Schmidt
- Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029 HUS, Finland
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42
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Corticosteroid receptor genes and childhood neglect influence susceptibility to crack/cocaine addiction and response to detoxification treatment. J Psychiatr Res 2015; 68:83-90. [PMID: 26228405 DOI: 10.1016/j.jpsychires.2015.06.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 06/08/2015] [Accepted: 06/11/2015] [Indexed: 01/21/2023]
Abstract
The aim of this study was to analyze hypotheses-driven gene-environment and gene-gene interactions in smoked (crack) cocaine addiction by evaluating childhood neglect and polymorphisms in mineralocorticoid and glucocorticoid receptor genes (NR3C2 and NR3C1, respectively). One hundred thirty-nine crack/cocaine-addicted women who completed 3 weeks of follow-up during early abstinence composed our sample. Childhood adversities were assessed using the Childhood Trauma Questionnaire (CTQ), and withdrawal symptoms were assessed using the Cocaine Selective Severity Assessment (CSSA) scale. Conditional logistic regression with counterfactuals and generalized estimating equation modeling were used to test gene-environment and gene-gene interactions. We found an interaction between the rs5522-Val allele and childhood physical neglect, which altered the risk of crack/cocaine addiction (Odds ratio = 4.0, P = 0.001). Moreover, a NR3C2-NR3C1 interaction (P = 0.002) was found modulating the severity of crack/cocaine withdrawal symptoms. In the post hoc analysis, concomitant carriers of the NR3C2 rs5522-Val and NR3C1 rs6198-G alleles showed lower overall severity scores when compared to other genotype groups (P-values ≤ 0.035). This gene-environment interaction is consistent with epidemiological and human experimental findings demonstrating a strong relationship between early life stress and the hypothalamic-pituitary-adrenal (HPA) axis dysregulation in cocaine addiction. Additionally, this study extended in crack/cocaine addiction the findings previously reported for tobacco smoking involving an interaction between NR3C2 and NR3C1 genes.
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43
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Sidow A, Spies N. Concepts in solid tumor evolution. Trends Genet 2015; 31:208-14. [PMID: 25733351 DOI: 10.1016/j.tig.2015.02.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 02/01/2015] [Accepted: 02/01/2015] [Indexed: 01/14/2023]
Abstract
Evolutionary mechanisms in cancer progression give tumors their individuality. Cancer evolution is different from organismal evolution, however, and we discuss where concepts from evolutionary genetics are useful or limited in facilitating an understanding of cancer. Based on these concepts we construct and apply the simplest plausible model of tumor growth and progression. Simulations using this simple model illustrate the importance of stochastic events early in tumorigenesis, highlight the dominance of exponential growth over linear growth and differentiation, and explain the clonal substructure of tumors.
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Affiliation(s)
- Arend Sidow
- Departments of Pathology and of Genetics, Stanford University, Stanford, CA 94305, USA.
| | - Noah Spies
- Departments of Pathology and of Genetics, Stanford University, Stanford, CA 94305, USA
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44
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Burgess DJ. Cancer genetics: Leveraging functional data for driver genes. Nat Rev Genet 2014; 16:4. [PMID: 25488582 DOI: 10.1038/nrg3875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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