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Baciu-Drăgan MA, Beerenwinkel N. Oncotree2vec - a method for embedding and clustering of tumor mutation trees. Bioinformatics 2024; 40:i180-i188. [PMID: 38940124 PMCID: PMC11211817 DOI: 10.1093/bioinformatics/btae214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis. RESULTS Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees. AVAILABILITY AND IMPLEMENTATION https://github.com/cbg-ethz/oncotree2vec.
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Affiliation(s)
- Monica-Andreea Baciu-Drăgan
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, Basel 4056, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, Basel 4056, Switzerland
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2
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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3
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Colyer B, Bak M, Basanta D, Noble R. A seven-step guide to spatial, agent-based modelling of tumour evolution. Evol Appl 2024; 17:e13687. [PMID: 38707992 PMCID: PMC11064804 DOI: 10.1111/eva.13687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 05/07/2024] Open
Abstract
Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.
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Affiliation(s)
- Blair Colyer
- Department of MathematicsCity, University of LondonLondonUK
| | - Maciej Bak
- Department of MathematicsCity, University of LondonLondonUK
| | - David Basanta
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Robert Noble
- Department of MathematicsCity, University of LondonLondonUK
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4
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Hirsch M, Pal S, Mehrabadi FR, Malikic S, Gruen C, Sassano A, Pérez-Guijarro E, Merlino G, Sahinalp C, Molloy EK, Day CP, Przytycka TM. Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.17.588869. [PMID: 38712152 PMCID: PMC11071284 DOI: 10.1101/2024.04.17.588869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.
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Affiliation(s)
- M.G. Hirsch
- National Library of Medicine, NIH, Bethesda, Maryland, USA
- Department of Computer Science, University of Maryland, College Park, Maryland USA
| | - Soumitra Pal
- Neurobiology Neurodegeneration and Repair Lab, National Eye Institute, NIH, Bethesda, Maryland, USA
| | - Farid Rashidi Mehrabadi
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer institute, NIH, Bethesda, Maryland, USA
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Salem Malikic
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer institute, NIH, Bethesda, Maryland, USA
| | - Charli Gruen
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Antonella Sassano
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
- Instituto de Investigaciones Biomédicas Sols-Morreale, Consejo Superior de Investigaciones Científicas, Universidad Autónoma de Madrid (IIBM, CSIC-UAM), Madrid, Spain
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer institute, NIH, Bethesda, Maryland, USA
| | - Erin K. Molloy
- Department of Computer Science, University of Maryland, College Park, Maryland USA
- University of Maryland Institute for Advanced Computer Studies, College Park, Maryland USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland, USA
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Dinh KN, Vázquez-García I, Chan A, Malhotra R, Weiner A, McPherson AW, Tavaré S. CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.587939. [PMID: 38617259 PMCID: PMC11014621 DOI: 10.1101/2024.04.03.587939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.
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Affiliation(s)
- Khanh N. Dinh
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Ignacio Vázquez-García
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Chan
- Case Western Reserve University, Cleveland, OH, USA
| | - Rhea Malhotra
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Stanford University, Palo Alto, CA, USA
| | - Adam Weiner
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Andrew W. McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simon Tavaré
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
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6
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Fu X, Luo Z, Deng Y, LaFramboise W, Bartlett D, Schwartz R. Marker selection strategies for circulating tumor DNA guided by phylogenetic inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.585352. [PMID: 38586041 PMCID: PMC10996527 DOI: 10.1101/2024.03.21.585352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Motivation Blood-based profiling of tumor DNA ("liquid biopsy") has offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but require further advances in computational methods to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific questions: 1) How to apply longitudinal ctDNA data to refine phylogeny models of clonal evolution, and 2) how to quantify changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions through a probabilistic framework for optimally identifying maximum likelihood markers and applying them to characterizing clonal evolution. Results We first estimate a distribution over plausible clonal lineage models, using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these lineage models and the clonal frequencies they imply over successive longitudinal samples. We use the resulting framework for modeling and refining tree distributions to pose a set of optimization problems to select ctDNA markers to maximize measures of utility capturing ability to solve the two questions of reducing uncertain in phylogeny models or quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining distributions of tree models and clonal frequencies so as to minimize measures of tree distance relative to the ground truth. Application of the tree refinement methods to real tumor data further demonstrated their effectiveness in refining a clonal lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of tumor progression. Availability https://github.com/CMUSchwartzLab/Mase-phi.git. Contact russells@andrew.cmu.edu.
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Affiliation(s)
- Xuecong Fu
- Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Avenue, 15217, Pittsburgh, PA, USA
| | - Zhicheng Luo
- Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Avenue, 15217, Pittsburgh, PA, USA
| | - Yueqian Deng
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, 15217, Pittsburgh, PA, USA
| | - William LaFramboise
- Allegheny Health Network Cancer Institute, Allegheny Health Network, 320 East North Avenue, 15212, Pittsburgh, PA, USA
| | - David Bartlett
- Allegheny Health Network Cancer Institute, Allegheny Health Network, 320 East North Avenue, 15212, Pittsburgh, PA, USA
| | - Russell Schwartz
- Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Avenue, 15217, Pittsburgh, PA, USA
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, 15217, Pittsburgh, PA, USA
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7
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Lacy MS, Jenner AL. Impact of Resistance on Therapeutic Design: A Moran Model of Cancer Growth. Bull Math Biol 2024; 86:43. [PMID: 38502371 PMCID: PMC10950993 DOI: 10.1007/s11538-024-01272-6] [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: 09/04/2023] [Accepted: 02/19/2024] [Indexed: 03/21/2024]
Abstract
Resistance of cancers to treatments, such as chemotherapy, largely arise due to cell mutations. These mutations allow cells to resist apoptosis and inevitably lead to recurrence and often progression to more aggressive cancer forms. Sustained-low dose therapies are being considered as an alternative over maximum tolerated dose treatments, whereby a smaller drug dosage is given over a longer period of time. However, understanding the impact that the presence of treatment-resistant clones may have on these new treatment modalities is crucial to validating them as a therapeutic avenue. In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Moran model, it is possible to explore the likelihood of treatment success given a non-negligible probability of treatment resistant mutations and suggest more robust therapeutic schedules.
