1
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Zhang Y, Wang W. Advances in tumor subclone formation and mechanisms of growth and invasion. J Transl Med 2025; 23:461. [PMID: 40259385 PMCID: PMC12012948 DOI: 10.1186/s12967-025-06486-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2025] [Accepted: 04/11/2025] [Indexed: 04/23/2025] Open
Abstract
Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.
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Affiliation(s)
- Yuhong Zhang
- Department of Oncology, Clinical Medical College, Southwest Medical University, No. 319, Section 3, Zhongshan Road, Luzhou, 646099, Sichuan, China
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Weidong Wang
- Department of Oncology, Clinical Medical College, Southwest Medical University, No. 319, Section 3, Zhongshan Road, Luzhou, 646099, Sichuan, China.
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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2
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Zhang W, Zhang X, Teng F, Yang Q, Wang J, Sun B, Liu J, Zhang J, Sun X, Zhao H, Xie Y, Liao K, Wang X. Research progress and the prospect of using single-cell sequencing technology to explore the characteristics of the tumor microenvironment. Genes Dis 2025; 12:101239. [PMID: 39552788 PMCID: PMC11566696 DOI: 10.1016/j.gendis.2024.101239] [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: 04/09/2023] [Revised: 11/23/2023] [Accepted: 12/01/2023] [Indexed: 11/19/2024] Open
Abstract
In precision cancer therapy, addressing intra-tumor heterogeneity poses a significant obstacle. Due to the heterogeneity of each cell subtype and between cells within the tumor, the sensitivity and resistance of different patients to targeted drugs, chemotherapy, etc., are inconsistent. Concerning a specific tumor type, many feasible treatments or combinations can be used by specifically targeting the tumor microenvironment. To solve this problem, it is necessary to further study the tumor microenvironment. Single-cell sequencing techniques can dissect distinct tumor cell populations by isolating cells and using statistical computational methods. This technology may assist in the selection of targeted combination therapy, and the obtained cell subset information is crucial for the rational application of targeted therapy. In this review, we summarized the research and application advances of single-cell sequencing technology in the tumor microenvironment, including the most commonly used single-cell genomic and transcriptomic sequencing, and their future development direction was proposed. The application of single-cell sequencing technology has been expanded to include epigenomics, proteomics, metabolomics, and microbiome analysis. The integration of these different omics approaches has significantly advanced the development of single-cell multiomics sequencing technology. This innovative approach holds immense potential for various fields, such as biological research and medical investigations. Finally, we discussed the advantages and disadvantages of using single-cell sequencing to explore the tumor microenvironment.
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Affiliation(s)
- Wenyige Zhang
- Department of Clinical Laboratory, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Xue Zhang
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Feifei Teng
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Qijun Yang
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Jiayi Wang
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Bing Sun
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Jie Liu
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Jingyan Zhang
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Xiaomeng Sun
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Hanqing Zhao
- Queen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Yuxuan Xie
- The Second Clinical Medical School, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Kaili Liao
- Department of Clinical Laboratory, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Xiaozhong Wang
- Department of Clinical Laboratory, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
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3
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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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4
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Weideman AMK, Wang R, Ibrahim JG, Jiang Y. Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585595. [PMID: 38562795 PMCID: PMC10983938 DOI: 10.1101/2024.03.18.585595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.
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Affiliation(s)
- Ann Marie K. Weideman
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rujin Wang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joseph G. Ibrahim
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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5
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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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6
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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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7
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Liu X, Griffiths JI, Bishara I, Liu J, Bild AH, Chang JT. Phylogenetic inference from single-cell RNA-seq data. Sci Rep 2023; 13:12854. [PMID: 37553438 PMCID: PMC10409753 DOI: 10.1038/s41598-023-39995-6] [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: 01/03/2023] [Accepted: 08/03/2023] [Indexed: 08/10/2023] Open
Abstract
Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and β-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.
