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Black GS, Huang X, Qiao Y, Moos P, Sampath D, Stephens DM, Woyach JA, Marth GT. Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotype and phenotype in chronic lymphocytic leukemia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585298. [PMID: 38559060 PMCID: PMC10979946 DOI: 10.1101/2024.03.15.585298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Bruton's tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTKC481S mutation that inhibits drug binding. In this study, we aimed to investigate whether the presence of additional CLL driver mutations in cancer subclones harboring a BTKC481S mutation accelerates subclone expansion. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we employ our recently published method (Qiao et al. 2024) that combines bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for the presence or absence of subclone-defining mutations. While the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNAseq technology, to substantially increase transcript coverage across the entire length of the transcripts and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTKC481S mutations during BTK inhibitor treatment. We found that BTK-mutated subclones often acquire additional mutations in CLL driver genes, leading to faster subclone proliferation. When examining subclone-specific gene expression, we found that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.
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Affiliation(s)
- Gage S Black
- Department of Human Genetics, University of Utah, Salt Lake City, UT
| | - Xiaomeng Huang
- Department of Human Genetics, University of Utah, Salt Lake City, UT
| | - Yi Qiao
- Department of Human Genetics, University of Utah, Salt Lake City, UT
| | - Philip Moos
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT
| | - Deepa Sampath
- Department of Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Gabor T Marth
- Department of Human Genetics, University of Utah, Salt Lake City, UT
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2
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Qiao Y, Huang X, Moos PJ, Ahmann JM, Pomicter AD, Deininger MW, Byrd JC, Woyach JA, Stephens DM, Marth GT. A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data. Genome Res 2024; 34:94-105. [PMID: 38195207 PMCID: PMC10903947 DOI: 10.1101/gr.278234.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024]
Abstract
Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.
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Affiliation(s)
- Yi Qiao
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Xiaomeng Huang
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Philip J Moos
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 84112, USA
| | - Jonathan M Ahmann
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Anthony D Pomicter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Michael W Deininger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
- Division of Hematology and Hematologic Malignancies, University of Utah, Salt Lake City, Utah 84112, USA
| | - John C Byrd
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Jennifer A Woyach
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Deborah M Stephens
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Gabor T Marth
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA;
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3
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Huang X, Qiao Y, Brady SW, Factor RE, Downs-Kelly E, Farrell A, McQuerry JA, Shrestha G, Jenkins D, Johnson WE, Cohen AL, Bild AH, Marth GT. Novel temporal and spatial patterns of metastatic colonization from breast cancer rapid-autopsy tumor biopsies. Genome Med 2021; 13:170. [PMID: 34711268 PMCID: PMC8555066 DOI: 10.1186/s13073-021-00989-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 10/13/2021] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. METHODS Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor's evolution at subclonal resolution. RESULTS The most unique, previously unreported aspect of the tumor's evolution that we observed in this patient was the presence of "subclone incubators," defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient's metastases for effective clinical intervention. CONCLUSIONS Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.
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Affiliation(s)
- Xiaomeng Huang
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, USA
- Department of Human Genetics, School of Medicine, University of Utah, Salt Lake City, USA
| | - Yi Qiao
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, USA
- Department of Human Genetics, School of Medicine, University of Utah, Salt Lake City, USA
| | - Samuel W Brady
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, USA
| | - Rachel E Factor
- Department of Pathology, School of Medicine, University of Utah, Salt Lake City, USA
| | - Erinn Downs-Kelly
- Department of Pathology, School of Medicine, University of Utah, Salt Lake City, USA
| | - Andrew Farrell
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, USA
- Department of Human Genetics, School of Medicine, University of Utah, Salt Lake City, USA
| | - Jasmine A McQuerry
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, USA
- Department of Oncological Sciences, School of Medicine, University of Utah, Salt Lake City, USA
| | - Gajendra Shrestha
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, USA
| | - David Jenkins
- Computational Biomedicine, Department of Medicine, Boston University, Boston, USA
| | - W Evan Johnson
- Computational Biomedicine, Department of Medicine, Boston University, Boston, USA
| | - Adam L Cohen
- Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, USA
| | - Andrea H Bild
- Department of Medical Oncology & Therapeutics Research, City of Hope, Duarte, USA
| | - Gabor T Marth
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, USA.
- Department of Human Genetics, School of Medicine, University of Utah, Salt Lake City, USA.
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Satas G, Zaccaria S, El-Kebir M, Raphael BJ. DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution. Cell Syst 2021; 12:1004-1018.e10. [PMID: 34416171 DOI: 10.1016/j.cels.2021.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/09/2021] [Accepted: 07/21/2021] [Indexed: 12/22/2022]
Abstract
The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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5
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Vavoulis DV, Cutts A, Taylor JC, Schuh A. A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data. Bioinformatics 2021; 37:147-154. [PMID: 32722772 PMCID: PMC8055230 DOI: 10.1093/bioinformatics/btaa672] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 05/13/2020] [Accepted: 07/20/2020] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so? RESULTS We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian Process priors and using as input a sequence of several sparsely collected samples, our method can reconstruct the temporal profile of the abundance of any mutation cluster supported by the data as a continuous function of time. We benchmarked our method on whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data and we found that incorporating information on the timing of tissue collection improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. Thus, our approach is particularly useful when collecting a relatively long sequence of tumour samples is feasible, as in liquid cancers (e.g. leukaemia) and liquid biopsies. AVAILABILITY AND IMPLEMENTATION The statistical methodology presented in this paper is freely available at github.com/dvav/clonosGP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dimitrios V Vavoulis
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
| | - Anthony Cutts
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
| | - Jenny C Taylor
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
| | - Anna Schuh
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
- Department of Haematology, Oxford University Hospitals NHS Trust, Oxford OX3 9DU, UK
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6
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Xiao Y, Wang X, Zhang H, Ulintz PJ, Li H, Guan Y. FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples. Nat Commun 2020; 11:4469. [PMID: 32901013 PMCID: PMC7478963 DOI: 10.1038/s41467-020-18169-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 08/06/2020] [Indexed: 02/06/2023] Open
Abstract
Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.
