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Barker-Clarke RJ, Gray JM, Strobl MAR, Tadele DS, Maltas J, Hinczewski M, Scott JG. The balance between intrinsic and ecological fitness defines new regimes in eco-evolutionary population dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.15.532871. [PMID: 36993598 PMCID: PMC10055088 DOI: 10.1101/2023.03.15.532871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Selection upon intrinsic fitness differences is one of the most basic mechanisms of evolution, fundamental to all biology. Equally, within macroscopic populations and microscopic environments, ecological interactions influence evolution. Direct experimental evidence of ecological selection between microscopic agents continues to grow. Whilst eco-evolutionary dynamics describes how interactions influence population fitness and composition, we build a model that allows ecological aspects of these interactions to fall on a spectrum independent of the intrinsic fitness of the population. With our mathematical framework, we show how ecological interactions between mutating populations modify the estimated evolutionary trajectories expected from monoculture fitnesses alone. We derive and validate analytical stationary solutions to our partial differential equations that depend on intrinsic and ecological terms, and mutation rates. We determine cases in which these interactions modify evolution in such ways as to, for example, maintain or invert existing monoculture fitness differences. This work discusses the importance of understanding ecological and intrinsic selection effects to avoid misleading conclusions from experiments and defines new ways to assess this balance from experimental results. Using published experimental data, we also show evidence that real microbiological systems can span intrinsic fitness dominant and ecological-effect dominant regimes and that ecological contributions can change with an environment to exaggerate or counteract the composite populations' intrinsic fitness differences.
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2
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Niu M, Zhang Y, Luo J, Sinson JC, Thompson AM, Zong C. Characterization of Cancer Evolution Landscape Based on Accurate Detection of Somatic Mutations in Single Tumor Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561356. [PMID: 37873375 PMCID: PMC10592685 DOI: 10.1101/2023.10.09.561356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Accurate detection of somatic mutations in single tumor cells is greatly desired as it allows us to quantify the single-cell mutation burden and construct the mutation-based phylogenetic tree. Here we developed scNanoSeq chemistry and profiled 842 single cells from 21 human breast cancer samples. The majority of the mutation-based phylogenetic trees comprise a characteristic stem evolution followed by the clonal sweep. We observed the subtype-dependent lengths in the stem evolution. To explain this phenomenon, we propose that the differences are related to different reprogramming required for different subtypes of breast cancer. Furthermore, we reason that the time that the tumor-initiating cell took to acquire the critical clonal-sweep-initiating mutation by random chance set the time limit for the reprogramming process. We refer to this model as a reprogramming and critical mutation co-timing (RCMC) subtype model. Next, in the sweeping clone, we observed that tumor cells undergo a branched evolution with rapidly decreasing selection. In the most recent clades, effectively neutral evolution has been reached, resulting in a substantially large number of mutational heterogeneities. Integrative analysis with 522-713X ultra-deep bulk whole genome sequencing (WGS) further validated this evolution mode. Mutation-based phylogenetic trees also allow us to identify the early branched cells in a few samples, whose phylogenetic trees support the gradual evolution of copy number variations (CNVs). Overall, the development of scNanoSeq allows us to unveil novel insights into breast cancer evolution.
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3
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Borgsmüller N, Valecha M, Kuipers J, Beerenwinkel N, Posada D. Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues. CELL GENOMICS 2023; 3:100380. [PMID: 37719146 PMCID: PMC10504633 DOI: 10.1016/j.xgen.2023.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 05/03/2023] [Accepted: 07/18/2023] [Indexed: 09/19/2023]
Abstract
Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.