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Affiliation(s)
- Xuan Liu
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA
| | - Jason I Griffiths
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Isaac Bishara
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Jiayi Liu
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA
| | - Andrea H Bild
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Jeffrey T Chang
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA.
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8
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Jun SH, Toosi H, Mold J, Engblom C, Chen X, O'Flanagan C, Hagemann-Jensen M, Sandberg R, Aparicio S, Hartman J, Roth A, Lagergren J. Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nat Commun 2023; 14:982. [PMID: 36813776 PMCID: PMC9946941 DOI: 10.1038/s41467-023-36202-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/20/2023] [Indexed: 02/24/2023] Open
Abstract
Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.
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Affiliation(s)
- Seong-Hwan Jun
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA
| | - Hosein Toosi
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Xinsong Chen
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.,Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. .,Department of Computer Science, University of British Columbia, Vancouver, Canada.
| | - Jens Lagergren
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
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9
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Pallikonda HA, Turajlic S. Predicting cancer evolution for patient benefit: Renal cell carcinoma paradigm. Biochim Biophys Acta Rev Cancer 2022; 1877:188759. [PMID: 35835341 DOI: 10.1016/j.bbcan.2022.188759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
Abstract
Evolutionary features of cancer have important clinical implications, but their evaluation in the clinic is currently limited. Here, we review current approaches to reconstruct tumour subclonal structure and discuss tumour sampling method and experimental design influence. We describe clear-cell renal cell carcinoma (ccRCC) as an exemplar for understanding and predicting cancer evolutionary dynamics. Finally, we discuss how understanding cancer evolution can benefit patients.
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Affiliation(s)
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom; Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom; Melanoma and Kidney Cancer Team, Institute of Cancer Research, London, United Kingdom.
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10
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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: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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11
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Jia Q, Chu H, Jin Z, Long H, Zhu B. High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther 2022; 7:145. [PMID: 35504878 PMCID: PMC9065032 DOI: 10.1038/s41392-022-00990-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/08/2022] [Indexed: 12/22/2022] Open
Abstract
With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.
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Affiliation(s)
- Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China
| | - Han Chu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Zheng Jin
- Research Institute, GloriousMed Clinical Laboratory Co., Ltd, Shanghai, 201318, China
| | - Haixia Long
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
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12
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Kozlov A, Alves JM, Stamatakis A, Posada D. CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data. Genome Biol 2022; 23:37. [PMID: 35081992 PMCID: PMC8790911 DOI: 10.1186/s13059-021-02583-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 12/20/2021] [Indexed: 01/15/2023] Open
Abstract
We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .
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Affiliation(s)
- Alexey Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany
| | - Joao M. Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
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13
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Lei H, Guo XA, Tao Y, Ding K, Fu X, Oesterreich S, Lee AV, Schwartz R. OUP accepted manuscript. Bioinformatics 2022; 38:i386-i394. [PMID: 35758822 PMCID: PMC9235482 DOI: 10.1093/bioinformatics/btac262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available. Results In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples. Availability and implementation Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.
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Affiliation(s)
| | | | - Yifeng Tao
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kai Ding
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
| | - Xuecong Fu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Steffi Oesterreich
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
| | - Adrian V Lee
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
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14
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Govek K, Sikes C, Zhou Y, Oesper L. GraPhyC: Using Consensus to Infer Tumor Evolution. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:465-478. [PMID: 33031032 DOI: 10.1109/tcbb.2020.3029689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We consider the problem of finding a consensus tumor evolution tree from a set of conflicting input trees. In contrast to traditional phylogenetic trees, the tumor trees we consider do not have the same set of labels applied to the leaves of each tree. We describe several distance measures between these tumor trees. Our GraPhyC algorithm solves the consensus problem using a weighted directed graph where vertices are sets of mutations and edges are weighted based on the number of times a parental relationship is observed between their constituent mutations in the input trees. We find a minimum weight spanning arborescence in this graph and prove that it minimizes the total distance to all input trees for one of our distance measures. We also describe several extensions of our GraPhyC approach. On simulated data we show that GraPhyC outperforms a baseline method and demonstrate that GraPhyC can be an effective means of computing centroids in k-medians clustering. We analyze two real sequencing datasets and find that GraPhyC is able to identify a tree not included in the set of input trees, but that contains characteristics supported by other reported evolutionary reconstructions of this tumor.
