1
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Gao Y, Feder AF. Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601073. [PMID: 39005367 PMCID: PMC11244928 DOI: 10.1101/2024.06.27.601073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on the J 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( ŝ and the Sackin index) show interior performance to J 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.
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2
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Xiang Z, Liu Z, Dinh KN. Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data. Sci Rep 2024; 14:17699. [PMID: 39085295 PMCID: PMC11291923 DOI: 10.1038/s41598-024-67842-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.
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Affiliation(s)
- Zijin Xiang
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA
| | - Zhihan Liu
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA
| | - Khanh N Dinh
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA.
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3
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Liu Y, Edrisi M, Yan Z, A Ogilvie H, Nakhleh L. NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model. Algorithms Mol Biol 2024; 19:18. [PMID: 38685065 PMCID: PMC11059640 DOI: 10.1186/s13015-024-00264-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 03/27/2024] [Indexed: 05/02/2024] Open
Abstract
Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .
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Affiliation(s)
- Yushu Liu
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA.
| | - Mohammadamin Edrisi
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Zhi Yan
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Huw A Ogilvie
- Department of Genetics, University of Texas MD Anderson Cancer Center, TX, 77030, Houston, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
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4
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Schneider MP, Cullen AE, Pangonyte J, Skelton J, Major H, Van Oudenhove E, Garcia MJ, Chaves Urbano B, Piskorz AM, Brenton JD, Macintyre G, Markowetz F. scAbsolute: measuring single-cell ploidy and replication status. Genome Biol 2024; 25:62. [PMID: 38438920 PMCID: PMC10910719 DOI: 10.1186/s13059-024-03204-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024] Open
Abstract
Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute's capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.
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Affiliation(s)
- Michael P Schneider
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Amy E Cullen
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Justina Pangonyte
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Jason Skelton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Harvey Major
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Elke Van Oudenhove
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Maria J Garcia
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Anna M Piskorz
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - James D Brenton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Geoff Macintyre
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Florian Markowetz
- University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK.
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5
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Chen L, Ma R, Luo C, Xie Q, Ning X, Sun K, Meng F, Zhou M, Sun J. Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA. Transl Res 2023; 262:12-24. [PMID: 37499745 DOI: 10.1016/j.trsl.2023.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 06/20/2023] [Accepted: 07/23/2023] [Indexed: 07/29/2023]
Abstract
Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.
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Affiliation(s)
- Lu Chen
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China; School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Wenzhou P. R. China
| | - Rong Ma
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
| | - Chang Luo
- Department of Birth Control, Red Cross Central Hospital of Harbin, Harbin, P. R. China
| | - Qin Xie
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
| | - Xin Ning
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
| | - Kaidi Sun
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
| | - Fanling Meng
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, P. R. China.
| | - Meng Zhou
- School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Wenzhou P. R. China.
| | - Jie Sun
- School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Wenzhou P. R. China.
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6
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. PLoS Comput Biol 2023; 19:e1011590. [PMID: 37943952 PMCID: PMC10662746 DOI: 10.1371/journal.pcbi.1011590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/21/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. RESULTS We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
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7
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Watkins TBK, Colliver EC, Huska MR, Kaufmann TL, Lim EL, Duncan CB, Haase K, Van Loo P, Swanton C, McGranahan N, Schwarz RF. Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity. PLoS Comput Biol 2023; 19:e1011379. [PMID: 37871126 PMCID: PMC10621967 DOI: 10.1371/journal.pcbi.1011379] [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: 12/13/2022] [Revised: 11/02/2023] [Accepted: 07/22/2023] [Indexed: 10/25/2023] Open
Abstract
Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient's disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample phasing. We demonstrate Refphase's ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.
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Affiliation(s)
- Thomas B. K. Watkins
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | | | - Matthew R. Huska
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
| | - Tom L. Kaufmann
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
- Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Berlin, Germany
- BIFOLD—Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Emilia L. Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | - Cody B. Duncan
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kerstin Haase
- German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- Department of Pediatric Oncology and Hematology, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Peter Van Loo
- The Francis Crick Institute, London, United Kingdom
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
- Department of Medical Oncology, University College London Hospitals, London, United Kingdom
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, United Kingdom
| | - Roland F. Schwarz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
- BIFOLD—Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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8
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Wang Y, Zhang M, Shi J, Zhu Y, Wang X, Zhang S, Wang F. Cracking the pattern of tumor evolution based on single-cell copy number alterations. Brief Bioinform 2023; 24:bbad341. [PMID: 37791583 DOI: 10.1093/bib/bbad341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 09/02/2023] [Accepted: 09/11/2023] [Indexed: 10/05/2023] Open
Abstract
Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.
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Affiliation(s)
- Ying Wang
- Guangdong Cardiovascular Institute,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences and Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
| | - Min Zhang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
| | - Jian Shi
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University
| | - Yue Zhu
- Department of Breast Surgery at Harbin Medical University Cancer Hospital and the Medical Research Institute of Guangdong Provincial People's Hospital
| | - Xin Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
| | - Shaojun Zhang
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
| | - Fang Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
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9
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Liu Y, Li XC, Rashidi Mehrabadi F, Schäffer AA, Pratt D, Crawford DR, Malikić S, Molloy EK, Gopalan V, Mount SM, Ruppin E, Aldape KD, Sahinalp SC. Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models. Genome Res 2023; 33:1089-1100. [PMID: 37316351 PMCID: PMC10538489 DOI: 10.1101/gr.277608.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.
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Affiliation(s)
- Yuelin Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Xuan Cindy Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
| | - Farid Rashidi Mehrabadi
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Drew Pratt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - David R Crawford
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Salem Malikić
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Erin K Molloy
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Vishaka Gopalan
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Stephen M Mount
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
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10
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Qiu MZ, Chen Q, Zheng DY, Zhao Q, Wu QN, Zhou ZW, Yang LQ, Luo QY, Sun YT, Lai MY, Yuan SS, Wang FH, Luo HY, Wang F, Li YH, Zhang HZ, Xu RH. Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins. Cell Rep 2023; 42:112576. [PMID: 37285266 DOI: 10.1016/j.celrep.2023.112576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/02/2023] [Accepted: 05/15/2023] [Indexed: 06/09/2023] Open
Abstract
Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.
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Affiliation(s)
- Miao-Zhen Qiu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qingjian Chen
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; State Key Laboratory of Systems Medicine for Cancer, Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
| | - Dan-Yang Zheng
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Qi Zhao
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qi-Nian Wu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Zhi-Wei Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Li-Qiong Yang
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qiu-Yun Luo
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Yu-Ting Sun
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Ming-Yu Lai
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Sha-Sha Yuan
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Feng-Hua Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Hui-Yan Luo
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Feng Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Yu-Hong Li
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Hui-Zhong Zhang
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Rui-Hua Xu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China.
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11
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Lu B, Curtius K, Graham TA, Yang Z, Barnes CP. CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples. Genome Biol 2023; 24:144. [PMID: 37340508 DOI: 10.1186/s13059-023-02983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/08/2023] [Indexed: 06/22/2023] Open
Abstract
Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.
