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Vasei H, Foroughmand-Araabi MH, Daneshgar A. Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference. Bioinformatics 2024; 40:btae120. [PMID: 38984735 DOI: 10.1093/bioinformatics/btae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/19/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024] Open
Abstract
MOTIVATION Tumor trees, which depict the evolutionary process of cancer, provide a backbone for discovering recurring evolutionary processes in cancer. While they are not the primary information extracted from genomic data, they are valuable for this purpose. One such extraction method involves summarizing multiple trees into a single representative tree, such as consensus trees or supertrees. RESULTS We define the "weighted centroid tree problem" to find the centroid tree of a set of single-labeled rooted trees through the following steps: (i) mapping the given trees into the Euclidean space, (ii) computing the weighted centroid matrix of the mapped trees, and (iii) finding the nearest mapped tree (NMTP) to the centroid matrix. We show that this setup encompasses previously studied parent-child and ancestor-descendent metrics as well as the GraPhyC and TuELiP consensus tree algorithms. Moreover, we show that, while the NMTP problem is polynomial-time solvable for the adjacency embedding, it is NP-hard for ancestry and distance mappings. We introduce integer linear programs for NMTP in different setups where we also provide a new algorithm for the case of ancestry embedding called 2-AncL2, that uses a novel weighting scheme for ancestry signals. Our experimental results show that 2-AncL2 has a superior performance compared to available consensus tree algorithms. We also illustrate our setup's application on providing representative trees for a large real breast cancer dataset, deducing that the cluster centroid trees summarize reliable evolutionary information about the original dataset. AVAILABILITY AND IMPLEMENTATION https://github.com/vasei/WAncILP.
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Affiliation(s)
- Hamed Vasei
- Department of Mathematical Sciences, Sharif University of Technology, Tehran 111559415, Iran
| | | | - Amir Daneshgar
- Department of Mathematical Sciences, Sharif University of Technology, Tehran 111559415, Iran
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2
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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3
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Qi Y, El-Kebir M. Consensus Tree Under the Ancestor-Descendant Distance is NP-Hard. J Comput Biol 2024; 31:58-70. [PMID: 38010616 DOI: 10.1089/cmb.2023.0262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023] Open
Abstract
Due to uncertainty in tumor phylogeny inference from sequencing data, many methods infer multiple, equally plausible phylogenies for the same cancer. To summarize the solution space T of tumor phylogenies, consensus tree methods seek a single best representative tree S under a specified pairwise tree distance function. One such distance function is the ancestor-descendant (AD) distance [Formula: see text] , which equals the size of the symmetric difference of the transitive closures of the edge sets [Formula: see text] and [Formula: see text] . Here, we show that finding a consensus tree S for tumor phylogenies T that minimizes the total AD distance [Formula: see text] is NP-hard.
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Affiliation(s)
- Yuanyuan Qi
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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4
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Yousaf I, Hannon WW, Donohue RC, Pfaller CK, Yadav K, Dikdan RJ, Tyagi S, Schroeder DC, Shieh WJ, Rota PA, Feder AF, Cattaneo R. Brain tropism acquisition: The spatial dynamics and evolution of a measles virus collective infectious unit that drove lethal subacute sclerosing panencephalitis. PLoS Pathog 2023; 19:e1011817. [PMID: 38127684 PMCID: PMC10735034 DOI: 10.1371/journal.ppat.1011817] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
It is increasingly appreciated that pathogens can spread as infectious units constituted by multiple, genetically diverse genomes, also called collective infectious units or genome collectives. However, genetic characterization of the spatial dynamics of collective infectious units in animal hosts is demanding, and it is rarely feasible in humans. Measles virus (MeV), whose spread in lymphatic tissues and airway epithelia relies on collective infectious units, can, in rare cases, cause subacute sclerosing panencephalitis (SSPE), a lethal human brain disease. In different SSPE cases, MeV acquisition of brain tropism has been attributed to mutations affecting either the fusion or the matrix protein, or both, but the overarching mechanism driving brain adaptation is not understood. Here we analyzed MeV RNA from several spatially distinct brain regions of an individual who succumbed to SSPE. Surprisingly, we identified two major MeV genome subpopulations present at variable frequencies in all 15 brain specimens examined. Both genome types accumulated mutations like those shown to favor receptor-independent cell-cell spread in other SSPE cases. Most infected cells carried both genome types, suggesting the possibility of genetic complementation. We cannot definitively chart the history of the spread of this virus in the brain, but several observations suggest that mutant genomes generated in the frontal cortex moved outwards as a collective and diversified. During diversification, mutations affecting the cytoplasmic tails of both viral envelope proteins emerged and fluctuated in frequency across genetic backgrounds, suggesting convergent and potentially frequency-dependent evolution for modulation of fusogenicity. We propose that a collective infectious unit drove MeV pathogenesis in this brain. Re-examination of published data suggests that similar processes may have occurred in other SSPE cases. Our studies provide a primer for analyses of the evolution of collective infectious units of other pathogens that cause lethal disease in humans.
