1
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Kayhanian H, Cross W, van der Horst SEM, Barmpoutis P, Lakatos E, Caravagna G, Zapata L, Van Hoeck A, Middelkamp S, Litchfield K, Steele C, Waddingham W, Patel D, Milite S, Jin C, Baker AM, Alexander DC, Khan K, Hochhauser D, Novelli M, Werner B, van Boxtel R, Hageman JH, Buissant des Amorie JR, Linares J, Ligtenberg MJL, Nagtegaal ID, Laclé MM, Moons LMG, Brosens LAA, Pillay N, Sottoriva A, Graham TA, Rodriguez-Justo M, Shiu KK, Snippert HJG, Jansen M. Homopolymer switches mediate adaptive mutability in mismatch repair-deficient colorectal cancer. Nat Genet 2024:10.1038/s41588-024-01777-9. [PMID: 38956208 DOI: 10.1038/s41588-024-01777-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/25/2024] [Indexed: 07/04/2024]
Abstract
Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, the MMR genes MutS homolog 6 (MSH6) and MutS homolog 3 (MSH3) also contain coding homopolymers, and these are frequent mutational targets in MMR-deficient cancers. The impact of incremental MMR mutations on MMR-deficient cancer evolution is unknown. Here we show that microsatellite instability modulates DNA repair by toggling hypermutable mononucleotide homopolymer runs in MSH6 and MSH3 through stochastic frameshift switching. Spontaneous mutation and reversion modulate subclonal mutation rate, mutation bias and HLA and neoantigen diversity. Patient-derived organoids corroborate these observations and show that MMR homopolymer sequences drift back into reading frame in the absence of immune selection, suggesting a fitness cost of elevated mutation rates. Combined experimental and simulation studies demonstrate that subclonal immune selection favors incremental MMR mutations. Overall, our data demonstrate that MMR-deficient colorectal cancers fuel intratumor heterogeneity by adapting subclonal mutation rate and diversity to immune selection.
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Affiliation(s)
| | - William Cross
- UCL Cancer Institute, University College London, London, UK
- Cancer Mechanisms and Biomarker Discovery Group, School of Life Sciences, University of Westminster, London, UK
| | - Suzanne E M van der Horst
- Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Panagiotis Barmpoutis
- UCL Cancer Institute, University College London, London, UK
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eszter Lakatos
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Giulio Caravagna
- Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy
| | - Luis Zapata
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Arne Van Hoeck
- Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sjors Middelkamp
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | | | | | | | - Dominic Patel
- UCL Cancer Institute, University College London, London, UK
| | - Salvatore Milite
- Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy
| | - Chen Jin
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Khurum Khan
- Department of Oncology, UCL Cancer Institute, University College London, London, UK
| | - Daniel Hochhauser
- UCL Cancer Institute, University College London, London, UK
- Department of Oncology, UCL Cancer Institute, University College London, London, UK
| | - Marco Novelli
- UCL Cancer Institute, University College London, London, UK
- Department of Pathology, University College London Hospital, London, UK
| | - Benjamin Werner
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Ruben van Boxtel
- Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Joris H Hageman
- Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | - Marjolijn J L Ligtenberg
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Iris D Nagtegaal
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Miangela M Laclé
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Leon M G Moons
- Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lodewijk A A Brosens
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Manuel Rodriguez-Justo
- UCL Cancer Institute, University College London, London, UK
- Department of Pathology, University College London Hospital, London, UK
| | - Kai-Keen Shiu
- UCL Cancer Institute, University College London, London, UK
- Department of Oncology, UCL Cancer Institute, University College London, London, UK
| | - Hugo J G Snippert
- Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Marnix Jansen
- UCL Cancer Institute, University College London, London, UK.
- Department of Pathology, University College London Hospital, London, UK.
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2
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Gao Y, Feder AF. Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601073. [PMID: 39005367 PMCID: PMC11244928 DOI: 10.1101/2024.06.27.601073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on the J 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( ŝ and the Sackin index) show interior performance to J 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.
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3
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Yu L, Renton J, Burian A, Khachaturyan M, Bayer T, Kotta J, Stachowicz JJ, DuBois K, Baums IB, Werner B, Reusch TBH. A somatic genetic clock for clonal species. Nat Ecol Evol 2024; 8:1327-1336. [PMID: 38858515 PMCID: PMC11239492 DOI: 10.1038/s41559-024-02439-z] [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: 11/30/2023] [Accepted: 05/07/2024] [Indexed: 06/12/2024]
Abstract
Age and longevity are key parameters for demography and life-history evolution of organisms. In clonal species, a widespread life history among animals, plants, macroalgae and fungi, the sexually produced offspring (genet) grows indeterminately by producing iterative modules, or ramets, and so obscure their age. Here we present a novel molecular clock based on the accumulation of fixed somatic genetic variation that segregates among ramets. Using a stochastic model, we demonstrate that the accumulation of fixed somatic genetic variation will approach linearity after a lag phase, and is determined by the mitotic mutation rate, without direct dependence on asexual generation time. The lag phase decreased with lower stem cell population size, number of founder cells for the formation of new modules, and the ratio of symmetric versus asymmetric cell divisions. We calibrated the somatic genetic clock on cultivated eelgrass Zostera marina genets (4 and 17 years respectively). In a global data set of 20 eelgrass populations, genet ages were up to 1,403 years. The somatic genetic clock is applicable to any multicellular clonal species where the number of founder cells is small, opening novel research avenues to study longevity and, hence, demography and population dynamics of clonal species.
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Affiliation(s)
- Lei Yu
- GEOMAR Helmholtz-Center for Ocean Research Kiel, Marine Evolutionary Ecology, Kiel, Germany
| | - Jessie Renton
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Agata Burian
- Institute of Biology, Biotechnology and Environmental Protection, University of Silesia in Katowice, Katowice, Poland
| | - Marina Khachaturyan
- GEOMAR Helmholtz-Center for Ocean Research Kiel, Marine Evolutionary Ecology, Kiel, Germany
- Institute of General Microbiology, Kiel University, Kiel, Germany
| | - Till Bayer
- GEOMAR Helmholtz-Center for Ocean Research Kiel, Marine Evolutionary Ecology, Kiel, Germany
| | - Jonne Kotta
- Estonian Marine Institute, University of Tartu, Tallinn, Estonia
| | - John J Stachowicz
- Department of Evolution and Ecology, University of California, Davis, CA, USA
| | - Katherine DuBois
- Department of Evolution and Ecology, University of California, Davis, CA, USA
| | - Iliana B Baums
- Helmholtz Institute for Functional Marine Biodiversity, University of Oldenburg, Oldenburg, Germany
- Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment (ICBM), School of Mathematics and Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Benjamin Werner
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
| | - Thorsten B H Reusch
- GEOMAR Helmholtz-Center for Ocean Research Kiel, Marine Evolutionary Ecology, Kiel, Germany.
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4
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Chen S, Xie D, Li Z, Wang J, Hu Z, Zhou D. Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response. Commun Biol 2024; 7:770. [PMID: 38918569 PMCID: PMC11199503 DOI: 10.1038/s42003-024-06460-7] [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: 09/01/2023] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.
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Affiliation(s)
- Shaoqing Chen
- School of Mathematical Sciences, Xiamen University, Xiamen, China
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Duo Xie
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Zan Li
- Life Science Research Center, Core Research Facilities, Southern University of Science and Technology, Shenzhen, China
| | - Jiguang Wang
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Hong Kong Center for Neurodegenerative Diseases, InnoHK, Hong Kong SAR, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China
| | - Zheng Hu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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5
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Yao Z, Jin S, Zhou F, Wang J, Wang K, Zou X. A novel multiscale framework for delineating cancer evolution from subclonal compositions. J Theor Biol 2024; 582:111743. [PMID: 38307450 DOI: 10.1016/j.jtbi.2024.111743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 12/21/2023] [Accepted: 01/20/2024] [Indexed: 02/04/2024]
Abstract
OBJECTIVE Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge. METHODS A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model. RESULTS The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm. CONCLUSION The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.
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Affiliation(s)
- Zhihao Yao
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei Province, China; Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, 0372, Oslo, Norway; Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, 1474, Viken, Norway
| | - Suoqin Jin
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei Province, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei Province, China
| | - Junbai Wang
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, 1474, Viken, Norway
| | - Kai Wang
- Department of Biostatistics, University of Iowa, Iowa City, 52242, IA, USA.
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei Province, China.