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Affiliation(s)
- Bingxin Lu
- Department of Cell and Developmental Biology, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
| | - Kit Curtius
- Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trevor A Graham
- Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Ziheng Yang
- Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
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12
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Liang J, Sun G, Pan X, Zhang M, Shen P, Zhu S, Zhao J, Zheng L, Zhao J, Chen Y, Yin X, Chen J, Hu X, Zeng Y, Chen J, Wang Y, Liu Z, Yao J, Su M, Huang R, Liao B, Wei Q, Li X, Zhou Q, Liu J, Shen Y, Liu Z, Chen N, Zeng H, Zhang X. Genomic and transcriptomic features between primary and paired metastatic fumarate hydratase-deficient renal cell carcinoma. Genome Med 2023; 15:31. [PMID: 37131267 PMCID: PMC10152735 DOI: 10.1186/s13073-023-01182-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/19/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Fumarate hydratase-deficient renal cell carcinoma (FH-RCC) is a rare highly aggressive subtype of kidney cancer for which the distinct genomic, transcriptomic, and evolutionary relationships between metastatic and primary lesions are still unclear. METHODS In this study, whole-exome, RNA-seq, and DNA methylation sequencing were performed on primary-metastatic paired specimens from 19 FH-RCC cases, including 23 primary and 35 matched metastatic lesions. Phylogenetic and clonal evolutionary analyses were used to investigate the evolutionary characteristics of FH-RCC. Transcriptomic analyses, immunohistochemistry, and multiple immunofluorescence experiments were performed to identify the tumor microenvironmental features of metastatic lesions. RESULTS Paired primary and metastatic lesions generally showed similar characteristics of tumor mutation burden, tumor neoantigen burden, microsatellite instability score, CNV burden, and genome instability index. Notably, we identified an FH-mutated founding MRCA (the most recent common ancestor) clone that dominated the early evolutionary trajectories in FH-RCC. Although both primary and metastatic lesions manifested high immunogenicity, metastatic lesions exhibited higher enrichment of T effector cells and immune-related chemokines, together with upregulation of PD-L1, TIGIT, and BTLA. In addition, we found that concurrent NF2 mutation may be associated with bone metastasis and upregulation of cell cycle signature in metastatic lesions. Furthermore, although in FH-RCC metastatic lesions in general shared similar CpG island methylator phenotype with primary lesions, we found metastatic lesions displaying hypomethylated chemokine and immune checkpoints related genomic loci. CONCLUSIONS Overall, our study demonstrated the genomic, epigenomic, and transcriptomic features of metastatic lesions in FH-RCC and revealed their early evolutionary trajectory. These results provided multi-omics evidence portraying the progression of FH-RCC.
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Affiliation(s)
- Jiayu Liang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Guangxi Sun
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiuyi Pan
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Mengni Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Pengfei Shen
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Sha Zhu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jinge Zhao
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Linmao Zheng
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Junjie Zhao
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxue Yin
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Junru Chen
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xu Hu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuhao Zeng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianhui Chen
- Department of Urology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yongquan Wang
- Department of Urology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Zhihong Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Minggang Su
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Rui Huang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Banghua Liao
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiang Li
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiao Zhou
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jiyan Liu
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yali Shen
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhenhua Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Ni Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Hao Zeng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Xingming Zhang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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13
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.10.536302. [PMID: 37090633 PMCID: PMC10120719 DOI: 10.1101/2023.04.10.536302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Motivation New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, NJ, USA
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14
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Delgado-Ortet M, Reinius MAV, McCague C, Bura V, Woitek R, Rundo L, Gill AB, Gehrung M, Ursprung S, Bolton H, Haldar K, Pathiraja P, Brenton JD, Crispin-Ortuzar M, Jimenez-Linan M, Escudero Sanchez L, Sala E. Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Front Oncol 2023; 13:1085874. [PMID: 36860310 PMCID: PMC9969130 DOI: 10.3389/fonc.2023.1085874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Background High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. Methods In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. Results Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. Conclusions We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.
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Affiliation(s)
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Marika A. V. Reinius
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Bolton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Krishnayan Haldar
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Pubudu Pathiraja
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
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15
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Yaeger R, Mezzadra R, Sinopoli J, Bian Y, Marasco M, Kaplun E, Gao Y, Zhao H, Paula ADC, Zhu Y, Perez AC, Chadalavada K, Tse E, Chowdhry S, Bowker S, Chang Q, Qeriqi B, Weigelt B, Nanjangud GJ, Berger MF, Der-Torossian H, Anderes K, Socci ND, Shia J, Riely GJ, Murciano-Goroff YR, Li BT, Christensen JG, Reis-Filho JS, Solit DB, de Stanchina E, Lowe SW, Rosen N, Misale S. Molecular Characterization of Acquired Resistance to KRASG12C-EGFR Inhibition in Colorectal Cancer. Cancer Discov 2023; 13:41-55. [PMID: 36355783 PMCID: PMC9827113 DOI: 10.1158/2159-8290.cd-22-0405] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/03/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022]
Abstract
With the combination of KRASG12C and EGFR inhibitors, KRAS is becoming a druggable target in colorectal cancer. However, secondary resistance limits its efficacy. Using cell lines, patient-derived xenografts, and patient samples, we detected a heterogeneous pattern of putative resistance alterations expected primarily to prevent inhibition of ERK signaling by drugs at progression. Serial analysis of patient blood samples on treatment demonstrates that most of these alterations are detected at a low frequency except for KRASG12C amplification, a recurrent resistance mechanism that rises in step with clinical progression. Upon drug withdrawal, resistant cells with KRASG12C amplification undergo oncogene-induced senescence, and progressing patients experience a rapid fall in levels of this alteration in circulating DNA. In this new state, drug resumption is ineffective as mTOR signaling is elevated. However, our work exposes a potential therapeutic vulnerability, whereby therapies that target the senescence response may overcome acquired resistance. SIGNIFICANCE Clinical resistance to KRASG12C-EGFR inhibition primarily prevents suppression of ERK signaling. Most resistance mechanisms are subclonal, whereas KRASG12C amplification rises over time to drive a higher portion of resistance. This recurrent resistance mechanism leads to oncogene-induced senescence upon drug withdrawal and creates a potential vulnerability to senolytic approaches. This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Rona Yaeger
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Riccardo Mezzadra
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jenna Sinopoli
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yu Bian
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michelangelo Marasco
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Esther Kaplun
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yijun Gao
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - HuiYong Zhao
- Antitumour Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Arnaud Da Cruz Paula
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yingjie Zhu
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Almudena Chaves Perez
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kalyani Chadalavada
- Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Edison Tse
- Boundless Bio, Inc., San Diego, California
| | | | - Sydney Bowker
- Antitumour Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Qing Chang
- Antitumour Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Besnik Qeriqi
- Antitumour Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gouri J. Nanjangud
- Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael F. Berger
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - Nicholas D. Socci
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gregory J. Riely
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Bob T. Li
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell Medical College, New York, New York
| | | | - Jorge S. Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David B. Solit
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elisa de Stanchina
- Antitumour Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Scott W. Lowe
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Howard Hughes Medical Institute, Chevy Chase, Maryland
| | - Neal Rosen
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York
- Center for Molecular-Based Therapy, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Sandra Misale
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York
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16
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Funnell T, O’Flanagan CH, Williams MJ, McPherson A, McKinney S, Kabeer F, Lee H, Salehi S, Vázquez-García I, Shi H, Leventhal E, Masud T, Eirew P, Yap D, Zhang AW, Lim JLP, Wang B, Brimhall J, Biele J, Ting J, Au V, Van Vliet M, Liu YF, Beatty S, Lai D, Pham J, Grewal D, Abrams D, Havasov E, Leung S, Bojilova V, Moore RA, Rusk N, Uhlitz F, Ceglia N, Weiner AC, Zaikova E, Douglas JM, Zamarin D, Weigelt B, Kim SH, Da Cruz Paula A, Reis-Filho JS, Martin SD, Li Y, Xu H, de Algara TR, Lee SR, Llanos VC, Huntsman DG, McAlpine JN, Shah SP, Aparicio S, Cannell IG, Casbolt H, Jauset C, Kovačević T, Mulvey CM, Nugent F, Ribes MP, Pearson I, Qosaj F, Sawicka K, Wild SA, Williams E, Laks E, Smith A, Lai D, Roth A, Balasubramanian S, Lee M, Bodenmiller B, Burger M, Kuett L, Tietscher S, Windhager J, Boyden ES, Alon S, Cui Y, Emenari A, Goodwin DR, Karagiannis ED, Sinha A, Wassie AT, Caldas C, Bruna A, Callari M, Greenwood W, Lerda G, Eyal-Lubling Y, Rueda OM, Shea A, Harris O, Becker R, Grimaldo F, Harris S, Vogl SL, Joyce JA, Watson SS, Tavare S, Dinh KN, Fisher E, Kunes R, Walton NA, Al Sa’d M, Chornay N, Dariush A, González-Solares EA, González-Fernández C, Yoldaş AK, Miller N, Zhuang X, Fan J, Lee H, Sepúlveda LA, Xia C, Zheng P, Shah SP, Aparicio S. Single-cell genomic variation induced by mutational processes in cancer. Nature 2022; 612:106-115. [PMID: 36289342 PMCID: PMC9712114 DOI: 10.1038/s41586-022-05249-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/17/2022] [Indexed: 12/15/2022]
Abstract
How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.