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Affiliation(s)
- Mason S Lacy
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
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8
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Metzcar J, Jutzeler CR, Macklin P, Köhn-Luque A, Brüningk SC. A review of mechanistic learning in mathematical oncology. Front Immunol 2024; 15:1363144. [PMID: 38533513 PMCID: PMC10963621 DOI: 10.3389/fimmu.2024.1363144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Affiliation(s)
- John Metzcar
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
- Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Catherine R. Jutzeler
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Paul Macklin
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Sarah C. Brüningk
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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9
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Fabre MA, Vassiliou GS. The lifelong natural history of clonal hematopoiesis and its links to myeloid neoplasia. Blood 2024; 143:573-581. [PMID: 37992214 DOI: 10.1182/blood.2023019964] [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: 07/28/2023] [Revised: 10/26/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
ABSTRACT The study of somatic mutations and the associated clonal mosaicism across the human body has transformed our understanding of aging and its links to cancer. In proliferative human tissues, stem cells compete for dominance, and those with an advantage expand clonally to outgrow their peers. In the hematopoietic system, such expansion is termed clonal hematopoiesis (CH). The forces driving competition, namely heterogeneity of the hematopoietic stem cell (HSC) pool and attrition of their environment, become increasingly prominent with age. As a result, CH becomes progressively more common through life to the point of becoming essentially ubiquitous. We are beginning to unravel the specific intracellular and extracellular factors underpinning clonal behavior, with somatic mutations in specific driver genes, inflammation, telomere maintenance, extraneous exposures, and inherited genetic variation among the important players. The inevitability of CH with age combined with its unequivocal links to myeloid cancers poses a scientific and clinical challenge. Specifically, we need to decipher the factors determining clonal behavior and develop prognostic tools to identify those at high risk of malignant progression, for whom preventive interventions may be warranted. Here, we discuss how recent advances in our understanding of the natural history of CH have provided important insights into these processes and helped define future avenues of investigation.
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Affiliation(s)
- Margarete A Fabre
- Department of Haematology, Cambridge University Hospitals National Health Service Trust, Cambridge, United Kingdom
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals Research & Development, AstraZeneca, Cambridge, United Kingdom
| | - George S Vassiliou
- Department of Haematology, Cambridge University Hospitals National Health Service Trust, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
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10
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Wang X, Jenner AL, Salomone R, Warne DJ, Drovandi C. Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation. J Math Biol 2024; 88:28. [PMID: 38358410 PMCID: PMC10869399 DOI: 10.1007/s00285-024-02045-4] [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: 06/28/2023] [Revised: 10/25/2023] [Accepted: 12/27/2023] [Indexed: 02/16/2024]
Abstract
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.
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Affiliation(s)
- Xiaoyu Wang
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Robert Salomone
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - David J Warne
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Christopher Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
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11
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Lai J, Liu Y, Scharpf RB, Karchin R. Evaluation of simulation methods for tumor subclonal reconstruction. ARXIV 2024:arXiv:2402.09599v1. [PMID: 38410652 PMCID: PMC10896360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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12
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Neagu AN, Whitham D, Bruno P, Arshad A, Seymour L, Morrissiey H, Hukovic AI, Darie CC. Onco-Breastomics: An Eco-Evo-Devo Holistic Approach. Int J Mol Sci 2024; 25:1628. [PMID: 38338903 PMCID: PMC10855488 DOI: 10.3390/ijms25031628] [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: 12/20/2023] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, “Alexandru Ioan Cuza” University of Iași, Carol I bvd. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Aneeta Arshad
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Logan Seymour
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Angiolina I. Hukovic
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Costel C. Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
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13
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Nair NU, Schäffer AA, Gertz EM, Cheng K, Zerbib J, Sahu AD, Leor G, Shulman ED, Aldape KD, Ben-David U, Ruppin E. Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576103. [PMID: 38313282 PMCID: PMC10836086 DOI: 10.1101/2024.01.17.576103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.
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14
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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15
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Bristy NA, Fu X, Schwartz R. Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570724. [PMID: 38106049 PMCID: PMC10723466 DOI: 10.1101/2023.12.07.570724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming (ILP) based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants (SNV), copy number alterations (CNA) and structural variations (SV) into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness on real data in resolving clonal evolution in the presence of multiple variant types, providing a path towards more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.
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16
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Baig Y, Ma HR, Xu H, You L. Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics. Nat Commun 2023; 14:7937. [PMID: 38049401 PMCID: PMC10696002 DOI: 10.1038/s41467-023-43455-0] [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: 01/30/2023] [Accepted: 11/09/2023] [Indexed: 12/06/2023] Open
Abstract
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.