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Affiliation(s)
- Iris Yousaf
- Department of Molecular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
| | - William W. Hannon
- Basic Sciences and Computational Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, United States of America
| | - Ryan C. Donohue
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
| | - Christian K. Pfaller
- Department of Molecular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
| | - Kalpana Yadav
- Department of Molecular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Ryan J. Dikdan
- Public Health Research Institute, Rutgers University, Newark, New Jersey, United States of America
| | - Sanjay Tyagi
- Public Health Research Institute, Rutgers University, Newark, New Jersey, United States of America
| | - Declan C. Schroeder
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, Minnesota, United States of America
| | - Wun-Ju Shieh
- Infectious Diseases Pathology Branch, Division of High Consequence Pathogens and Pathology, Center for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Paul A. Rota
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Center for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alison F. Feder
- Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Public Health Sciences and Computational Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Roberto Cattaneo
- Department of Molecular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, United States of America
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5
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol 2023; 24:272. [PMID: 38037115 PMCID: PMC10688497 DOI: 10.1186/s13059-023-03106-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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6
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Luo XG, Kuipers J, Beerenwinkel N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat Commun 2023; 14:3676. [PMID: 37344522 DOI: 10.1038/s41467-023-39400-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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Affiliation(s)
- Xiang Ge Luo
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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7
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Sandmann S, Richter S, Jiang X, Varghese J. Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5128. [PMID: 36982036 PMCID: PMC10049679 DOI: 10.3390/ijerph20065128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/04/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, 48149 Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
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8
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Haughey MJ, Bassolas A, Sousa S, Baker AM, Graham TA, Nicosia V, Huang W. First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal cancer. PLoS Comput Biol 2023; 19:e1010952. [PMID: 36913406 PMCID: PMC10035892 DOI: 10.1371/journal.pcbi.1010952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 03/23/2023] [Accepted: 02/14/2023] [Indexed: 03/14/2023] Open
Abstract
The signature of early cancer dynamics on the spatial arrangement of tumour cells is poorly understood, and yet could encode information about how sub-clones grew within the expanding tumour. Novel methods of quantifying spatial tumour data at the cellular scale are required to link evolutionary dynamics to the resulting spatial architecture of the tumour. Here, we propose a framework using first passage times of random walks to quantify the complex spatial patterns of tumour cell population mixing. First, using a simple model of cell mixing we demonstrate how first passage time statistics can distinguish between different pattern structures. We then apply our method to simulated patterns of mutated and non-mutated tumour cell population mixing, generated using an agent-based model of expanding tumours, to explore how first passage times reflect mutant cell replicative advantage, time of emergence and strength of cell pushing. Finally, we explore applications to experimentally measured human colorectal cancer, and estimate parameters of early sub-clonal dynamics using our spatial computational model. We infer a wide range of sub-clonal dynamics, with mutant cell division rates varying between 1 and 4 times the rate of non-mutated cells across our sample set. Some mutated sub-clones emerged after as few as 100 non-mutant cell divisions, and others only after 50,000 divisions. The majority were consistent with boundary driven growth or short-range cell pushing. By analysing multiple sub-sampled regions in a small number of samples, we explore how the distribution of inferred dynamics could inform about the initial mutational event. Our results demonstrate the efficacy of first passage time analysis as a new methodology in spatial analysis of solid tumour tissue, and suggest that patterns of sub-clonal mixing can provide insights into early cancer dynamics.
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Affiliation(s)
- Magnus J. Haughey
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Aleix Bassolas
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Sandro Sousa
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Trevor A. Graham
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Weini Huang
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
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9
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522408. [PMID: 36711528 PMCID: PMC9882003 DOI: 10.1101/2023.01.05.522408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study. Code availability Software is available at https://github.com/raphael-group/constrained-Dollo.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A. Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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10
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Pellegrina L, Vandin F. Discovering significant evolutionary trajectories in cancer phylogenies. Bioinformatics 2022; 38:ii49-ii55. [PMID: 36124798 DOI: 10.1093/bioinformatics/btac467] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge. RESULTS We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies. AVAILABILITY AND IMPLEMENTATION MASTRO is available at https://github.com/VandinLab/MASTRO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonardo Pellegrina
- Department of Information Engineering, University of Padova, Padova, 35129, Italy
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova, 35129, Italy
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11
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Markowska M, Cąkała T, Miasojedow B, Aybey B, Juraeva D, Mazur J, Ross E, Staub E, Szczurek E. CONET: copy number event tree model of evolutionary tumor history for single-cell data. Genome Biol 2022; 23:128. [PMID: 35681161 PMCID: PMC9185904 DOI: 10.1186/s13059-022-02693-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.
