1
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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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2
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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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3
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Frankell AM, Dietzen M, Al Bakir M, Lim EL, Karasaki T, Ward S, Veeriah S, Colliver E, Huebner A, Bunkum A, Hill MS, Grigoriadis K, Moore DA, Black JRM, Liu WK, Thol K, Pich O, Watkins TBK, Naceur-Lombardelli C, Cook DE, Salgado R, Wilson GA, Bailey C, Angelova M, Bentham R, Martínez-Ruiz C, Abbosh C, Nicholson AG, Le Quesne J, Biswas D, Rosenthal R, Puttick C, Hessey S, Lee C, Prymas P, Toncheva A, Smith J, Xing W, Nicod J, Price G, Kerr KM, Naidu B, Middleton G, Blyth KG, Fennell DA, Forster MD, Lee SM, Falzon M, Hewish M, Shackcloth MJ, Lim E, Benafif S, Russell P, Boleti E, Krebs MG, Lester JF, Papadatos-Pastos D, Ahmad T, Thakrar RM, Lawrence D, Navani N, Janes SM, Dive C, Blackhall FH, Summers Y, Cave J, Marafioti T, Herrero J, Quezada SA, Peggs KS, Schwarz RF, Van Loo P, Miedema DM, Birkbak NJ, Hiley CT, Hackshaw A, Zaccaria S, Jamal-Hanjani M, McGranahan N, Swanton C. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 2023; 616:525-533. [PMID: 37046096 PMCID: PMC10115649 DOI: 10.1038/s41586-023-05783-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/02/2023] [Indexed: 04/14/2023]
Abstract
Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource.
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Affiliation(s)
- Alexander M Frankell
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emilia L Lim
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Takahiro Karasaki
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Abigail Bunkum
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Wing Kin Liu
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Kerstin Thol
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Chris Bailey
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mihaela Angelova
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Robert Bentham
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John Le Quesne
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Pathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Dhruva Biswas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Rachel Rosenthal
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Clare Puttick
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Claudia Lee
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Division of Medicine, University College London, London, UK
| | - Paulina Prymas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Jon Smith
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Wei Xing
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Jerome Nicod
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Gillian Price
- Department of Medical Oncology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
- University of Aberdeen, Aberdeen, UK
| | - Keith M Kerr
- University of Aberdeen, Aberdeen, UK
- Department of Pathology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
| | - Babu Naidu
- Birmingham Acute Care Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | - Gary Middleton
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Kevin G Blyth
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | - Dean A Fennell
- University of Leicester, Leicester, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Martin D Forster
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Siow Ming Lee
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Mary Falzon
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Madeleine Hewish
- Royal Surrey Hospital, Royal Surrey Hospitals NHS Foundation Trust, Guilford, UK
- University of Surrey, Guilford, UK
| | | | - Eric Lim
- Academic Division of Thoracic Surgery, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sarah Benafif
- Department of Oncology, University College London Hospitals, London, UK
| | - Peter Russell
- Princess Alexandra Hospital, The Princess Alexandra Hospital NHS Trust, Harlow, UK
| | - Ekaterini Boleti
- Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK
| | - Matthew G Krebs
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Jason F Lester
- Singleton Hospital, Swansea Bay University Health Board, Swansea, UK
| | | | - Tanya Ahmad
- Department of Oncology, University College London Hospitals, London, UK
| | - Ricky M Thakrar
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - David Lawrence
- Department of Thoracic Surgery, University College London Hospital NHS Trust, London, UK
| | - Neal Navani
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Fiona H Blackhall
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Yvonne Summers
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Judith Cave
- Department of Oncology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Javier Herrero
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Immune Regulation and Tumour Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Karl S Peggs
- Department of Haematology, University College London Hospitals, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Roland F Schwarz
- Institute for Computational Cancer Biology, Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
| | - Peter Van Loo
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Daniël M Miedema
- LEXOR, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Nicolai J Birkbak
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Crispin T Hiley
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Allan Hackshaw
- Cancer Research UK and UCL Cancer Trials Centre, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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4
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Zapata L, Caravagna G, Williams MJ, Lakatos E, AbdulJabbar K, Werner B, Chowell D, James C, Gourmet L, Milite S, Acar A, Riaz N, Chan TA, Graham TA, Sottoriva A. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat Genet 2023; 55:451-460. [PMID: 36894710 PMCID: PMC10011129 DOI: 10.1038/s41588-023-01313-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/25/2023] [Indexed: 03/11/2023]
Abstract
In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.
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Affiliation(s)
- Luis Zapata
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Cancer Data Science Laboratory, Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Trieste, Italy
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan 10 Kettering Cancer Center, New York, NY, USA
| | - Eszter Lakatos
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Khalid AbdulJabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Benjamin Werner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Diego Chowell
- The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chela James
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Lucie Gourmet
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Salvatore Milite
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Ahmet Acar
- Department of Biological Sciences, Middle East Technical University, Universiteler Mah, Ankara, Turkey
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timothy A Chan
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Computational Biology Research Centre, Human Technopole, Milan, Italy.