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Affiliation(s)
- Yao Xiao
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Xueqing Wang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.,Microsoft Inc., Redmond, WA, USA
| | - Peter J Ulintz
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA. .,Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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Abstract
BACKGROUND Bacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations. RESULTS In this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli. CONCLUSION We developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments. AVAILABILITY The program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE.
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Affiliation(s)
- Wazim Mohammed Ismail
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Haixu Tang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
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8
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Abstract
BACKGROUND Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease. While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these reconstructions. METHODS We investigate when and how well these histories can be reconstructed from multi-sample bulk sequencing data when considering only single nucleotide variants (SNVs). Specifically, we examine the space of all possible tumor phylogenies under the infinite sites assumption (ISA) using several approaches for enumerating phylogenies consistent with the sequencing data. RESULTS On noisy simulated data, we find that the ISA is often violated and that low coverage and high noise make it more difficult to identify phylogenies. Additionally, we find that evolutionary trees with branching topologies are easier to reconstruct accurately. We also apply our reconstruction methods to both chronic lymphocytic leukemia and clear cell renal cell carcinoma datasets and confirm that ISA violations are common in practice, especially in lower-coverage sequencing data. Nonetheless, we show that an ISA-based approach can be relaxed to produce high-quality phylogenies. CONCLUSIONS Consideration of practical aspects of sequencing data such as coverage or the model of tumor evolution (branching, linear, etc.) is essential to effectively using the output of tumor phylogeny inference methods. Additionally, these factors should be considered in the development of new inference methods.
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Affiliation(s)
- Kiran Tomlinson
- Department of Computer Science, Carleton College, 1 N College St, Northfield, 55057, MN, USA
- Department of Computer Science, Cornell University, 402 Gates Hall, Ithaca, 14853, NY, USA
| | - Layla Oesper
- Department of Computer Science, Carleton College, 1 N College St, Northfield, 55057, MN, USA.
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9
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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: 1.0] [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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10
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Deveau P, Colmet Daage L, Oldridge D, Bernard V, Bellini A, Chicard M, Clement N, Lapouble E, Combaret V, Boland A, Meyer V, Deleuze JF, Janoueix-Lerosey I, Barillot E, Delattre O, Maris JM, Schleiermacher G, Boeva V. QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction. Bioinformatics 2019; 34:1808-1816. [PMID: 29342233 PMCID: PMC5972665 DOI: 10.1093/bioinformatics/bty016] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 01/10/2018] [Indexed: 01/13/2023] Open
Abstract
Motivation In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants. Results We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution. Availability and implementation Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone. Contact gudrun.schleiermacher@curie.fr or valentina.boeva@inserm.fr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Paul Deveau
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
- University of Paris-Sud, Orsay, France
| | - Leo Colmet Daage
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Derek Oldridge
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Childhood Cancer Research Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Virginie Bernard
- Institut Curie, PSL Research University, NGS platform ICGex, Paris, France
| | - Angela Bellini
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Mathieu Chicard
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Nathalie Clement
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Eve Lapouble
- Unité de Génétique Somatique, Institut Curie, PSL Research University, Paris, France
| | - Valerie Combaret
- Centre Léon-Bérard Laboratoire de Recherche Translationnelle, Lyon, France
| | - Anne Boland
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Vincent Meyer
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Jean-Francois Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Isabelle Janoueix-Lerosey
- Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Olivier Delattre
- Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris, France
| | - John M Maris
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Childhood Cancer Research Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Gudrun Schleiermacher
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
- Département de Pédiatrie, Institut Curie, PSL Research University, Paris, France
- To whom correspondence should be addressed. or
| | - Valentina Boeva
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
- Institut Cochin, INSERM U1016, CNRS UMR 8104, Université Paris Descartes UMR-S1016, Paris, France
- To whom correspondence should be addressed. or
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11
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Abstract
Cancer is an evolutionary process. Recent advances in sequencing technologies have allowed us to investigate intratumor heterogeneity at the single nucleotide level. Here, we describe computational methods that use sequencing data to identify genetically distinct tumor subclones and reconstruct tumor evolutionary histories.
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12
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Ricketts C, Popic V, Toosi H, Hajirasouliha I. Using LICHeE and BAMSE for Reconstructing Cancer Phylogenetic Trees. CURRENT PROTOCOLS IN BIOINFORMATICS 2018; 62:e49. [PMID: 29927069 PMCID: PMC6020047 DOI: 10.1002/cpbi.49] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The reconstruction of cancer phylogeny trees and quantifying the evolution of the disease is a challenging task. LICHeE and BAMSE are two computational tools designed and implemented recently for this purpose. They both utilize estimated variant allele fraction of somatic mutations across multiple samples to infer the most likely cancer phylogenies. This unit provides extensive guidelines for installing and running both LICHeE and BAMSE. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Camir Ricketts
- Tri-I Computational Biology & Medicine Graduate Program, Cornell University, New York, New York
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Victoria Popic
- Illumina Inc. Illumina Mission Bay, San Francisco, California
| | - Hosein Toosi
- Department of Bioinformatics, Institute for Biochemistry and Biophysics, University of Tehran, Iran
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
- Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York
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13
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Finding cancer driver mutations in the era of big data research. Biophys Rev 2018; 11:21-29. [PMID: 29611034 DOI: 10.1007/s12551-018-0415-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 03/16/2018] [Indexed: 12/20/2022] Open
Abstract
In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.