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6
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Weng C, Yu F, Yang D, Poeschla M, Liggett LA, Jones MG, Qiu X, Wahlster L, Caulier A, Hussmann JA, Schnell A, Yost KE, Koblan LW, Martin-Rufino JD, Min J, Hammond A, Ssozi D, Bueno R, Mallidi H, Kreso A, Escabi J, Rideout WM, Jacks T, Hormoz S, van Galen P, Weissman JS, Sankaran VG. Deciphering cell states and genealogies of human haematopoiesis. Nature 2024; 627:389-398. [PMID: 38253266 PMCID: PMC10937407 DOI: 10.1038/s41586-024-07066-z] [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: 12/01/2022] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.
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Affiliation(s)
- Chen Weng
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fulong Yu
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, P.R. China
| | - Dian Yang
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Molecular Pharmacology and Therapeutics, Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Michael Poeschla
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - L Alexander Liggett
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew G Jones
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Dermatology, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Genetics and Computer Science, BASE Research Initiative, Betty Irene Moore Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Lara Wahlster
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexis Caulier
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey A Hussmann
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alexandra Schnell
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kathryn E Yost
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Luke W Koblan
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jorge D Martin-Rufino
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph Min
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alessandro Hammond
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel Ssozi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Raphael Bueno
- Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Hari Mallidi
- Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Antonia Kreso
- Division of Cardiac Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Javier Escabi
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - William M Rideout
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA
| | - Tyler Jacks
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA
| | - Sahand Hormoz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Peter van Galen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA.
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
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7
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Wu Z, Gu T, Xiong C, Shi J, Wang J, Guo T, Xing X, Pang F, He N, Miao R, Shan F, Zhou Y, Li Z, Ji J. Genomic characterization of peritoneal lavage cytology-positive gastric cancer. Chin J Cancer Res 2024; 36:66-77. [PMID: 38455368 PMCID: PMC10915641 DOI: 10.21147/j.issn.1000-9604.2024.01.07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 02/04/2024] [Indexed: 03/09/2024] Open
Abstract
Objective Positive peritoneal lavege cytology (CY1) gastric cancer is featured by dismal prognosis, with high risks of peritoneal metastasis. However, there is a lack of evidence on pathogenic mechanism and signature of CY1 and there is a continuous debate on CY1 therapy. Therefore, exploring the mechanism of CY1 is crucial for treatment strategies and targets for CY1 gastric cancer. Methods In order to figure out specific driver genes and marker genes of CY1 gastric cancer, and ultimately offer clues for potential marker and risk assessment of CY1, 17 cytology-positive gastric cancer patients and 31 matched cytology-negative gastric cancer patients were enrolled in this study. The enrollment criteria were based on the results of diagnostic laparoscopy staging and cytology inspection of exfoliated cells. Whole exome sequencing was then performed on tumor samples to evaluate genomic characterization of cytology-positive gastric cancer. Results Least absolute shrinkage and selection operator (LASSO) algorithm identified 43 cytology-positive marker genes, while MutSigCV identified 42 cytology-positive specific driver genes. CD3G and CDKL2 were both driver and marker genes of CY1. Regarding mutational signatures, driver gene mutation and tumor subclone architecture, no significant differences were observed between CY1 and negative peritoneal lavege cytology (CY0). Conclusions There might not be distinct differences between CY1 and CY0, and CY1 might represent the progression of CY0 gastric cancer rather than constituting an independent subtype. This genomic analysis will thus provide key molecular insights into CY1, which may have a direct effect on treatment recommendations for CY1 and CY0 patients, and provides opportunities for genome-guided clinical trials and drug development.
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Affiliation(s)
- Zhouqiao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Tingfei Gu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Changxian Xiong
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Jinyao Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jingpu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ting Guo
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Xiaofang Xing
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fei Pang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ning He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Rulin Miao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fei Shan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yuan Zhou
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Ziyu Li
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jiafu Ji
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
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8
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Zhang L, Bass HW, Irianto J, Mallory X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res 2023; 33:gr.277249.122. [PMID: 37993137 PMCID: PMC10760445 DOI: 10.1101/gr.277249.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Jerome Irianto
- College of Medicine, Florida State University, Tallahassee, Florida 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA;
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9
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Li H, Yang Z, Tu F, Deng L, Han Y, Fu X, Wang L, Gu D, Werner B, Huang W. Mutation divergence over space in tumour expansion. J R Soc Interface 2023; 20:20230542. [PMID: 37989227 PMCID: PMC10681009 DOI: 10.1098/rsif.2023.0542] [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: 09/16/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.
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Affiliation(s)
- Haiyang Li
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Zixuan Yang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Fengyu Tu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Lijuan Deng
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Yuqing Han
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Xing Fu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Long Wang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Di Gu
- The first affiliated hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Benjamin Werner
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Weini Huang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- School of Mathematical Sciences, Queen Mary University of London, London, UK
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10
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Yang LP, Jiang TJ, He MM, Ling YH, Wang ZX, Wu HX, Zhang Z, Xu RH, Wang F, Yuan SQ, Zhao Q. Comprehensive genomic characterization of sporadic synchronous colorectal cancer: Implications for treatment optimization and clinical outcome. Cell Rep Med 2023; 4:101222. [PMID: 37794586 PMCID: PMC10591049 DOI: 10.1016/j.xcrm.2023.101222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 07/12/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023]
Abstract
Sporadic synchronous colorectal cancer (SCRC) refers to multiple primary CRC tumors detected simultaneously in an individual without predisposing hereditary conditions, which accounts for the majority of multiple CRCs while lacking a profound understanding of the genomic landscape and evolutionary dynamics to optimize its treatment. In this study, 103 primary tumor samples from 51 patients with SCRC undergo whole-exome sequencing. The germline and somatic mutations and evolutionary and clinical features are comprehensively investigated. Somatic genetic events are largely inconsistent between paired tumors. Compared with solitary CRC, SCRCs have higher prevalence of tumor mutation burden high (TMB-H; 33.3%) and microsatellite-instability high (MSI-H; 29.4%) and different mutation frequencies in oncogenic signaling pathways. Moreover, neutrally evolving SCRC tumors are associated with higher intratumoral heterogeneity and better prognosis. These findings unveil special molecular features, carcinogenesis, and prognosis of sporadic SCRC. Strategies for targeted therapy and immunotherapy should be optimized accordingly.
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Affiliation(s)
- Lu-Ping Yang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China
| | - Teng-Jia Jiang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China
| | - Ming-Ming He
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China
| | - Yi-Hong Ling
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China
| | - Zi-Xian Wang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China
| | - Hao-Xiang Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China
| | - Zhen Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China
| | - Rui-Hua Xu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China
| | - Feng Wang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China
| | - Shu-Qiang Yuan
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China.
| | - Qi Zhao
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, P.R. China.
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11
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Pradeu T, Daignan-Fornier B, Ewald A, Germain PL, Okasha S, Plutynski A, Benzekry S, Bertolaso M, Bissell M, Brown JS, Chin-Yee B, Chin-Yee I, Clevers H, Cognet L, Darrason M, Farge E, Feunteun J, Galon J, Giroux E, Green S, Gross F, Jaulin F, Knight R, Laconi E, Larmonier N, Maley C, Mantovani A, Moreau V, Nassoy P, Rondeau E, Santamaria D, Sawai CM, Seluanov A, Sepich-Poore GD, Sisirak V, Solary E, Yvonnet S, Laplane L. Reuniting philosophy and science to advance cancer research. Biol Rev Camb Philos Soc 2023; 98:1668-1686. [PMID: 37157910 PMCID: PMC10869205 DOI: 10.1111/brv.12971] [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: 09/22/2022] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.