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Affiliation(s)
- Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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4
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Alfieri F, Caravagna G, Schaefer MH. Cancer genomes tolerate deleterious coding mutations through somatic copy number amplifications of wild-type regions. Nat Commun 2023; 14:3594. [PMID: 37328455 PMCID: PMC10276008 DOI: 10.1038/s41467-023-39313-8] [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: 09/27/2022] [Accepted: 06/01/2023] [Indexed: 06/18/2023] Open
Abstract
Cancers evolve under the accumulation of thousands of somatic mutations and chromosomal aberrations. While most coding mutations are deleterious, almost all protein-coding genes lack detectable signals of negative selection. This raises the question of how tumors tolerate such large amounts of deleterious mutations. Using 8,690 tumor samples from The Cancer Genome Atlas, we demonstrate that copy number amplifications frequently cover haploinsufficient genes in mutation-prone regions. This could increase tolerance towards the deleterious impact of mutations by creating safe copies of wild-type regions and, hence, protecting the genes therein. Our findings demonstrate that these potential buffering events are highly influenced by gene functions, essentiality, and mutation impact and that they occur early during tumor evolution. We show how cancer type-specific mutation landscapes drive copy number alteration patterns across cancer types. Ultimately, our work paves the way for the detection of novel cancer vulnerabilities by revealing genes that fall within amplifications likely selected during evolution to mitigate the effect of mutations.
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Affiliation(s)
- Fabio Alfieri
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, 20139, Italy
| | - Giulio Caravagna
- Department of Mathematics and Geosciences, University of Trieste, Trieste, 34127, Italy
| | - Martin H Schaefer
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, 20139, Italy.
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5
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West J, Robertson-Tessi M, Anderson ARA. Agent-based methods facilitate integrative science in cancer. Trends Cell Biol 2023; 33:300-311. [PMID: 36404257 PMCID: PMC10918696 DOI: 10.1016/j.tcb.2022.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/25/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022]
Abstract
In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.
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Affiliation(s)
- Jeffrey West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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6
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Wu L, Wu W, Zhang J, Zhao Z, Li L, Zhu M, Wu M, Wu F, Zhou F, Du Y, Chai RC, Zhang W, Qiu X, Liu Q, Wang Z, Li J, Li K, Chen A, Jiang Y, Xiao X, Zou H, Srivastava R, Zhang T, Cai Y, Liang Y, Huang B, Zhang R, Lin F, Hu L, Wang X, Qian X, Lv S, Hu B, Zheng S, Hu Z, Shen H, You Y, Verhaak RG, Jiang T, Wang Q. Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma. Cancer Discov 2022; 12:2820-2837. [PMID: 36122307 PMCID: PMC9716251 DOI: 10.1158/2159-8290.cd-22-0196] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/05/2022] [Accepted: 09/15/2022] [Indexed: 01/12/2023]
Abstract
Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM's natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711.
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Affiliation(s)
- Lingxiang Wu
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Wei Wu
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Junxia Zhang
- Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Zhao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Liangyu Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Mengyan Zhu
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Min Wu
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Fan Wu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Fengqi Zhou
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuxin Du
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Rui-Chao Chai
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wei Zhang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoguang Qiu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Quanzhong Liu
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Ziyu Wang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Jie Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Kening Li
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Apeng Chen
- State Key Laboratory of Veterinary Etiological Biology, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yinan Jiang
- John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania.,Department of Pediatric Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Xiangwei Xiao
- John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania.,Department of Pediatric Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Han Zou
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Rashmi Srivastava
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Tingting Zhang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Cai
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Yuan Liang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Bin Huang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Ruohan Zhang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Fan Lin
- Department of Cell Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors and Key Laboratory of Rare Metabolic Diseases, Nanjing Medical University, Nanjing, China
| | - Lang Hu
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Xiuxing Wang
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Xu Qian
- Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Nutrition and Food Hygiene, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sali Lv
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Baoli Hu
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,John G. Rangos Sr. Research Center, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Siyuan Zheng
- Greehey Children's Cancer Research Institute, UT Health San Antonio, San Antonio, Texas.,Department of Population Health Sciences, UT Health San Antonio, San Antonio, Texas
| | - Zhibin Hu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China.,Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China.,Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yongping You
- Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,Corresponding Authors: Qianghu Wang, Nanjing Medical University, 211166 Nanjing, China. Phone: 8602-5868-69330; E-mail: ; Tao Jiang, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China. Phone: 8601-0599-75624; E-mail: ; Roel G.W. Verhaak, The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032. Phone: 860-837-2140; E-mail: ; and Yongping You, Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China. Phone: 8602-5681-36679; E-mail:
| | - Roel G.W. Verhaak
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.,Corresponding Authors: Qianghu Wang, Nanjing Medical University, 211166 Nanjing, China. Phone: 8602-5868-69330; E-mail: ; Tao Jiang, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China. Phone: 8601-0599-75624; E-mail: ; Roel G.W. Verhaak, The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032. Phone: 860-837-2140; E-mail: ; and Yongping You, Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China. Phone: 8602-5681-36679; E-mail:
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Corresponding Authors: Qianghu Wang, Nanjing Medical University, 211166 Nanjing, China. Phone: 8602-5868-69330; E-mail: ; Tao Jiang, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China. Phone: 8601-0599-75624; E-mail: ; Roel G.W. Verhaak, The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032. Phone: 860-837-2140; E-mail: ; and Yongping You, Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China. Phone: 8602-5681-36679; E-mail:
| | - Qianghu Wang
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.,Department of Bioinformatics, Nanjing Medical University, Nanjing, China.,Institute for Brain Tumors, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Corresponding Authors: Qianghu Wang, Nanjing Medical University, 211166 Nanjing, China. Phone: 8602-5868-69330; E-mail: ; Tao Jiang, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China. Phone: 8601-0599-75624; E-mail: ; Roel G.W. Verhaak, The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032. Phone: 860-837-2140; E-mail: ; and Yongping You, Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China. Phone: 8602-5681-36679; E-mail:
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7
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Chen B, Wu X, Ruan Y, Zhang Y, Cai Q, Zapata L, Wu CI, Lan P, Wen H. Very large hidden genetic diversity in one single tumor: evidence for tumors-in-tumor. Natl Sci Rev 2022; 9:nwac250. [PMID: 36694802 PMCID: PMC9869076 DOI: 10.1093/nsr/nwac250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/13/2022] Open
Abstract
Despite the concern of within-tumor genetic diversity, this diversity is in fact limited by the kinship among cells in the tumor. Indeed, genomic studies have amply supported the 'Nowell dogma' whereby cells of the same tumor descend from a single progenitor cell. In parallel, genomic data also suggest that the diversity could be >10-fold larger if tumor cells are of multiple origins. We develop an evolutionary hypothesis that a single tumor may often harbor multiple cell clones of independent origins, but only one would be large enough to be detected. To test the hypothesis, we search for independent tumors within a larger one (or tumors-in-tumor). Very high density sampling was done on two cases of colon tumors. Case 1 indeed has 13 independent clones of disparate sizes, many having heavy mutation burdens and potentially highly tumorigenic. In Case 2, despite a very intensive search, only two small independent clones could be found. The two cases show very similar movements and metastasis of the dominant clone. Cells initially move actively in the expanding tumor but become nearly immobile in late stages. In conclusion, tumors-in-tumor are plausible but could be very demanding to find. Despite their small sizes, they can enhance the within-tumor diversity by orders of magnitude. Such increases may contribute to the missing genetic diversity associated with the resistance to cancer therapy.
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Affiliation(s)
| | | | - Yongsen Ruan
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou510275, China
| | - Yulin Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou510275, China
| | - Qichun Cai
- Cancer Center, Clifford Hospital, Jinan University, Guangzhou 511495, China
| | - Luis Zapata
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London SW7 3RP, UK
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8
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Genomic Analysis of SARS-CoV-2 Alpha, Beta and Delta Variants of Concern Uncovers Signatures of Neutral and Non-Neutral Evolution. Viruses 2022; 14:v14112375. [PMID: 36366473 PMCID: PMC9695218 DOI: 10.3390/v14112375] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 01/31/2023] Open
Abstract
Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths-Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung-Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation "bands" persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a "toy" mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations.
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9
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Ní Leathlobhair M, Lenski RE. Population genetics of clonally transmissible cancers. Nat Ecol Evol 2022; 6:1077-1089. [PMID: 35879542 DOI: 10.1038/s41559-022-01790-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 05/12/2022] [Indexed: 11/08/2022]
Abstract
Populations of cancer cells are subject to the same core evolutionary processes as asexually reproducing, unicellular organisms. Transmissible cancers are particularly striking examples of these processes. These unusual cancers are clonal lineages that can spread through populations via physical transfer of living cancer cells from one host individual to another, and they have achieved long-term success in the colonization of at least eight different host species. Population genetic theory provides a useful framework for understanding the shift from a multicellular sexual animal into a unicellular asexual clone and its long-term effects on the genomes of these cancers. In this Review, we consider recent findings from transmissible cancer research with the goals of developing an evolutionarily informed perspective on transmissible cancers, examining possible implications for their long-term fate and identifying areas for future research on these exceptional lineages.