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14
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Than H, Qiao Y, Huang X, Yan D, Khorashad JS, Pomicter AD, Kovacsovics TJ, Marth GT, O'Hare T, Deininger MW. Ongoing clonal evolution in chronic myelomonocytic leukemia on hypomethylating agents: a computational perspective. Leukemia 2018; 32:2049-2054. [PMID: 29588547 PMCID: PMC6128729 DOI: 10.1038/s41375-018-0050-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 01/04/2018] [Accepted: 01/09/2018] [Indexed: 01/20/2023]
Affiliation(s)
- Hein Than
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA.,Department of Hematology, Singapore General Hospital, Singapore, Singapore
| | - Yi Qiao
- Eccles Institute of Human Genetics, The University of Utah, Salt Lake City, UT, USA
| | - Xiaomeng Huang
- Eccles Institute of Human Genetics, The University of Utah, Salt Lake City, UT, USA
| | - Dongqing Yan
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA
| | - Jamshid S Khorashad
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA.,Centre for Hematology, Department of Medicine, Imperial College, London, UK
| | - Anthony D Pomicter
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA
| | | | - Gabor T Marth
- Eccles Institute of Human Genetics, The University of Utah, Salt Lake City, UT, USA
| | - Thomas O'Hare
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA.,Division of Hematology and Hematologic Malignancies, The University of Utah, Salt Lake City, UT, USA
| | - Michael W Deininger
- Huntsman Cancer Institute, The University of Utah, Salt Lake City, UT, USA. .,Division of Hematology and Hematologic Malignancies, The University of Utah, Salt Lake City, UT, USA.
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15
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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. Sci Rep 2017; 7:16943. [PMID: 29208983 PMCID: PMC5717219 DOI: 10.1038/s41598-017-16813-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 11/17/2017] [Indexed: 11/20/2022] Open
Abstract
A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.
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16
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Combating subclonal evolution of resistant cancer phenotypes. Nat Commun 2017; 8:1231. [PMID: 29093439 PMCID: PMC5666005 DOI: 10.1038/s41467-017-01174-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 08/24/2017] [Indexed: 12/24/2022] Open
Abstract
Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.
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17
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Farahani H, de Souza CPE, Billings R, Yap D, Shumansky K, Wan A, Lai D, Mes-Masson AM, Aparicio S, P Shah S. Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer. Sci Rep 2017; 7:13467. [PMID: 29044127 PMCID: PMC5647443 DOI: 10.1038/s41598-017-13338-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 09/19/2017] [Indexed: 11/30/2022] Open
Abstract
Characterization and quantification of tumour clonal populations over time via longitudinal sampling are essential components in understanding and predicting the response to therapeutic interventions. Computational methods for inferring tumour clonal composition from deep-targeted sequencing data are ubiquitous, however due to the lack of a ground truth biological data, evaluating their performance is difficult. In this work, we generate a benchmark data set that simulates tumour longitudinal growth and heterogeneity by in vitro mixing of cancer cell lines with known proportions. We apply four different algorithms to our ground truth data set and assess their performance in inferring clonal composition using different metrics. We also analyse the performance of these algorithms on breast tumour xenograft samples. We conclude that methods that can simultaneously analyse multiple samples while accounting for copy number alterations as a factor in allelic measurements exhibit the most accurate predictions. These results will inform future functional genomics oriented studies of model systems where time series measurements in the context of therapeutic interventions are becoming increasingly common. These studies will need computational models which accurately reflect the multi-factorial nature of allele measurement in cancer including, as we show here, segmental aneuploidies.
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Affiliation(s)
- Hossein Farahani
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada.,University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, V6T 2B5, Canada
| | - Camila P E de Souza
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada.,University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, V6T 2B5, Canada
| | - Raewyn Billings
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada
| | - Damian Yap
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada
| | - Karey Shumansky
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada
| | - Adrian Wan
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada
| | - Daniel Lai
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada
| | - Anne-Marie Mes-Masson
- Centre de recherche du Centre hospitalier de l' Université de Montréal (CRCHUM), Montreal, Canada.,Institut du cancer de Montréal, Montreal, Canada.,Department of Medicine, Université de Montréal, Montreal, Canada
| | - Samuel Aparicio
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada.,University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, V6T 2B5, Canada
| | - Sohrab P Shah
- BC Cancer Agency, Department of Molecular Oncology, Vancouver, V5Z 1L3, Canada. .,University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, V6T 2B5, Canada. .,BC Cancer Agency, Michael Smith Genome Sciences Centre, Vancouver, V5Z 1L3, Canada.