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Affiliation(s)
- Yasa Baig
- Department of Physics, Duke University, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Helena R Ma
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA
| | - Helen Xu
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
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17
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. PLoS Comput Biol 2023; 19:e1011590. [PMID: 37943952 PMCID: PMC10662746 DOI: 10.1371/journal.pcbi.1011590] [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: 07/18/2023] [Revised: 11/21/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. RESULTS We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
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18
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Romero-Arias JR, González-Castro CA, Ramírez-Santiago G. A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development. PLoS Comput Biol 2023; 19:e1011673. [PMID: 37992135 DOI: 10.1371/journal.pcbi.1011673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 12/06/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
We analyzed a quantitative multiscale model that describes the epigenetic dynamics during the growth and evolution of an avascular tumor. A gene regulatory network (GRN) formed by a set of ten genes that are believed to play an important role in breast cancer development was kinetically coupled to the microenvironmental agents: glucose, estrogens, and oxygen. The dynamics of spontaneous mutations was described by a Yule-Furry master equation whose solution represents the probability that a given cell in the tissue undergoes a certain number of mutations at a given time. We assumed that the mutation rate is modified by a spatial gradient of nutrients. The tumor mass was simulated by means of cellular automata supplemented with a set of reaction diffusion equations that described the transport of microenvironmental agents. By analyzing the epigenetic state space described by the GRN dynamics, we found three attractors that were identified with cellular epigenetic states: normal, precancer and cancer. For two-dimensional (2D) and three-dimensional (3D) tumors we calculated the spatial distribution of the following quantities: (i) number of mutations, (ii) mutation of each gene and, (iii) phenotypes. Using estrogen as the principal microenvironmental agent that regulates cell proliferation process, we obtained tumor shapes for different values of estrogen consumption and supply rates. It was found that he majority of mutations occurred in cells that were located close to the 2D tumor perimeter or close to the 3D tumor surface. Also, it was found that the occurrence of different phenotypes in the tumor are controlled by estrogen concentration levels since they can change the individual cell threshold and gene expression levels. All results were consistently observed for 2D and 3D tumors.
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Affiliation(s)
- J Roberto Romero-Arias
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
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19
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Amaro A, Pfeffer U. Clonal Extinction Drives Tumorigenesis. Cancers (Basel) 2023; 15:4761. [PMID: 37835454 PMCID: PMC10571900 DOI: 10.3390/cancers15194761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Before a tumor is diagnosed and surgically removed, it has been growing for many months or even years [...].
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Affiliation(s)
- Adriana Amaro
- Laboratory of Regulation of Gene Expression, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy
| | - Ulrich Pfeffer
- Laboratory of Regulation of Gene Expression, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy
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20
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Deutscher K, Hillen T, Newby J. A computational model for the cancer field effect. Front Artif Intell 2023; 6:1060879. [PMID: 37469932 PMCID: PMC10352683 DOI: 10.3389/frai.2023.1060879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 06/05/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction The Cancer Field Effect describes an area of pre-cancerous cells that results from continued exposure to carcinogens. Cells in the cancer field can easily develop into cancer. Removal of the main tumor mass might leave the cancer field behind, increasing risk of recurrence. Methods The model we propose for the cancer field effect is a hybrid cellular automaton (CA), which includes a multi-layer perceptron (MLP) to compute the effects of the carcinogens on the gene expression of the genes related to cancer development. We use carcinogen interactions that are typically associated with smoking and alcohol consumption and their effect on cancer fields of the tongue. Results Using simulations we support the understanding that tobacco smoking is a potent carcinogen, which can be reinforced by alcohol consumption. The effect of alcohol alone is significantly less than the effect of tobacco. We further observe that pairing tumor excision with field removal delays recurrence compared to tumor excision alone. We track cell lineages and find that, in most cases, a polyclonal field develops, where the number of distinct cell lineages decreases over time as some lineages become dominant over others. Finally, we find tumor masses rarely form via monoclonal origin.
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21
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Ivanovic S, El-Kebir M. Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Res 2023; 33:1078-1088. [PMID: 37344104 PMCID: PMC10538496 DOI: 10.1101/gr.277672.123] [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: 01/06/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.
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Affiliation(s)
- Stefan Ivanovic
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
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22
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Liu Y, Li XC, Rashidi Mehrabadi F, Schäffer AA, Pratt D, Crawford DR, Malikić S, Molloy EK, Gopalan V, Mount SM, Ruppin E, Aldape KD, Sahinalp SC. Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models. Genome Res 2023; 33:1089-1100. [PMID: 37316351 PMCID: PMC10538489 DOI: 10.1101/gr.277608.122] [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: 01/12/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.
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Affiliation(s)
- Yuelin Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Xuan Cindy Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
| | - Farid Rashidi Mehrabadi
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Drew Pratt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - David R Crawford
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Salem Malikić
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Erin K Molloy
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Vishaka Gopalan
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Stephen M Mount
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
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23
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Luo XG, Kuipers J, Beerenwinkel N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat Commun 2023; 14:3676. [PMID: 37344522 DOI: 10.1038/s41467-023-39400-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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Affiliation(s)
- Xiang Ge Luo
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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24
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Huzar J, Shenoy M, Sanderford MD, Kumar S, Miura S. Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data. FRONTIERS IN BIOINFORMATICS 2023; 3:1090730. [PMID: 37261293 PMCID: PMC10228696 DOI: 10.3389/fbinf.2023.1090730] [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: 11/05/2022] [Accepted: 03/28/2023] [Indexed: 06/02/2023] Open
Abstract
Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.
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Affiliation(s)
- Jared Huzar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Madelyn Shenoy
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
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25
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Fritz M. Tumor Evolution Models of Phase-Field Type with Nonlocal Effects and Angiogenesis. Bull Math Biol 2023; 85:44. [PMID: 37081144 DOI: 10.1007/s11538-023-01151-6] [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: 12/01/2022] [Accepted: 03/27/2023] [Indexed: 04/22/2023]
Abstract
In this survey article, a variety of systems modeling tumor growth are discussed. In accordance with the hallmarks of cancer, the described models incorporate the primary characteristics of cancer evolution. Specifically, we focus on diffusive interface models and follow the phase-field approach that describes the tumor as a collection of cells. Such systems are based on a multiphase approach that employs constitutive laws and balance laws for individual constituents. In mathematical oncology, numerous biological phenomena are involved, including temporal and spatial nonlocal effects, complex nonlinearities, stochasticity, and mixed-dimensional couplings. Using the models, for instance, we can express angiogenesis and cell-to-matrix adhesion effects. Finally, we offer some methods for numerically approximating the models and show simulations of the tumor's evolution in response to various biological effects.