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15
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Vendramin R, Litchfield K, Swanton C. Cancer evolution: Darwin and beyond. EMBO J 2021; 40:e108389. [PMID: 34459009 PMCID: PMC8441388 DOI: 10.15252/embj.2021108389] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/04/2021] [Accepted: 06/25/2021] [Indexed: 12/16/2022] Open
Abstract
Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.
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Affiliation(s)
- Roberto Vendramin
- Cancer Research UK Lung Cancer Centre of ExcellenceUniversity College London Cancer InstituteLondonUK
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of ExcellenceUniversity College London Cancer InstituteLondonUK
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of ExcellenceUniversity College London Cancer InstituteLondonUK
- Cancer Evolution and Genome Instability LaboratoryThe Francis Crick InstituteLondonUK
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16
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Baghaarabani L, Goliaei S, Foroughmand-Araabi MH, Shariatpanahi SP, Goliaei B. Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data. BMC Bioinformatics 2021; 22:416. [PMID: 34461827 PMCID: PMC8404257 DOI: 10.1186/s12859-021-04338-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 08/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. RESULT In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. CONCLUSIONS In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.
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Affiliation(s)
- Leila Baghaarabani
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sama Goliaei
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | | | | | - Bahram Goliaei
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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17
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Xu J, Liao K, Yang X, Wu C, Wu W, Han S. Using single-cell sequencing technology to detect circulating tumor cells in solid tumors. Mol Cancer 2021; 20:104. [PMID: 34412644 PMCID: PMC8375060 DOI: 10.1186/s12943-021-01392-w] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022] Open
Abstract
Circulating tumor cells are tumor cells with high vitality and high metastatic potential that invade and shed into the peripheral blood from primary solid tumors or metastatic foci. Due to the heterogeneity of tumors, it is difficult for high-throughput sequencing analysis of tumor tissues to find the genomic characteristics of low-abundance tumor stem cells. Single-cell sequencing of circulating tumor cells avoids interference from tumor heterogeneity by comparing the differences between single-cell genomes, transcriptomes, and epigenetic groups among circulating tumor cells, primary and metastatic tumors, and metastatic lymph nodes in patients' peripheral blood, providing a new perspective for understanding the biological process of tumors. This article describes the identification, biological characteristics, and single-cell genome-wide variation in circulating tumor cells and summarizes the application of single-cell sequencing technology to tumor typing, metastasis analysis, progression detection, and adjuvant therapy.
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Affiliation(s)
- Jiasheng Xu
- Department of Oncology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District Zhejiang Province, Huzhou, China.,Department of Vascular Surgery, the Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, 330006, Jiangxi, China
| | - Kaili Liao
- Department of Clinical Laboratory, the Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, 330006, Jiangxi, China
| | - Xi Yang
- Department of Oncology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District Zhejiang Province, Huzhou, China
| | - Chengfeng Wu
- Department of Vascular Surgery, the Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, 330006, Jiangxi, China
| | - Wei Wu
- Department of Gastroenterology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District Zhejiang Province, 313000, Huzhou, China
| | - Shuwen Han
- Department of Oncology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District Zhejiang Province, Huzhou, China.