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Affiliation(s)
- Máire Ní Leathlobhair
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Microbiology, Moyne Institute of Preventive Medicine, School of Genetics and Microbiology, Trinity College Dublin, Dublin, Ireland.
| | - Richard E Lenski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA
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10
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Kurpas MK, Kimmel M. Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra. Front Ecol Evol 2022; 10:889438. [PMID: 37333691 PMCID: PMC10275603 DOI: 10.3389/fevo.2022.889438] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024] Open
Abstract
The tug-of-war model was developed in a series of papers of McFarland and co-authors to account for existence of mutually counteracting rare advantageous driver mutations and more frequent slightly deleterious passenger mutations in cancer. In its original version, it was a state-dependent branching process. Because of its formulation, the tug-of-war model is of importance for tackling the problem as to whether evolution of cancerous tumors is "Darwinian" or "non-Darwinian." We define two Time-Continuous Markov Chain versions of the model, including identical mutation processes but adopting different drift and selection components. In Model A, drift and selection process preserves expected fitness whereas in Model B it leads to non-decreasing expected fitness. We investigate these properties using mathematical analysis and extensive simulations, which detect the effect of the so-called drift barrier in Model B but not in Model A. These effects are reflected in different structure of clone genealogies in the two models. Our work is related to the past theoretical work in the field of evolutionary genetics, concerning the interplay among mutation, drift and selection, in absence of recombination (asexual reproduction), where epistasis plays a major role. Finally, we use the statistics of mutation frequencies known as the Site Frequency Spectra (SFS), to compare the variant frequencies in DNA of sequenced HER2+ breast cancers, to those based on Model A and B simulations. The tumor-based SFS are better reproduced by Model A, pointing out a possible selection pattern of HER2+ tumor evolution. To put our models in context, we carried out an exploratory study of how publicly accessible data from breast, prostate, skin and ovarian cancers fit a range of models found in the literature.
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Affiliation(s)
- Monika K. Kurpas
- Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Marek Kimmel
- Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
- Department of Statistics and Bioengineering, Rice University, Houston, TX, United States
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11
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van den Bosch T, Vermeulen L, Miedema DM. Quantitative models for the inference of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1034] [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] Open
Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
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12
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Wu T, Wang G, Wang X, Wang S, Zhao X, Wu C, Ning W, Tao Z, Chen F, Liu XS. Quantification of neoantigen-mediated immunoediting in cancer evolution. Cancer Res 2022; 82:2226-2238. [PMID: 35486454 DOI: 10.1158/0008-5472.can-21-3717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/08/2022] [Accepted: 04/19/2022] [Indexed: 11/16/2022]
Abstract
Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual cancer patients. Moreover, this method demonstrated the prevalence of immunoediting in immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer.
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Affiliation(s)
- Tao Wu
- ShanghaiTech University, shanghai, China
| | | | - Xuan Wang
- ShanghaiTech University, shanghai, China
| | | | | | - Chenxu Wu
- ShanghaiTech University, shanghai, China
| | - Wei Ning
- ShanghaiTech University, Shanghai, China
| | - Ziyu Tao
- ShanghaiTech University, shanghai, China
| | - Fuxiang Chen
- NO.9 People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China
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13
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Schenck RO, Brosula G, West J, Leedham S, Shibata D, Anderson AR. Gattaca: Base-Pair Resolution Mutation Tracking for Somatic Evolution Studies using Agent-based Models. Mol Biol Evol 2022; 39:msac058. [PMID: 35298641 PMCID: PMC9034688 DOI: 10.1093/molbev/msac058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution.
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Affiliation(s)
- Ryan O. Schenck
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX37BN, United Kingdom
| | - Gabriel Brosula
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Jeffrey West
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Simon Leedham
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Darryl Shibata
- Department of Pathology, University of Southern California, Keck School of Medicine, Los Angeles, CA 90033, USA
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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14
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Li G, Yang Z, Wu D, Liu S, Li X, Li T, Li Y, Liang L, Zou W, Wu CI, Wang HY, Lu X. Evolution under spatially heterogeneous selection in solid tumors. Mol Biol Evol 2021; 39:6440067. [PMID: 34850073 PMCID: PMC8788224 DOI: 10.1093/molbev/msab335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.