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Affiliation(s)
- Tyler Funnell
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ciara H. O’Flanagan
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Marc J. Williams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Andrew McPherson
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Steven McKinney
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Farhia Kabeer
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Hakwoo Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Sohrab Salehi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ignacio Vázquez-García
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Hongyu Shi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Emily Leventhal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Tehmina Masud
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Peter Eirew
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Damian Yap
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Allen W. Zhang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jamie L. P. Lim
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Beixi Wang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jazmine Brimhall
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Justina Biele
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jerome Ting
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Vinci Au
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Michael Van Vliet
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Yi Fei Liu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Sean Beatty
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Daniel Lai
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jenifer Pham
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Diljot Grewal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Douglas Abrams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Eliyahu Havasov
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samantha Leung
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Viktoria Bojilova
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Richard A. Moore
- grid.434706.20000 0004 0410 5424Michael Smith Genome Sciences Centre, Vancouver, British Columbia Canada
| | - Nicole Rusk
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Florian Uhlitz
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Nicholas Ceglia
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Adam C. Weiner
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Elena Zaikova
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - J. Maxwell Douglas
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Dmitriy Zamarin
- grid.51462.340000 0001 2171 9952GYN Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Britta Weigelt
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Sarah H. Kim
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Arnaud Da Cruz Paula
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Jorge S. Reis-Filho
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Spencer D. Martin
- grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Yangguang Li
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Hong Xu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Teresa Ruiz de Algara
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - So Ra Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Viviana Cerda Llanos
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - David G. Huntsman
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jessica N. McAlpine
- grid.17091.3e0000 0001 2288 9830Department of Gynecology and Obstetrics, University of British Columbia, Vancouver, British Columbia Canada
| | | | - Sohrab P. Shah
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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17
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Kaufmann TL, Petkovic M, Watkins TBK, Colliver EC, Laskina S, Thapa N, Minussi DC, Navin N, Swanton C, Van Loo P, Haase K, Tarabichi M, Schwarz RF. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution. Genome Biol 2022; 23:241. [PMID: 36376909 PMCID: PMC9661799 DOI: 10.1186/s13059-022-02794-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.
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Affiliation(s)
- Tom L Kaufmann
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany.
- Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
| | - Marina Petkovic
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
- Department of Biology, Humboldt University of Berlin, Unter den Linden 6, 10099, Berlin, Germany
- Division of Oncology and Hematology, Department of Pediatrics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | | | | | - Sofya Laskina
- Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany
| | - Nisha Thapa
- UCL Medical School, University College London, London, UK
| | - Darlan C Minussi
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles Swanton
- The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals, London, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kerstin Haase
- Division of Oncology and Hematology, Department of Pediatrics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK
- Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Roland F Schwarz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
- Institute for Computational Cancer Biology, Center for Integrated Oncology (CIO) and Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
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18
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Hui S, Nielsen R. SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances. BMC Bioinformatics 2022; 23:348. [PMID: 35986254 PMCID: PMC9392257 DOI: 10.1186/s12859-022-04890-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/11/2022] [Indexed: 11/24/2022] Open
Abstract
Background Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors. Results We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method. Conclusions SCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04890-w.
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19
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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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20
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van den Bosch T, Vermeulen L, Miedema DM. Quantitative models for the inference of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
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21
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Chaves-Urbano B, Hernando B, Garcia MJ, Macintyre G. CNpare: matching DNA copy number profiles. Bioinformatics 2022; 38:3638-3641. [PMID: 35640971 PMCID: PMC9272807 DOI: 10.1093/bioinformatics/btac371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 04/19/2022] [Accepted: 05/27/2022] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number. AVAILABILITY CNpare is available as an R package at https://github.com/macintyrelab/CNpare. All analysis performed in the manuscript can be reproduced via the code found at https://github.com/macintyrelab/CNpare_analyses. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Blas Chaves-Urbano
- Computational Oncology Group, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Barbara Hernando
- Computational Oncology Group, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Maria J Garcia
- Computational Oncology Group, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Geoff Macintyre
- Computational Oncology Group, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
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22
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Chen Q, Wu QN, Rong YM, Wang S, Zuo Z, Bai L, Zhang B, Yuan S, Zhao Q. Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol. Brief Bioinform 2022; 23:6590438. [DOI: 10.1093/bib/bbac175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/30/2022] [Accepted: 04/18/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Multiple primary tumor (MPT) is a special and rare cancer type, defined as more than two primary tumors presenting at the diagnosis in a single patient. The molecular characteristics and tumorigenesis of MPT remain unclear due to insufficient approaches. Here, we present MPTevol, a practical computational framework for comprehensively exploring the MPT from multiregion sequencing (MRS) experiments. To verify the utility of MPTevol, we performed whole-exome MRS for 33 samples of a rare patient with triple-primary tumors and three metastatic sites and systematically investigated clonal dynamics and metastatic routines. MPTevol assists in comparing genomic profiles across samples, detecting clonal evolutionary history and metastatic routines and quantifying the metastatic history. All triple-primary tumors were independent origins and their genomic characteristics were consistent with corresponding sporadic tumors, strongly supporting their independent tumorigenesis. We further showed two independent early monoclonal seeding events for the metastases in the ovary and uterus. We revealed that two ovarian metastases were disseminated from the same subclone of the primary tumor through undergoing whole-genome doubling processes, suggesting metastases-to-metastases seeding occurred when tumors had similar microenvironments. Surprisingly, according to the metastasis timing model of MPTevol, we found that primary tumors of about 0.058–0.124 cm diameter have been disseminating to distant organs, which is much earlier than conventional clinical views. We developed MPT-specialized analysis framework MPTevol and demonstrated its utility in explicitly resolving clonal evolutionary history and metastatic seeding routines with a rare MPT case. MPTevol is implemented in R and is available at https://github.com/qingjian1991/MPTevol under the GPL v3 license.
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Affiliation(s)
- Qingjian Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Qi-Nian Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P. R. China
| | - Yu-Ming Rong
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Shixiang Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
| | - Long Bai
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Bei Zhang
- Department of VIP Region, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
| | - Shuqiang Yuan
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
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23
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Bowes A, Tarabichi M, Pillay N, Van Loo P. Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers. J Pathol 2022; 257:466-478. [PMID: 35438189 PMCID: PMC9322001 DOI: 10.1002/path.5914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amy Bowes
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK
| | - Maxime Tarabichi
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Nischalan Pillay
- Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK.,Department of Histopathology, The Royal National Orthopaedic Hospital NHS Trust, London, UK
| | - Peter Van Loo
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Department of Genetics, The University of Texas MD Anderson Cancer Centre, Houston, USA.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Centre, Houston, USA
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24
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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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25
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Kader T, Zethoven M, Gorringe KL. Evaluating statistical approaches to define clonal origin of tumours using bulk DNA sequencing: context is everything. Genome Biol 2022; 23:43. [PMID: 35109903 PMCID: PMC8809045 DOI: 10.1186/s13059-022-02600-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/03/2022] [Indexed: 12/19/2022] Open
Abstract
Clonal analysis of tumour sequencing data enables the evaluation of the relationship of histologically distinct synchronous lesions, such as co-existing benign areas, and temporally distinct tumours, such as primary-recurrence comparisons. In this review, we summarise statistical approaches that are commonly employed to define tumour clonal relatedness using data from bulk DNA technologies. We discuss approaches using total copy number, allele-specific copy number and mutation data, and the relative genomic resolution required for analysis and summarise some of the current tools for inferring clonal relationships. We argue that the impact of the biological context is critical in selecting any particular approach, such as the relative genomic complexity of the lesions being compared, and we recommend considering this context before employing any method to a new dataset.