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Affiliation(s)
- Magda Markowska
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland.,Medical University of Warsaw, Postgraduate School of Molecular Medicine, Ks. Trojdena 2a Street, Warsaw, Poland
| | - Tomasz Cąkała
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - BłaŻej Miasojedow
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - Bogac Aybey
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Dilafruz Juraeva
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Johanna Mazur
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Edith Ross
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Eike Staub
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293, Germany
| | - Ewa Szczurek
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland.
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12
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Yang D, Jones MG, Naranjo S, Rideout WM, Min KHJ, Ho R, Wu W, Replogle JM, Page JL, Quinn JJ, Horns F, Qiu X, Chen MZ, Freed-Pastor WA, McGinnis CS, Patterson DM, Gartner ZJ, Chow ED, Bivona TG, Chan MM, Yosef N, Jacks T, Weissman JS. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 2022; 185:1905-1923.e25. [PMID: 35523183 DOI: 10.1016/j.cell.2022.04.015] [Citation(s) in RCA: 103] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/09/2022] [Accepted: 04/08/2022] [Indexed: 12/19/2022]
Abstract
Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.
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Affiliation(s)
- Dian Yang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - William M Rideout
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Kyung Hoi Joseph Min
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Raymond Ho
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joseph M Replogle
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer L Page
- Cell and Genome Engineering Core, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Felix Horns
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xiaojie Qiu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Michael Z Chen
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Medical Scientist Training Program, Harvard Medical School, Boston, MA 02115, USA
| | - William A Freed-Pastor
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Christopher S McGinnis
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David M Patterson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Cellular Construction, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eric D Chow
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Advanced Technology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michelle M Chan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Chan Zuckerberg BioHub Investigator, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA 94720, USA; Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA.
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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13
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Sashittal P, Zaccaria S, El-Kebir M. Parsimonious Clone Tree Integration in cancer. Algorithms Mol Biol 2022; 17:3. [PMID: 35282838 PMCID: PMC8919608 DOI: 10.1186/s13015-022-00209-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/25/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. RESULTS To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. CONCLUSION PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
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14
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Sandmann S, Richter S, Jiang X, Varghese J. Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution. Cancer Genomics Proteomics 2022; 19:194-204. [PMID: 35181588 DOI: 10.21873/cgp.20314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges. MATERIALS AND METHODS By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool. RESULTS Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms. CONCLUSION Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany;
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
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15
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Laganà A. Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:101-118. [DOI: 10.1007/978-3-030-91836-1_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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OUP accepted manuscript. Bioinformatics 2022; 38:i125-i133. [PMID: 35758777 PMCID: PMC9236577 DOI: 10.1093/bioinformatics/btac253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. RESULTS In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. AVAILABILITY AND IMPLEMENTATION https://github.com/CMUSchwartzLab/TUSV-ext. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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17
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Laganà A. The Architecture of a Precision Oncology Platform. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:1-22. [DOI: 10.1007/978-3-030-91836-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Govek K, Sikes C, Zhou Y, Oesper L. GraPhyC: Using Consensus to Infer Tumor Evolution. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:465-478. [PMID: 33031032 DOI: 10.1109/tcbb.2020.3029689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We consider the problem of finding a consensus tumor evolution tree from a set of conflicting input trees. In contrast to traditional phylogenetic trees, the tumor trees we consider do not have the same set of labels applied to the leaves of each tree. We describe several distance measures between these tumor trees. Our GraPhyC algorithm solves the consensus problem using a weighted directed graph where vertices are sets of mutations and edges are weighted based on the number of times a parental relationship is observed between their constituent mutations in the input trees. We find a minimum weight spanning arborescence in this graph and prove that it minimizes the total distance to all input trees for one of our distance measures. We also describe several extensions of our GraPhyC approach. On simulated data we show that GraPhyC outperforms a baseline method and demonstrate that GraPhyC can be an effective means of computing centroids in k-medians clustering. We analyze two real sequencing datasets and find that GraPhyC is able to identify a tree not included in the set of input trees, but that contains characteristics supported by other reported evolutionary reconstructions of this tumor.