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Affiliation(s)
- Thomas Pradeu
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
- CNRS UMR8590, Institut d’Histoire et Philosophie des Sciences et des Technique, University Paris I Panthéon-Sorbonne, 13 rue du Four, Paris 75006, France
| | - Bertrand Daignan-Fornier
- CNRS UMR 5095 Institut de Biochimie et Génétique Cellulaires, University of Bordeaux, 1 rue Camille St Saens, Bordeaux 33077, France
| | - Andrew Ewald
- Departments of Cell Biology and Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Pierre-Luc Germain
- Department of Health Sciences and Technology, Institute for Neurosciences, Eidgenössische Technische Hochschule (ETH) Zürich, Universitätstrasse 2, Zürich 8092, Switzerland
- Department of Molecular Life Sciences, Laboratory of Statistical Bioinformatics, Universität Zürich, Winterthurerstrasse 190, Zurich 8057, Switzerland
| | - Samir Okasha
- Department of Philosophy, University of Bristol, Cotham House, Bristol, BS6 6JL, UK
| | - Anya Plutynski
- Department of Philosophy, Washington University in St. Louis, and Associate with Division of Biology and Biomedical Sciences, St. Louis, MO 63105, USA
| | - Sébastien Benzekry
- Computational Pharmacology and Clinical Oncology (COMPO) Unit, Inria Sophia Antipolis-Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 27, bd Jean Moulin, Marseille 13005, France
| | - Marta Bertolaso
- Research Unit of Philosophy of Science and Human Development, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21-00128, Rome, Italy
- Centre for Cancer Biomarkers, University of Bergen, Bergen 5007, Norway
| | - Mina Bissell
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Benjamin Chin-Yee
- Division of Hematology, Department of Medicine, Schulich School of Medicine and Dentistry, Western University, 800 Commissioners Rd E, London, ON, Canada
- Rotman Institute of Philosophy, Western University, 1151 Richmond Street North, London, ON, Canada
| | - Ian Chin-Yee
- Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, 800 Commissioners Rd E, London, ON, Canada
| | - Hans Clevers
- Pharma, Research and Early Development (pRED) of F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel 4070, Switzerland
- Oncode Institute, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center, Uppsalalaan 8, Utrecht 3584 CT, The Netherlands
| | - Laurent Cognet
- CNRS UMR 5298, Laboratoire Photonique Numérique et Nanosciences, University of Bordeaux, Rue François Mitterrand, Talence 33400, France
| | - Marie Darrason
- Department of Pneumology and Thoracic Oncology, University Hospital of Lyon, 165 Chem. du Grand Revoyet, 69310 Pierre Bénite, Lyon, France
- Lyon Institute of Philosophical Research, Lyon 3 Jean Moulin University, 1 Av. des Frères Lumière, Lyon 69007, France
| | - Emmanuel Farge
- Mechanics and Genetics of Embryonic and Tumor Development group, Institut Curie, CNRS, UMR168, Inserm, Centre Origines et conditions d’apparition de la vie (OCAV) Paris Sciences Lettres Research University, Sorbonne University, Institut Curie, 11 rue Pierre et Marie Curie, Paris 75005, France
| | - Jean Feunteun
- INSERM U981, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
| | - Jérôme Galon
- INSERM UMRS1138, Integrative Cancer Immunology, Cordelier Research Center, Sorbonne Université, Université Paris Cité, 15 rue de l’École de Médecine, Paris 75006, France
| | - Elodie Giroux
- Lyon Institute of Philosophical Research, Lyon 3 Jean Moulin University, 1 Av. des Frères Lumière, Lyon 69007, France
| | - Sara Green
- Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Rådmandsgade 64, Copenhagen 2200, Denmark
| | - Fridolin Gross
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Fanny Jaulin
- INSERM U1279, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, 3223 Voigt Dr, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Ezio Laconi
- Department of Biomedical Sciences, School of Medicine, University of Cagliari, Via Università 40, Cagliari 09124, Italy
| | - Nicolas Larmonier
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Carlo Maley
- Arizona Cancer Evolution Center, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
- School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
- Biodesign Center for Biocomputing, Security and Society, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287, USA
- Biodesign Center for Mechanisms of Evolution, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287, USA
- Center for Evolution and Medicine, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
| | - Alberto Mantovani
- Department of Biomedical Sciences, Humanitas University, 4 Via Rita Levi Montalcini, 20090 Pieve Emanuele, Milan, Italy
- Department of Immunology and Inflammation, Istituto Clinico Humanitas Humanitas Cancer Center (IRCCS) Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
- The William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Violaine Moreau
- INSERM UMR1312, Bordeaux Institute of Oncology (BRIC), University of Bordeaux, 146 Rue Léo Saignat, Bordeaux 33076, France
| | - Pierre Nassoy
- CNRS UMR 5298, Laboratoire Photonique Numérique et Nanosciences, University of Bordeaux, Rue François Mitterrand, Talence 33400, France
| | - Elena Rondeau
- INSERM U1111, ENS Lyon and Centre International de Recherche en Infectionlogie (CIRI), 46 Allée d’Italie, Lyon 69007, France
| | - David Santamaria
- Molecular Mechanisms of Cancer Program, Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC)-University of Salamanca, Salamanca 37007, Spain
| | - Catherine M. Sawai
- INSERM UMR1312, Bordeaux Institute of Oncology (BRIC), University of Bordeaux, 146 Rue Léo Saignat, Bordeaux 33076, France
| | - Andrei Seluanov
- Department of Biology and Medicine, University of Rochester, Rochester, NY 14627, USA
| | | | - Vanja Sisirak
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Eric Solary
- INSERM U1287, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Département d’hématologie, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Université Paris-Saclay, Faculté de Médecine, 63 Rue Gabriel Péri, Le Kremlin-Bicêtre 94270, France
| | - Sarah Yvonnet
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen DK-2200, Denmark
| | - Lucie Laplane
- CNRS UMR8590, Institut d’Histoire et Philosophie des Sciences et des Technique, University Paris I Panthéon-Sorbonne, 13 rue du Four, Paris 75006, France
- INSERM U1287, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Center for Biology and Society, College of Liberal Arts and Sciences, Arizona State University, 1100 S McAllister Ave, Tempe, AZ 85281, USA
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12
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Liu G, Bu C, Guo G, Zhang Z, Sheng Z, Deng K, Wu S, Xu S, Bu Y, Gao Y, Wang M, Liu G, Kong L, Li T, Li M, Bu X. Molecular and clonal evolution in vivo reveal a common pathway of distant relapse gliomas. iScience 2023; 26:107528. [PMID: 37649695 PMCID: PMC10462858 DOI: 10.1016/j.isci.2023.107528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 06/18/2023] [Accepted: 07/28/2023] [Indexed: 09/01/2023] Open
Abstract
The evolutionary trajectories of genomic alterations underlying distant recurrence in glioma remain largely unknown. To elucidate glioma evolution, we analyzed the evolutionary trajectories of matched pairs of primary tumors and relapse tumors or tumor in situ fluid (TISF) based on deep whole-genome sequencing data (ctDNA). We found that MMR gene mutations occurred in the late stage in IDH-mutant glioma during gene evolution, which activates multiple signaling pathways and significantly increases distant recurrence potential. The proneural subtype characterized by PDGFRA amplification was likely prone to hypermutation and distant recurrence following treatment. The classical and mesenchymal subtypes tended to progress locally through subclonal reconstruction, trunk genes transformation, and convergence evolution. EGFR and NOTCH signaling pathways and CDNK2A mutation play an important role in promoting tumor local progression. Glioma subtypes displayed distinct preferred evolutionary patterns. ClinicalTrials.gov, NCT05512325.
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Affiliation(s)
- Guanzheng Liu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Chaojie Bu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Guangzhong Guo
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Zhiyue Zhang
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Zhiyuan Sheng
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Kaiyuan Deng
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Shuang Wu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Sensen Xu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Yage Bu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Yushuai Gao
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Gang Liu
- Department of Center for Clinical Single Cell Biomedicine, Clinical Research Center, Department of Oncology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou 450003, China
| | - Lingfei Kong
- Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Tianxiao Li
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Ming Li
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Xingyao Bu
- Department of Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, China
- Juha International Central Laboratory of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou 450003, China
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13
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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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14
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Nicholson MD, Cheek D, Antal T. Sequential mutations in exponentially growing populations. PLoS Comput Biol 2023; 19:e1011289. [PMID: 37428805 PMCID: PMC10359018 DOI: 10.1371/journal.pcbi.1011289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 07/20/2023] [Accepted: 06/21/2023] [Indexed: 07/12/2023] Open
Abstract
Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.
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Affiliation(s)
- Michael D. Nicholson
- Edinburgh Cancer Research, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - David Cheek
- Center for Systems Biology, Department of Radiology, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Tibor Antal
- School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh, United Kingdom
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15
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Ingram D, Stan GB. Modelling genetic stability in engineered cell populations. Nat Commun 2023; 14:3471. [PMID: 37308512 DOI: 10.1038/s41467-023-38850-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/19/2023] [Indexed: 06/14/2023] Open
Abstract
Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation heterogeneity to explore, after which our model generates host-aware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a device's components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.
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Affiliation(s)
- Duncan Ingram
- Centre of Excellence in Synthetic Biology and Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Centre of Excellence in Synthetic Biology and Department of Bioengineering, Imperial College London, London, United Kingdom.