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18
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Ciccolella S, Ricketts C, Soto Gomez M, Patterson M, Silverbush D, Bonizzoni P, Hajirasouliha I, Della Vedova G. Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses. Bioinformatics 2021; 37:326-333. [PMID: 32805010 PMCID: PMC8058767 DOI: 10.1093/bioinformatics/btaa722] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 08/06/2020] [Accepted: 08/11/2020] [Indexed: 01/21/2023] Open
Abstract
Motivation In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. Results We present Simulated Annealing Single-Cell inference (SASC): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. Availability and implementation The SASC tool is open source and available at https://github.com/sciccolella/sasc. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Simone Ciccolella
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Camir Ricketts
- Department of Physiology and Biophysics, Tri-I Computational Biology & Medicine Graduate Program, Weill Cornell Medicine of Cornell University, New York, NY 10021, USA.,Institute for Computational Biomedicine, Englander Institute for Precision Medicine, The Meyer Cancer Center, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York City, NY 10021, USA
| | - Mauricio Soto Gomez
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Murray Patterson
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.,Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA
| | - Dana Silverbush
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Paola Bonizzoni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, The Meyer Cancer Center, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York City, NY 10021, USA
| | - Gianluca Della Vedova
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
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19
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Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am 2021; 35:279-293. [PMID: 33641869 PMCID: PMC7935666 DOI: 10.1016/j.hoc.2021.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Philadelphia-negative myeloproliferative neoplasms (MPNs) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue-associated fibrosis, and cancer stem cells. In this review, we discuss recent insights into MPN biology gained from the application of a number of new single-cell technologies to study human disease, with a specific focus on single-cell genomics, single-cell transcriptomics, and digital pathology.
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Affiliation(s)
- Daniel Royston
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine and NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX39DS, UK
| | - Adam J Mead
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK.
| | - Bethan Psaila
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK
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20
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Tarabichi M, Salcedo A, Deshwar AG, Leathlobhair MN, Wintersinger J, Wedge DC, Loo PV, Morris QD, Boutros PC. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 2021; 18:144-155. [PMID: 33398189 PMCID: PMC7867630 DOI: 10.1038/s41592-020-01013-2] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 11/09/2020] [Indexed: 01/28/2023]
Abstract
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
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Affiliation(s)
- Maxime Tarabichi
- The Francis Crick Institute, London, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Adriana Salcedo
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Amit G. Deshwar
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Canada
| | - Máire Ni Leathlobhair
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom
| | - Jeff Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - David C. Wedge
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford, United Kingdom
- Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | | | - Quaid D. Morris
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute, Toronto, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York
- Donnelly Centre, University of Toronto, Toronto, Canada
| | - Paul C. Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Vector Institute, Toronto, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles
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21
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Ciccolella S, Soto Gomez M, Patterson MD, Della Vedova G, Hajirasouliha I, Bonizzoni P. gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinformatics 2020; 21:413. [PMID: 33297943 PMCID: PMC7725124 DOI: 10.1186/s12859-020-03736-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 09/03/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. RESULTS We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps . CONCLUSIONS gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.
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Affiliation(s)
- Simone Ciccolella
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.
| | - Mauricio Soto Gomez
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
| | - Murray D Patterson
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.,Georgia State University, Atlanta, GA, USA
| | - Gianluca Della Vedova
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York City, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, NewYork City, 10021, NY, USA
| | - Paola Bonizzoni
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
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22
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Dai Z, Gu XY, Xiang SY, Gong DD, Man CF, Fan Y. Research and application of single-cell sequencing in tumor heterogeneity and drug resistance of circulating tumor cells. Biomark Res 2020; 8:60. [PMID: 33292625 PMCID: PMC7653877 DOI: 10.1186/s40364-020-00240-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023] Open
Abstract
Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes studying it further difficult for us. In recent years, using single-cell sequencing technology to study and analyse circulating tumor cells can avoid the interference of tumor heterogeneity and provide a new perspective for us to understand tumor drug resistance.