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18
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Gu JL, Chukhman M, Lu Y, Liu C, Liu SY, Lu H. RNA-seq Based Transcription Characterization of Fusion Breakpoints as a Potential Estimator for Its Oncogenic Potential. BIOMED RESEARCH INTERNATIONAL 2017; 2017:9829175. [PMID: 29181411 PMCID: PMC5664375 DOI: 10.1155/2017/9829175] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 08/23/2017] [Indexed: 12/20/2022]
Abstract
Based on high-throughput sequencing technology, the detection of gene fusions is no longer a big challenge but estimating the oncogenic potential of fusion genes remains challenging. Recent studies successfully applied machine learning methods and gene structural and functional features of fusion mutation to predict their oncogenic potentials. However, the transcription characterizations features of fusion genes have not yet been studied. In this study, based on the clonal evolution theory, we hypothesized that a fusion gene is more likely to be an oncogenic genomic alteration, if the neoplastic cells harboring this fusion mutation have larger clonal size than other neoplastic cells in a tumor. We proposed a novel method, called iFCR (internal Fusion Clone Ratio), given an estimation of oncogenic potential for fusion mutations. We have evaluated the iFCR method in three public cancer transcriptome sequencing datasets; the results demonstrated that the fusion mutations occurring in tumor samples have higher internal fusion clone ratio than normal samples. And the most frequent prostate cancer fusion mutation, TMPRSS2-ERG, appears to have a remarkably higher iFCR value in all three independent patients. The preliminary results suggest that the internal fusion clone ratio might potentially advantage current fusion mutation oncogenic potential prediction methods.
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Affiliation(s)
- Jian-lei Gu
- Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200040, China
- Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai 200040, China
| | - Morris Chukhman
- Department of Bioengineering, Bioinformatics Program, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Yao Lu
- Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200040, China
- Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cong Liu
- Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200040, China
- Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Bioengineering, Bioinformatics Program, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Shi-yi Liu
- Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hui Lu
- Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai 200040, China
- Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai 200040, China
- Department of Bioengineering, Bioinformatics Program, University of Illinois at Chicago, Chicago, IL 60607, USA
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19
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Kuipers J, Jahn K, Raphael BJ, Beerenwinkel N. Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors. Genome Res 2017; 27:1885-1894. [PMID: 29030470 PMCID: PMC5668945 DOI: 10.1101/gr.220707.117] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 09/20/2017] [Indexed: 01/04/2023]
Abstract
Intra-tumor heterogeneity poses substantial challenges for cancer treatment. A tumor's composition can be deduced by reconstructing its mutational history. Central to current approaches is the infinite sites assumption that every genomic position can only mutate once over the lifetime of a tumor. The validity of this assumption has never been quantitatively assessed. We developed a rigorous statistical framework to test the infinite sites assumption with single-cell sequencing data. Our framework accounts for the high noise and contamination present in such data. We found strong evidence for the same genomic position being mutationally affected multiple times in individual tumors for 11 of 12 single-cell sequencing data sets from a variety of human cancers. Seven cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large-scale genomic deletions. Four cases exhibited a parallel mutation, potentially indicating convergent evolution at the base pair level. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity for more effective cancer treatment.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, 4058, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, 4058, Switzerland
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, 4058, Switzerland
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20
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Sakai K, Ukita M, Schmidt J, Wu L, De Velasco MA, Roter A, Jevons L, Nishio K, Mandai M. Clonal composition of human ovarian cancer based on copy number analysis reveals a reciprocal relation with oncogenic mutation status. Cancer Lett 2017; 405:22-28. [PMID: 28734796 DOI: 10.1016/j.canlet.2017.07.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 07/11/2017] [Accepted: 07/13/2017] [Indexed: 12/17/2022]
Abstract
Intratumoral heterogeneity of cancer cells remains largely unexplored. Here we investigated the composition of ovarian cancer and its biological relevance. A whole-genome single nucleotide polymorphism array was applied to detect the clonal composition of 24 formalin-fixed, paraffin-embedded samples of human ovarian cancer. Genome-wide segmentation data consisting of the log2 ratio (log2R) and B allele frequency (BAF) were used to calculate an estimate of the clonal composition number (CC number) for each tumor. Somatic mutation profiles of cancer-related genes were also determined for the same 24 samples by next-generation sequencing. The CC number was estimated successfully for 23 of the 24 cancer samples. The mean ± SD value for the CC number was 1.7 ± 1.1 (range of 0-4). A somatic mutation in at least one gene was identified in 22 of the 24 ovarian cancer samples, with the mutations including those in the oncogenes KRAS (29.2%), PIK3CA (12.5%), BRAF (8.3%), FGFR2 (4.2%), and JAK2 (4.2%) as well as those in the tumor suppressor genes TP53 (54.2%), FBXW7 (8.3%), PTEN (4.2%), and RB1 (4.2%). Tumors with one or more oncogenic mutations had a significantly lower CC number than did those without such a mutation (1.0 ± 0.8 versus 2.3 ± 0.9, P = 0.0027), suggesting that cancers with driver oncogene mutations are less heterogeneous than those with other mutations. Our results thus reveal a reciprocal relation between oncogenic mutation status and clonal composition in ovarian cancer using the established method for the estimation of the CC number.
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Affiliation(s)
- Kazuko Sakai
- Department of Genome Biology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-Sayama, Osaka, 589-8511, Japan
| | - Masayo Ukita
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-Sayama, Osaka, 589-8511, Japan
| | | | - Longyang Wu
- Thermo Fisher Scientific, 3420 Central Expy, Santa Clara, USA
| | - Marco A De Velasco
- Department of Genome Biology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-Sayama, Osaka, 589-8511, Japan
| | - Alan Roter
- Thermo Fisher Scientific, 3420 Central Expy, Santa Clara, USA
| | - Luis Jevons
- Thermo Fisher Scientific, 3420 Central Expy, Santa Clara, USA
| | - Kazuto Nishio
- Department of Genome Biology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
| | - Masaki Mandai
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-Sayama, Osaka, 589-8511, Japan
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21
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Sun R, Hu Z, Sottoriva A, Graham TA, Harpak A, Ma Z, Fischer JM, Shibata D, Curtis C. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat Genet 2017; 49:1015-1024. [PMID: 28581503 PMCID: PMC5643198 DOI: 10.1038/ng.3891] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 05/05/2017] [Indexed: 12/17/2022]
Abstract
Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.