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16
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Karlsson K, Przybilla MJ, Kotler E, Khan A, Xu H, Karagyozova K, Sockell A, Wong WH, Liu K, Mah A, Lo YH, Lu B, Houlahan KE, Ma Z, Suarez CJ, Barnes CP, Kuo CJ, Curtis C. Deterministic evolution and stringent selection during preneoplasia. Nature 2023; 618:383-393. [PMID: 37258665 PMCID: PMC10247377 DOI: 10.1038/s41586-023-06102-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/19/2023] [Indexed: 06/02/2023]
Abstract
The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.
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Affiliation(s)
- Kasper Karlsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Science for Life Laboratory and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Moritz J Przybilla
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Wellcome Sanger Institute & University of Cambridge, Hinxton, UK
| | - Eran Kotler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Aziz Khan
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Hang Xu
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kremena Karagyozova
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra Sockell
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wing H Wong
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine Liu
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Amanda Mah
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Bingxin Lu
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Kathleen E Houlahan
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Zhicheng Ma
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Calvin J Kuo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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17
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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18
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Xie L, Cai Z, Lu H, Meng F, Zhang X, Luo K, Su X, Lei Y, Xu J, Lou J, Wang H, Du Z, Wang Y, Li Y, Ren T, Xu J, Sun X, Tang X, Guo W. Distinct genomic features between osteosarcomas firstly metastasing to bone and to lung. Heliyon 2023; 9:e15527. [PMID: 37205995 PMCID: PMC10189180 DOI: 10.1016/j.heliyon.2023.e15527] [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: 10/08/2022] [Revised: 03/26/2023] [Accepted: 04/12/2023] [Indexed: 05/21/2023] Open
Abstract
Background Osteosarcoma initially metastasing to bone only shows distinct biological features compared to osteosarcoma that firstly metastasizes to the lung, which suggests us underlying different genomic pathogenetic mechanism. Methods We analyzed whole-exome sequencing (WES) data for 38 osteosarcoma with paired samples in different relapse patterns. We also sought to redefine disease subclassifications for osteosarcoma based on genetic alterations and correlate these genetic profiles with clinical treatment courses to elucidate potential evolving cladograms. Results We investigated WES of 12/38 patients with high-grade osteosarcoma (31.6%) with initial bone metastasis (group A) and 26/38 (68.4%) with initial pulmonary metastasis (group B), of whom 15/38 (39.5%) had paired samples of primary lesions and metastatic lesions. We found that osteosarcoma in group A mainly carries single-nucleotide variations displaying higher tumor mutation burden and neoantigen load and more tertiary lymphoid structures, while those in group B mainly exhibits structural variants. High conservation of reported genetic sequencing over time in their evolving cladograms. Conclusions Osteosarcoma with mainly single-nucleotide variations other than structural variants might exhibit biological behavior predisposing toward bone metastases as well as better immunogenicity in tumor microenvironment.
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Affiliation(s)
- Lu Xie
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Zhenyu Cai
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Hezhe Lu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, No. A3 Datun Road, Chaoyang District, Beijing 100101, China
| | - Fanfei Meng
- Shanghai OrigiMed Co., Ltd, Shanghai, No. 3576 Zhaolou Road, Minhang District, Shanghai, 201112, China
| | - Xin Zhang
- Shanghai OrigiMed Co., Ltd, Shanghai, No. 3576 Zhaolou Road, Minhang District, Shanghai, 201112, China
| | - Kun Luo
- Shanghai OrigiMed Co., Ltd, Shanghai, No. 3576 Zhaolou Road, Minhang District, Shanghai, 201112, China
| | - Xiaoxing Su
- Berry Oncology Corporation, Fuzhou, 350200, China
| | - Yan Lei
- Berry Oncology Corporation, Fuzhou, 350200, China
| | - Jiuhui Xu
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Jingbing Lou
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Han Wang
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Zhiye Du
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Yunfan Wang
- Pathology Department, Peking University Shougang Hospital, No. 9 Jinyuanzhuang Road, Shijingshan District, Beijing, 100144, China
| | - Yuan Li
- Radiology Department & Nuclear Medicine Department, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, China
| | - Tingting Ren
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Jie Xu
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Xin Sun
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
| | - Xiaodong Tang
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
- Corresponding author.
| | - Wei Guo
- Musculoskeletal Tumor Center, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China
- Corresponding author.
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19
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Körber V, Stainczyk SA, Kurilov R, Henrich KO, Hero B, Brors B, Westermann F, Höfer T. Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome. Nat Genet 2023; 55:619-630. [PMID: 36973454 PMCID: PMC10101850 DOI: 10.1038/s41588-023-01332-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023]
Abstract
AbstractNeuroblastoma, the most frequent solid tumor in infants, shows very diverse outcomes from spontaneous regression to fatal disease. When these different tumors originate and how they evolve are not known. Here we quantify the somatic evolution of neuroblastoma by deep whole-genome sequencing, molecular clock analysis and population-genetic modeling in a comprehensive cohort covering all subtypes. We find that tumors across the entire clinical spectrum begin to develop via aberrant mitoses as early as the first trimester of pregnancy. Neuroblastomas with favorable prognosis expand clonally after short evolution, whereas aggressive neuroblastomas show prolonged evolution during which they acquire telomere maintenance mechanisms. The initial aneuploidization events condition subsequent evolution, with aggressive neuroblastoma exhibiting early genomic instability. We find in the discovery cohort (n = 100), and validate in an independent cohort (n = 86), that the duration of evolution is an accurate predictor of outcome. Thus, insight into neuroblastoma evolution may prospectively guide treatment decisions.
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20
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Haughey MJ, Bassolas A, Sousa S, Baker AM, Graham TA, Nicosia V, Huang W. First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal cancer. PLoS Comput Biol 2023; 19:e1010952. [PMID: 36913406 PMCID: PMC10035892 DOI: 10.1371/journal.pcbi.1010952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 03/23/2023] [Accepted: 02/14/2023] [Indexed: 03/14/2023] Open
Abstract
The signature of early cancer dynamics on the spatial arrangement of tumour cells is poorly understood, and yet could encode information about how sub-clones grew within the expanding tumour. Novel methods of quantifying spatial tumour data at the cellular scale are required to link evolutionary dynamics to the resulting spatial architecture of the tumour. Here, we propose a framework using first passage times of random walks to quantify the complex spatial patterns of tumour cell population mixing. First, using a simple model of cell mixing we demonstrate how first passage time statistics can distinguish between different pattern structures. We then apply our method to simulated patterns of mutated and non-mutated tumour cell population mixing, generated using an agent-based model of expanding tumours, to explore how first passage times reflect mutant cell replicative advantage, time of emergence and strength of cell pushing. Finally, we explore applications to experimentally measured human colorectal cancer, and estimate parameters of early sub-clonal dynamics using our spatial computational model. We infer a wide range of sub-clonal dynamics, with mutant cell division rates varying between 1 and 4 times the rate of non-mutated cells across our sample set. Some mutated sub-clones emerged after as few as 100 non-mutant cell divisions, and others only after 50,000 divisions. The majority were consistent with boundary driven growth or short-range cell pushing. By analysing multiple sub-sampled regions in a small number of samples, we explore how the distribution of inferred dynamics could inform about the initial mutational event. Our results demonstrate the efficacy of first passage time analysis as a new methodology in spatial analysis of solid tumour tissue, and suggest that patterns of sub-clonal mixing can provide insights into early cancer dynamics.
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Affiliation(s)
- Magnus J. Haughey
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Aleix Bassolas
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Sandro Sousa
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Trevor A. Graham
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Weini Huang
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
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21
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Bosque JJ, Calvo GF, Molina-García D, Pérez-Beteta J, García Vicente AM, Pérez-García VM. Metabolic activity grows in human cancers pushed by phenotypic variability. iScience 2023; 26:106118. [PMID: 36843844 PMCID: PMC9950952 DOI: 10.1016/j.isci.2023.106118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/30/2022] [Accepted: 01/28/2023] [Indexed: 02/05/2023] Open
Abstract
Different evolutionary processes push cancers to increasingly aggressive behaviors, energetically sustained by metabolic reprogramming. The collective signature emerging from this transition is macroscopically displayed by positron emission tomography (PET). In fact, the most readily PET measure, the maximum standardized uptake value (SUVmax), has been found to have prognostic value in different cancers. However, few works have linked the properties of this metabolic hotspot to cancer evolutionary dynamics. Here, by analyzing diagnostic PET images from 512 patients with cancer, we found that SUVmax scales superlinearly with the mean metabolic activity (SUVmean), reflecting a dynamic preferential accumulation of activity on the hotspot. Additionally, SUVmax increased with metabolic tumor volume (MTV) following a power law. The behavior from the patients data was accurately captured by a mechanistic evolutionary dynamics model of tumor growth accounting for phenotypic transitions. This suggests that non-genetic changes may suffice to fuel the observed sustained increases in tumor metabolic activity.