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Affiliation(s)
- Guanghao Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zuyu Yang
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,Institute of Environmental Science and Research, Porirua, New Zealand
| | - Dafei Wu
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Sixue Liu
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuening Li
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tao Li
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yawei Li
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Liji Liang
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Weilong Zou
- Surgery of Liver Transplant, The Third Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.,Surgery of Hepatopancreatobiliary, Peking University Shougang Hospital, Beijing, 100144, China
| | - Chung-I Wu
- China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,State Key Laboratory of Biocontrol, College of Ecology and Evolution, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Hurng-Yi Wang
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, 11031, Taiwan.,Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, 106, Taiwan
| | - Xuemei Lu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,China National Center for Bioinformation, Beijing, 100101, China.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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15
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Gunnarsson EB, Leder K, Foo J. Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability. Theor Popul Biol 2021; 142:67-90. [PMID: 34560155 DOI: 10.1016/j.tpb.2021.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 08/24/2021] [Accepted: 09/11/2021] [Indexed: 11/28/2022]
Abstract
The site frequency spectrum (SFS) is a popular summary statistic of genomic data. While the SFS of a constant-sized population undergoing neutral mutations has been extensively studied in population genetics, the rapidly growing amount of cancer genomic data has attracted interest in the spectrum of an exponentially growing population. Recent theoretical results have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large-time or large-population limits. In this work, we derive exact expressions for the expected SFS of a cell population that evolves according to a stochastic branching process, first for cells with an infinite line of descent and then for the total population, evaluated either at a fixed time (fixed-time spectrum) or at the stochastic time at which the population reaches a certain size (fixed-size spectrum). We find that while the rate of mutation scales the SFS of the total population linearly, the rates of cell birth and cell death change the shape of the spectrum at the small-frequency end, inducing a transition between a 1/j2 power-law spectrum and a 1/j spectrum as cell viability decreases. We show that this insight can in principle be used to estimate the ratio between the rate of cell death and cell birth, as well as the mutation rate, using the site frequency spectrum alone. Although the discussion is framed in terms of tumor dynamics, our results apply to any exponentially growing population of individuals undergoing neutral mutations.
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Affiliation(s)
- Einar Bjarki Gunnarsson
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA.
| | - Kevin Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA.
| | - Jasmine Foo
- School of Mathematics, University of Minnesota, Twin Cities, MN 55455, USA.
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16
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Clonal Evolution of Multiple Myeloma-Clinical and Diagnostic Implications. Diagnostics (Basel) 2021; 11:diagnostics11091534. [PMID: 34573876 PMCID: PMC8469181 DOI: 10.3390/diagnostics11091534] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/17/2021] [Accepted: 08/19/2021] [Indexed: 12/22/2022] Open
Abstract
Plasma cell dyscrasias are a heterogeneous group of diseases characterized by the expansion of bone marrow plasma cells. Malignant transformation of plasma cells depends on the continuity of events resulting in a sequence of well-defined disease stages, from monoclonal gammopathy of undetermined significance (MGUS) through smoldering myeloma (SMM) to symptomatic multiple myeloma (MM). Evolution of a pre-malignant cell into a malignant cell, as well as further tumor progression, dissemination, and relapse, require development of multiple driver lesions conferring selective advantage of the dominant clone and allowing subsequent evolution under selective pressure of microenvironment and treatment. This process of natural selection facilitates tumor plasticity leading to the formation of genetically complex and heterogenous tumors that are notoriously difficult to treat. Better understanding of the mechanisms underlying tumor evolution in MM and identification of lesions driving the evolution from the premalignant clone is therefore a key to development of effective treatment and long-term disease control. Here, we review recent advances in clonal evolution patterns and genomic landscape dynamics of MM, focusing on their clinical implications.