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19
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Richard G, Princiotta MF, Bridon D, Martin WD, Steinberg GD, De Groot AS. Neoantigen-based personalized cancer vaccines: the emergence of precision cancer immunotherapy. Expert Rev Vaccines 2021; 21:173-184. [PMID: 34882038 DOI: 10.1080/14760584.2022.2012456] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION The field of cancer therapy has undergone a major transformation in less than a decade due to the introduction of checkpoint inhibitors, the advent of next generation sequencing and the discovery of neoantigens. The key observation that the breadth of each patient's immune response to the unique mutations or neoantigens present in their tumor is directly related to their survival has led oncologists to focus on driving immune responses to neoantigens through vaccination. Oncology has entered the era of precision immunotherapy, and cancer vaccine development is undergoing a paradigm shift. AREAS COVERED Neoantigens are short peptide sequences found in tumors, but not noncancerous tissues, the vast majority of which are unique to each patient. In addition to providing a description of the distinguishing features of neoantigen discovery platforms, this review will address cross-cutting personalized cancer vaccine design themes and developmental stumbling blocks. EXPERT OPINION Immunoinformatic pipelines that can rapidly scan cancer genomes and identify 'the best' neoantigens are in high demand. Despite the need for such tools, immunoinformatic methods for identifying neoepitopes in cancer genomes are diverse and have not been well-validated. Validation of 'personalized vaccine design pipelines' will bring about a revolution in neoantigen-based vaccine design and delivery.
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Affiliation(s)
| | | | | | | | - Gary D Steinberg
- EpiVax Therapeutics, Inc., Providence, RI, USA.,Perlmutter Cancer Center, Department of Urology at NYU Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Anne S De Groot
- EpiVax, Inc., Providence, RI, USA.,Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
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20
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Ogundijo OE, Zhu K, Wang X, Anastassiou D. Characterizing Intra-Tumor Heterogeneity From Somatic Mutations Without Copy-Neutral Assumption. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2271-2280. [PMID: 32070995 DOI: 10.1109/tcbb.2020.2973635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2, to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2.
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21
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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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22
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Ali S, Ciccolella S, Lucarella L, Vedova GD, Patterson M. Simpler and Faster Development of Tumor Phylogeny Pipelines. J Comput Biol 2021; 28:1142-1155. [PMID: 34698531 DOI: 10.1089/cmb.2021.0271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
In the recent years, there has been an increasing amount of single-cell sequencing studies, producing a considerable number of new data sets. This has particularly affected the field of cancer analysis, where more and more articles are published using this sequencing technique that allows for capturing more detailed information regarding the specific genetic mutations on each individually sampled cell. As the amount of information increases, it is necessary to have more sophisticated and rapid tools for analyzing the samples. To this goal, we developed plastic (PipeLine Amalgamating Single-cell Tree Inference Components), an easy-to-use and quick to adapt pipeline that integrates three different steps: (1) to simplify the input data, (2) to infer tumor phylogenies, and (3) to compare the phylogenies. We have created a pipeline submodule for each of those steps and developed new in-memory data structures that allow for easy and transparent sharing of the information across the tools implementing the above steps. While we use existing open source tools for those steps, we have extended the tool used for simplifying the input data, incorporating two machine learning procedures-which greatly reduce the running time without affecting the quality of the downstream analysis. Moreover, we have introduced the capability of producing some plots to quickly visualize results.
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Affiliation(s)
- Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Simone Ciccolella
- Department of Informatics, Systems, and Communications, University of Milano-Bicocca, Milano, Italy
| | - Lorenzo Lucarella
- Department of Informatics, Systems, and Communications, University of Milano-Bicocca, Milano, Italy
| | - Gianluca Della Vedova
- Department of Informatics, Systems, and Communications, University of Milano-Bicocca, Milano, Italy
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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23
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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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24
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Andersson N, Chattopadhyay S, Valind A, Karlsson J, Gisselsson D. DEVOLUTION-A method for phylogenetic reconstruction of aneuploid cancers based on multiregional genotyping data. Commun Biol 2021; 4:1103. [PMID: 34545199 PMCID: PMC8452746 DOI: 10.1038/s42003-021-02637-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 09/03/2021] [Indexed: 02/05/2023] Open
Abstract
Phylogenetic reconstruction of cancer cell populations remains challenging. There is a particular lack of tools that deconvolve clones based on copy number aberration analyses of multiple tumor biopsies separated in time and space from the same patient. This has hampered investigations of tumors rich in aneuploidy but few point mutations, as in many childhood cancers and high-risk adult cancer. Here, we present DEVOLUTION, an algorithm for subclonal deconvolution followed by phylogenetic reconstruction from bulk genotyping data. It integrates copy number and sequencing information across multiple tumor regions throughout the inference process, provided that the mutated clone fraction for each mutation is known. We validate DEVOLUTION on data from 56 pediatric tumors comprising 253 tumor biopsies and show a robust performance on simulations of bulk genotyping data. We also benchmark DEVOLUTION to similar bioinformatic tools using an external dataset. DEVOLUTION holds the potential to facilitate insights into the development, progression, and response to treatment, particularly in tumors with high burden of chromosomal copy number alterations.
