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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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2
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Lynch A, Bradford S, Burkard ME. The reckoning of chromosomal instability: past, present, future. Chromosome Res 2024; 32:2. [PMID: 38367036 DOI: 10.1007/s10577-024-09746-y] [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: 01/11/2024] [Revised: 01/11/2024] [Accepted: 01/27/2024] [Indexed: 02/19/2024]
Abstract
Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.
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Affiliation(s)
- Andrew Lynch
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Shermineh Bradford
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mark E Burkard
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA.
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
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Ban I, Tomašić L, Trakala M, Tolić IM, Pavin N. Proliferative advantage of specific aneuploid cells drives evolution of tumor karyotypes. Biophys J 2023; 122:632-645. [PMID: 36654508 PMCID: PMC9989886 DOI: 10.1016/j.bpj.2023.01.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/19/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Most tumors have abnormal karyotypes, which arise from mistakes during mitotic division of healthy euploid cells and evolve through numerous complex mechanisms. In a recent mouse model with increased chromosome missegregation, chromosome gains dominate over losses both in pretumor and tumor tissues, whereas T-cell lymphomas are characterized by gains of chromosomes 14 and 15. However, the quantitative understanding of clonal selection leading to tumor karyotype evolution remains unknown. Here we show, by introducing a mathematical model based on a concept of a macro-karyotype, that tumor karyotypes can be explained by proliferation-driven evolution of aneuploid cells. In pretumor cells, increased apoptosis and slower proliferation of cells with monosomies lead to predominant chromosome gains over losses. Tumor karyotypes with gain of one chromosome can be explained by karyotype-dependent proliferation, whereas, for those with two chromosomes, an interplay with karyotype-dependent apoptosis is an additional possible pathway. Thus, evolution of tumor-specific karyotypes requires proliferative advantage of specific aneuploid karyotypes.
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Affiliation(s)
- Ivana Ban
- Department of Physics, Faculty of Science, University of Zagreb, Bijenička cesta 32, 10000 Zagreb, Croatia
| | - Lucija Tomašić
- Department of Physics, Faculty of Science, University of Zagreb, Bijenička cesta 32, 10000 Zagreb, Croatia
| | - Marianna Trakala
- David H. Koch Institute for Integrative Cancer Research, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Iva M Tolić
- Division of Molecular Biology, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
| | - Nenad Pavin
- Department of Physics, Faculty of Science, University of Zagreb, Bijenička cesta 32, 10000 Zagreb, Croatia.
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Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction. PLoS Comput Biol 2023; 19:e1010815. [PMID: 36689467 PMCID: PMC9917311 DOI: 10.1371/journal.pcbi.1010815] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/10/2023] [Accepted: 12/12/2022] [Indexed: 01/24/2023] Open
Abstract
The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cell's SCNA burden-a chromosome missegregation. A stochastic model of chromosome mis-segregations has been previously developed to describe the evolution of SCNAs of a single chromosome type. Building upon this work, we derive a general deterministic framework for modeling missegregations of multiple chromosome types. The framework offers flexibility to model intra-tumor heterogeneity in the SCNAs of all chromosomes, as well as in missegregation- and turnover rates. The model can be used to test how selection acts upon coexisting karyotypes over hundreds of generations. We use the model to calculate missegregation-induced population extinction (MIE) curves, that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Turnover- and missegregation rates estimated from scRNA-seq data are then compared to theoretical predictions. We find convergence of theoretical and empirical results in both the location of MIE curves and the necessary conditions for MIE. When a dependency of missegregation rate on karyotype is introduced, karyotypes associated with low missegregation rates act as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely.
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Lynch AR, Arp NL, Zhou AS, Weaver BA, Burkard ME. Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference. eLife 2022; 11:e69799. [PMID: 35380536 PMCID: PMC9054132 DOI: 10.7554/elife.69799] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 04/01/2022] [Indexed: 12/03/2022] Open
Abstract
Chromosomal instability (CIN)-persistent chromosome gain or loss through abnormal mitotic segregation-is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rate, a measure of CIN, can inform prognosis and is a promising biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by selection against aneuploid cells, which reduces observable diversity. We developed a framework to measure CIN, accounting for karyotype selection, using simulations with various levels of CIN and models of selection. To identify the model parameters that best fit karyotype data from single-cell sequencing, we used approximate Bayesian computation to infer mis-segregation rates and karyotype selection. Experimental validation confirmed the extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (18.5 ± 0.5/division). Extending this approach to clinical samples revealed that inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.