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17
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Leroi AM, Lambert B, Rosindell J, Kokkoris GD. Neutral Theory is a tool that should be wielded with care. Nat Hum Behav 2021; 5:809. [PMID: 34239078 DOI: 10.1038/s41562-021-01150-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Armand M Leroi
- Department of Life Sciences, Imperial College London, London, UK. .,Data Science Institute, Imperial College London, London, UK.
| | - Ben Lambert
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - James Rosindell
- Department of Life Sciences, Imperial College London, Ascot, UK
| | - Giorgos D Kokkoris
- Department of Marine Sciences, University of the Aegean, Mytilene, Greece
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18
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Positive selection as the unifying force for clonal evolution in multiple myeloma. Leukemia 2021; 35:1511-1515. [PMID: 33483619 DOI: 10.1038/s41375-021-01130-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/25/2020] [Accepted: 01/07/2021] [Indexed: 01/30/2023]
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19
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West J, Schenck RO, Gatenbee C, Robertson-Tessi M, Anderson ARA. Normal tissue architecture determines the evolutionary course of cancer. Nat Commun 2021; 12:2060. [PMID: 33824323 PMCID: PMC8024392 DOI: 10.1038/s41467-021-22123-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/24/2021] [Indexed: 12/17/2022] Open
Abstract
Cancer growth can be described as a caricature of the renewal process of the tissue of origin, where the tissue architecture has a strong influence on the evolutionary dynamics within the tumor. Using a classic, well-studied model of tumor evolution (a passenger-driver mutation model) we systematically alter spatial constraints and cell mixing rates to show how tissue structure influences functional (driver) mutations and genetic heterogeneity over time. This approach explores a key mechanism behind both inter-patient and intratumoral tumor heterogeneity: competition for space. Time-varying competition leads to an emergent transition from Darwinian premalignant growth to subsequent invasive neutral tumor growth. Initial spatial constraints determine the emergent mode of evolution (Darwinian to neutral) without a change in cell-specific mutation rate or fitness effects. Driver acquisition during the Darwinian precancerous stage may be modulated en route to neutral evolution by the combination of two factors: spatial constraints and limited cellular mixing. These two factors occur naturally in ductal carcinomas, where the branching topology of the ductal network dictates spatial constraints and mixing rates.
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Affiliation(s)
- Jeffrey West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Ryan O Schenck
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Chandler Gatenbee
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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20
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Tung HR, Durrett R. Signatures of neutral evolution in exponentially growing tumors: A theoretical perspective. PLoS Comput Biol 2021; 17:e1008701. [PMID: 33571199 PMCID: PMC7904140 DOI: 10.1371/journal.pcbi.1008701] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/24/2021] [Accepted: 01/13/2021] [Indexed: 11/18/2022] Open
Abstract
Recent work of Sottoriva, Graham, and collaborators have led to the controversial claim that exponentially growing tumors have a site frequency spectrum that follows the 1/f law consistent with neutral evolution. This conclusion has been criticized based on data quality issues, statistical considerations, and simulation results. Here, we use rigorous mathematical arguments to investigate the site frequency spectrum in the two-type model of clonal evolution. If the fitnesses of the two types are λ0 < λ1, then the site frequency spectrum is c/fα where α = λ0/λ1. This is due to the advantageous mutations that produce the founders of the type 1 population. Mutations within the growing type 0 and type 1 populations follow the 1/f law. Our results show that, in contrast to published criticisms, neutral evolution in an exponentially growing tumor can be distinguished from the two-type model using the site frequency spectrum.
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Affiliation(s)
- Hwai-Ray Tung
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - Rick Durrett
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
- * E-mail:
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21
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Stadler T, Pybus OG, Stumpf MPH. Phylodynamics for cell biologists. Science 2021; 371:371/6526/eaah6266. [PMID: 33446527 DOI: 10.1126/science.aah6266] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 08/13/2020] [Indexed: 12/12/2022]
Abstract
Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.