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Affiliation(s)
- Natalie Andersson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden.
| | - Subhayan Chattopadhyay
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Anders Valind
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Department of Pediatrics, Skåne University Hospital, Lund, Sweden
| | - Jenny Karlsson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - David Gisselsson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Division of Oncology-Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Clinical Genetics and Pathology, Laboratory Medicine, Lund University Hospital, Skåne Healthcare Region, Lund, Sweden
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25
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Lei H, Gertz EM, Schäffer AA, Fu X, Tao Y, Heselmeyer-Haddad K, Torres I, Li G, Xu L, Hou Y, Wu K, Shi X, Dean M, Ried T, Schwartz R. Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data. Bioinformatics 2021; 37:4704-4711. [PMID: 34289030 PMCID: PMC8665747 DOI: 10.1093/bioinformatics/btab504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/19/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation (SV) events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS In the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming (ILP) framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence, and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution.
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Affiliation(s)
- Haoyun Lei
- Computational Biology Dept, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - E Michael Gertz
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Xuecong Fu
- Shenzhen Luohu People's Hospital, Shenzhen, 518000, China
| | - Yifeng Tao
- Computational Biology Dept, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Kerstin Heselmeyer-Haddad
- Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Irianna Torres
- Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Guibo Li
- Department of Biology, University of Copenhagen, Copenhagen, 1599, Denmark
| | - Liqin Xu
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Soltofts Plads, 2800 Kongens Lyngby, Denmark
| | - Yong Hou
- Department of Biology, University of Copenhagen, Copenhagen, 1599, Denmark
| | - Kui Wu
- Department of Biology, University of Copenhagen, Copenhagen, 1599, Denmark
| | - Xulian Shi
- Shenzhen Luohu People's Hospital, Shenzhen, 518000, China
| | - Michael Dean
- Laboratory of Translational Genomics, Division of Cancer Epidemiology & Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, MD, 20814, USA
| | - Thomas Ried
- Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Russell Schwartz
- Computational Biology Dept, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.,Dept. of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
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Weber LL, El-Kebir M. Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors. Algorithms Mol Biol 2021; 16:14. [PMID: 34229713 PMCID: PMC8259357 DOI: 10.1186/s13015-021-00194-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/22/2021] [Indexed: 01/24/2023] Open
Abstract
Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.
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Ciccolella S, Ricketts C, Soto Gomez M, Patterson M, Silverbush D, Bonizzoni P, Hajirasouliha I, Della Vedova G. Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses. Bioinformatics 2021; 37:326-333. [PMID: 32805010 PMCID: PMC8058767 DOI: 10.1093/bioinformatics/btaa722] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 08/06/2020] [Accepted: 08/11/2020] [Indexed: 01/21/2023] Open
Abstract
Motivation In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. Results We present Simulated Annealing Single-Cell inference (SASC): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. Availability and implementation The SASC tool is open source and available at https://github.com/sciccolella/sasc. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Simone Ciccolella
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Camir Ricketts
- Department of Physiology and Biophysics, Tri-I Computational Biology & Medicine Graduate Program, Weill Cornell Medicine of Cornell University, New York, NY 10021, USA.,Institute for Computational Biomedicine, Englander Institute for Precision Medicine, The Meyer Cancer Center, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York City, NY 10021, USA
| | - Mauricio Soto Gomez
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Murray Patterson
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.,Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA
| | - Dana Silverbush
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Paola Bonizzoni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, The Meyer Cancer Center, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York City, NY 10021, USA
| | - Gianluca Della Vedova
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
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28
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Zhang C, El-Kebir M, Ochoa I. Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples. Nat Commun 2021; 12:2204. [PMID: 33850139 PMCID: PMC8044184 DOI: 10.1038/s41467-021-22466-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 03/05/2021] [Indexed: 11/17/2022] Open
Abstract
Intra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor's mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss' improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics.
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Affiliation(s)
- Chuanyi Zhang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Idoia Ochoa
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical Engineering, University of Navarra, Tecnun, San Sebastian, Spain.