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Affiliation(s)
- Zhe Dai
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
| | - Xu-yu Gu
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
| | - Shou-yan Xiang
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
| | - Dan-dan Gong
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
| | - Chang-feng Man
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
| | - Yu Fan
- Cancer Institution, Affiliated People’s Hospital of Jiangsu University, No.8 Dianli Road, Zhenjiang, Jiangsu Province 212002 People’s Republic of China
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23
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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24
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McCarthy DJ, Rostom R, Huang Y, Kunz DJ, Danecek P, Bonder MJ, Hagai T, Lyu R, Wang W, Gaffney DJ, Simons BD, Stegle O, Teichmann SA. Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nat Methods 2020; 17:414-421. [PMID: 32203388 DOI: 10.1038/s41592-020-0766-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 01/31/2020] [Indexed: 02/03/2023]
Abstract
Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino (https://github.com/single-cell-genetics/cardelino), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin.
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Affiliation(s)
- Davis J McCarthy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.,St Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia.,Melbourne Integrative Genomics, School of Mathematics and Statistics/School of Biosciences, University of Melbourne, Parkville, Victoria, Australia
| | - Raghd Rostom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Yuanhua Huang
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Daniel J Kunz
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Department of Physics, Cavendish Laboratory, Cambridge, UK.,The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK
| | - Petr Danecek
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Tzachi Hagai
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,School of Molecular Cell Biology and Biotechnology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ruqian Lyu
- St Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia.,Melbourne Integrative Genomics, School of Mathematics and Statistics/School of Biosciences, University of Melbourne, Parkville, Victoria, Australia
| | | | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Benjamin D Simons
- Department of Physics, Cavendish Laboratory, Cambridge, UK.,The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK.,The Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. .,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. .,European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. .,Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany.
| | - Sarah A Teichmann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. .,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. .,Department of Physics, Cavendish Laboratory, Cambridge, UK.
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25
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Sun W, Jin C, Gelfond JA, Chen MH, Ibrahim JG. Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity. Biometrics 2019; 76:983-994. [PMID: 31813161 DOI: 10.1111/biom.13198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 10/23/2019] [Accepted: 11/25/2019] [Indexed: 11/28/2022]
Abstract
Many computational methods have been developed to discern intratumor heterogeneity (ITH) using DNA sequence data from bulk tumor samples. These methods share an assumption that two mutations arise from the same subclone if they have similar mutant allele-frequencies (MAFs), and thus it is difficult or impossible to distinguish two subclones with similar MAFs. Single-cell DNA sequencing (scDNA-seq) data can be very informative for ITH inference. However, due to the difficulty of DNA amplification, scDNA-seq data are often very noisy. A promising new study design is to collect both bulk and single-cell DNA-seq data and jointly analyze them to mitigate the limitations of each data type. To address the analytic challenges of this new study design, we propose a computational method named BaSiC (Bulk tumor and Single Cell), to discern ITH by jointly analyzing DNA-seq data from bulk tumor and single cells. We demonstrate that BaSiC has comparable or better performance than the methods using either data type. We further evaluate BaSiC using bulk tumor and single-cell DNA-seq data from a breast cancer patient and several leukemia patients.
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Affiliation(s)
- Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Chong Jin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jonathan A Gelfond
- Department of Epidemiology and Biostatistics, UT Health Science Center, San Antonio, Texas
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut, Storrs, Connecticut
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
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26
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Ismail WM, Nzabarushimana E, Tang H. Algorithmic approaches to clonal reconstruction in heterogeneous cell populations. QUANTITATIVE BIOLOGY 2019; 7:255-265. [PMID: 32431959 PMCID: PMC7236794 DOI: 10.1007/s40484-019-0188-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/09/2019] [Accepted: 08/25/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology. The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones. RESULTS In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. We formally define the problem and then discuss the complexity and solution space of the problem. Various methods have been proposed to find the phylogeny that best explains the observed data. We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem. Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction. We briefly review the existing algorithms and their relationships. Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem. CONCLUSIONS In this review, we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data, review existing algorithms and catergorize them according to their problem formulation and solution approaches. We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed. We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.