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Affiliation(s)
- Tanjina Kader
- , Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, 3000, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, 3010, Australia
| | - Magnus Zethoven
- , Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, 3000, Australia
| | - Kylie L Gorringe
- , Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, 3000, Australia.
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, 3010, Australia.
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26
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Chen HN, Shu Y, Liao F, Liao X, Zhang H, Qin Y, Wang Z, Luo M, Liu Q, Xue Z, Cao M, Zhang S, Zhang WH, Hou Q, Xia X, Luo H, Zhang Y, Yang L, Hu JK, Fu X, Liu B, Hu H, Huang C, Peng Y, Cheng W, Dai L, Yang L, Zhang W, Dong B, Li Y, Wei Y, Xu H, Zhou ZG. Genomic evolution and diverse models of systemic metastases in colorectal cancer. Gut 2022; 71:322-332. [PMID: 33632712 PMCID: PMC8762014 DOI: 10.1136/gutjnl-2020-323703] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The systemic spread of colorectal cancer (CRC) is dominated by the portal system and exhibits diverse patterns of metastasis without systematical genomic investigation. Here, we evaluated the genomic evolution of CRC with multiorgan metastases using multiregion sequencing. DESIGN Whole-exome sequencing was performed on multiple regions (n=74) of matched primary tumour, adjacent non-cancerous mucosa, liver metastasis and lung metastasis from six patients with CRC. Phylogenetic reconstruction and evolutionary analyses were used to investigate the metastatic seeding pattern and clonal origin. Recurrent driver gene mutations were analysed across patients and validated in two independent cohorts. Metastatic assays were performed to examine the effect of the novel driver gene on the malignant behaviour of CRC cells. RESULTS Based on the migration patterns and clonal origins, three models were revealed (sequential, branch-off and diaspora), which not only supported the anatomic assumption that CRC cells spread to lung after clonally expanding in the liver, but also illustrated the direct seeding of extrahepatic metastases from primary tumours independently. Unlike other cancer types, polyphyletic seeding occurs in CRC, which may result in late metastases with intermetastatic driver gene heterogeneity. In cases with rapid dissemination, we found recurrent trunk loss-of-function mutations in ZFP36L2, which is enriched in metastatic CRC and associated with poor overall survival. CRISPR/Cas9-mediated knockout of ZFP36L2 enhances the metastatic potential of CRC cells. CONCLUSION Our results provide genomic evidence for metastatic evolution and indicate that biopsy/sequencing of metastases may be considered for patients with CRC with multiorgan or late postoperative metastasis.
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Affiliation(s)
- Hai-Ning Chen
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yang Shu
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fei Liao
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xue Liao
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongying Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhu Wang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Maochao Luo
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiuluo Liu
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhinan Xue
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Minyuan Cao
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shouyue Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei-Han Zhang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qianqian Hou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xuyang Xia
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Han Luo
- Department of Thyroid and Parathyroid Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yan Zhang
- Department of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lie Yang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jian-Kun Hu
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xianghui Fu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bo Liu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongbo Hu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yong Peng
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Cheng
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lunzhi Dai
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital Central South University, Changsha, Hunan, China
| | - Biao Dong
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuan Li
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuquan Wei
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China .,Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zong-Guang Zhou
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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27
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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28
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Schmelz K, Toedling J, Huska M, Cwikla MC, Kruetzfeldt LM, Proba J, Ambros PF, Ambros IM, Boral S, Lodrini M, Chen CY, Burkert M, Guergen D, Szymansky A, Astrahantseff K, Kuenkele A, Haase K, Fischer M, Deubzer HE, Hertwig F, Hundsdoerfer P, Henssen AG, Schwarz RF, Schulte JH, Eggert A. Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions. Nat Commun 2021; 12:6804. [PMID: 34815394 PMCID: PMC8611017 DOI: 10.1038/s41467-021-26870-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 10/18/2021] [Indexed: 01/12/2023] Open
Abstract
Intratumour heterogeneity is a major cause of treatment failure in cancer. We present in-depth analyses combining transcriptomic and genomic profiling with ultra-deep targeted sequencing of multiregional biopsies in 10 patients with neuroblastoma, a devastating childhood tumour. We observe high spatial and temporal heterogeneity in somatic mutations and somatic copy-number alterations which are reflected on the transcriptomic level. Mutations in some druggable target genes including ALK and FGFR1 are heterogeneous at diagnosis and/or relapse, raising the issue whether current target prioritization and molecular risk stratification procedures in single biopsies are sufficiently reliable for therapy decisions. The genetic heterogeneity in gene mutations and chromosome aberrations observed in deep analyses from patient courses suggest clonal evolution before treatment and under treatment pressure, and support early emergence of metastatic clones and ongoing chromosomal instability during disease evolution. We report continuous clonal evolution on mutational and copy number levels in neuroblastoma, and detail its implications for therapy selection, risk stratification and therapy resistance.
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Affiliation(s)
- Karin Schmelz
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Joern Toedling
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matt Huska
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Maja C Cwikla
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | | | - Jutta Proba
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Peter F Ambros
- Children's Cancer Research Institute, St. Anna Kinderkrebsforschung, 1090, Vienna, Austria
| | - Inge M Ambros
- Children's Cancer Research Institute, St. Anna Kinderkrebsforschung, 1090, Vienna, Austria
| | - Sengül Boral
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marco Lodrini
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Celine Y Chen
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center (ECRC) of the Charité and Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Martin Burkert
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Dennis Guergen
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
| | | | | | - Annette Kuenkele
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Kerstin Haase
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Matthias Fischer
- Department of Experimental Pediatric Oncology, Medical Faculty, University Children's Hospital of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Hedwig E Deubzer
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
- Experimental and Clinical Research Center (ECRC) of the Charité and Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Falk Hertwig
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- The German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Hundsdoerfer
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- Helios Klinikum Berlin-Buch, Berlin, Germany
| | - Anton G Henssen
- Charité-Universitätsmedizin Berlin, Berlin, Germany.
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany.
- The German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- Experimental and Clinical Research Center (ECRC) of the Charité and Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Berlin Institute of Health (BIH), Berlin, Germany.
| | - Roland F Schwarz
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
| | - Johannes H Schulte
- Charité-Universitätsmedizin Berlin, Berlin, Germany.
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany.
- The German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Berlin Institute of Health (BIH), Berlin, Germany.
| | - Angelika Eggert
- Charité-Universitätsmedizin Berlin, Berlin, Germany.
- The German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany.
- The German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Berlin Institute of Health (BIH), Berlin, Germany.
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29
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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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30
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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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31
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Montemurro M, Grassi E, Pizzino CG, Bertotti A, Ficarra E, Urgese G. PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity. BMC Bioinformatics 2021; 22:360. [PMID: 34217219 PMCID: PMC8254361 DOI: 10.1186/s12859-021-04277-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. RESULTS We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. CONCLUSIONS PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.