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Affiliation(s)
- Marvin Fritz
- Computational Methods for PDEs, Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria.
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26
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.10.536302. [PMID: 37090633 PMCID: PMC10120719 DOI: 10.1101/2023.04.10.536302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Motivation New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, NJ, USA
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27
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Moravec JC, Lanfear R, Spector DL, Diermeier SD, Gavryushkin A. Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data. J Comput Biol 2023; 30:518-537. [PMID: 36475926 PMCID: PMC10125402 DOI: 10.1089/cmb.2022.0357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.
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Affiliation(s)
- Jiří C. Moravec
- Department of Computer Science, University of Otago, Dunedin, New Zealand
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Robert Lanfear
- Division of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, Australia
| | | | | | - Alex Gavryushkin
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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28
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Caravagna G. Mathematical modeling of neuroblastoma associates evolutionary patterns with outcomes. Nat Genet 2023; 55:530-531. [PMID: 36973453 DOI: 10.1038/s41588-023-01358-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Affiliation(s)
- Giulio Caravagna
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy.
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Streck A, Kaufmann TL, Schwarz RF. SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity. Bioinformatics 2023; 39:7056642. [PMID: 36825830 PMCID: PMC10010604 DOI: 10.1093/bioinformatics/btad102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/10/2023] [Accepted: 02/23/2023] [Indexed: 02/25/2023] Open
Abstract
MOTIVATION Simulations of cancer evolution are highly useful to study the effects of selection and mutation rates on cellular fitness. However, most methods are either lattice-based and cannot simulate realistically sized tumours, or they omit spatial constraints and lack the clonal dynamics of real-world tumours. RESULTS Stochastic model of intra-tumour heterogeneity (SMITH) is an efficient and explainable model of cancer evolution that combines a branching process with a new confinement mechanism limiting clonal growth based on the size of the individual clones as well as the overall tumour population. We demonstrate how confinement is sufficient to induce the rich clonal dynamics observed in spatial models and cancer samples across tumour types, while allowing for a clear geometric interpretation and simulation of 1 billion cells within a few minutes on a desktop PC. AVAILABILITY AND IMPLEMENTATION SMITH is implemented in C# and freely available at https://bitbucket.org/schwarzlab/smith. For visualizations, we provide the accompanying Python package PyFish at https://bitbucket.org/schwarzlab/pyfish. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Adam Streck
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.,Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Tom L Kaufmann
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Roland F Schwarz
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
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30
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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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31
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Shan NL, Kahn A, Pusztai L. Intratumour heterogeneity, from hypothesis to the clinic. Br J Cancer 2023; 128:459-460. [PMID: 36216884 PMCID: PMC9938204 DOI: 10.1038/s41416-022-02008-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
Recent technological advances uncovered intricate biological processes underlying intratumor heterogeneity with clinical implications. These insights led to novel biomarkers for immunotherapies, justified serial tumour biopsies for therapeutic target profiling, inspired new treatment strategies, and ultimately might yield novel therapeutics that target clonal interdependence.
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Affiliation(s)
- Naing Lin Shan
- Yale Cancer Center, Yale School of Medicine, 333 Cedar St., PO Box 208032, New Haven, CT, 05620, USA
| | - Adriana Kahn
- Yale Cancer Center, Yale School of Medicine, 333 Cedar St., PO Box 208032, New Haven, CT, 05620, USA
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, 333 Cedar St., PO Box 208032, New Haven, CT, 05620, USA.
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32
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Peixoto C, Lopes MB, Martins M, Casimiro S, Sobral D, Grosso AR, Abreu C, Macedo D, Costa AL, Pais H, Alvim C, Mansinho A, Filipe P, Costa PMD, Fernandes A, Borralho P, Ferreira C, Malaquias J, Quintela A, Kaplan S, Golkaram M, Salmans M, Khan N, Vijayaraghavan R, Zhang S, Pawlowski T, Godsey J, So A, Liu L, Costa L, Vinga S. Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization. BMC Bioinformatics 2023; 24:17. [PMID: 36647008 PMCID: PMC9841719 DOI: 10.1186/s12859-022-05104-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 12/07/2022] [Indexed: 01/18/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner-a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods' accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models' predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients' groups based on RNA-seq data.
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Affiliation(s)
- Carolina Peixoto
- grid.9983.b0000 0001 2181 4263INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), NOVA School of Science and Technology, 2829-516 Caparica, Portugal ,Center for Mathematics and Applications (NOVA MATH), NOVA School of Science and Technology (FCT NOVA), 2829-516 Caparica, Portugal
| | - Marta Martins
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Sandra Casimiro
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Daniel Sobral
- grid.10772.330000000121511713Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal ,grid.10772.330000000121511713UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Ana Rita Grosso
- grid.10772.330000000121511713Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal ,grid.10772.330000000121511713UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Catarina Abreu
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Daniela Macedo
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Ana Lúcia Costa
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Helena Pais
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Cecília Alvim
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - André Mansinho
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal ,grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Pedro Filipe
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Pedro Marques da Costa
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Afonso Fernandes
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Paula Borralho
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Cristina Ferreira
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - João Malaquias
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - António Quintela
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Shannon Kaplan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Mahdi Golkaram
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Michael Salmans
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Nafeesa Khan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Raakhee Vijayaraghavan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Shile Zhang
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Traci Pawlowski
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Jim Godsey
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Alex So
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Li Liu
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Luís Costa
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal ,grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Susana Vinga
- grid.9983.b0000 0001 2181 4263INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal ,grid.9983.b0000 0001 2181 4263IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
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Georg P, Grasedyck L, Klever M, Schill R, Spang R, Wettig T. Low-rank tensor methods for Markov chains with applications to tumor progression models. J Math Biol 2023; 86:7. [PMID: 36460900 PMCID: PMC9718722 DOI: 10.1007/s00285-022-01846-9] [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: 09/13/2021] [Revised: 09/19/2022] [Accepted: 11/22/2022] [Indexed: 12/05/2022]
Abstract
Cancer progression can be described by continuous-time Markov chains whose state space grows exponentially in the number of somatic mutations. The age of a tumor at diagnosis is typically unknown. Therefore, the quantity of interest is the time-marginal distribution over all possible genotypes of tumors, defined as the transient distribution integrated over an exponentially distributed observation time. It can be obtained as the solution of a large linear system. However, the sheer size of this system renders classical solvers infeasible. We consider Markov chains whose transition rates are separable functions, allowing for an efficient low-rank tensor representation of the linear system's operator. Thus we can reduce the computational complexity from exponential to linear. We derive a convergent iterative method using low-rank formats whose result satisfies the normalization constraint of a distribution. We also perform numerical experiments illustrating that the marginal distribution is well approximated with low rank.