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Affiliation(s)
- Wazim Mohammed Ismail
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Etienne Nzabarushimana
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Haixu Tang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
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27
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Malikic S, Mehrabadi FR, Ciccolella S, Rahman MK, Ricketts C, Haghshenas E, Seidman D, Hach F, Hajirasouliha I, Sahinalp SC. PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data. Genome Res 2019; 29:1860-1877. [PMID: 31628256 PMCID: PMC6836735 DOI: 10.1101/gr.234435.118] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 09/11/2019] [Indexed: 12/29/2022]
Abstract
Available computational methods for tumor phylogeny inference via single-cell sequencing (SCS) data typically aim to identify the most likely perfect phylogeny tree satisfying the infinite sites assumption (ISA). However, the limitations of SCS technologies including frequent allele dropout and variable sequence coverage may prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions, and convergent evolution. In order to address such limitations, we introduce the optimal subperfect phylogeny problem which asks to integrate SCS data with matching bulk sequencing data by minimizing a linear combination of potential false negatives (due to allele dropout or variance in sequence coverage), false positives (due to read errors) among mutation calls, and the number of mutations that violate ISA (real or because of incorrect copy number estimation). We then describe a combinatorial formulation to solve this problem which ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and—as a first in tumor phylogeny reconstruction—a Boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data while accounting for ISA violating mutations. In contrast to the alternative methods, typically based on probabilistic approaches, PhISCS provides a guarantee of optimality in reported solutions. Using simulated and real data sets, we demonstrate that PhISCS is more general and accurate than all available approaches.
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Affiliation(s)
- Salem Malikic
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Farid Rashidi Mehrabadi
- Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA.,Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Simone Ciccolella
- Department of Computer Systems and Communication, University of Milano-Bicocca, 20136 Milan, Italy.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York 10065, USA
| | - Md Khaledur Rahman
- Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA
| | - Camir Ricketts
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York 10065, USA.,Tri-I Computational Biology and Medicine Graduate Program, Cornell University, New York, New York 10065, USA
| | - Ehsan Haghshenas
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Daniel Seidman
- Tri-I Computational Biology and Medicine Graduate Program, Cornell University, New York, New York 10065, USA
| | - Faraz Hach
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Department of Urologic Sciences, University of British Columbia, Vancouver, BC V5Z 1M9, Canada.,Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York 10065, USA.,Department of Physiology and Biophysics, Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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28
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Chkhaidze K, Heide T, Werner B, Williams MJ, Huang W, Caravagna G, Graham TA, Sottoriva A. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLoS Comput Biol 2019; 15:e1007243. [PMID: 31356595 PMCID: PMC6687187 DOI: 10.1371/journal.pcbi.1007243] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 08/08/2019] [Accepted: 07/05/2019] [Indexed: 12/19/2022] Open
Abstract
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.
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Affiliation(s)
- Ketevan Chkhaidze
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Timon Heide
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Marc J. Williams
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Weini Huang
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Trevor A. Graham
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
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Malikic S, Jahn K, Kuipers J, Sahinalp SC, Beerenwinkel N. Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Nat Commun 2019; 10:2750. [PMID: 31227714 PMCID: PMC6588593 DOI: 10.1038/s41467-019-10737-5] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 05/30/2019] [Indexed: 02/07/2023] Open
Abstract
Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees. Intra-tumour heterogeneity provides important information about subclonal tumour evolution. Here, the authors develop B-SCITE, a computational method for inferring tumour phylogenies from combined single-cell and bulk sequencing data.
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Affiliation(s)
- Salem Malikic
- School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada.,Vancouver Prostate Centre, Vancouver, V6H 3Z6, BC, Canada
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - S Cenk Sahinalp
- Department of Computer Science, Indiana University, Bloomington, 47405, IN, USA.
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
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30
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Williams MJ, Sottoriva A, Graham TA. Measuring Clonal Evolution in Cancer with Genomics. Annu Rev Genomics Hum Genet 2019; 20:309-329. [PMID: 31059289 DOI: 10.1146/annurev-genom-083117-021712] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.