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Affiliation(s)
- Marilisa Montemurro
- Department of Control and Computer Science, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
| | - Elena Grassi
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Carmelo Gabriele Pizzino
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Andrea Bertotti
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Elisa Ficarra
- Enzo Ferrari Engineering Dept, University of Modena and Reggio Emilia, Via Vivarelli 10/1, 41125, Modena, Italy
| | - Gianvito Urgese
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy
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32
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Clonal relationship and directionality of progression of synchronous endometrial and ovarian carcinomas in patients with DNA mismatch repair-deficiency associated syndromes. Mod Pathol 2021; 34:994-1007. [PMID: 33328602 PMCID: PMC8076061 DOI: 10.1038/s41379-020-00721-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 01/01/2023]
Abstract
Sporadic synchronous endometrial (ECs) and ovarian cancers (OCs), although clinically considered to be independent primaries, have been shown to be clonally related and likely constitute metastases from each other. We sought to define whether synchronous ECs/OCs in patients with DNA mismatch repair (MMR)-deficiency syndromes would be clonally related. We subjected synchronous ECs/OCs from four patients (LS3-LS6) with clinically confirmed Lynch syndrome (LS) and one patient with constitutional mismatch repair-deficiency syndrome (CMMRD) to massively parallel sequencing targeting 468 cancer-related genes. Somatic mutations, copy number alterations (CNAs), clonal relatedness and clonal decomposition analyses were performed using previously described bioinformatics methods. All synchronous ECs/OCs analyzed were considered independent primaries based on clinicopathologic criteria. Sequencing analysis revealed that the ECs/OCs of three cases (LS2-CMMRD, L3, L4) harbored similar repertoires of somatic mutations and CNAs and were clonally related. In these three cases, a subset of subclonal mutations in the EC became clonal in the OC, suggesting that the EC was likely the substrate from which the OC developed. LS5's EC/OC had distinct mutational profiles but shared specific CNAs. In contrast, LS6's EC/OC harbored distinct somatic mutations and lacked CNAs, consistent with each tumor constituting an independent primary lesion. In LS5 and LS6, PTEN mutations and PTEN loss of protein expression were found to be restricted to the EC. Finally, re-analysis of sequencing data of sporadic synchronous ECs/OCs supported the observations made in the current study that the directionality of progression is likely from the endometrium to the ovary. In conclusion, contrary to sporadic synchronous ECs/OCs, which are almost invariably clonally related, ECs/OCs simultaneously involving the uterus and ovary in LS patients may represent distinct primary tumors. A subset of MMR-deficiency syndrome-related synchronous ECs/OCs, however, may originate from a single primary tumor at variance with their clinical diagnosis, with the endometrium being the likeliest site of origin.
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33
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Vergara IA, Mintoff CP, Sandhu S, McIntosh L, Young RJ, Wong SQ, Colebatch A, Cameron DL, Kwon JL, Wolfe R, Peng A, Ellul J, Dou X, Fedele C, Boyle S, Arnau GM, Raleigh J, Hatzimihalis A, Szeto P, Mooi J, Widmer DS, Cheng PF, Amann V, Dummer R, Hayward N, Wilmott J, Scolyer RA, Cho RJ, Bowtell D, Thorne H, Alsop K, Cordner S, Woodford N, Leditschke J, O'Brien P, Dawson SJ, McArthur GA, Mann GJ, Levesque MP, Papenfuss AT, Shackleton M. Evolution of late-stage metastatic melanoma is dominated by aneuploidy and whole genome doubling. Nat Commun 2021; 12:1434. [PMID: 33664264 PMCID: PMC7933255 DOI: 10.1038/s41467-021-21576-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 01/26/2021] [Indexed: 12/24/2022] Open
Abstract
Although melanoma is initiated by acquisition of point mutations and limited focal copy number alterations in melanocytes-of-origin, the nature of genetic changes that characterise lethal metastatic disease is poorly understood. Here, we analyze the evolution of human melanoma progressing from early to late disease in 13 patients by sampling their tumours at multiple sites and times. Whole exome and genome sequencing data from 88 tumour samples reveals only limited gain of point mutations generally, with net mutational loss in some metastases. In contrast, melanoma evolution is dominated by whole genome doubling and large-scale aneuploidy, in which widespread loss of heterozygosity sculpts the burden of point mutations, neoantigens and structural variants even in treatment-naïve and primary cutaneous melanomas in some patients. These results imply that dysregulation of genomic integrity is a key driver of selective clonal advantage during melanoma progression.
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Affiliation(s)
- Ismael A Vergara
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Melanoma Institute of Australia, Sydney, Australia
| | | | | | - Lachlan McIntosh
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
- Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| | | | - Stephen Q Wong
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | - Daniel L Cameron
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Julia Lai Kwon
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Rory Wolfe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Angela Peng
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Jason Ellul
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Xuelin Dou
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Clare Fedele
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Samantha Boyle
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | | | | | - Pacman Szeto
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Jennifer Mooi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Daniel S Widmer
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Phil F Cheng
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Valerie Amann
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Reinhard Dummer
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Nicholas Hayward
- Melanoma Institute of Australia, Sydney, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Richard A Scolyer
- Melanoma Institute of Australia, Sydney, Australia
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, Australia
- Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Raymond J Cho
- Department of Dermatology, University of California, San Francisco, CA, USA
| | - David Bowtell
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Heather Thorne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Kathryn Alsop
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephen Cordner
- The Victorian Institute of Forensic Medicine, Melbourne, Australia
| | - Noel Woodford
- The Victorian Institute of Forensic Medicine, Melbourne, Australia
| | - Jodie Leditschke
- The Victorian Institute of Forensic Medicine, Melbourne, Australia
| | - Patricia O'Brien
- The Victorian Institute of Forensic Medicine, Melbourne, Australia
| | - Sarah-Jane Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Centre of Cancer Research, The University of Melbourne, Parkville, VIC, Australia
| | - Grant A McArthur
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham J Mann
- Melanoma Institute of Australia, Sydney, Australia
- Centre for Cancer Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Mitchell P Levesque
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Anthony T Papenfuss
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia.
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia.
| | - Mark Shackleton
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia.
- Department of Oncology, Alfred Health, Melbourne, Australia.
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Wang F, Wang Q, Mohanty V, Liang S, Dou J, Han J, Minussi DC, Gao R, Ding L, Navin N, Chen K. MEDALT: single-cell copy number lineage tracing enabling gene discovery. Genome Biol 2021; 22:70. [PMID: 33622385 PMCID: PMC7901082 DOI: 10.1186/s13059-021-02291-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 02/09/2021] [Indexed: 12/20/2022] Open
Abstract
We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT .
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Affiliation(s)
- Fang Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA
- Present Address: Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qihan Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA
| | - Shaoheng Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA
| | - Jincheng Han
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Ruli Gao
- Department of Cardiovascular Sciences, Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, USA
| | - Li Ding
- Department of Medicine, McDonnell Genome Institute Washington University School of Medicine, St. Louis, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA.
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35
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Ali SI, Sheikh WM, Rather MA, Venkatesalu V, Muzamil Bashir S, Nabi SU. Medicinal plants: Treasure for antiviral drug discovery. Phytother Res 2021; 35:3447-3483. [PMID: 33590931 PMCID: PMC8013762 DOI: 10.1002/ptr.7039] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 01/04/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022]
Abstract
The pandemic of viral diseases like novel coronavirus (2019-nCoV) prompted the scientific world to examine antiviral bioactive compounds rather than nucleic acid analogous, protease inhibitors, or other toxic synthetic molecules. The emerging viral infections significantly associated with 2019-nCoV have challenged humanity's survival. Further, there is a constant emergence of new resistant viral strains that demand novel antiviral agents with fewer side effects and cell toxicity. Despite significant progress made in immunization and regenerative medicine, numerous viruses still lack prophylactic vaccines and specific antiviral treatments that are so often influenced by the generation of viral escape mutants. Of importance, medicinal herbs offer a wide variety of therapeutic antiviral chemotypes that can inhibit viral replication by preventing viral adsorption, adhering to cell receptors, inhibiting virus penetration in the host cell, and competing for pathways of activation of intracellular signals. The present review will comprehensively summarize the promising antiviral activities of medicinal plants and their bioactive molecules. Furthermore, it will elucidate their mechanism of action and possible implications in the treatment/prevention of viral diseases even when their mechanism of action is not fully understood, which could serve as the base for the future development of novel or complementary antiviral treatments.