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Affiliation(s)
- Peter Georg
- Department of Physics, University of Regensburg, 93040 Regensburg, Germany
| | - Lars Grasedyck
- Institute for Geometry and Applied Mathematics, RWTH Aachen University, 52062 Aachen, Germany
| | - Maren Klever
- Institute for Geometry and Applied Mathematics, RWTH Aachen University, 52062 Aachen, Germany
| | - Rudolf Schill
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, 93040 Regensburg, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, 93040 Regensburg, Germany
| | - Tilo Wettig
- Department of Physics, University of Regensburg, 93040 Regensburg, Germany
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Chen J. Timed hazard networks: Incorporating temporal difference for oncogenetic analysis. PLoS One 2023; 18:e0283004. [PMID: 36928529 PMCID: PMC10019724 DOI: 10.1371/journal.pone.0283004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
Oncogenetic graphical models are crucial for understanding cancer progression by analyzing the accumulation of genetic events. These models are used to identify statistical dependencies and temporal order of genetic events, which helps design targeted therapies. However, existing algorithms do not account for temporal differences between samples in oncogenetic analysis. This paper introduces Timed Hazard Networks (TimedHN), a new statistical model that uses temporal differences to improve accuracy and reliability. TimedHN models the accumulation process as a continuous-time Markov chain and includes an efficient gradient computation algorithm for optimization. Our simulation experiments demonstrate that TimedHN outperforms current state-of-the-art graph reconstruction methods. We also compare TimedHN with existing methods on a luminal breast cancer dataset, highlighting its potential utility. The Matlab implementation and data are available at https://github.com/puar-playground/TimedHN.
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Affiliation(s)
- Jian Chen
- Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, United States of America
- * E-mail:
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35
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Seillier L, Peifer M. Reconstructing Phylogenetic Relationship in Bladder Cancer: A Methodological Overview. Methods Mol Biol 2023; 2684:113-132. [PMID: 37410230 DOI: 10.1007/978-1-0716-3291-8_6] [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] [Indexed: 07/07/2023]
Abstract
Bladder cancer (BC) expresses itself as a highly heterogeneous disease both at the histological and molecular level, often occurring as synchronous or metachronous multifocal disease with high risk of recurrence and potential to metastasize. Multiple sequencing studies focusing on both non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) gave insights into the extent of both inter- and intrapatient heterogeneity, but many questions on clonal evolution in BC remain unanswered. In this review article, we provide an overview over the technical and theoretical concepts linked to reconstructing evolutionary trajectories in BC and propose a set of tools and established software for phylogenetic analysis.
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Affiliation(s)
| | - Martin Peifer
- Department of Translational Genomics, University of Cologne, Cologne, Germany
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36
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Moen MT, Johnston IG. HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs. Bioinformatics 2022; 39:6895098. [PMID: 36511587 PMCID: PMC9848056 DOI: 10.1093/bioinformatics/btac803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use. RESULTS Here, we introduce HyperHMM, an adapted Baum-Welch (expectation-maximization) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to previous hypercubic inference approaches. We show that HyperHMM allows any combination of traits to exert arbitrary positive or negative influence on the acquisition of other traits, relaxing a common limitation of only independent trait influences. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways. AVAILABILITY AND IMPLEMENTATION Code for inference and visualization, and data for example cases, is freely available at https://github.com/StochasticBiology/hypercube-hmm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marcus T Moen
- Department of Mathematics, University of Bergen, Bergen, Vestland, Norway
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37
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ToMExO: A probabilistic tree-structured model for cancer progression. PLoS Comput Biol 2022; 18:e1010732. [DOI: 10.1371/journal.pcbi.1010732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 12/15/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.
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Gao Y, Gaither J, Chifman J, Kubatko L. A phylogenetic approach to inferring the order in which mutations arise during cancer progression. PLoS Comput Biol 2022; 18:e1010560. [PMID: 36459515 DOI: 10.1371/journal.pcbi.1010560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 12/14/2022] [Accepted: 09/12/2022] [Indexed: 12/05/2022] Open
Abstract
Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.
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Affiliation(s)
- Yuan Gao
- Division of Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Gaither
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, United States of America
| | - Julia Chifman
- Dept of Mathematics and Statistics, American University, Washington D. C., United States of America
| | - Laura Kubatko
- Dept of Statistics, The Ohio State University, Columbus, Ohio, United States of America
- Dept of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
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El-Kafrawy SA, El-Daly MM, Bajrai LH, Alandijany TA, Faizo AA, Mobashir M, Ahmed SS, Ahmed S, Alam S, Jeet R, Kamal MA, Anwer ST, Khan B, Tashkandi M, Rizvi MA, Azhar EI. Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma. Front Genet 2022; 13:880440. [PMID: 36479247 PMCID: PMC9720179 DOI: 10.3389/fgene.2022.880440] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 11/02/2022] [Indexed: 12/11/2023] Open
Abstract
Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.