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Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London SM2 5NG, United Kingdom
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
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31
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Ramazzotti D, Graudenzi A, De Sano L, Antoniotti M, Caravagna G. Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC Bioinformatics 2019; 20:210. [PMID: 31023236 PMCID: PMC6485126 DOI: 10.1186/s12859-019-2795-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 04/08/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.
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Affiliation(s)
| | - Alex Graudenzi
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
- Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Viale F.lli Cervi 93, Segrate, Milan, 20090 Italy
| | - Luca De Sano
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
| | - Marco Antoniotti
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
- Milan Center for Neuroscience, Università degli Studi di Milano-Bicocca, San Gerardo Hospital, Via Pergolesi 33, Monza, 20052 Italy
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG UK
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32
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Abstract
Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.
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Affiliation(s)
- Xianwen Ren
- Beijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, 100871, China.
| | - Boxi Kang
- Beijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, 100871, China
| | - Zemin Zhang
- Beijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, 100871, China.
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33
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Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol 2018; 20:1349-1360. [PMID: 30482943 DOI: 10.1038/s41556-018-0236-7] [Citation(s) in RCA: 399] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 10/24/2018] [Indexed: 02/07/2023]
Abstract
Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.
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34
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Caravagna G, Giarratano Y, Ramazzotti D, Tomlinson I, Graham TA, Sanguinetti G, Sottoriva A. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat Methods 2018; 15:707-714. [PMID: 30171232 PMCID: PMC6380470 DOI: 10.1038/s41592-018-0108-x] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 07/23/2018] [Indexed: 12/13/2022]
Abstract
Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.
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Affiliation(s)
- Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Ylenia Giarratano
- School of Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Trevor A Graham
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | | | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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35
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Abstract
Cancer arises through the accumulation of somatic mutations over time. An understanding of the sequence of events during this process should allow both earlier diagnosis and better prediction of cancer progression. However, the pathways of tumor evolution have not yet been comprehensively characterized. With the advent of whole genome sequencing, it is now possible to infer the evolutionary history of single tumors from the snapshot of their genome taken at diagnosis, giving new insights into the biology of tumorigenesis.
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MESH Headings
- BRCA1 Protein/genetics
- BRCA1 Protein/metabolism
- Breast Neoplasms/genetics
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinogenesis/genetics
- Carcinogenesis/metabolism
- Carcinogenesis/pathology
- Clonal Evolution
- Female
- Gene Expression Regulation, Neoplastic
- Genome, Human
- Humans
- Janus Kinase 2/genetics
- Janus Kinase 2/metabolism
- Leukemia, Lymphocytic, Chronic, B-Cell/genetics
- Leukemia, Lymphocytic, Chronic, B-Cell/metabolism
- Leukemia, Lymphocytic, Chronic, B-Cell/pathology
- Male
- Mutation
- Neoplasm Proteins/genetics
- Neoplasm Proteins/metabolism
- STAT3 Transcription Factor/genetics
- STAT3 Transcription Factor/metabolism
- Time Factors
- Whole Genome Sequencing
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Affiliation(s)
- Clemency Jolly
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Peter Van Loo
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.
- Department of Human Genetics, University of Leuven, B-3000, Leuven, Belgium.
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36
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Pogrebniak KL, Curtis C. Harnessing Tumor Evolution to Circumvent Resistance. Trends Genet 2018; 34:639-651. [PMID: 29903534 DOI: 10.1016/j.tig.2018.05.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 05/16/2018] [Accepted: 05/21/2018] [Indexed: 02/08/2023]
Abstract
High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.
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Affiliation(s)
- Katherine L Pogrebniak
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; http://med.stanford.edu/curtislab.html
| | - Christina Curtis
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; http://med.stanford.edu/curtislab.html.