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Affiliation(s)
- Sofi Imtiyaz Ali
- Biochemistry & Molecular Biology Lab, Division of veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India
| | - Wajid Mohammad Sheikh
- Biochemistry & Molecular Biology Lab, Division of veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India
| | - Muzafar Ahmad Rather
- Biochemistry & Molecular Biology Lab, Division of veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India
| | | | - Showkeen Muzamil Bashir
- Biochemistry & Molecular Biology Lab, Division of veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India
| | - Showkat Ul Nabi
- Large Animal Diagnostic Laboratory, Department of Clinical Veterinary Medicine, Ethics & Jurisprudence, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India
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36
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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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37
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Tarabichi M, Salcedo A, Deshwar AG, Leathlobhair MN, Wintersinger J, Wedge DC, Loo PV, Morris QD, Boutros PC. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 2021; 18:144-155. [PMID: 33398189 PMCID: PMC7867630 DOI: 10.1038/s41592-020-01013-2] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 11/09/2020] [Indexed: 01/28/2023]
Abstract
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
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Affiliation(s)
- Maxime Tarabichi
- The Francis Crick Institute, London, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Adriana Salcedo
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Amit G. Deshwar
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Canada
| | - Máire Ni Leathlobhair
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom
| | - Jeff Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - David C. Wedge
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford, United Kingdom
- Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | | | - Quaid D. Morris
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute, Toronto, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York
- Donnelly Centre, University of Toronto, Toronto, Canada
| | - Paul C. Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Vector Institute, Toronto, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles
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38
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Watkins TBK, Lim EL, Petkovic M, Elizalde S, Birkbak NJ, Wilson GA, Moore DA, Grönroos E, Rowan A, Dewhurst SM, Demeulemeester J, Dentro SC, Horswell S, Au L, Haase K, Escudero M, Rosenthal R, Bakir MA, Xu H, Litchfield K, Lu WT, Mourikis TP, Dietzen M, Spain L, Cresswell GD, Biswas D, Lamy P, Nordentoft I, Harbst K, Castro-Giner F, Yates LR, Caramia F, Jaulin F, Vicier C, Tomlinson IPM, Brastianos PK, Cho RJ, Bastian BC, Dyrskjøt L, Jönsson GB, Savas P, Loi S, Campbell PJ, Andre F, Luscombe NM, Steeghs N, Tjan-Heijnen VCG, Szallasi Z, Turajlic S, Jamal-Hanjani M, Van Loo P, Bakhoum SF, Schwarz RF, McGranahan N, Swanton C. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature 2020; 587:126-132. [PMID: 32879494 PMCID: PMC7611706 DOI: 10.1038/s41586-020-2698-6] [Citation(s) in RCA: 195] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Chromosomal instability in cancer consists of dynamic changes to the number and structure of chromosomes1,2. The resulting diversity in somatic copy number alterations (SCNAs) may provide the variation necessary for tumour evolution1,3,4. Here we use multi-sample phasing and SCNA analysis of 1,421 samples from 394 tumours across 22 tumour types to show that continuous chromosomal instability results in pervasive SCNA heterogeneity. Parallel evolutionary events, which cause disruption in the same genes (such as BCL9, MCL1, ARNT (also known as HIF1B), TERT and MYC) within separate subclones, were present in 37% of tumours. Most recurrent losses probably occurred before whole-genome doubling, that was found as a clonal event in 49% of tumours. However, loss of heterozygosity at the human leukocyte antigen (HLA) locus and loss of chromosome 8p to a single haploid copy recurred at substantial subclonal frequencies, even in tumours with whole-genome doubling, indicating ongoing karyotype remodelling. Focal amplifications that affected chromosomes 1q21 (which encompasses BCL9, MCL1 and ARNT), 5p15.33 (TERT), 11q13.3 (CCND1), 19q12 (CCNE1) and 8q24.1 (MYC) were frequently subclonal yet appeared to be clonal within single samples. Analysis of an independent series of 1,024 metastatic samples revealed that 13 focal SCNAs were enriched in metastatic samples, including gains in chromosome 8q24.1 (encompassing MYC) in clear cell renal cell carcinoma and chromosome 11q13.3 (encompassing CCND1) in HER2+ breast cancer. Chromosomal instability may enable the continuous selection of SCNAs, which are established as ordered events that often occur in parallel, throughout tumour evolution.
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Affiliation(s)
- Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Emilia L Lim
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Marina Petkovic
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Sergi Elizalde
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Nicolai J Birkbak
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Eva Grönroos
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Andrew Rowan
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Sally M Dewhurst
- Laboratory for Cell Biology and Genetics, Rockefeller University, New York, NY, USA
| | - Jonas Demeulemeester
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Stefan C Dentro
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
- Oxford Big Data Institute, University of Oxford, Oxford, UK
- Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Stuart Horswell
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - Lewis Au
- Renal and Skin Units, The Royal Marsden Hospital NHS Foundation Trust, London, UK
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Kerstin Haase
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Mickael Escudero
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - Rachel Rosenthal
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Hang Xu
- Stanford Cancer Institute, Stanford, CA, USA
| | - Kevin Litchfield
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Wei Ting Lu
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thanos P Mourikis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, University College London Cancer Institute, University College London, London, UK
| | - Michelle Dietzen
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, University College London Cancer Institute, University College London, London, UK
| | - Lavinia Spain
- Renal and Skin Units, The Royal Marsden Hospital NHS Foundation Trust, London, UK
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - George D Cresswell
- Bioinformatics and Computational Biology Laboratory, The Francis Crick Institute, London, UK
| | - Dhruva Biswas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Philippe Lamy
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Iver Nordentoft
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Katja Harbst
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
- Lund University Cancer Centre, Lund University, Lund, Sweden
| | - Francesc Castro-Giner
- Department of Biomedicine, Cancer Metastasis Laboratory, University of Basel and University Hospital Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Lucy R Yates
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Franco Caramia
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Cécile Vicier
- Department of Medical Oncology, Institut Paoli-Calmettes, Aix-Marseille University, Marseille, France
| | - Ian P M Tomlinson
- Edinburgh Cancer Research Centre, IGMM, University of Edinburgh, Edinburgh, UK
| | - Priscilla K Brastianos
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Raymond J Cho
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Boris C Bastian
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Lars Dyrskjøt
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Göran B Jönsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
- Lund University Cancer Centre, Lund University, Lund, Sweden
| | - Peter Savas
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Sherene Loi
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Fabrice Andre
- INSERM U981, PRISM Institute, Gustave Roussy, Villejuif, France
- Department of Medical Oncology, Gustave Roussy, Villejuif, France
- Medical School, Université Paris Saclay, Kremlin Bicetre, France
| | - Nicholas M Luscombe
- Bioinformatics and Computational Biology Laboratory, The Francis Crick Institute, London, UK
- UCL Genetics Institute, Department of Genetics, Evolution & Environment, University College London, London, UK
- Okinawa Institute of Science & Technology, Okinawa, Japan
| | - Neeltje Steeghs
- Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Vivianne C G Tjan-Heijnen
- Department of Medical Oncology, School of GROW, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Zoltan Szallasi
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- 2nd Department of Pathology, SE-NAP Brain Metastasis Research Group, Semmelweis University, Budapest, Hungary
| | - Samra Turajlic
- Renal and Skin Units, The Royal Marsden Hospital NHS Foundation Trust, London, UK
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals, London, UK
| | - Peter Van Loo
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Samuel F Bakhoum
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roland F Schwarz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
- German Cancer Consortium (DKTK), partner site Berlin, Berlin, Germany.