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Affiliation(s)
- Sherif A. El-Kafrawy
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mai M. El-Daly
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Leena H. Bajrai
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Biochemistry Department, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Thamir A. Alandijany
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Arwa A. Faizo
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohammad Mobashir
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institute, Stockholm, Sweden
- Genome Biology Lab, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Sunbul S. Ahmed
- Genome Biology Lab, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Sarfraz Ahmed
- Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Shoaib Alam
- Department of Biotechnology, Jamia Millia Islamia, New Delhi, India
| | - Raja Jeet
- Botany Department, Ganesh Dutt College, Begusarai, Bihar, India
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
- Enzymoics, Hebersham, NSW, Australia
- Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Syed Tauqeer Anwer
- Genome Biology Lab, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Bushra Khan
- Genome Biology Lab, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Manal Tashkandi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Moshahid A. Rizvi
- Genome Biology Lab, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
| | - Esam Ibraheem Azhar
- Special Infectious Agents Unit-BSL3, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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40
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Kaufmann TL, Petkovic M, Watkins TBK, Colliver EC, Laskina S, Thapa N, Minussi DC, Navin N, Swanton C, Van Loo P, Haase K, Tarabichi M, Schwarz RF. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution. Genome Biol 2022; 23:241. [PMID: 36376909 PMCID: PMC9661799 DOI: 10.1186/s13059-022-02794-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.
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Affiliation(s)
- Tom L Kaufmann
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany.
- Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
| | - Marina Petkovic
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
- Department of Biology, Humboldt University of Berlin, Unter den Linden 6, 10099, Berlin, Germany
- Division of Oncology and Hematology, Department of Pediatrics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | | | | | - Sofya Laskina
- Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany
| | - Nisha Thapa
- UCL Medical School, University College London, London, UK
| | - Darlan C Minussi
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles Swanton
- The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals, London, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kerstin Haase
- Division of Oncology and Hematology, Department of Pediatrics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK
- Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Roland F Schwarz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
- Institute for Computational Cancer Biology, Center for Integrated Oncology (CIO) and Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
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41
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Diaz-Uriarte R, Herrera-Nieto P. EvAM-Tools: tools for evolutionary accumulation and cancer progression models. Bioinformatics 2022; 38:5457-5459. [PMID: 36287062 PMCID: PMC9750106 DOI: 10.1093/bioinformatics/btac710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 10/03/2022] [Accepted: 10/25/2022] [Indexed: 12/25/2022] Open
Abstract
SUMMARY EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application, users can easily construct models (modifying directed acyclic graphs of restrictions, matrices of mutual hazards or specifying genotype composition), generate data from them (with user-specified observational/genotyping error) and analyze the data. AVAILABILITY AND IMPLEMENTATION Implemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-Tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Pablo Herrera-Nieto
- Department of Biochemistry, Universidad Autónoma de Madrid, Instituto de Investigaciones Biomédicas “Alberto Sols” (UAM-CSIC), Madrid, Spain
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42
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Dynamic Co-Evolution of Cancer Cells and Cancer-Associated Fibroblasts: Role in Right- and Left-Sided Colon Cancer Progression and Its Clinical Relevance. BIOLOGY 2022; 11:biology11071014. [PMID: 36101394 PMCID: PMC9312176 DOI: 10.3390/biology11071014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary The versatile crosstalk between cancer cells and cancer-associated fibroblasts (CAFs) of the tumour microenvironment (TME) drives colorectal carcinogenesis and heterogeneity. Colorectal cancer (CRC) can be classified by the anatomical sites from which the cancer arises, either from the right or left colon. Although the cancer cell–CAF interaction is being widely studied, its role in the progression of cancer in the right and left colon and cancer heterogeneity are still yet to be elucidated. Further insight into the complex interaction between different cellular components in the cancer niche, their evolutionary process and their influence on cancer progression would propel the discovery of effective targeted CRC therapy. Abstract Cancer is a result of a dynamic evolutionary process. It is composed of cancer cells and the tumour microenvironment (TME). One of the major cellular constituents of TME, cancer-associated fibroblasts (CAFs) are known to interact with cancer cells and promote colorectal carcinogenesis. The accumulation of these activated fibroblasts is linked to poor diagnosis in colorectal cancer (CRC) patients and recurrence of the disease. However, the interplay between cancer cells and CAFs is yet to be described, especially in relation to the sidedness of colorectal carcinogenesis. CRC, which is the third most commonly diagnosed cancer globally, can be classified according to the anatomical region from which they originate: left-sided (LCRC) and right-sided CRC (RCR). Both cancers differ in many aspects, including in histology, evolution, and molecular signatures. Despite occurring at lower frequency, RCRC is often associated with worse diagnosis compared to LCRC. The differences in molecular profiles between RCRC and LCRC also influence the mode of treatment that can be used to specifically target these cancer entities. A better understanding of the cancer cell–CAF interplay and its association with RCRC and LRCR progression will provide better insight into potential translational aspects of targeted treatment for CRC.
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43
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Markowska M, Cąkała T, Miasojedow B, Aybey B, Juraeva D, Mazur J, Ross E, Staub E, Szczurek E. CONET: copy number event tree model of evolutionary tumor history for single-cell data. Genome Biol 2022; 23:128. [PMID: 35681161 PMCID: PMC9185904 DOI: 10.1186/s13059-022-02693-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.
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Affiliation(s)
- Magda Markowska
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland.,Medical University of Warsaw, Postgraduate School of Molecular Medicine, Ks. Trojdena 2a Street, Warsaw, Poland
| | - Tomasz Cąkała
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - BłaŻej Miasojedow
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - Bogac Aybey
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Dilafruz Juraeva
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Johanna Mazur
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Edith Ross
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Eike Staub
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Ewa Szczurek
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland.