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29
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Sadeqi Azer E, Rashidi Mehrabadi F, Malikić S, Li XC, Bartok O, Litchfield K, Levy R, Samuels Y, Schäffer AA, Gertz EM, Day CP, Pérez-Guijarro E, Marie K, Lee MP, Merlino G, Ergun F, Sahinalp SC. PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem. Bioinformatics 2021; 36:i169-i176. [PMID: 32657358 DOI: 10.1093/bioinformatics/btaa464] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. RESULTS We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and bound algorithm to compute the most likely perfect phylogeny on an input genotype matrix extracted from an SCS dataset. PhISCS-BnB not only offers an optimality guarantee, but is also 10-100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma dataset derived from the sublineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in <4 h. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor. AVAILABILITY AND IMPLEMENTATION https://github.com/algo-cancer/PhISCS-BnB. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erfan Sadeqi Azer
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Farid Rashidi Mehrabadi
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA.,Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Salem Malikić
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Xuan Cindy Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.,Program in Computational Biology, Bioinformatics and Genomics, University of Maryland, College Park, MD 20742, USA
| | - Osnat Bartok
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Kevin Litchfield
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London NW1 1AT, UK.,Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ronen Levy
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - E Michael Gertz
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kerrie Marie
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Maxwell P Lee
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Funda Ergun
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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30
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Hodzic E, Shrestha R, Malikic S, Collins CC, Litchfield K, Turajlic S, Sahinalp SC. Identification of conserved evolutionary trajectories in tumors. Bioinformatics 2021; 36:i427-i435. [PMID: 32657374 DOI: 10.1093/bioinformatics/btaa453] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). RESULTS In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. AVAILABILITY AND IMPLEMENTATION CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ermin Hodzic
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Raunak Shrestha
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Salem Malikic
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA
| | - Colin C Collins
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.,aboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, BC, Canada
| | - Kevin Litchfield
- Cancer Dynamics Laboratory, the Francis Crick institute, Genome Instability Laboratory, Francis Crick Institute, London, UK
| | - Samra Turajlic
- Cancer Dynamics Laboratory, the Francis Crick institute, Genome Instability Laboratory, Francis Crick Institute, London, UK.,Skin and Renal Units, The royal Marsden NHS Foundation Trust, London, UK
| | - S Cenk Sahinalp
- Cancer Data Science Lab., National Cancer Institute, NIH, Bethesda, MD, USA
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31
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Christensen S, Kim J, Chia N, Koyejo O, El-Kebir M. Detecting evolutionary patterns of cancers using consensus trees. Bioinformatics 2021; 36:i684-i691. [PMID: 33381820 DOI: 10.1093/bioinformatics/btaa801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. RESULTS To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. AVAILABILITY AND IMPLEMENTATION https://github.com/elkebir-group/RECAP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Juho Kim
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine.,Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
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32
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Zaccaria S, Raphael BJ. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat Biotechnol 2021; 39:207-214. [PMID: 32879467 PMCID: PMC9876616 DOI: 10.1038/s41587-020-0661-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 08/04/2020] [Indexed: 01/28/2023]
Abstract
Single-cell barcoding technologies enable genome sequencing of thousands of individual cells in parallel, but with extremely low sequencing coverage (<0.05×) per cell. While the total copy number of large multi-megabase segments can be derived from such data, important allele-specific mutations-such as copy-neutral loss of heterozygosity (LOH) in cancer-are missed. We introduce copy-number haplotype inference in single cells using evolutionary links (CHISEL), a method to infer allele- and haplotype-specific copy numbers in single cells and subpopulations of cells by aggregating sparse signal across hundreds or thousands of individual cells. We applied CHISEL to ten single-cell sequencing datasets of ~2,000 cells from two patients with breast cancer. We identified extensive allele-specific copy-number aberrations (CNAs) in these samples, including copy-neutral LOHs, whole-genome duplications (WGDs) and mirrored-subclonal CNAs. These allele-specific CNAs affect genomic regions containing well-known breast-cancer genes. We also refined the reconstruction of tumor evolution, timing allele-specific CNAs before and after WGDs, identifying low-frequency subpopulations distinguished by unique CNAs and uncovering evidence of convergent evolution.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
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33
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Sundermann LK, Wintersinger J, Rätsch G, Stoye J, Morris Q. Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine. PLoS Comput Biol 2021; 17:e1008400. [PMID: 33465079 PMCID: PMC7845980 DOI: 10.1371/journal.pcbi.1008400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 01/29/2021] [Accepted: 09/22/2020] [Indexed: 11/18/2022] Open
Abstract
Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine. Cancer cells accumulate mutations over time and consist of genetically distinct subpopulations. Their evolutionary history (as represented by tumor phylogenies) can be inferred from bulk cancer genome sequencing data. Current tumor phylogeny reconstruction methods have two main issues: they are slow, and they do not efficiently represent uncertainty in the reconstruction. To address these issues, we developed SubMARine, a fast algorithm that summarizes all valid phylogenies in an intuitive format. SubMARine solved all reconstruction problems in this manuscript in less than 70 seconds, orders of magnitude faster than other methods. These reconstruction problems included those with up to 50 subclones; problems that are too large for other algorithms to even attempt. SubMARine achieves these result because, unlike other algorithms, it performs its reconstruction by identifying an upper-bound on the solution set of trees and the amount of noise in the estimates of the subclonal frequencies. In the vast majority of cases we checked, i. e. an extensive noise-free simulation, a lung cancer and a prostate cancer dataset, this upper bound is tight: when only a single solution exists, SubMARine converges to it every time. When multiple solutions exist, our algorithm correctly recovers the uncertain relationships in 71% of cases. In addition to solving these two major challenges, we introduce some useful new concepts for and open research problems in the field of tumor phylogeny reconstruction. Specifically, we formalize the concept of a partial clone tree which provides a set of constraints on the solution set of clone trees; and provide a complete set of conditions under which a partial clone tree is valid. These conditions guarantee that all trees in the solution set satisfy the constraints implied by the partial clone tree.