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5
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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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6
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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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7
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Ouellette TW, Awadalla P. Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning. PLoS Comput Biol 2022; 18:e1010007. [PMID: 35482653 PMCID: PMC9049314 DOI: 10.1371/journal.pcbi.1010007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/09/2022] [Indexed: 11/18/2022] Open
Abstract
Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.
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Affiliation(s)
- Tom W. Ouellette
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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8
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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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9
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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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10
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Azimzade Y, Saberi AA, Gatenby RA. Superlinear growth reveals the Allee effect in tumors. Phys Rev E 2021; 103:042405. [PMID: 34005934 DOI: 10.1103/physreve.103.042405] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 03/16/2021] [Indexed: 12/17/2022]
Abstract
Integrating experimental data into ecological models plays a central role in understanding biological mechanisms that drive tumor progression where such knowledge can be used to develop new therapeutic strategies. While the current studies emphasize the role of competition among tumor cells, they fail to explain recently observed superlinear growth dynamics across human tumors. Here we study tumor growth dynamics by developing a model that incorporates evolutionary dynamics inside tumors with tumor-microenvironment interactions. Our results reveal that tumor cells' ability to manipulate the environment and induce angiogenesis drives superlinear growth-a process compatible with the Allee effect. In light of this understanding, our model suggests that, for high-risk tumors that have a higher growth rate, suppressing angiogenesis can be the appropriate therapeutic intervention.
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Affiliation(s)
- Youness Azimzade
- Department of Physics, University of Tehran, Tehran 14395-547, Iran
| | - Abbas Ali Saberi
- Department of Physics, University of Tehran, Tehran 14395-547, Iran and Institut für Theoretische Physik, Universitat zu Köln, Zülpicher Strasse 77, 50937 Köln, Germany
| | - Robert A Gatenby
- Cancer Biology and Evolution Program, Integrated Mathematical Oncology Department, and Diagnostic Imaging Department, Moffitt Cancer Center, Tampa, Florida 33612, USA
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11
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Tarabichi M, Salcedo A, Deshwar AG, Ni Leathlobhair M, Wintersinger J, Wedge DC, Van Loo P, Morris QD, Boutros PC. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 2021; 18:144-155. [PMID: 33398189 PMCID: PMC7867630 DOI: 10.1038/s41592-020-01013-2] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 11/09/2020] [Indexed: 01/28/2023]
Abstract
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
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Affiliation(s)
- Maxime Tarabichi
- The Francis Crick Institute, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Adriana Salcedo
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Amit G Deshwar
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Máire Ni Leathlobhair
- Big Data Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
| | - Jeff Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - David C Wedge
- Big Data Institute, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford, UK
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK
| | | | - Quaid D Morris
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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12
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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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13
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Salichos L, Meyerson W, Warrell J, Gerstein M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nat Commun 2020; 11:732. [PMID: 32024824 PMCID: PMC7002450 DOI: 10.1038/s41467-020-14407-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 11/26/2019] [Indexed: 01/01/2023] Open
Abstract
Tumors accumulate thousands of mutations, and sequencing them has given rise to methods for finding cancer drivers via mutational recurrence. However, these methods require large cohorts and underperform for low recurrence. Recently, ultra-deep sequencing has enabled accurate measurement of VAFs (variant-allele frequencies) for mutations, allowing the determination of evolutionary trajectories. Here, based solely on the VAF spectrum for an individual sample, we report on a method that identifies drivers and quantifies tumor growth. Drivers introduce perturbations into the spectrum, and our method uses the frequency of hitchhiking mutations preceding a driver to measure this. As validation, we use simulation models and 993 tumors from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium with previously identified drivers. Then we apply our method to an ultra-deep sequenced acute myeloid leukemia (AML) tumor and identify known cancer genes and additional driver candidates. In summary, our framework presents opportunities for personalized driver diagnosis using sequencing data from a single individual.
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Affiliation(s)
- Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - William Meyerson
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Center for Biomedical Data Science, Yale University, New Haven, CT, 06520, USA.
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14
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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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15
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Abstract
To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, cancer is a particular case owing to the vast size of tumour cell populations, chromosomal instability and its potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid to understand cancer progression and therapy failure. Indeed, such a framework could be applied to predict individual tumour behaviour and support treatment strategies.
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Affiliation(s)
- Samra Turajlic
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, UK
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Trevor Graham
- Tumour Biology, Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK.
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London, UK.
- Department of Medical Oncology, University College London Hospitals, London, UK.
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16
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Williams MJ, Sottoriva A, Graham TA. Measuring Clonal Evolution in Cancer with Genomics. Annu Rev Genomics Hum Genet 2019; 20:309-329. [PMID: 31059289 DOI: 10.1146/annurev-genom-083117-021712] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.
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Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London SM2 5NG, United Kingdom
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
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