- German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, University College London Cancer Institute, University College London, London, UK.
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Department of Medical Oncology, University College London Hospitals, London, UK.
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39
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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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40
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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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41
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Mallory XF, Edrisi M, Navin N, Nakhleh L. Methods for copy number aberration detection from single-cell DNA-sequencing data. Genome Biol 2020; 21:208. [PMID: 32807205 PMCID: PMC7433197 DOI: 10.1186/s13059-020-02119-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.
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Affiliation(s)
- Xian F. Mallory
- Department of Computer Science, Rice University, Houston, TX USA
- Department of Computer Science, Florida State University, Tallahassee, FL USA
| | | | - Nicholas Navin
- Department of Genetics, the University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, TX USA
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42
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Abstract
MOTIVATION Copy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, because different CNAs may overlap with one another on the genome. A recent popular model for copy number evolution is the copy number distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. In the CND, all events contribute equally; however, it is well known that rates of CNAs vary by length, genomic position and type (amplification versus deletion). RESULTS We introduce a weighted CND that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted CND as well as the associated transformation. This algorithm is based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We show that the weighted CND improves phylogenetic reconstruction on simulated data where CNAs occur with varying probabilities, aids in the derivation of phylogenies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large pan-cancer dataset. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/raphael-group/WCND. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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43
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Loveday C, Litchfield K, Proszek PZ, Cornish AJ, Santo F, Levy M, Macintyre G, Holryod A, Broderick P, Dudakia D, Benton B, Bakir MA, Hiley C, Grist E, Swanton C, Huddart R, Powles T, Chowdhury S, Shipley J, O'Connor S, Brenton JD, Reid A, de Castro DG, Houlston RS, Turnbull C. Genomic landscape of platinum resistant and sensitive testicular cancers. Nat Commun 2020; 11:2189. [PMID: 32366847 PMCID: PMC7198558 DOI: 10.1038/s41467-020-15768-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 03/23/2020] [Indexed: 12/11/2022] Open
Abstract
While most testicular germ cell tumours (TGCTs) exhibit exquisite sensitivity to platinum chemotherapy, ~10% are platinum resistant. To gain insight into the underlying mechanisms, we undertake whole exome sequencing and copy number analysis in 40 tumours from 26 cases with platinum-resistant TGCT, and combine this with published genomic data on an additional 624 TGCTs. We integrate analyses for driver mutations, mutational burden, global, arm-level and focal copy number (CN) events, and SNV and CN signatures. Albeit preliminary and observational in nature, these analyses provide support for a possible mechanistic link between early driver mutations in RAS and KIT and the widespread copy number events by which TGCT is characterised.
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Affiliation(s)
- Chey Loveday
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Kevin Litchfield
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Paula Z Proszek
- The Centre for Molecular Pathology, The Royal Marsden NHS Trust, Sutton, London, UK
| | - Alex J Cornish
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Flavia Santo
- The Centre for Molecular Pathology, The Royal Marsden NHS Trust, Sutton, London, UK
| | - Max Levy
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Geoff Macintyre
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Amy Holryod
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Peter Broderick
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Darshna Dudakia
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Barbara Benton
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Crispin Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emily Grist
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK
- Translational Cancer Therapeutics Laboratory, UCL Cancer Institute, London, UK
| | - Robert Huddart
- Academic Radiotherapy Unit, Institute of Cancer Research, London, UK
| | - Tom Powles
- Barts Cancer Institute, Queen Mary University, London, UK
| | - Simon Chowdhury
- Department of Oncology, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Janet Shipley
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
- Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
| | - Simon O'Connor
- The Centre for Molecular Pathology, The Royal Marsden NHS Trust, Sutton, London, UK
- Addenbrooke's Hospital, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Alison Reid
- Academic Uro-oncology Unit, The Royal Marsden NHS Foundation Trust, Sutton, London, UK
| | | | - Richard S Houlston
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Clare Turnbull
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK.
- William Harvey Research Institute, Queen Mary University, London, UK.
- Guys and St Thomas' NHS Foundation Trust, Great Maze Pond, London, UK.
- Public Health England, National Cancer Registration and Analysis Service, London, UK.
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44
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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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45
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Abstract
BACKGROUND During cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by 1 or -1, even though these events can affect large portions of a chromosome. RESULTS We propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor 2 approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called cnp2cnp. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by cnp2cnp and compare it against two other alternatives, namely the MEDICC distance and the Euclidean distance. CONCLUSIONS The experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the MEDICC distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the MEDICC approach should preferred over the Euclidean distance.
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Affiliation(s)
| | - Manuel Lafond
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada.
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46
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Pareja F, Brown DN, Lee JY, Da Cruz Paula A, Selenica P, Bi R, Geyer FC, Gazzo A, da Silva EM, Vahdatinia M, Stylianou AA, Ferrando L, Wen HY, Hicks JB, Weigelt B, Reis-Filho JS. Whole-Exome Sequencing Analysis of the Progression from Non-Low-Grade Ductal Carcinoma In Situ to Invasive Ductal Carcinoma. Clin Cancer Res 2020; 26:3682-3693. [PMID: 32220886 DOI: 10.1158/1078-0432.ccr-19-2563] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 01/28/2020] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE Ductal carcinoma in situ (DCIS) is a nonobligate precursor of invasive breast cancer. Here, we sought to investigate the level of intralesion genetic heterogeneity in DCIS and the patterns of clonal architecture changes in the progression from DCIS to invasive disease. EXPERIMENTAL DESIGN Synchronous DCIS (n = 27) and invasive ductal carcinomas of no special type (IDC-NSTs; n = 26) from 25 patients, and pure DCIS (n = 7) from 7 patients were microdissected separately and subjected to high-depth whole-exome (n = 56) or massively parallel sequencing targeting ≥410 key cancer-related genes (n = 4). Somatic genetic alterations, mutational signatures, clonal composition, and phylogenetic analyses were defined using validated computational methods. RESULTS DCIS revealed genetic alterations similar to those of synchronously diagnosed IDC-NSTs and of non-related IDC-NSTs from The Cancer Genome Atlas (TCGA), whereas pure DCIS lacked PIK3CA mutations. Clonal decomposition and phylogenetic analyses based on somatic mutations and copy number alterations revealed that the mechanisms of progression of DCIS to invasive carcinoma are diverse, and that clonal selection might have constituted the mechanism of progression from DCIS to invasive disease in 28% (7/25) of patients. DCIS displaying a pattern of clonal selection in the progression to invasive cancer harbored higher levels of intralesion genetic heterogeneity than DCIS where no clonal selection was observed. CONCLUSIONS Intralesion genetic heterogeneity is a common feature in DCIS synchronously diagnosed with IDC-NST. DCIS is a nonobligate precursor of IDC-NST, whose mechanisms of progression to invasive breast cancer are diverse and vary from case to case.