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44
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Achimas-Cadariu P, Kubelac P, Irimie A, Berindan-Neagoe I, Rühli F. Evolutionary perspectives, heterogeneity and ovarian cancer: a complicated tale from past to present. J Ovarian Res 2022; 15:67. [PMID: 35659345 PMCID: PMC9164402 DOI: 10.1186/s13048-022-01004-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 05/24/2022] [Indexed: 11/21/2022] Open
Abstract
Ovarian cancer is composed of a complex system of cells best described by features such as clonal evolution, spatial and temporal genetic heterogeneity, and development of drug resistance, thus making it the most lethal gynecologic cancer. Seminal work on cancer as an evolutionary process has a long history; however, recent cost-effective large-scale molecular profiling has started to provide novel insights coupled with the development of mathematical algorithms. In the current review, we have systematically searched for articles that focused on the clonal evolution of ovarian cancer to offer the whole landscape of research that has been done and highlight future research avenues given its characteristic features and connections to evolutionary biology.
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Affiliation(s)
- Patriciu Achimas-Cadariu
- Department of Surgery, The Oncology Institute 'Prof. Dr. Ion Chiricuta', 34-36 Republicii street, 400015 , Cluj-Napoca, Romania. .,Department of Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
| | - Paul Kubelac
- Department of Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Medical Oncology, The Oncology Institute 'Prof. Dr. Ion Chiricuta', Cluj-Napoca, Romania
| | - Alexandru Irimie
- Department of Surgery, The Oncology Institute 'Prof. Dr. Ion Chiricuta', 34-36 Republicii street, 400015 , Cluj-Napoca, Romania.,Department of Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ioana Berindan-Neagoe
- Research Centre for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Research Center for Advanced Medicine Medfuture, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Functional Genomics and Experimental Pathology, The Oncology Institute 'Prof. Dr. Ion Chiricuta', Cluj-Napoca, Romania
| | - Frank Rühli
- Institute of Evolutionary Medicine, Zurich, Switzerland
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45
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Fabre MA, de Almeida JG, Fiorillo E, Mitchell E, Damaskou A, Rak J, Orrù V, Marongiu M, Chapman MS, Vijayabaskar MS, Baxter J, Hardy C, Abascal F, Williams N, Nangalia J, Martincorena I, Campbell PJ, McKinney EF, Cucca F, Gerstung M, Vassiliou GS. The longitudinal dynamics and natural history of clonal haematopoiesis. Nature 2022; 606:335-342. [PMID: 35650444 PMCID: PMC9177423 DOI: 10.1038/s41586-022-04785-z] [Citation(s) in RCA: 129] [Impact Index Per Article: 64.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/19/2022] [Indexed: 12/13/2022]
Abstract
Clonal expansions driven by somatic mutations become pervasive across human tissues with age, including in the haematopoietic system, where the phenomenon is termed clonal haematopoiesis1-4. The understanding of how and when clonal haematopoiesis develops, the factors that govern its behaviour, how it interacts with ageing and how these variables relate to malignant progression remains limited5,6. Here we track 697 clonal haematopoiesis clones from 385 individuals 55 years of age or older over a median of 13 years. We find that 92.4% of clones expanded at a stable exponential rate over the study period, with different mutations driving substantially different growth rates, ranging from 5% (DNMT3A and TP53) to more than 50% per year (SRSF2P95H). Growth rates of clones with the same mutation differed by approximately ±5% per year, proportionately affecting slow drivers more substantially. By combining our time-series data with phylogenetic analysis of 1,731 whole-genome sequences of haematopoietic colonies from 7 individuals from an older age group, we reveal distinct patterns of lifelong clonal behaviour. DNMT3A-mutant clones preferentially expanded early in life and displayed slower growth in old age, in the context of an increasingly competitive oligoclonal landscape. By contrast, splicing gene mutations drove expansion only later in life, whereas TET2-mutant clones emerged across all ages. Finally, we show that mutations driving faster clonal growth carry a higher risk of malignant progression. Our findings characterize the lifelong natural history of clonal haematopoiesis and give fundamental insights into the interactions between somatic mutation, ageing and clonal selection.
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Affiliation(s)
- Margarete A Fabre
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - José Guilherme de Almeida
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Cambridge, UK
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Lanusei, Italy
| | - Emily Mitchell
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Aristi Damaskou
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Justyna Rak
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Lanusei, Italy
| | - Michele Marongiu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Lanusei, Italy
| | - Michael Spencer Chapman
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - M S Vijayabaskar
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Joanna Baxter
- Cambridge Blood and Stem Cell Biobank, Department of Haematology, University of Cambridge, Cambridge, UK
| | - Claire Hardy
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Federico Abascal
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Nicholas Williams
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Jyoti Nangalia
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Peter J Campbell
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Eoin F McKinney
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Lanusei, Italy
- Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany.
| | - George S Vassiliou
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- Department of Haematology, University of Cambridge, Cambridge, UK.