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Affiliation(s)
- Ruping Sun
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Zheng Hu
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Trevor A. Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Arbel Harpak
- Department of Biology, Stanford University, Stanford, California, USA
| | - Zhicheng Ma
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Jared M. Fischer
- Oregon Health and Science University, Department of Molecular and Medical Genetics, Portland, Oregon, USA
| | - Darryl Shibata
- Department of Pathology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
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22
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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: 3.0] [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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23
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Smida J, Xu H, Zhang Y, Baumhoer D, Ribi S, Kovac M, von Luettichau I, Bielack S, O'Leary VB, Leib-Mösch C, Frishman D, Nathrath M. Genome-wide analysis of somatic copy number alterations and chromosomal breakages in osteosarcoma. Int J Cancer 2017; 141:816-828. [DOI: 10.1002/ijc.30778] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 04/19/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Jan Smida
- Institute of Radiation Biology, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
- Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
- Pediatric Oncology Center; Department of Pediatrics, Technical University of Munich and Comprehensive Cancer Center; Munich Germany
| | - Hongen Xu
- Department of Bioinformatics; Wissenschaftszentrum Weihenstephan, Technical University of Munich; Freising Germany
| | - Yanping Zhang
- Department of Bioinformatics; Wissenschaftszentrum Weihenstephan, Technical University of Munich; Freising Germany
| | - Daniel Baumhoer
- Bone Tumour Reference Center; Institute of Pathology, University Hospital Basel; Switzerland
| | - Sebastian Ribi
- Bone Tumour Reference Center; Institute of Pathology, University Hospital Basel; Switzerland
| | - Michal Kovac
- Bone Tumour Reference Center; Institute of Pathology, University Hospital Basel; Switzerland
| | - Irene von Luettichau
- Pediatric Oncology Center; Department of Pediatrics, Technical University of Munich and Comprehensive Cancer Center; Munich Germany
| | - Stefan Bielack
- Pediatrics 5 (Oncology, Hematology, Immunology), Klinikum Stuttgart Olgahospital; Stuttgart Germany
| | - Valerie B. O'Leary
- Institute of Radiation Biology, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
| | - Christine Leib-Mösch
- Institute of Virology, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
| | - Dmitrij Frishman
- Department of Bioinformatics; Wissenschaftszentrum Weihenstephan, Technical University of Munich; Freising Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
- St Petersburg State Polytechnic University; St Petersburg Russia
| | - Michaela Nathrath
- Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum Munich - German Research Center for Environmental Health; Neuherberg Germany
- Pediatric Oncology Center; Department of Pediatrics, Technical University of Munich and Comprehensive Cancer Center; Munich Germany
- Department of Pediatric Hematology and Oncology; Klinikum Kassel; Germany
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24
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phyC: Clustering cancer evolutionary trees. PLoS Comput Biol 2017; 13:e1005509. [PMID: 28459850 PMCID: PMC5432190 DOI: 10.1371/journal.pcbi.1005509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 05/15/2017] [Accepted: 04/10/2017] [Indexed: 01/06/2023] Open
Abstract
Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC. Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.
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25
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Abstract
Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.
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Affiliation(s)
- Russell Schwartz
- Department of Biological Sciences and Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, USA
| | - Alejandro A Schäffer
- Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland 20892, USA
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Kuipers J, Jahn K, Beerenwinkel N. Advances in understanding tumour evolution through single-cell sequencing. Biochim Biophys Acta Rev Cancer 2017; 1867:127-138. [PMID: 28193548 PMCID: PMC5813714 DOI: 10.1016/j.bbcan.2017.02.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/02/2017] [Accepted: 02/04/2017] [Indexed: 12/14/2022]
Abstract
The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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MESH Headings
- Adaptation, Physiological
- Animals
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Cell Transformation, Neoplastic/genetics
- Cell Transformation, Neoplastic/metabolism
- Cell Transformation, Neoplastic/pathology
- Evolution, Molecular
- Gene Expression Regulation, Neoplastic
- Genetic Fitness
- Genetic Heterogeneity
- Genetic Predisposition to Disease
- Heredity
- Humans
- Models, Genetic
- Mutation
- Neoplasms/drug therapy
- Neoplasms/genetics
- Neoplasms/metabolism
- Neoplasms/pathology
- Pedigree
- Phenotype
- Phylogeny
- Sequence Analysis, DNA
- Signal Transduction/genetics
- Single-Cell Analysis/methods
- Time Factors
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
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Davis A, Gao R, Navin N. Tumor evolution: Linear, branching, neutral or punctuated? Biochim Biophys Acta Rev Cancer 2017; 1867:151-161. [PMID: 28110020 DOI: 10.1016/j.bbcan.2017.01.003] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/14/2017] [Accepted: 01/16/2017] [Indexed: 02/08/2023]
Abstract
Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Alexander Davis
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruli Gao
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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28
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Miller CA, McMichael J, Dang HX, Maher CA, Ding L, Ley TJ, Mardis ER, Wilson RK. Visualizing tumor evolution with the fishplot package for R. BMC Genomics 2016; 17:880. [PMID: 27821060 PMCID: PMC5100182 DOI: 10.1186/s12864-016-3195-z] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 10/22/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Massively-parallel sequencing at depth is now enabling tumor heterogeneity and evolution to be characterized in unprecedented detail. Tracking these changes in clonal architecture often provides insight into therapeutic response and resistance. In complex cases involving multiple timepoints, standard visualizations, such as scatterplots, can be difficult to interpret. Current data visualization methods are also typically manual and laborious, and often only approximate subclonal fractions. RESULTS We have developed an R package that accurately and intuitively displays changes in clonal structure over time. It requires simple input data and produces illustrative and easy-to-interpret graphs suitable for diagnosis, presentation, and publication. CONCLUSIONS The simplicity, power, and flexibility of this tool make it valuable for visualizing tumor evolution, and it has potential utility in both research and clinical settings. The fishplot package is available at https://github.com/chrisamiller/fishplot .