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Affiliation(s)
- T Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
| | - M P H Stumpf
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
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22
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Hoang PH, Cornish AJ, Sherborne AL, Chubb D, Kimber S, Jackson G, Morgan GJ, Cook G, Kinnersley B, Kaiser M, Houlston RS. An enhanced genetic model of relapsed IGH-translocated multiple myeloma evolutionary dynamics. Blood Cancer J 2020; 10:101. [PMID: 33057009 PMCID: PMC7560599 DOI: 10.1038/s41408-020-00367-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/15/2020] [Accepted: 09/28/2020] [Indexed: 01/11/2023] Open
Abstract
Most patients with multiple myeloma (MM) die from progressive disease after relapse. To advance our understanding of MM evolution mechanisms, we performed whole-genome sequencing of 80 IGH-translocated tumour-normal newly diagnosed pairs and 24 matched relapsed tumours from the Myeloma XI trial. We identify multiple events as potentially important for survival and therapy-resistance at relapse including driver point mutations (e.g., TET2), translocations (MAP3K14), lengthened telomeres, and increased genomic instability (e.g., 17p deletions). Despite heterogeneous mutational processes contributing to relapsed mutations across MM subtypes, increased AID/APOBEC activity is particularly associated with shorter progression time to relapse, and contributes to higher mutational burden at relapse. In addition, we identify three enhanced major clonal evolution patterns of MM relapse, independent of treatment strategies and molecular karyotypes, questioning the viability of "evolutionary herding" approach in treating drug-resistant MM. Our data show that MM relapse is associated with acquisition of new mutations and clonal selection, and suggest APOBEC enzymes among potential targets for therapy-resistant MM.
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Affiliation(s)
- Phuc H Hoang
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Alex J Cornish
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Amy L Sherborne
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Daniel Chubb
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Scott Kimber
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Graham Jackson
- Department of Haematology, University of Newcastle, Newcastle Upon Tyne, UK
| | | | - Gordon Cook
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Martin Kaiser
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK.
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.
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23
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Bozic I, Wu CJ. Delineating the evolutionary dynamics of cancer from theory to reality. ACTA ACUST UNITED AC 2020; 1:580-588. [DOI: 10.1038/s43018-020-0079-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 05/18/2020] [Indexed: 01/08/2023]
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24
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Abstract
Neutral models of evolution assume the absence of natural selection. Formerly confined to ecology and evolutionary biology, neutral models are spreading. In recent years they've been applied to explaining the diversity of baby names, scientific citations, cryptocurrencies, pot decorations, literary lexica, tumour variants and much more besides. Here, we survey important neutral models and highlight their similarities. We investigate the most widely used tests of neutrality, show that they are weak and suggest more powerful methods. We conclude by discussing the role of neutral models in the explanation of diversity. We suggest that the ability of neutral models to fit low-information distributions should not be taken as evidence for the absence of selection. Nevertheless, many studies, in increasingly diverse fields, make just such claims. We call this tendency 'neutral syndrome'.
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25
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Abstract
Tumours vary in gene expression programmes and genetic alterations. Understanding this diversity and its biological meaning requires a theoretical framework, which could in turn guide the development of more accurate prognosis and therapy. Here, we review the theory of multi-task evolution of cancer, which is based upon the premise that tumours evolve in the host and face selection trade-offs between multiple biological functions. This theory can help identify the major biological tasks that cancer cells perform and the trade-offs between these tasks. It introduces the concept of specialist tumours, which focus on one task, and generalist tumours, which perform several tasks. Specialist tumours are suggested to be sensitive to therapy targeting their main task. Driver mutations tune gene expression towards specific tasks in a tissue-dependent manner and thus help to determine whether a tumour is specialist or generalist. We discuss potential applications of the theory of multi-task evolution to interpret the spatial organization of tumours and intratumour heterogeneity.
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Affiliation(s)
- Jean Hausser
- Department of Cellular and Molecular Biology, Karolinska Institutet, Solna, Sweden.
- SciLifeLab, Solna, Sweden.