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37
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El-Kebir M, Satas G, Raphael BJ. Inferring parsimonious migration histories for metastatic cancers. Nat Genet 2018; 50:718-726. [PMID: 29700472 PMCID: PMC6103651 DOI: 10.1038/s41588-018-0106-z] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 03/09/2018] [Indexed: 01/29/2023]
Abstract
Metastasis is the migration of cancerous cells from a primary tumor to other anatomical sites. Although metastasis was long thought to result from monoclonal seeding, or single cellular migrations, recent phylogenetic analyses of metastatic cancers have reported complex patterns of cellular migrations between sites, including polyclonal migrations and reseeding. However, accurate determination of migration patterns from somatic mutation data is complicated by intratumor heterogeneity and discordance between clonal lineage and cellular migration. We introduce MACHINA, a multi-objective optimization algorithm that jointly infers clonal lineages and parsimonious migration histories of metastatic cancers from DNA sequencing data. MACHINA analysis of data from multiple cancers shows that migration patterns are often not uniquely determined from sequencing data alone and that complicated migration patterns among primary tumors and metastases may be less prevalent than previously reported. MACHINA's rigorous analysis of migration histories will aid in studies of the drivers of metastasis.
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Affiliation(s)
- Mohammed El-Kebir
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Brown University, Providence, RI, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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38
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Povinelli BJ, Rodriguez-Meira A, Mead AJ. Single cell analysis of normal and leukemic hematopoiesis. Mol Aspects Med 2018; 59:85-94. [PMID: 28863981 PMCID: PMC5771467 DOI: 10.1016/j.mam.2017.08.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 08/17/2017] [Accepted: 08/28/2017] [Indexed: 01/06/2023]
Abstract
The hematopoietic system is well established as a paradigm for the study of cellular hierarchies, their disruption in disease and therapeutic use in regenerative medicine. Traditional approaches to study hematopoiesis involve purification of cell populations based on a small number of surface markers. However, such population-based analysis obscures underlying heterogeneity contained within any phenotypically defined cell population. This heterogeneity can only be resolved through single cell analysis. Recent advances in single cell techniques allow analysis of the genome, transcriptome, epigenome and proteome in single cells at an unprecedented scale. The application of these new single cell methods to investigate the hematopoietic system has led to paradigm shifts in our understanding of cellular heterogeneity in hematopoiesis and how this is disrupted in disease. In this review, we summarize how single cell techniques have been applied to the analysis of hematopoietic stem/progenitor cells in normal and malignant hematopoiesis, with a particular focus on recent advances in single-cell genomics, including how these might be utilized for clinical application.
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Affiliation(s)
- Benjamin J Povinelli
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; Haematopoietic Stem Cell Biology Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Alba Rodriguez-Meira
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; Haematopoietic Stem Cell Biology Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Adam J Mead
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; Haematopoietic Stem Cell Biology Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; NIHR Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom.
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39
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40
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Ortega MA, Poirion O, Zhu X, Huang S, Wolfgruber TK, Sebra R, Garmire LX. Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin Transl Med 2017; 6:46. [PMID: 29285690 PMCID: PMC5746494 DOI: 10.1186/s40169-017-0177-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 12/06/2017] [Indexed: 12/31/2022] Open
Abstract
It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.
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Affiliation(s)
- Michael A. Ortega
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
| | - Olivier Poirion
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
| | - Xun Zhu
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
- Department of Molecular Biosciences and Bioengineering, Honolulu, HI USA
| | - Sijia Huang
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
- Department of Molecular Biosciences and Bioengineering, Honolulu, HI USA
| | - Thomas K. Wolfgruber
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
| | - Robert Sebra
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Lana X. Garmire
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
- Department of Molecular Biosciences and Bioengineering, Honolulu, HI USA
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Vandin F. Computational Methods for Characterizing Cancer Mutational Heterogeneity. Front Genet 2017; 8:83. [PMID: 28659971 PMCID: PMC5469877 DOI: 10.3389/fgene.2017.00083] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 05/30/2017] [Indexed: 12/21/2022] Open
Abstract
Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.
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Affiliation(s)
- Fabio Vandin
- Department of Information Engineering, University of PadovaPadova, Italy
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