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Affiliation(s)
- Fresia Pareja
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David N Brown
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ju Youn Lee
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Arnaud Da Cruz Paula
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pier Selenica
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Rui Bi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Felipe C Geyer
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrea Gazzo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Edaise M da Silva
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mahsa Vahdatinia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anthe A Stylianou
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lorenzo Ferrando
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Internal Medicine, University of Genoa, Genova, Italy
| | - Hannah Y Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James B Hicks
- Department of Biological Sciences, University of Southern California, Los Angeles, California
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Jorge S Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York. .,Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
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47
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Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CSO, Aparicio S, Baaijens J, Balvert M, Barbanson BD, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BP, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Rączkowska A, Reinders M, Ridder JD, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21:31. [PMID: 32033589 PMCID: PMC7007675 DOI: 10.1186/s13059-020-1926-6] [Citation(s) in RCA: 565] [Impact Index Per Article: 141.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/02/2020] [Indexed: 02/08/2023] Open
Abstract
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
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Affiliation(s)
- David Lähnemann
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Department of Paediatric Oncology, Haematology and Immunology, Medical Faculty, Heinrich Heine University, University Hospital, Düsseldorf, Germany
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johannes Köster
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - Ewa Szczurek
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Davis J. McCarthy
- Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia
- Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD USA
| | - Mark D. Robinson
- Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zürich, Zürich, Switzerland
| | - Catalina A. Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- The Alan Turing Institute, British Library, London, UK
| | - Kieran R. Campbell
- Department of Statistics, University of British Columbia, Vancouver, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Data Science Institute, University of British Columbia, Vancouver, Canada
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Jasmijn Baaijens
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Marleen Balvert
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
| | - Buys de Barbanson
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Antonio Cappuccio
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - Giacomo Corleone
- Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK
| | - Bas E. Dutilh
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maria Florescu
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rens Holmer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thamar Jessurun Lobo
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Emma M. Keizer
- Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Indu Khatri
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Szymon M. Kielbasa
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan O. Korbel
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Alexey M. Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Tzu-Hao Kuo
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Boudewijn P.F. Lelieveldt
- PRB lab, Delft University of Technology, Delft, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ion I. Mandoiu
- Computer Science & Engineering Department, University of Connecticut, Storrs, USA
| | - John C. Marioni
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Felix Mölder
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Amir Niknejad
- Computation molecular design, Zuse Institute Berlin, Berlin, Germany
- Mathematics Department, Mount Saint Vincent, New York, USA
| | - Alicja Rączkowska
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Marcel Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection Research, Würzburg, Germany
| | - Antonios Somarakis
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center–DKFZ, Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Huan Yang
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research–LACDR–Leiden University, Leiden, The Netherlands
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alice C. McHardy
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | - Sohrab P. Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Alexander Schönhuth
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
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48
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Wang M, Yu T, Liu J, Chen L, Stromberg AJ, Villano JL, Arnold SM, Liu C, Wang C. A probabilistic method for leveraging functional annotations to enhance estimation of the temporal order of pathway mutations during carcinogenesis. BMC Bioinformatics 2019; 20:620. [PMID: 31791231 PMCID: PMC6889196 DOI: 10.1186/s12859-019-3218-2] [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: 06/01/2019] [Accepted: 11/12/2019] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. Although a number of statistical and computational methods have been proposed to estimate the temporal order of mutations, they do not account for the differences in the functional impacts of mutations and thus are likely to be obscured by the presence of passenger mutations that do not contribute to cancer progression. In addition, many methods infer the order of mutations at the gene level, which have limited power due to the low mutation rate in most genes. RESULTS In this paper, we develop a Probabilistic Approach for estimating the Temporal Order of Pathway mutations by leveraging functional Annotations of mutations (PATOPA). PATOPA infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Simulation studies and analysis of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that PATOPA is able to accurately estimate the temporal order of pathway mutations and provides new biological insights on carcinogenesis of colorectal and lung cancers. CONCLUSIONS PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.
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Affiliation(s)
- Menghan Wang
- Department of Statistics, University of Kentucky, Lexington, USA
| | - Tianxin Yu
- Department of Molecular & Cellular Biology, Roswell Park Comprehensive Cancer Center, Buffalo, USA
| | - Jinpeng Liu
- Markey Cancer Center, University of Kentucky, Lexington, USA
| | - Li Chen
- Markey Cancer Center, University of Kentucky, Lexington, USA
- Department of Biostatistics, University of Kentucky, Lexington, USA
| | | | - John L. Villano
- Markey Cancer Center, University of Kentucky, Lexington, USA
- Department of Internal Medicine, University of Kentucky, Lexington, USA
| | - Susanne M. Arnold
- Markey Cancer Center, University of Kentucky, Lexington, USA
- Department of Internal Medicine, University of Kentucky, Lexington, USA
| | - Chunming Liu
- Markey Cancer Center, University of Kentucky, Lexington, USA
- Department of Molecular & Cellular Biochemistry, University of Kentucky, Lexington, USA
| | - Chi Wang
- Markey Cancer Center, University of Kentucky, Lexington, USA
- Department of Biostatistics, University of Kentucky, Lexington, USA
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Eaton J, Wang J, Schwartz R. Deconvolution and phylogeny inference of structural variations in tumor genomic samples. Bioinformatics 2019; 34:i357-i365. [PMID: 29950001 PMCID: PMC6022719 DOI: 10.1093/bioinformatics/bty270] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Motivation Phylogenetic reconstruction of tumor evolution has emerged as a crucial tool for making sense of the complexity of emerging cancer genomic datasets. Despite the growing use of phylogenetics in cancer studies, though, the field has only slowly adapted to many ways that tumor evolution differs from classic species evolution. One crucial question in that regard is how to handle inference of structural variations (SVs), which are a major mechanism of evolution in cancers but have been largely neglected in tumor phylogenetics to date, in part due to the challenges of reliably detecting and typing SVs and interpreting them phylogenetically. Results We present a novel method for reconstructing evolutionary trajectories of SVs from bulk whole-genome sequence data via joint deconvolution and phylogenetics, to infer clonal sub-populations and reconstruct their ancestry. We establish a novel likelihood model for joint deconvolution and phylogenetic inference on bulk SV data and formulate an associated optimization algorithm. We demonstrate the approach to be efficient and accurate for realistic scenarios of SV mutation on simulated data. Application to breast cancer genomic data from The Cancer Genome Atlas shows it to be practical and effective at reconstructing features of SV-driven evolution in single tumors. Availability and implementation Python source code and associated documentation are available at https://github.com/jaebird123/tusv.
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Affiliation(s)
- Jesse Eaton
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jingyi Wang
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Russell Schwartz
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
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50
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Deveau P, Colmet Daage L, Oldridge D, Bernard V, Bellini A, Chicard M, Clement N, Lapouble E, Combaret V, Boland A, Meyer V, Deleuze JF, Janoueix-Lerosey I, Barillot E, Delattre O, Maris JM, Schleiermacher G, Boeva V. QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction. Bioinformatics 2019; 34:1808-1816. [PMID: 29342233 PMCID: PMC5972665 DOI: 10.1093/bioinformatics/bty016] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 01/10/2018] [Indexed: 01/13/2023] Open
Abstract
Motivation In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants. Results We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution. Availability and implementation Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone. Contact gudrun.schleiermacher@curie.fr or valentina.boeva@inserm.fr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Paul Deveau
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
- University of Paris-Sud, Orsay, France
| | - Leo Colmet Daage
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Derek Oldridge
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Childhood Cancer Research Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Virginie Bernard
- Institut Curie, PSL Research University, NGS platform ICGex, Paris, France
| | - Angela Bellini
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Mathieu Chicard
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Nathalie Clement
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
| | - Eve Lapouble
- Unité de Génétique Somatique, Institut Curie, PSL Research University, Paris, France
| | - Valerie Combaret
- Centre Léon-Bérard Laboratoire de Recherche Translationnelle, Lyon, France
| | - Anne Boland
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Vincent Meyer
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Jean-Francois Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry, France
| | - Isabelle Janoueix-Lerosey
- Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Olivier Delattre
- Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris, France
| | - John M Maris
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Childhood Cancer Research Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Gudrun Schleiermacher
- Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris, France
- Département de Pédiatrie, Institut Curie, PSL Research University, Paris, France
- To whom correspondence should be addressed. or
| | - Valentina Boeva
- Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
- Institut Cochin, INSERM U1016, CNRS UMR 8104, Université Paris Descartes UMR-S1016, Paris, France
- To whom correspondence should be addressed. or
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