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46
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Nicol PB, Barabási DL, Coombes KR, Asiaee A. SITH
: An R package for visualizing and analyzing a spatial model of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022; 2. [DOI: 10.1002/cso2.1033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Phillip B. Nicol
- Department of Biostatistics Harvard University Boston Massachusetts USA
| | | | - Kevin R. Coombes
- Department of Biomedical Informatics Ohio State University Columbus Ohio USA
| | - Amir Asiaee
- Department of Biostatistics Vanderbilt University Nashville TN USA
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47
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Huang Y, Holcombe SA, Zhou Q, Wang SC, Tang J, Nie B. A GAN based approach for inferring progression trajectories of costal cartilage calcification from cross-sectional data at image level. Comput Biol Med 2022; 146:105647. [PMID: 35617729 DOI: 10.1016/j.compbiomed.2022.105647] [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: 01/24/2022] [Revised: 05/02/2022] [Accepted: 05/14/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Costal cartilage calcification (CCC) increases with age and presents differently for men and women. In individuals, however, the cross-sectional studies that show such trends do not reveal the geometric trajectories through which calcification might accumulate across a lifetime. Generative adversarial networks have the potential to reveal such trajectories from cross-sectional data by learning population trends and synthesizing individualized images at progressive levels of calcification. METHODS Chest wall mid-surface CT images with normalized cartilage morphologies were produced for 379 subjects aged 6 to 90, and labeled by sex and calcification severity. A conditional GAN with added loss terms to favor one-way accumulation of CCC was trained using organized image batches. GAN performance was assessed by comparing the distributions of images between the training and synthetic groups. RESULTS Synthetic images generated from a common seed for a given sex and at successive calcification severity levels showed incremental and regional growth of calcification sites. CCC patterns for synthetic male and female images matched known sex-based differences, and individual CCC growth in synthetic images was consistent with previously observed population trends. These trends in the synthetic images were also quantified by structural similarity scores. Synthetic images generated from different input seeds further showed individual variance in specific regions and trajectories of CCC accumulation. CONCLUSION This study inferred individual progression of CCC accumulation from uncalcified to severely calcified using cross-sectional image data. This information can inform computational models of the changing chest wall biomechanics with age, and the GAN-based technique shows potential for inferring longitudinal data from population trends in other clinical areas.
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Affiliation(s)
- Yuan Huang
- State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China; International Center for Automotive Medicine (ICAM), University of Michigan, USA.
| | - Sven A Holcombe
- International Center for Automotive Medicine (ICAM), University of Michigan, USA.
| | - Qing Zhou
- State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China.
| | - Stewart C Wang
- International Center for Automotive Medicine (ICAM), University of Michigan, USA.
| | - Jisi Tang
- State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China.
| | - Bingbing Nie
- State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China.
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48
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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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49
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Zhang W, Dong Y, Sartor O, Zhang K. Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer. Cancer Inform 2022; 21:11769351221087046. [PMID: 35392296 PMCID: PMC8980432 DOI: 10.1177/11769351221087046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/22/2022] [Indexed: 12/30/2022] Open
Abstract
The prevalence of TP53 mutations in advanced prostate cancers (PCa) is 3 to 5 times of the quantity in primary PCa. By an integrative analysis of the Cancer Genome Atlas and Catalogue of Somatic Mutations in Cancer data, we revealed the supporting evidence for 2 complementary hypotheses: H1 - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H2—part of TP53 mutations in PCa metastases occur after the diagnosis of original cancers. The plausibility of these hypotheses can explain the increased prevalence of TP53 mutations in PCa metastases. With H1 and H2 as the general assumptions, we developed mathematical models to decipher the change of the percentage frequency (prevalence) of TP53 mutations from primary tumors to metastases. The following results were obtained. Compared to TP53-normal patients, TP53-mutated patients had poorer biochemical relapse-free survival, higher Gleason scores, and more advanced t-stages (P < .01). Single-nucleotide variants in metastases more frequently occurred on G bases of the coding sequence than those in primary cancers (P = .03). The profile of TP53 hotspot mutations was significantly different between primary and metastatic PCa as demonstrated in a set of statistical tests (P < .05). By the derived formulae, we estimated that about 40% TP53 mutation records collected from metastases occurred after the diagnosis of the original cancers. Our study provided significant insight into PCa progression. The proposed models can also be applied to decipher the prevalence of mutations on TP53 (or other driver genes) in other cancer types.
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Affiliation(s)
- Wensheng Zhang
- Bioinformatics Core of Xavier NIH RCMI Center of Cancer Research, Xavier University of Louisiana, New Orleans, LA, USA
| | - Yan Dong
- Department of Structural and Cellular Biology, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA
| | - Oliver Sartor
- Department of Medicine, Tulane University School of Medicine, Tulane Cancer Center, New Orleans, LA, USA
| | - Kun Zhang
- Bioinformatics Core of Xavier NIH RCMI Center of Cancer Research, Xavier University of Louisiana, New Orleans, LA, USA
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA, USA
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50
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Genetic Clonality as the Hallmark Driving Evolution of Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14:cancers14071813. [PMID: 35406585 PMCID: PMC8998004 DOI: 10.3390/cancers14071813] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Limited knowledge about NSCLC evolution has affected therapeutic strategies for many decades. The application of NGS-based techniques to studies on ITH has provided genetic insight into the contribution of clonality primary seeding, as well as to distant dissemination. To date, multiregional ITH affects accurate diagnosis and treatment decisions and is considered the main hallmark of anticancer therapy failure. Understanding the evolutionary trajectories that drive the metastatic process is critical for improving treatment strategies for this deadly condition. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC, highlighting that wide genetic analyses may reveal the phylogenetic lineages of NSCLC evolution. Abstract Data indicate that many driver alterations from the primary tumor of non-small cell lung cancer (NSCLC) are predominantly shared across all metastases; however, disseminating cells may also acquire a new genetic landscape across their journey. By comparing the constituent subclonal mutations between pairs of primary and metastatic samples, it is possible to derive the ancestral relationships between tumor clones, rather than between tumor samples. Current treatment strategies mostly rely on the theory that metastases are genetically similar to the primary lesions from which they arise. However, intratumor heterogeneity (ITH) affects accurate diagnosis and treatment decisions and it is considered the main hallmark of anticancer therapy failure. Understanding the genetic changes that drive the metastatic process is critical for improving the treatment strategies of this deadly condition. Application of next generation sequencing (NGS) techniques has already created knowledge about tumorigenesis and cancer evolution; however, further NGS implementation may also allow to reconstruct phylogenetic clonal lineages and clonal expansion. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC. We highlight that wide genetic analyses may reveal the phylogenetic trajectories of NSCLC evolution, and may pave the way to better management of follow-up and treatment.
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