| | - Uri Alon
- Department of Molecular and Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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26
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Ryser MD, Mallo D, Hall A, Hardman T, King LM, Tatishchev S, Sorribes IC, Maley CC, Marks JR, Hwang ES, Shibata D. Minimal barriers to invasion during human colorectal tumor growth. Nat Commun 2020; 11:1280. [PMID: 32152322 PMCID: PMC7062901 DOI: 10.1038/s41467-020-14908-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 02/10/2020] [Indexed: 12/15/2022] Open
Abstract
Intra-tumoral heterogeneity (ITH) could represent clonal evolution where subclones with greater fitness confer more malignant phenotypes and invasion constitutes an evolutionary bottleneck. Alternatively, ITH could represent branching evolution with invasion of multiple subclones. The two models respectively predict a hierarchy of subclones arranged by phenotype, or multiple subclones with shared phenotypes. We delineate these modes of invasion by merging ancestral, topographic, and phenotypic information from 12 human colorectal tumors (11 carcinomas, 1 adenoma) obtained through saturation microdissection of 325 small tumor regions. The majority of subclones (29/46, 60%) share superficial and invasive phenotypes. Of 11 carcinomas, 9 show evidence of multiclonal invasion, and invasive and metastatic subclones arise early along the ancestral trees. Early multiclonal invasion in the majority of these tumors indicates the expansion of co-evolving subclones with similar malignant potential in absence of late bottlenecks and suggests that barriers to invasion are minimal during colorectal cancer growth.
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Affiliation(s)
- Marc D Ryser
- Department of Population Health Sciences, Duke University Medical Center, Durham, NC, USA.
- Department of Mathematics, Duke University, Durham, NC, USA.
- Duke Cancer Institute, Durham, NC, USA.
| | - Diego Mallo
- Arizona Cancer Evolution Center and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Allison Hall
- Department of Pathology, Duke University Medical Center, Durham, NC, USA
| | - Timothy Hardman
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - Lorraine M King
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - Sergei Tatishchev
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | - Carlo C Maley
- Arizona Cancer Evolution Center and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jeffrey R Marks
- Duke Cancer Institute, Durham, NC, USA
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - E Shelley Hwang
- Duke Cancer Institute, Durham, NC, USA
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - Darryl Shibata
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA.
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Laajala TD, Gerke T, Tyekucheva S, Costello JC. Modeling genetic heterogeneity of drug response and resistance in cancer. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:8-14. [PMID: 37736115 PMCID: PMC10512436 DOI: 10.1016/j.coisb.2019.09.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.
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Affiliation(s)
- Teemu D. Laajala
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Travis Gerke
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Svitlana Tyekucheva
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - James C Costello
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Univeristy of Colorado Comprehensive Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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28
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Bozic I, Paterson C, Waclaw B. On measuring selection in cancer from subclonal mutation frequencies. PLoS Comput Biol 2019; 15:e1007368. [PMID: 31557163 PMCID: PMC6788714 DOI: 10.1371/journal.pcbi.1007368] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 10/11/2019] [Accepted: 09/01/2019] [Indexed: 11/19/2022] Open
Abstract
Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides quantification of the validity of the 1/f statistic across the entire range of relevant parameter values. We also show that our conclusions remain valid for non-exponential models: spatial 3d model and sigmoidal growth, relevant for early- and late stages of cancer growth. Darwinian evolution in cancer is responsible for the emergence of malignant traits in initially benign tumors. As tumor cells divide, they accumulate new mutations and while most of them are “passengers” which do not confer any selective growth advantage, “driver” mutations endow cells with traits that contribute to cancer spread. Identifying driver mutations that are under selection in cancer can point to new targets for cancer therapeutics and open new avenues for personalized cancer treatment. It has recently been argued that the presence or absence of selection in cancer can be deduced from deviation of mutant allele frequencies from 1/f power law in an intermediate frequency range. Using a stochastic mathematical model of cancer evolution we derive a formula for the frequency of a subclonal driver and show that frequencies of cancer drivers are biased towards 0 and 1; thus most mutations will inevitably appear to be either neutral (frequency ≈ 0) or clonal (frequency ≈ 1) despite very different levels of selection. Consequently, the proposed 1/f statistic will significantly overestimate the number of cancers deemed to be evolving neutrally. Our work quantifies the validity of the proposed neutral evolution statistic across the entire range of relevant parameter values.
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Affiliation(s)
- Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- * E-mail:
| | - Chay Paterson
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Bartlomiej Waclaw
- School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
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29
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Werner B, Williams MJ, Barnes CP, Graham TA, Sottoriva A. Reply to 'Currently available bulk sequencing data do not necessarily support a model of neutral tumor evolution'. Nat Genet 2018; 50:1624-1626. [PMID: 30374070 DOI: 10.1038/s41588-018-0235-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK
- Department of Cell and Developmental Biology, University College London, London, UK
- Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
- Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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