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Affiliation(s)
- Christopher A Miller
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA. .,Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St. Louis, MO, 63108, USA.
| | - Joshua McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Ha X Dang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Christopher A Maher
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Li Ding
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Timothy J Ley
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Elaine R Mardis
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Richard K Wilson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, 63108, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108, USA
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29
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Somarelli JA, Ware KE, Kostadinov R, Robinson JM, Amri H, Abu-Asab M, Fourie N, Diogo R, Swofford D, Townsend JP. PhyloOncology: Understanding cancer through phylogenetic analysis. Biochim Biophys Acta Rev Cancer 2016; 1867:101-108. [PMID: 27810337 DOI: 10.1016/j.bbcan.2016.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 10/14/2016] [Accepted: 10/26/2016] [Indexed: 11/30/2022]
Abstract
Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Jason A Somarelli
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States.
| | - Kathryn E Ware
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States
| | - Rumen Kostadinov
- Pediatric Oncology, School of Medicine, Johns Hopkins University, United States
| | - Jeffrey M Robinson
- Anatomy Department, College of Medicine, Howard University, Washington, DC 20059, United States; Digestive Disorders Unit, National Institute of Nursing Research, NIH, Bethesda, MD 20892, United States
| | - Hakima Amri
- Department of Biochemistry and Cellular and Molecular Biology, Georgetown University Medical Center, Washington, DC 20007, United States
| | - Mones Abu-Asab
- Section of Ultrastructural Biology, National Eye Institute, NIH, Bethesda, MD 20892, United States
| | - Nicolaas Fourie
- Digestive Disorders Unit, National Institute of Nursing Research, NIH, Bethesda, MD 20892, United States
| | - Rui Diogo
- Anatomy Department, College of Medicine, Howard University, Washington, DC 20059, United States
| | - David Swofford
- Department of Biology, Duke University Trinity College of Arts and Sciences, Durham, NC 27710, United States
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale University, United States; Department of Ecology and Evolutionary Biology, Yale University, United States; Department of Program in Computational Biology and Bioinformatics, Yale University, United States.
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Abstract
Computational methods have been developed to reconstruct evolutionary lineages from tumors using single-cell genomic data. The resulting tumor trees have important applications in cancer research and clinical oncology. Please see related Research articles: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0929-9 and http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0936-x.
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Affiliation(s)
- Alexander Davis
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Nicholas E Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. .,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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31
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Jahn K, Kuipers J, Beerenwinkel N. Tree inference for single-cell data. Genome Biol 2016; 17:86. [PMID: 27149953 PMCID: PMC4858868 DOI: 10.1186/s13059-016-0936-x] [Citation(s) in RCA: 175] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 04/08/2016] [Indexed: 02/02/2023] Open
Abstract
Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.
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Affiliation(s)
- Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB, Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB, Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,SIB, Swiss Institute of Bioinformatics, Basel, Switzerland.
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32
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Ryu D, Joung JG, Kim NKD, Kim KT, Park WY. Deciphering intratumor heterogeneity using cancer genome analysis. Hum Genet 2016; 135:635-42. [DOI: 10.1007/s00439-016-1670-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 04/08/2016] [Indexed: 10/21/2022]
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Abstract
Despite the enormous medical impact of cancers and intensive study of their biology, detailed characterization of tumor growth and development remains elusive. This difficulty occurs in large part because of enormous heterogeneity in the molecular mechanisms of cancer progression, both tumor-to-tumor and cell-to-cell in single tumors. Advances in genomic technologies, especially at the single-cell level, are improving the situation, but these approaches are held back by limitations of the biotechnologies for gathering genomic data from heterogeneous cell populations and the computational methods for making sense of those data. One popular way to gain the advantages of whole-genome methods without the cost of single-cell genomics has been the use of computational deconvolution (unmixing) methods to reconstruct clonal heterogeneity from bulk genomic data. These methods, too, are limited by the difficulty of inferring genomic profiles of rare or subtly varying clonal subpopulations from bulk data, a problem that can be computationally reduced to that of reconstructing the geometry of point clouds of tumor samples in a genome space. Here, we present a new method to improve that reconstruction by better identifying subspaces corresponding to tumors produced from mixtures of distinct combinations of clonal subpopulations. We develop a nonparametric clustering method based on medoidshift clustering for identifying subgroups of tumors expected to correspond to distinct trajectories of evolutionary progression. We show on synthetic and real tumor copy-number data that this new method substantially improves our ability to resolve discrete tumor subgroups, a key step in the process of accurately deconvolving tumor genomic data and inferring clonal heterogeneity from bulk data.
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Affiliation(s)
- Theodore Roman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA. .,Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA.
| | - Lu Xie
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA. .,Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA.
| | - Russell Schwartz
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA. .,Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, PA, USA.