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Affiliation(s)
- Linda K. Sundermann
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Jeff Wintersinger
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zurich, Zurich, Zurich, Switzerland
- Biomedical Informatics, University Hospital Zurich, Zurich, Zurich, Switzerland
| | - Jens Stoye
- Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany
| | - Quaid Morris
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York City, New York, United States of America
- * E-mail:
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34
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Ciccolella S, Soto Gomez M, Patterson MD, Della Vedova G, Hajirasouliha I, Bonizzoni P. gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinformatics 2020; 21:413. [PMID: 33297943 PMCID: PMC7725124 DOI: 10.1186/s12859-020-03736-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 09/03/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. RESULTS We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps . CONCLUSIONS gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.
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Affiliation(s)
- Simone Ciccolella
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.
| | - Mauricio Soto Gomez
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
| | - Murray D Patterson
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.,Georgia State University, Atlanta, GA, USA
| | - Gianluca Della Vedova
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York City, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, NewYork City, 10021, NY, USA
| | - Paola Bonizzoni
- Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy
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35
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Sadeqi Azer E, Haghir Ebrahimabadi M, Malikić S, Khardon R, Sahinalp SC. Tumor Phylogeny Topology Inference via Deep Learning. iScience 2020; 23:101655. [PMID: 33117968 PMCID: PMC7582044 DOI: 10.1016/j.isci.2020.101655] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/10/2020] [Accepted: 10/02/2020] [Indexed: 01/24/2023] Open
Abstract
Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents genotype calls of single cells. This problem is NP-hard, and as a result, existing approaches aim to solve relatively small instances of it through combinatorial optimization techniques or Bayesian inference. As expected, even when the goal is to infer basic topological features of the tumor phylogeny, rather than reconstructing the topology entirely, these approaches could be prohibitively slow. In this paper, we introduce fast deep learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible from a given genotype matrix. We also present a reinforcement learning approach for reconstructing the most likely tumor phylogeny. This preliminary work demonstrates that data-driven approaches can reconstruct key features of tumor evolution.
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Affiliation(s)
- Erfan Sadeqi Azer
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Mohammad Haghir Ebrahimabadi
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Salem Malikić
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Roni Khardon
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - S. Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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36
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Chen Z, Gong F, Wan L, Ma L. RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data. Bioinformatics 2020; 36:3299-3306. [PMID: 32159762 DOI: 10.1093/bioinformatics/btaa172] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 02/10/2020] [Accepted: 03/06/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging. RESULTS To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. AVAILABILITY AND IMPLEMENTATION RobustClone software is available at https://github.com/ucasdp/RobustClone. CONTACT lwan@amss.ac.cn or maliang@ioz.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziwei Chen
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fuzhou Gong
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Wan
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
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37
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Weber LL, Aguse N, Chia N, El-Kebir M. PhyDOSE: Design of follow-up single-cell sequencing experiments of tumors. PLoS Comput Biol 2020; 16:e1008240. [PMID: 33001973 PMCID: PMC7553321 DOI: 10.1371/journal.pcbi.1008240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 10/13/2020] [Accepted: 08/12/2020] [Indexed: 01/07/2023] Open
Abstract
The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.
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Affiliation(s)
- Leah L Weber
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nuraini Aguse
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Mohammed El-Kebir
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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38
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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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39
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Zaccaria S, Raphael BJ. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nat Commun 2020; 11:4301. [PMID: 32879317 PMCID: PMC7468132 DOI: 10.1038/s41467-020-17967-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022] Open
Abstract
Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity), an algorithm that infers allele- and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumor samples from 14 prostate and pancreas cancer patients, we identify subclonal CNAs and WGDs that are more plausible than previously published analyses and more consistent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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40
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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41
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Abstract
MOTIVATION Cancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing. RESULTS This article introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ∼280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intra-tumor heterogeneity. AVAILABILITY AND IMPLEMENTATION https://github.com/elkebir-group/ClonArch.
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Affiliation(s)
- Jiaqi Wu
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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42
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Adam G, Rampášek L, Safikhani Z, Smirnov P, Haibe-Kains B, Goldenberg A. Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis Oncol 2020; 4:19. [PMID: 32566759 PMCID: PMC7296033 DOI: 10.1038/s41698-020-0122-1] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 04/17/2020] [Indexed: 12/24/2022] Open
Abstract
Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.