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Affiliation(s)
- Jesús J. Bosque
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain,Corresponding author
| | - Gabriel F. Calvo
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - David Molina-García
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Julián Pérez-Beteta
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Ana M. García Vicente
- Nuclear Medicine Unit, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
| | - Víctor M. Pérez-García
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
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22
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Feller G, Khammissa RAG, Ballyram R, Beetge MM, Lemmer J, Feller L. Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2392. [PMID: 36767758 PMCID: PMC9915085 DOI: 10.3390/ijerph20032392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.
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Affiliation(s)
- Gal Feller
- Department of Radiation Oncology, University of Witwatersrand, Johannesburg and Charlotte Maxeke Academic Hospital, Johannesburg 2193, South Africa
| | - Razia Abdool Gafaar Khammissa
- Department of Periodontics and Oral Medicine, School of Oral Health Sciences, Faculty of Health Sciences, University of Pretoria, Pretoria 0084, South Africa
| | - Raoul Ballyram
- Department of Periodontology and Oral Medicine, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa
| | - Mia-Michaela Beetge
- Department of Periodontics and Oral Medicine, School of Oral Health Sciences, Faculty of Health Sciences, University of Pretoria, Pretoria 0084, South Africa
| | - Johan Lemmer
- Retired Professor, Silvela Street, Sandton, Johannesburg 2031, South Africa
| | - Liviu Feller
- Retired Professor, Bantry Bay, Cape Town 8005, South Africa
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23
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Intratumoral heterogeneity affects tumor regression and Ki67 proliferation index in perioperatively treated gastric carcinoma. Br J Cancer 2023; 128:375-386. [PMID: 36347963 PMCID: PMC9902476 DOI: 10.1038/s41416-022-02047-3] [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: 06/25/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Intratumoral heterogeneity (ITH) is a major problem in gastric cancer (GC). We tested Ki67 and tumor regression for ITH after neoadjuvant/perioperative chemotherapy. METHODS 429 paraffin blocks were obtained from 106 neoadjuvantly/perioperatively treated GCs (one to five blocks per case). Serial sections were stained with Masson's trichrome, antibodies directed against cytokeratin and Ki67, and finally digitalized. Tumor regression and three different Ki67 proliferation indices (PI), i.e., maximum PI (KiH), minimum PI (KiL), and the difference between KiH/KiL (KiD) were obtained per block. Statistics were performed in a block-wise (all blocks irrespective of their case-origin) and case-wise manner. RESULTS Ki67 and tumor regression showed extensive ITH in our series (maximum ITH within a case: 31% to 85% for KiH; 4.5% to 95.6% for tumor regression). In addition, Ki67 was significantly associated with tumor regression (p < 0.001). Responders (<10% residual tumor, p = 0.016) exhibited prolonged survival. However, there was no significant survival benefit after cut-off values were increased ≥20% residual tumor mass. Ki67 remained without prognostic value. CONCLUSIONS Digital image analysis in tumor regression evaluation might help overcome inter- and intraobserver variability and validate classification systems. Ki67 may serve as a sensitivity predictor for chemotherapy and an indicator of ITH.
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24
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Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction. J Mol Med (Berl) 2022; 100:1755-1769. [PMID: 36367565 DOI: 10.1007/s00109-022-02266-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/04/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022]
Abstract
There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy. KEY MESSAGES: We creatively developed a prognostic signature (57-MPS) based on clonal diversity. The high-risk samples displayed an underlying immunosuppressive mechanism. 57-MPS improved the predictive performance of PD-L1 for immunotherapy.
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25
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Xu BL, Wang XM, Chen GY, Yuan P, Han L, Qin P, Li TP, You HQ, Zhang CJ, Fu XM, Yuan L, Wang ZB, Gao QL. In vivo growth of subclones derived from Lewis lung carcinoma is determined by the tumor microenvironment. Am J Cancer Res 2022; 12:5255-5270. [PMID: 36504888 PMCID: PMC9729899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/02/2022] [Indexed: 12/15/2022] Open
Abstract
Heterogeneity is a fundamental feature of human tumors and plays a major role in drug resistance and disease progression. In the present study, we selected single-cell-derived cell lines (SCDCLs) derived from Lewis lung carcinoma (LLC1) cells to investigate tumorigenesis and heterogeneity. SCDCLs were generated using limiting dilution. Five SCDCLs were subcutaneously injected into wild-type C57BL/6N mice; however, they displayed significant differences in tumor growth. Subclone SCC1 grew the fastest in vivo, whereas it grew slower in vitro. The growth pattern of SCC2 was the opposite to that of SCC1. Genetic differences in these two subclones showed marked differences in cell adhesion and proliferation. Pathway enrichment results indicate that signal transduction and immune system responses were the most significantly altered functional categories in SCC2 cells compared to those in SCC1 cells in vitro. The number and activation of CD3+ and CD8+ T cells and NK cells in the tumor tissue of tumor-bearing mice inoculated with SCC2 were significantly higher, whereas those of myeloid cells were significantly lower, than those in the SCC1 and LLC1 groups. Our results suggest that the in vivo growth of two subclones derived from LLC1 was determined by the tumor microenvironment rather than their intrinsic proliferative cell characteristics.
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Affiliation(s)
- Ben-Ling Xu
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Xiao-Ming Wang
- Department of General Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Guang-Yu Chen
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Peng Yuan
- Department of Breast Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Lu Han
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Peng Qin
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Tie-Peng Li
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Hong-Qin You
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Cheng-Juan Zhang
- Center of Bio Repository, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Xiao-Min Fu
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Long Yuan
- Department of General Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Zi-Bing Wang
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Quan-Li Gao
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
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26
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Inferring parameters of cancer evolution in chronic lymphocytic leukemia. PLoS Comput Biol 2022; 18:e1010677. [DOI: 10.1371/journal.pcbi.1010677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 11/16/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer, where two longitudinal samples are available for sequencing. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. Chronic lymphocytic leukemia (CLL), which often does not require treatment for years after diagnosis, presents an optimal system to study the untreated, natural evolution of cancer cell populations. When we apply our methodology to reconstruct the individual evolutionary histories of CLL patients, we find that the parental leukemic clone typically appears within the first fifteen years of life.
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27
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Househam J, Heide T, Cresswell GD, Spiteri I, Kimberley C, Zapata L, Lynn C, James C, Mossner M, Fernandez-Mateos J, Vinceti A, Baker AM, Gabbutt C, Berner A, Schmidt M, Chen B, Lakatos E, Gunasri V, Nichol D, Costa H, Mitchinson M, Ramazzotti D, Werner B, Iorio F, Jansen M, Caravagna G, Barnes CP, Shibata D, Bridgewater J, Rodriguez-Justo M, Magnani L, Sottoriva A, Graham TA. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 2022; 611:744-753. [PMID: 36289336 PMCID: PMC9684078 DOI: 10.1038/s41586-022-05311-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/01/2022] [Indexed: 12/12/2022]
Abstract
Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.
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Affiliation(s)
- Jacob Househam
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Timon Heide
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - George D Cresswell
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Inmaculada Spiteri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chris Kimberley
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Luis Zapata
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Claire Lynn
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chela James
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Maximilian Mossner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | | | | | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Calum Gabbutt
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Alison Berner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Melissa Schmidt
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Bingjie Chen
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Eszter Lakatos
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Vinaya Gunasri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Daniel Nichol
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Helena Costa
- UCL Cancer Institute, University College London, London, UK
| | - Miriam Mitchinson
- Histopathology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Daniele Ramazzotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Benjamin Werner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Francesco Iorio
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Marnix Jansen
- UCL Cancer Institute, University College London, London, UK
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Darryl Shibata
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | | | - Luca Magnani
- Department of Surgery and Cancer, Imperial College 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.
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
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28
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Su X, Bai S, Xie G, Shi Y, Zhao L, Yang G, Tian F, He KY, Wang L, Li X, Long Q, Han ZG. Accurate tumor clonal structures require single-cell analysis. Ann N Y Acad Sci 2022; 1517:213-224. [PMID: 36081327 DOI: 10.1111/nyas.14897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.