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Affiliation(s)
- Andrew R Lynch
- Carbone Cancer Center, University of Wisconsin-MadisonMadisonUnited States
- McArdle Laboratory for Cancer Research, University of Wisconsin-MadisonMadisonUnited States
| | - Nicholas L Arp
- Carbone Cancer Center, University of Wisconsin-MadisonMadisonUnited States
| | - Amber S Zhou
- Carbone Cancer Center, University of Wisconsin-MadisonMadisonUnited States
- McArdle Laboratory for Cancer Research, University of Wisconsin-MadisonMadisonUnited States
| | - Beth A Weaver
- Carbone Cancer Center, University of Wisconsin-MadisonMadisonUnited States
- McArdle Laboratory for Cancer Research, University of Wisconsin-MadisonMadisonUnited States
- Department of Cell and Regenerative Biology, University of WisconsinMadisonUnited States
| | - Mark E Burkard
- Carbone Cancer Center, University of Wisconsin-MadisonMadisonUnited States
- McArdle Laboratory for Cancer Research, University of Wisconsin-MadisonMadisonUnited States
- Division of Hematology Medical Oncology and Palliative Care, Department of Medicine University of WisconsinMadisonUnited States
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Elizalde S, Laughney AM, Bakhoum SF. A Markov chain for numerical chromosomal instability in clonally expanding populations. PLoS Comput Biol 2018; 14:e1006447. [PMID: 30204765 PMCID: PMC6150543 DOI: 10.1371/journal.pcbi.1006447] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 09/21/2018] [Accepted: 08/18/2018] [Indexed: 01/17/2023] Open
Abstract
Cancer cells frequently undergo chromosome missegregation events during mitosis, whereby the copies of a given chromosome are not distributed evenly among the two daughter cells, thus creating cells with heterogeneous karyotypes. A stochastic model tracing cellular karyotypes derived from clonal populations over hundreds of generations was recently developed and experimentally validated, and it was capable of predicting favorable karyotypes frequently observed in cancer. Here, we construct and study a Markov chain that precisely describes karyotypic evolution during clonally expanding cancer cell populations. The Markov chain allows us to directly predict the distribution of karyotypes and the expected size of the tumor after many cell divisions without resorting to computationally expensive simulations. We determine the limiting karyotype distribution of an evolving tumor population, and quantify its dependency on several key parameters including the initial karyotype of the founder cell, the rate of whole chromosome missegregation, and chromosome-specific cell viability. Using this model, we confirm the existence of an optimal rate of chromosome missegregation probabilities that maximizes karyotypic heterogeneity, while minimizing the occurrence of nullisomy. Interestingly, karyotypic heterogeneity is significantly more dependent on chromosome missegregation probabilities rather than the number of cell divisions, so that maximal heterogeneity can be reached rapidly (within a few hundred generations of cell division) at chromosome missegregation rates commonly observed in cancer cell lines. Conversely, at low missegregation rates, heterogeneity is constrained even after thousands of cell division events. This leads us to conclude that chromosome copy number heterogeneity is primarily constrained by chromosome missegregation rates and the risk for nullisomy and less so by the age of the tumor. This model enables direct integration of karyotype information into existing models of tumor evolution based on somatic mutations. Chromosomal instability (CIN) is a hallmark of cancer and it results from persistent chromosome segregation errors during cell division. CIN has been shown to play a key role in drug resistance and tumor metastasis. While our understanding of CIN on the cellular level has grown over the past decade, our ability to predict the behavior of tumors containing billions of cells remains limited due to the paucity of adequate mathematical models. Here, we develop a Markov-chain model that is capable of providing exact solutions for long-term chromosome copy number distributions during tumor growth. Using this model we confirm the presence of optimal chromosome missegregation rates that balance genomic heterogeneity required for tumor evolution and survival. Interestingly, we show that chromosome copy number heterogeneity is primarily influenced by the rate of chromosome segregation errors rather than the age of the tumor. At chromosome missegregation rates frequently observed in cancer, tumors can acquire maximal genomic heterogeneity after a few hundred cell divisions. This model enables the integration of selection imparted by CIN into existing models of tumor evolution based on somatic mutations to explore their mutual effects.
