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Nair NU, Schäffer AA, Gertz EM, Cheng K, Zerbib J, Sahu AD, Leor G, Shulman ED, Aldape KD, Ben-David U, Ruppin E. Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576103. [PMID: 38313282 PMCID: PMC10836086 DOI: 10.1101/2024.01.17.576103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.
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Gao Y, Gaither J, Chifman J, Kubatko L. A phylogenetic approach to inferring the order in which mutations arise during cancer progression. PLoS Comput Biol 2022; 18:e1010560. [PMID: 36459515 DOI: 10.1371/journal.pcbi.1010560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 12/14/2022] [Accepted: 09/12/2022] [Indexed: 12/05/2022] Open
Abstract
Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.
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Affiliation(s)
- Yuan Gao
- Division of Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Gaither
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, United States of America
| | - Julia Chifman
- Dept of Mathematics and Statistics, American University, Washington D. C., United States of America
| | - Laura Kubatko
- Dept of Statistics, The Ohio State University, Columbus, Ohio, United States of America
- Dept of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
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Sashittal P, Zaccaria S, El-Kebir M. Parsimonious Clone Tree Integration in cancer. Algorithms Mol Biol 2022; 17:3. [PMID: 35282838 PMCID: PMC8919608 DOI: 10.1186/s13015-022-00209-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/25/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. RESULTS To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. CONCLUSION PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
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Han X, Schubert CJ, Fiskal A, Dubois N, Lever MA. Eutrophication as a driver of microbial community structure in lake sediments. Environ Microbiol 2020; 22:3446-3462. [PMID: 32510812 DOI: 10.1111/1462-2920.15115] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 05/27/2020] [Accepted: 06/01/2020] [Indexed: 11/27/2022]
Abstract
Lake sediments are globally important carbon sinks. Although the fate of organic carbon in lake sediments depends significantly on microorganisms, only few studies have investigated controls on lake sedimentary microbial communities. Here we investigate the impact of anthropogenic eutrophication, which affects redox chemistry and organic matter (OM) sources in sediments, on microbial communities across five lakes in central Switzerland. Lipid biomarkers and distributions of microbial respiration reactions indicate strong increases in aquatic OM contributions and microbial activity with increasing trophic state. Across all lakes, 16S rRNA genes analyses indicate similar depth-dependent zonations at the phylum- and class-level that follow vertical distributions of OM sources and respiration reactions. Yet, there are notable differences, such as higher abundances of nitrifying Bacteria and Archaea in an oligotrophic lake. Furthermore, analyses at the order-level and below suggest that changes in OM sources due to eutrophication cause permanent changes in bacterial community structure. By contrast, archaeal communities are differentiated according to trophic state in recently deposited layers, but converge in older sediments deposited under different trophic regimes. Our study indicates an important role for trophic state in driving lacustrine sediment microbial communities and reveals fundamental differences in the temporal responses of sediment Bacteria and Archaea to eutrophication.
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Affiliation(s)
- Xingguo Han
- Institute of Biogeochemistry and Pollutant Dynamics, Swiss Federal Institute of Technology, Zurich (ETH Zurich), Universitätstrasse 16, Zurich, 8092, Switzerland
| | - Carsten Johnny Schubert
- Department of Surface Waters - Research and Management, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Seestrasse 79, Kastanienbaum, 6047, Switzerland
| | - Annika Fiskal
- Institute of Biogeochemistry and Pollutant Dynamics, Swiss Federal Institute of Technology, Zurich (ETH Zurich), Universitätstrasse 16, Zurich, 8092, Switzerland
| | - Nathalie Dubois
- Department of Earth Sciences, Swiss Federal Institute of Technology, Zurich (ETH Zurich), Sonneggstrasse 5, Zurich, 8092, Switzerland.,Department of Surface Waters - Research and Management, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Überlandstrasse 133, Dübendorf, 8600, Switzerland
| | - Mark Alexander Lever
- Institute of Biogeochemistry and Pollutant Dynamics, Swiss Federal Institute of Technology, Zurich (ETH Zurich), Universitätstrasse 16, Zurich, 8092, Switzerland
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Nouri-Vaskeh M, Alizadeh L, Hajiasgharzadeh K, Mokhtarzadeh A, Halimi M, Baradaran B. The role of HSP90 molecular chaperones in hepatocellular carcinoma. J Cell Physiol 2020; 235:9110-9120. [PMID: 32452023 DOI: 10.1002/jcp.29776] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Abstract
Misfolded proteins have enhanced formation of toxic oligomers and nonfunctional protein copies lead to recruiting wild-type protein types. Heat shock protein 90 (HSP90) is a molecular chaperone generated by cells that are involved in many cellular functions through regulation of folding and/or localization of large multi-protein complexes as well as client proteins. HSP90 can regulate a number of different cellular processes including cell proliferation, motility, angiogenesis, signal transduction, and adaptation to stress. HSP90 makes the mutated oncoproteins able to avoid misfolding and degradation and permits the malignant transformation. As a result, HSP90 is an important factor in several signaling pathways associated with tumorigenicity, therapy resistance, and inhibiting apoptosis. Clinically, the upregulation of HSP90 expression in hepatocellular carcinoma (HCC) is linked with advanced stages and inappropriate survival in cases suffering from this kind of cancer. The present review comprehensively assesses HSP90 functions and its possible usefulness as a potential diagnostic biomarker and therapeutic option for HCC.
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Affiliation(s)
- Masoud Nouri-Vaskeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Leila Alizadeh
- Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Ahad Mokhtarzadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Monireh Halimi
- Department of Pathology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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Mazaya M, Trinh HC, Kwon YK. Effects of ordered mutations on dynamics in signaling networks. BMC Med Genomics 2020; 13:13. [PMID: 32075651 PMCID: PMC7032007 DOI: 10.1186/s12920-019-0651-z] [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: 12/06/2019] [Accepted: 12/19/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations. METHODS To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model. RESULTS Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter. CONCLUSION Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.
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Affiliation(s)
- Maulida Mazaya
- School of IT Convergence, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610, Republic of Korea
| | - Hung-Cuong Trinh
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Yung-Keun Kwon
- School of IT Convergence, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610, Republic of Korea.
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Williams MJ, Werner B, Barnes CP, Graham TA, Sottoriva A. Identification of neutral tumor evolution across cancer types. Nat Genet 2016; 48:238-244. [PMID: 26780609 PMCID: PMC4934603 DOI: 10.1038/ng.3489] [Citation(s) in RCA: 388] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 12/18/2015] [Indexed: 12/17/2022]
Abstract
Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.
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Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, WC1E 6BT, UK
| | - Benjamin Werner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
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Bauer R, Kaiser M, Stoll E. A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population. PLoS One 2014; 9:e111219. [PMID: 25409511 PMCID: PMC4237327 DOI: 10.1371/journal.pone.0111219] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 09/22/2014] [Indexed: 02/01/2023] Open
Abstract
Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated) decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.
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Affiliation(s)
- Roman Bauer
- Interdisciplinary Computing and Complex BioSystems Research Group (ICOS), School of Computing Science, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems Research Group (ICOS), School of Computing Science, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom
| | - Elizabeth Stoll
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom
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