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Affiliation(s)
- Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shihao Bai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gangcai Xie
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Linan Zhao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guoliang Yang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Futong Tian
- Department of Design, Politecnico di Milano, Milan, Italy
| | - Kun-Yan He
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lan Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolin Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Long
- Joint School of Life Sciences, Guangzhou Medical University & Guangzhou Institutes of Biomedicine and Health-Chinese Academy of Sciences, Guangzhou, China
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
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29
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Rouhani FJ, Zou X, Danecek P, Badja C, Amarante TD, Koh G, Wu Q, Memari Y, Durbin R, Martincorena I, Bassett AR, Gaffney D, Nik-Zainal S. Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells. Nat Genet 2022; 54:1406-1416. [PMID: 35953586 PMCID: PMC9470532 DOI: 10.1038/s41588-022-01147-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/24/2022] [Indexed: 12/27/2022]
Abstract
We explored human induced pluripotent stem cells (hiPSCs) derived from different tissues to gain insights into genomic integrity at single-nucleotide resolution. We used genome sequencing data from two large hiPSC repositories involving 696 hiPSCs and daughter subclones. We find ultraviolet light (UV)-related damage in ~72% of skin fibroblast-derived hiPSCs (F-hiPSCs), occasionally resulting in substantial mutagenesis (up to 15 mutations per megabase). We demonstrate remarkable genomic heterogeneity between independent F-hiPSC clones derived during the same round of reprogramming due to oligoclonal fibroblast populations. In contrast, blood-derived hiPSCs (B-hiPSCs) had fewer mutations and no UV damage but a high prevalence of acquired BCOR mutations (26.9% of lines). We reveal strong selection pressure for BCOR mutations in F-hiPSCs and B-hiPSCs and provide evidence that they arise in vitro. Directed differentiation of hiPSCs and RNA sequencing showed that BCOR mutations have functional consequences. Our work strongly suggests that detailed nucleotide-resolution characterization is essential before using hiPSCs.
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Affiliation(s)
- Foad J Rouhani
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Xueqing Zou
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK
- Academic Department of Medical Genetics, Addenbrooke's Treatment Centre, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Petr Danecek
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Cherif Badja
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK
- Academic Department of Medical Genetics, Addenbrooke's Treatment Centre, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Tauanne Dias Amarante
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Gene Koh
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK
- Academic Department of Medical Genetics, Addenbrooke's Treatment Centre, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Qianxin Wu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yasin Memari
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Richard Durbin
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Inigo Martincorena
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Andrew R Bassett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel Gaffney
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Genomics plc, King Charles House, Oxford, UK
| | - Serena Nik-Zainal
- Early Cancer Institute, Hutchison/MRC Research Centre, Cambridge Biomedical Research Campus, Cambridge, UK.
- Academic Department of Medical Genetics, Addenbrooke's Treatment Centre, Cambridge Biomedical Research Campus, Cambridge, UK.
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30
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Dubois F, Sidiropoulos N, Weischenfeldt J, Beroukhim R. Structural variations in cancer and the 3D genome. Nat Rev Cancer 2022; 22:533-546. [PMID: 35764888 PMCID: PMC10423586 DOI: 10.1038/s41568-022-00488-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/18/2022] [Indexed: 12/21/2022]
Abstract
Structural variations (SVs) affect more of the cancer genome than any other type of somatic genetic alteration but difficulties in detecting and interpreting them have limited our understanding. Clinical cancer sequencing also increasingly aims to detect SVs, leading to a widespread necessity to interpret their biological and clinical relevance. Recently, analyses of large whole-genome sequencing data sets revealed features that impact rates of SVs across the genome in different cancers. A striking feature has been the extent to which, in both their generation and their influence on the selective fitness of cancer cells, SVs are more specific to individual cancer types than other genetic alterations such as single-nucleotide variants. This Perspective discusses how the folding of the 3D genome, and differences in its folding across cell types, affect observed SV rates in different cancer types as well as how SVs can impact cancer cell fitness.
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Affiliation(s)
- Frank Dubois
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nikos Sidiropoulos
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
- The Finsen Laboratory, Rigshospitalet, Copenhagen, Denmark
| | - Joachim Weischenfeldt
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark.
- The Finsen Laboratory, Rigshospitalet, Copenhagen, Denmark.
- Department of Urology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Rameen Beroukhim
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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31
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Homeostasis limits keratinocyte evolution. Proc Natl Acad Sci U S A 2022; 119:e2006487119. [PMID: 35998218 PMCID: PMC9436311 DOI: 10.1073/pnas.2006487119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Human skin is riddled with mutations creating subclones of variable sizes. Some of these mutations are driver mutations, implicated in cancer development and progression, that appear to be under positive selection due to their relative sizes. We show how these driver and nondriver “passenger” mutations encode their history of division and loss within the tissue using a simple model combined with realistic mutation tracking. Using a three-dimensional in silico homeostatic epidermis model, we reveal that many mutations likely lack functional heterogeneity and are, instead, simply those that arise earlier in life within the basal layer. We use our model to reveal how functional differences conveyed by driver mutations could lead to a persistence phenotype while maintaining homeostasis. Recent studies have revealed that normal human tissues accumulate many somatic mutations. In particular, human skin is riddled with mutations, with multiple subclones of variable sizes. Driver mutations are frequent and tend to have larger subclone sizes, suggesting selection. To begin to understand the histories encoded by these complex somatic mutations, we incorporated genomes into a simple agent-based skin-cell model whose prime directive is homeostasis. In this model, stem-cell survival is random and dependent on proximity to the basement membrane. This simple homeostatic skin model recapitulates the observed log-linear distributions of somatic mutations, where most mutations are found in increasingly smaller subclones that are typically lost with time. Hence, neutral mutations are “passengers” whose fates depend on the random survival of their stem cells, where a rarer larger subclone reflects the survival and spread of mutations acquired earlier in life. The model can also maintain homeostasis and accumulate more frequent and larger driver subclones if these mutations (NOTCH1 and TP53) confer relatively higher persistence in normal skin or during tissue damage (sunlight). Therefore, a relatively simple model of epithelial turnover indicates how observed passenger and driver somatic mutations could accumulate without violating the prime directive of homeostasis in normal human tissues.
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32
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Iranzo J, Gruenhagen G, Calle-Espinosa J, Koonin EV. Pervasive conditional selection of driver mutations and modular epistasis networks in cancer. Cell Rep 2022; 40:111272. [PMID: 36001960 DOI: 10.1016/j.celrep.2022.111272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/18/2022] [Accepted: 08/05/2022] [Indexed: 11/19/2022] Open
Abstract
Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.
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Affiliation(s)
- Jaime Iranzo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
| | - George Gruenhagen
- Institute of Bioengineering and Biosciences, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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33
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Wong WH, Curtis C. "Fateful" encounter: Lineage tracing meets phylogeny to unravel mysteries of cancer progression. Dev Cell 2022; 57:1680-1682. [PMID: 35901781 DOI: 10.1016/j.devcel.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In a recent issue of Cell, Yang et al. utilize evolvable cellular barcodes to investigate the evolutionary trajectories of murine lung adenocarcinoma. Reconstructing the life histories of these tumors based on cellular barcodes reveals stringent selection and phenotypic differences across subclonal lineages.
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Affiliation(s)
- Wing Hing Wong
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
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34
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Gabbutt C, Wright NA, Baker A, Shibata D, Graham TA. Lineage tracing in human tissues. J Pathol 2022; 257:501-512. [PMID: 35415852 PMCID: PMC9253082 DOI: 10.1002/path.5911] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/21/2022] [Accepted: 04/09/2022] [Indexed: 11/11/2022]
Abstract
The dynamical process of cell division that underpins homeostasis in the human body cannot be directly observed in vivo, but instead is measurable from the pattern of somatic genetic or epigenetic mutations that accrue in tissues over an individual's lifetime. Because somatic mutations are heritable, they serve as natural lineage tracing markers that delineate clonal expansions. Mathematical analysis of the distribution of somatic clone sizes gives a quantitative readout of the rates of cell birth, death, and replacement. In this review we explore the broad range of somatic mutation types that have been used for lineage tracing in human tissues, introduce the mathematical concepts used to infer dynamical information from these clone size data, and discuss the insights of this lineage tracing approach for our understanding of homeostasis and cancer development. We use the human colon as a particularly instructive exemplar tissue. There is a rich history of human somatic cell dynamics surreptitiously written into the cell genomes that is being uncovered by advances in sequencing and careful mathematical analysis lineage of tracing data. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Calum Gabbutt
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and DentistryQueen Mary University of LondonLondonUK
- Centre for Evolution and CancerInstitute of Cancer ResearchSuttonUK
- London Interdisciplinary Doctoral Training Programme (LIDo)LondonUK
| | - Nicholas A Wright
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and DentistryQueen Mary University of LondonLondonUK
| | - Ann‐Marie Baker
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and DentistryQueen Mary University of LondonLondonUK
- Centre for Evolution and CancerInstitute of Cancer ResearchSuttonUK
| | - Darryl Shibata
- Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Trevor A Graham
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and DentistryQueen Mary University of LondonLondonUK
- Centre for Evolution and CancerInstitute of Cancer ResearchSuttonUK
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35
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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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36
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In Silico Investigations of Multi-Drug Adaptive Therapy Protocols. Cancers (Basel) 2022; 14:cancers14112699. [PMID: 35681680 PMCID: PMC9179496 DOI: 10.3390/cancers14112699] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/21/2022] [Accepted: 05/25/2022] [Indexed: 11/17/2022] Open
Abstract
The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control.