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Affiliation(s)
- Sergi Elizalde
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, United States of America
- * E-mail:
| | - Ashley M. Laughney
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Samuel F. Bakhoum
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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Solé RV, Valverde S, Rodriguez-Caso C, Sardanyés J. Can a minimal replicating construct be identified as the embodiment of cancer? Bioessays 2015; 36:503-12. [PMID: 24723412 DOI: 10.1002/bies.201300098] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Genomic instability is a hallmark of cancer. Cancer cells that exhibit abnormal chromosomes are characteristic of most advanced tumours, despite the potential threat represented by accumulated genetic damage. Carcinogenesis involves a loss of key components of the genetic and signalling molecular networks; hence some authors have suggested that this is part of a trend of cancer cells to behave as simple, minimal replicators. In this study, we explore this conjecture and suggest that, in the case of cancer, genomic instability has an upper limit that is associated with a minimal cancer cell network. Such a network would include (for a given microenvironment) the basic molecular components that allow cells to replicate and respond to selective pressures. However, it would also exhibit internal fragilities that could be exploited by appropriate therapies targeting the DNA repair machinery. The implications of this hypothesis are discussed.
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Affiliation(s)
- Ricard V Solé
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Biologia Evolutiva, CSIC-UPF, Barcelona, Spain; Santa Fe Institute, Santa Fe, NM, USA
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Desper R, Difilippantonio MJ, Ried T, Schäffer AA. A comprehensive continuous-time model for the appearance of CGH signal due to chromosomal missegregations during mitosis. Math Biosci 2005; 197:67-87. [PMID: 16043196 PMCID: PMC1356526 DOI: 10.1016/j.mbs.2005.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2004] [Revised: 03/31/2005] [Accepted: 05/16/2005] [Indexed: 11/27/2022]
Abstract
Aneuploidy, the gain or loss of large regions of the genome, is a common feature in cancer cells. Irregularities in chromosomal copy number caused by missegregations of chromosomes during mitosis can be visualized by cytogenetic techniques including fluorescence in situ hybridization (FISH), spectral karyotyping (SKY) and comparative genomic hybridization (CGH). In the current work, we consider the propagation of irregular copy numbers throughout a cell population as the individual cells progress through ordinary mitotic cell cycles. We use an algebraic model to track the different copy numbers as states in a stochastic process, based on the model of chromosome instability of Gusev, Kagansky, and Dooley, and consider the average copy number of a particular chromosome within a cell population as a function of the cell division rate. We review a number of mathematical models for determining the length of the cell cycle, including the Smith-Martin transition probability model and the 'sloppy size' model of Wheals, Tyson and Diekmann. The program MITOSIM simulates the growth of a population of cells using the aforementioned models of the cell cycle. MITOSIM allows the cell population to grow, with occasional resampling, until the average copy number of a given chromosome in the population reaches a preset threshold signifying a positive copy number alteration in this region. MITOSIM calculates the relationship between the missegregation rate and the growth rate of the cell population. This allows the user to test hypotheses regarding the effect chromosomal aberrations have upon the cell cycle, cell growth rates, and time to population dominance.
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Affiliation(s)
- Richard Desper
- Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
- Present affiliation: Department of Biology, University College London, UK
| | - Michael J. Difilippantonio
- Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Thomas Ried
- Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Alejandro A. Schäffer
- Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
- *Address for Correspondence: Alejandro A. Schäffer, NCBI/NLM/NIH, Building 38A, Room 6S608, 8600 Rockville Pike, Bethesda, MD 20894 USA, E-mail:, FAX: 301-480-2288
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