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Affiliation(s)
- George Adam
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON Canada
- Department of Computer Science, University of Toronto, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
| | - Ladislav Rampášek
- Department of Computer Science, University of Toronto, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
- Ontario Institute for Cancer Research, Toronto, ON Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON Canada
- Department of Computer Science, University of Toronto, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
- Ontario Institute for Cancer Research, Toronto, ON Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON Canada
- Vector Institute, Toronto, ON Canada
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON Canada
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43
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Satas G, Zaccaria S, Mon G, Raphael BJ. SCARLET: Single-cell tumor phylogeny inference with copy-number constrained mutation losses. Cell Syst 2020; 10:323-332.e8. [PMID: 32864481 PMCID: PMC7451135 DOI: 10.1016/j.cels.2020.04.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A small number of somatic mutations drive the development of cancer, but all somatic mutations are markers of the evolutionary history of a tumor. Prominent methods to construct phylogenies from single-cell sequencing data use single-nucleotide variants (SNVs) as markers but fail to adequately account for copy-number aberrations (CNAs), which can overlap SNVs and result in SNV losses. Here, we introduce SCARLET, an algorithm that infers tumor phylogenies from single-cell DNA sequencing data while accounting for both CNA-driven loss of SNVs and sequencing errors. SCARLET outperforms existing methods on simulated data, with more accurate inference of the order in which mutations were acquired and the mutations present in individual cells. Using a single-cell dataset from a patient with colorectal cancer, SCARLET constructs a tumor phylogeny that is consistent with the observed CNAs and suggests an alternate origin for the patient's metastases. SCARLET is available at: github.com/raphael-group/scarlet.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Brown University, Providence, RI 02912
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Geoffrey Mon
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540
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44
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DiNardo Z, Tomlinson K, Ritz A, Oesper L. Distance measures for tumor evolutionary trees. Bioinformatics 2020; 36:2090-2097. [PMID: 31750900 PMCID: PMC7141873 DOI: 10.1093/bioinformatics/btz869] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/19/2019] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zach DiNardo
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
| | - Kiran Tomlinson
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, USA
| | - Layla Oesper
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
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45
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Lei H, Lyu B, Gertz EM, Schäffer AA, Shi X, Wu K, Li G, Xu L, Hou Y, Dean M, Schwartz R. Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data. J Comput Biol 2020; 27:565-598. [PMID: 32181683 DOI: 10.1089/cmb.2019.0302] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.
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Affiliation(s)
- Haoyun Lei
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Bochuan Lyu
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, Indiana
| | - E Michael Gertz
- National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, Maryland.,Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, Maryland
| | - Alejandro A Schäffer
- National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, Maryland.,Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, Maryland
| | | | - Kui Wu
- BGI-Shenzhen, Shenzhen, China
| | | | | | | | - Michael Dean
- Laboratory of Translational Genomics, Division of Cancer Epidemiology & Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, Maryland
| | - Russell Schwartz
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania
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46
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Przytycki PF, Singh M. Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations. Cell Syst 2020; 10:193-203.e4. [PMID: 32078798 PMCID: PMC7457951 DOI: 10.1016/j.cels.2020.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/04/2019] [Accepted: 01/22/2020] [Indexed: 01/23/2023]
Abstract
Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.
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Affiliation(s)
- Pawel F Przytycki
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
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47
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Husić E, Li X, Hujdurović A, Mehine M, Rizzi R, Mäkinen V, Milanič M, Tomescu AI. MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP. Bioinformatics 2019; 35:769-777. [PMID: 30101335 PMCID: PMC6394401 DOI: 10.1093/bioinformatics/bty683] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 07/08/2018] [Accepted: 08/07/2018] [Indexed: 12/31/2022] Open
Abstract
Motivation Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny. Results We study a natural problem formulation requiring to decompose the tumor samples into several subclones with the objective of forming a minimum perfect phylogeny. We propose an Integer Linear Programming formulation for it, and implement it into a method called MIPUP. We tested the ability of MIPUP and of four popular tools LICHeE, AncesTree, CITUP, Treeomics to reconstruct the tumor phylogeny. On simulated data, MIPUP shows up to a 34% improvement under the ancestor-descendant relations metric. On four real datasets, MIPUP’s reconstructions proved to be generally more faithful than those of LICHeE. Availability and implementation MIPUP is available at https://github.com/zhero9/MIPUP as open source. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Edin Husić
- Department of Mathematics, London School of Economics and Political Science, London, UK
| | - Xinyue Li
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland
| | - Ademir Hujdurović
- University of Primorska, UP IAM, Koper, Slovenia.,University of Primorska, UP FAMNIT, Koper, Slovenia
| | - Miika Mehine
- Genome-Scale Biology Research Program, Research Programs Unit, Department of Medical and Clinical Genetics, Faculty of Medicine, University of Helsinki, Medicum, Helsinki, Finland
| | - Romeo Rizzi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Veli Mäkinen
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland
| | - Martin Milanič
- University of Primorska, UP IAM, Koper, Slovenia.,University of Primorska, UP FAMNIT, Koper, Slovenia
| | - Alexandru I Tomescu
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland
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48
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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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49
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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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50
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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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