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37
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Yang D, Jones MG, Naranjo S, Rideout WM, Min KHJ, Ho R, Wu W, Replogle JM, Page JL, Quinn JJ, Horns F, Qiu X, Chen MZ, Freed-Pastor WA, McGinnis CS, Patterson DM, Gartner ZJ, Chow ED, Bivona TG, Chan MM, Yosef N, Jacks T, Weissman JS. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 2022; 185:1905-1923.e25. [PMID: 35523183 DOI: 10.1016/j.cell.2022.04.015] [Citation(s) in RCA: 100] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/09/2022] [Accepted: 04/08/2022] [Indexed: 12/19/2022]
Abstract
Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.
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Affiliation(s)
- Dian Yang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - William M Rideout
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Kyung Hoi Joseph Min
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Raymond Ho
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joseph M Replogle
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer L Page
- Cell and Genome Engineering Core, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Felix Horns
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xiaojie Qiu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Michael Z Chen
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, Harvard Medical School, Boston, MA 02115, USA
| | - William A Freed-Pastor
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Christopher S McGinnis
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David M Patterson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Cellular Construction, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eric D Chow
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Advanced Technology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michelle M Chan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA 94720, USA; Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA.
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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38
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Eghdami A, Paulose J, Fusco D. Branching structure of genealogies in spatially growing populations and its implications for population genetics inference. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:294008. [PMID: 35510713 DOI: 10.1088/1361-648x/ac6cd9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 05/04/2022] [Indexed: 06/14/2023]
Abstract
Spatial models where growth is limited to the population edge have been instrumental to understanding the population dynamics and the clone size distribution in growing cellular populations, such as microbial colonies and avascular tumours. A complete characterization of the coalescence process generated by spatial growth is still lacking, limiting our ability to apply classic population genetics inference to spatially growing populations. Here, we start filling this gap by investigating the statistical properties of the cell lineages generated by the two dimensional Eden model, leveraging their physical analogy with directed polymers. Our analysis provides quantitative estimates for population measurements that can easily be assessed via sequencing, such as the average number of segregating sites and the clone size distribution of a subsample of the population. Our results not only reveal remarkable features of the genealogies generated during growth, but also highlight new properties that can be misinterpreted as signs of selection if non-spatial models are inappropriately applied.
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Affiliation(s)
- Armin Eghdami
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, United Kingdom
| | - Jayson Paulose
- Department of Physics and Institute for Fundamental Science, University of Oregon, Eugene, OR 97401, United States of America
| | - Diana Fusco
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, United Kingdom
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Wintersinger JA, Dobson SM, Kulman E, Stein LD, Dick JE, Morris Q. Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree. Blood Cancer Discov 2022; 3:208-219. [PMID: 35247876 PMCID: PMC9780082 DOI: 10.1158/2643-3230.bcd-21-0092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/23/2021] [Accepted: 02/28/2022] [Indexed: 01/25/2023] Open
Abstract
Cancers are composed of genetically distinct subpopulations of malignant cells. DNA-sequencing data can be used to determine the somatic point mutations specific to each population and build clone trees describing the evolutionary relationships between them. These clone trees can reveal critical points in disease development and inform treatment. Pairtree is a new method that constructs more accurate and detailed clone trees than previously possible using variant allele frequency data from one or more bulk cancer samples. It does so by first building a Pairs Tensor that captures the evolutionary relationships between pairs of subpopulations, and then it uses these relations to constrain clone trees and infer violations of the infinite sites assumption. Pairtree can accurately build clone trees using up to 100 samples per cancer that contain 30 or more subclonal populations. On 14 B-progenitor acute lymphoblastic leukemias, Pairtree replicates or improves upon expert-derived clone tree reconstructions. SIGNIFICANCE Clone trees illustrate the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA-sequencing data from many samples of the same cancer to build more detailed and accurate clone trees than previously possible. See related commentary by Miller, p. 176. This article is highlighted in the In This Issue feature, p. 171.
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Affiliation(s)
- Jeff A. Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Stephanie M. Dobson
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ethan Kulman
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lincoln D. Stein
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - John E. Dick
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Quaid Morris
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Memorial Sloan Kettering Cancer Center, New York, New York.,Corresponding Author: Quaid Morris, Sloan Kettering Institute, 417 East 68th Street, New York, NY 10021. Phone: 646-888-2201; E-mail:
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40
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Gabbutt C, Schenck RO, Weisenberger DJ, Kimberley C, Berner A, Househam J, Lakatos E, Robertson-Tessi M, Martin I, Patel R, Clark SK, Latchford A, Barnes CP, Leedham SJ, Anderson ARA, Graham TA, Shibata D. Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat Biotechnol 2022; 40:720-730. [PMID: 34980912 PMCID: PMC9110299 DOI: 10.1038/s41587-021-01109-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 09/28/2021] [Indexed: 02/07/2023]
Abstract
Molecular clocks that record cell ancestry mutate too slowly to measure the short-timescale dynamics of cell renewal in adult tissues. Here, we show that fluctuating DNA methylation marks can be used as clocks in cells where ongoing methylation and demethylation cause repeated 'flip-flops' between methylated and unmethylated states. We identify endogenous fluctuating CpG (fCpG) sites using standard methylation arrays and develop a mathematical model to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than the colon, with slower stem cell replacement in the small intestine. Germline APC mutation increased the number of replacements per crypt. In blood, we measured rapid expansion of acute leukemia and slower growth of chronic disease. Thus, the patterns of human somatic cell birth and death are measurable with fluctuating methylation clocks (FMCs).
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Affiliation(s)
- Calum Gabbutt
- Evolution and Cancer Laboratory, 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
- Department of Cell and Developmental Biology, University College London, London, UK
- London Interdisciplinary Doctoral Training Programme (LIDo), London, UK
| | - Ryan O Schenck
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, FL, USA
- Intestinal Stem Cell Biology Lab, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Daniel J Weisenberger
- Department of Biochemistry and Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher Kimberley
- Evolution and Cancer Laboratory, 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
| | - Alison Berner
- Evolution and Cancer Laboratory, 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
| | - Jacob Househam
- Evolution and Cancer Laboratory, 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
| | - Eszter Lakatos
- Evolution and Cancer Laboratory, 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
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, FL, USA
| | - Isabel Martin
- Evolution and Cancer Laboratory, 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
- St. Mark's Hospital, Harrow, London, UK
| | - Roshani Patel
- Evolution and Cancer Laboratory, 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
- St. Mark's Hospital, Harrow, London, UK
| | - Susan K Clark
- St. Mark's Hospital, Harrow, London, UK
- Department of Surgery and Cancer, Imperial College, London, UK
| | - Andrew Latchford
- St. Mark's Hospital, Harrow, London, UK
- Department of Surgery and Cancer, Imperial College, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Simon J Leedham
- Intestinal Stem Cell Biology Lab, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Trevor A Graham
- Evolution and Cancer Laboratory, 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.
| | - Darryl Shibata
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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41
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Sepich-Poore GD, Guccione C, Laplane L, Pradeu T, Curtius K, Knight R. Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution. Bioessays 2022; 44:e2100252. [PMID: 35253252 PMCID: PMC10506734 DOI: 10.1002/bies.202100252] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/31/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022]
Abstract
The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.