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34
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Smith KS, Yadav VK, Pei S, Pollyea DA, Jordan CT, De S. SomVarIUS: somatic variant identification from unpaired tissue samples. Bioinformatics 2015; 32:808-13. [DOI: 10.1093/bioinformatics/btv685] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 11/13/2015] [Indexed: 12/15/2022] Open
Abstract
Abstract
Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples.
Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples.
Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/
Contact: subhajyoti.de@ucdenver.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kyle S. Smith
- Department of Medicine,
- Department of Pharmacology,
- Computational Biosciences Training Program, University of Colorado School of Medicine, Aurora, CO, USA,
| | | | - Shanshan Pei
- University of Colorado Cancer Center, Aurora, CO, USA and
| | - Daniel A. Pollyea
- Department of Medicine,
- University of Colorado Cancer Center, Aurora, CO, USA and
| | - Craig T. Jordan
- Department of Medicine,
- University of Colorado Cancer Center, Aurora, CO, USA and
| | - Subhajyoti De
- Department of Medicine,
- Department of Pharmacology,
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
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35
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SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing. PLoS Comput Biol 2015; 11:e1004416. [PMID: 26436540 PMCID: PMC4593588 DOI: 10.1371/journal.pcbi.1004416] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 06/27/2015] [Indexed: 01/10/2023] Open
Abstract
Recent improvements in next-generation sequencing of tumor samples and the ability to identify somatic mutations at low allelic fractions have opened the way for new approaches to model the evolution of individual cancers. The power and utility of these models is increased when tumor samples from multiple sites are sequenced. Temporal ordering of the samples may provide insight into the etiology of both primary and metastatic lesions and rationalizations for tumor recurrence and therapeutic failures. Additional insights may be provided by temporal ordering of evolving subclones—cellular subpopulations with unique mutational profiles. Current methods for subclone hierarchy inference tightly couple the problem of temporal ordering with that of estimating the fraction of cancer cells harboring each mutation. We present a new framework that includes a rigorous statistical hypothesis test and a collection of tools that make it possible to decouple these problems, which we believe will enable substantial progress in the field of subclone hierarchy inference. The methods presented here can be flexibly combined with methods developed by others addressing either of these problems. We provide tools to interpret hypothesis test results, which inform phylogenetic tree construction, and we introduce the first genetic algorithm designed for this purpose. The utility of our framework is systematically demonstrated in simulations. For most tested combinations of tumor purity, sequencing coverage, and tree complexity, good power (≥ 0.8) can be achieved and Type 1 error is well controlled when at least three tumor samples are available from a patient. Using data from three published multi-region tumor sequencing studies of (murine) small cell lung cancer, acute myeloid leukemia, and chronic lymphocytic leukemia, in which the authors reconstructed subclonal phylogenetic trees by manual expert curation, we show how different configurations of our tools can identify either a single tree in agreement with the authors, or a small set of trees, which include the authors’ preferred tree. Our results have implications for improved modeling of tumor evolution and the importance of multi-region tumor sequencing. Cancer is a genetic disease, driven by DNA mutations. Each tumor is composed of millions of cells with differing genetic profiles that compete with each other for resources in a process similar to Darwinian evolution. We describe a computational framework to model tumor evolution on the cellular level, using next-generation sequencing. The framework is the first to apply a rigorous statistical hypothesis test designed to inform a new search algorithm. Both the test and the algorithm are based on evolutionary principles. The utility of the framework is shown in computer simulations and by automated reconstruction of the cellular evolution underlying murine small cell lung cancers, acute myeloid leukemias and chronic lymophocytic leukemias, from three recent published studies.
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36
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Aparicio S, Mardis E. Tumor heterogeneity: next-generation sequencing enhances the view from the pathologist's microscope. Genome Biol 2015; 15:463. [PMID: 25315013 PMCID: PMC4318188 DOI: 10.1186/s13059-014-0463-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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37
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38
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Roman T, Nayyeri A, Fasy BT, Schwartz R. A simplicial complex-based approach to unmixing tumor progression data. BMC Bioinformatics 2015; 16:254. [PMID: 26264682 PMCID: PMC4534068 DOI: 10.1186/s12859-015-0694-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 08/03/2015] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Tumorigenesis is an evolutionary process by which tumor cells acquire mutations through successive diversification and differentiation. There is much interest in reconstructing this process of evolution due to its relevance to identifying drivers of mutation and predicting future prognosis and drug response. Efforts are challenged by high tumor heterogeneity, though, both within and among patients. In prior work, we showed that this heterogeneity could be turned into an advantage by computationally reconstructing models of cell populations mixed to different degrees in distinct tumors. Such mixed membership model approaches, however, are still limited in their ability to dissect more than a few well-conserved cell populations across a tumor data set. RESULTS We present a method to improve on current mixed membership model approaches by better accounting for conserved progression pathways between subsets of cancers, which imply a structure to the data that has not previously been exploited. We extend our prior methods, which use an interpretation of the mixture problem as that of reconstructing simple geometric objects called simplices, to instead search for structured unions of simplices called simplicial complexes that one would expect to emerge from mixture processes describing branches along an evolutionary tree. We further improve on the prior work with a novel objective function to better identify mixtures corresponding to parsimonious evolutionary tree models. We demonstrate that this approach improves on our ability to accurately resolve mixtures on simulated data sets and demonstrate its practical applicability on a large RNASeq tumor data set. CONCLUSIONS Better exploiting the expected geometric structure for mixed membership models produced from common evolutionary trees allows us to quickly and accurately reconstruct models of cell populations sampled from those trees. In the process, we hope to develop a better understanding of tumor evolution as well as other biological problems that involve interpreting genomic data gathered from heterogeneous populations of cells.