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Affiliation(s)
| | - Caitlin Guccione
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Lucie Laplane
- Institut d’histoire et de philosophie des sciences et des techniques (UMR8590), CNRS & Panthéon-Sorbonne University, 75006 Paris, France
- Hematopoietic stem cells and the development of myeloid malignancies (UMR1287), Gustave Roussy Cancer Campus, 94800 Villejuif, France
| | - Thomas Pradeu
- ImmunoConcept (UMR5164), CNRS & University of Bordeaux, 33076 Bordeaux Cedex, France
| | - Kit Curtius
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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42
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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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43
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Distinguishing excess mutations and increased cell death based on variant allele frequencies. PLoS Comput Biol 2022; 18:e1010048. [PMID: 35468135 PMCID: PMC9071171 DOI: 10.1371/journal.pcbi.1010048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 05/05/2022] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high. Tumors frequently harbor an elevated number of mutations, compared to healthy tissue. These extra mutations may be generated either by an increased mutation rate or the presence of cell death resulting in increased cellular turnover and additional cell divisions for tumor growth. Separating the effects of these two factors is a nontrivial problem. Here we present a method which can simultaneously estimate cell turnover rate and genomic mutation rate from bulk sequencing data. Our method is based on the estimation of the parameters of a generative model of tumor growth and mutations. Applying our method to a human hepatocellular carcinoma sample reveals an elevated per cell division mutation rate and high cell turnover.
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44
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Gatenbee CD, Baker AM, Schenck RO, Strobl M, West J, Neves MP, Hasan SY, Lakatos E, Martinez P, Cross WCH, Jansen M, Rodriguez-Justo M, Whelan CJ, Sottoriva A, Leedham S, Robertson-Tessi M, Graham TA, Anderson ARA. Immunosuppressive niche engineering at the onset of human colorectal cancer. Nat Commun 2022; 13:1798. [PMID: 35379804 PMCID: PMC8979971 DOI: 10.1038/s41467-022-29027-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 02/24/2022] [Indexed: 12/13/2022] Open
Abstract
The evolutionary dynamics of tumor initiation remain undetermined, and the interplay between neoplastic cells and the immune system is hypothesized to be critical in transformation. Colorectal cancer (CRC) presents a unique opportunity to study the transition to malignancy as pre-cancers (adenomas) and early-stage cancers are frequently resected. Here, we examine tumor-immune eco-evolutionary dynamics from pre-cancer to carcinoma using a computational model, ecological analysis of digital pathology data, and neoantigen prediction in 62 patient samples. Modeling predicted recruitment of immunosuppressive cells would be the most common driver of transformation. As predicted, ecological analysis reveals that progressed adenomas co-localized with immunosuppressive cells and cytokines, while benign adenomas co-localized with a mixed immune response. Carcinomas converge to a common immune "cold" ecology, relaxing selection against immunogenicity and high neoantigen burdens, with little evidence for PD-L1 overexpression driving tumor initiation. These findings suggest re-engineering the immunosuppressive niche may prove an effective immunotherapy in CRC.
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Affiliation(s)
- Chandler D Gatenbee
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA.
| | - Ann-Marie Baker
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Ryan O Schenck
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX37BN, UK
| | - Maximilian Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Jeffrey West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Margarida P Neves
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Sara Yakub Hasan
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Eszter Lakatos
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Pierre Martinez
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
- Lyon Cancer Institute, Lyon, France
| | - William C H Cross
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Marnix Jansen
- Department of Pathology, University College London Hospital, London, UK
| | | | - Christopher J Whelan
- Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
- Department of Biological Sciences, University of Illinois at Chicago, 845 West Taylor Street, Chicago, IL, 60607, USA
| | - Andrea Sottoriva
- Center for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Simon Leedham
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX37BN, UK
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA.
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45
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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: 2] [Impact Index Per Article: 1.0] [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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46
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Demeter M, Derényi I, Szöllősi GJ. Trade-off between reducing mutational accumulation and increasing commitment to differentiation determines tissue organization. Nat Commun 2022; 13:1666. [PMID: 35351889 PMCID: PMC8964737 DOI: 10.1038/s41467-022-29004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 02/23/2022] [Indexed: 11/09/2022] Open
Abstract
Species-specific differences control cancer risk across orders of magnitude variation in body size and lifespan, e.g., by varying the copy numbers of tumor suppressor genes. It is unclear, however, how different tissues within an organism can control somatic evolution despite being subject to markedly different constraints, but sharing the same genome. Hierarchical differentiation, characteristic of self-renewing tissues, can restrain somatic evolution both by limiting divisional load, thereby reducing mutation accumulation, and by increasing cells’ commitment to differentiation, which can “wash out” mutants. Here, we explore the organization of hierarchical tissues that have evolved to limit their lifetime incidence of cancer. Estimating the likelihood of cancer in the presence of mutations that enhance self-proliferation, we demonstrate that a trade-off exists between mutation accumulation and the strength of washing out. Our results explain differences in the organization of widely different hierarchical tissues, such as colon and blood. The observation that tissues that undergo more stem cell divisions are less prone to develop cancer presents a paradox as these tissues should have more opportunity to accumulate cancer-causing mutations. Here, the authors present a solution to the paradox by showing how hierarchical tissues can maintain low cancer incidence by balancing mutation accumulation and the cells’ commitment to differentiation.
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Abstract
This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.
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Affiliation(s)
- James J Saller
- Departments of Pathology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Theresa A Boyle
- Departments of Pathology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
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48
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Kerr L, Sproul D, Grima R. Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns. J R Soc Interface 2022; 19:20210707. [PMID: 35078341 PMCID: PMC8790364 DOI: 10.1098/rsif.2021.0707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/13/2021] [Indexed: 11/12/2022] Open
Abstract
The accurate establishment and maintenance of DNA methylation patterns is vital for mammalian development and disruption to these processes causes human disease. Our understanding of DNA methylation mechanisms has been facilitated by mathematical modelling, particularly stochastic simulations. Megabase-scale variation in DNA methylation patterns is observed in development, cancer and ageing and the mechanisms generating these patterns are little understood. However, the computational cost of stochastic simulations prevents them from modelling such large genomic regions. Here, we test the utility of three different mean-field models to predict summary statistics associated with large-scale DNA methylation patterns. By comparison to stochastic simulations, we show that a cluster mean-field model accurately predicts the statistical properties of steady-state DNA methylation patterns, including the mean and variance of methylation levels calculated across a system of CpG sites, as well as the covariance and correlation of methylation levels between neighbouring sites. We also demonstrate that a cluster mean-field model can be used within an approximate Bayesian computation framework to accurately infer model parameters from data. As mean-field models can be solved numerically in a few seconds, our work demonstrates their utility for understanding the processes underpinning large-scale DNA methylation patterns.
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Affiliation(s)
- Lyndsay Kerr
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Duncan Sproul
- MRC Human Genetics Unit and CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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49
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Bozic I. Quantification of the Selective Advantage of Driver Mutations Is Dependent on the Underlying Model and Stage of Tumor Evolution. Cancer Res 2022; 82:21-24. [PMID: 34983781 DOI: 10.1158/0008-5472.can-21-1064] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/11/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Measuring the selective fitness advantages provided by driver mutations has the potential to facilitate a precise quantitative understanding of cancer evolution. However, accurately measuring the selective advantage of driver mutations has remained a challenge in the field. Early studies reported small selective advantages of drivers, on the order of 1%, whereas newer studies report much larger selective advantages, as high as 1,200%. In this article, we argue that the calculated selective advantages of cancer drivers are dependent on the underlying mathematical model and stage of cancer evolution and that comparisons of numerical values of selective advantage without regard for the underlying model and stage can lead to spurious conclusions.
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Affiliation(s)
- Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, Washington. .,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
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50
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Noble R, Burri D, Le Sueur C, Lemant J, Viossat Y, Kather JN, Beerenwinkel N. Spatial structure governs the mode of tumour evolution. Nat Ecol Evol 2022; 6:207-217. [PMID: 34949822 PMCID: PMC8825284 DOI: 10.1038/s41559-021-01615-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 11/10/2021] [Indexed: 12/12/2022]
Abstract
Characterizing the mode-the way, manner or pattern-of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes: rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell-cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.
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Affiliation(s)
- Robert Noble
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Basel, Switzerland. .,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland. .,Department of Mathematics, City, University of London, London, UK.
| | - Dominik Burri
- grid.5801.c0000 0001 2156 2780Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland ,grid.6612.30000 0004 1937 0642Biozentrum, University of Basel, Basel, Switzerland
| | - Cécile Le Sueur
- grid.5801.c0000 0001 2156 2780Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Jeanne Lemant
- grid.5801.c0000 0001 2156 2780Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Yannick Viossat
- grid.11024.360000000120977052Ceremade, Université Paris Dauphine-PSL, Paris, France
| | - Jakob Nikolas Kather
- grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.412301.50000 0000 8653 1507Internal Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
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