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Affiliation(s)
- Theodore Roman
- Computatational Biology Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA.
| | - Amir Nayyeri
- Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA.
| | - Brittany Terese Fasy
- Department of Computer Science, Tulane University, 6834 St. Charles St., New Orleans, USA.
| | - Russell Schwartz
- Computatational Biology Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA. .,Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA.
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39
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Guerini-Rocco E, Hodi Z, Piscuoglio S, Ng CKY, Rakha EA, Schultheis AM, Marchiò C, da Cruz Paula A, De Filippo MR, Martelotto LG, De Mattos-Arruda L, Edelweiss M, Jungbluth AA, Fusco N, Norton L, Weigelt B, Ellis IO, Reis-Filho JS. The repertoire of somatic genetic alterations of acinic cell carcinomas of the breast: an exploratory, hypothesis-generating study. J Pathol 2015; 237:166-78. [PMID: 26011570 DOI: 10.1002/path.4566] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 05/01/2015] [Accepted: 05/19/2015] [Indexed: 12/12/2022]
Abstract
Acinic cell carcinoma (ACC) of the breast is a rare form of triple-negative (that is, oestrogen receptor-negative, progesterone receptor-negative, HER2-negative) salivary gland-type tumour displaying serous acinar differentiation. Despite its triple-negative phenotype, breast ACCs are reported to have an indolent clinical behaviour. Here, we sought to define whether ACCs have a mutational repertoire distinct from that of other triple-negative breast cancers (TNBCs). DNA was extracted from microdissected formalin-fixed, paraffin-embedded sections of tumour and normal tissue from two pure and six mixed breast ACCs. Each tumour component of the mixed cases was microdissected separately. Tumour and normal samples were subjected to targeted capture massively parallel sequencing targeting all exons of 254 genes, including genes most frequently mutated in breast cancer and related to DNA repair. Selected somatic mutations were validated by targeted amplicon resequencing and Sanger sequencing. Akin to other forms of TNBC, the most frequently mutated gene found in breast ACCs was TP53 (one pure and six mixed cases). Additional somatic mutations affecting breast cancer-related genes found in ACCs included PIK3CA, MTOR, CTNNB1, BRCA1, ERBB4, ERBB3, INPP4B, and FGFR2. Copy number alteration analysis revealed complex patterns of gains and losses similar to those of common forms of TNBCs. Of the mixed cases analysed, identical somatic mutations were found in the acinic and the high-grade non-acinic components in two out of four cases analysed, providing evidence of their clonal relatedness. In conclusion, breast ACCs display the hallmark somatic genetic alterations found in high-grade forms of TNBC, including complex patterns of gene copy number alterations and recurrent TP53 mutations. Furthermore, we provide circumstantial genetic evidence to suggest that ACCs may constitute the substrate for the development of more aggressive forms of triple-negative disease.
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Affiliation(s)
- Elena Guerini-Rocco
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,School of Pathology, University of Milan, Italy
| | - Zsolt Hodi
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Salvatore Piscuoglio
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charlotte K Y Ng
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emad A Rakha
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Anne M Schultheis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Caterina Marchiò
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medical Sciences, University of Turin, Turin, Italy
| | - Arnaud da Cruz Paula
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria R De Filippo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luciano G Martelotto
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Leticia De Mattos-Arruda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Vall d'Hebron Institute of Oncology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marcia Edelweiss
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Achim A Jungbluth
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicola Fusco
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,School of Pathology, University of Milan, Italy
| | - Larry Norton
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ian O Ellis
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Jorge S Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Popic V, Salari R, Hajirasouliha I, Kashef-Haghighi D, West RB, Batzoglou S. Fast and scalable inference of multi-sample cancer lineages. Genome Biol 2015; 16:91. [PMID: 25944252 PMCID: PMC4501097 DOI: 10.1186/s13059-015-0647-8] [Citation(s) in RCA: 126] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2014] [Accepted: 04/07/2015] [Indexed: 01/06/2023] Open
Abstract
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee.
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Affiliation(s)
- Victoria Popic
- Department of Computer Science, Stanford University, Stanford, CA, USA.
| | - Raheleh Salari
- Department of Computer Science, Stanford University, Stanford, CA, USA.
| | | | | | - Robert B West
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Serafim Batzoglou
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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Turajlic S, McGranahan N, Swanton C. Inferring mutational timing and reconstructing tumour evolutionary histories. BIOCHIMICA ET BIOPHYSICA ACTA 2015; 1855:264-75. [PMID: 25827356 DOI: 10.1016/j.bbcan.2015.03.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Revised: 03/17/2015] [Accepted: 03/19/2015] [Indexed: 12/28/2022]
Abstract
Cancer evolution can be considered within a Darwinian framework. Both micro and macro-evolutionary theories can be applied to understand tumour progression and treatment failure. Owing to cancers' complexity and heterogeneity the rules of tumour evolution, such as the role of selection, remain incompletely understood. The timing of mutational events during tumour evolution presents diagnostic, prognostic and therapeutic opportunities. Here we review the current sampling and computational approaches for inferring mutational timing and the evidence from next generation sequencing-informed data on mutational timing across all tumour types. We discuss how this knowledge can be used to illuminate the genes and pathways that drive cancer initiation and relapse; and to support drug development and clinical trial design.
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Affiliation(s)
- Samra Turajlic
- The Francis Crick Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK
| | | | - Charles Swanton
- The Francis Crick Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK; UCL Cancer Institute, CRUK Lung Cancer Centre of Excellence, Huntley Street, WC1E 6DD, UK.
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