1
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Serio RN, Scheben A, Lu B, Gargiulo DV, Patruno L, Buckholtz CL, Chaffee RJ, Jibilian MC, Persaud SG, Staklinski SJ, Hassett R, Brault LM, Ramazzotti D, Barbieri CE, Siepel AC, Nowak DG. Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer. Cancer Discov 2024; 14:1990-2009. [PMID: 38969342 DOI: 10.1158/2159-8290.cd-23-1332] [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: 11/10/2023] [Revised: 04/23/2024] [Accepted: 07/03/2024] [Indexed: 07/07/2024]
Abstract
The patterns by which primary tumors spread to metastatic sites remain poorly understood. Here, we define patterns of metastatic seeding in prostate cancer using a novel injection-based mouse model-EvoCaP (Evolution in Cancer of the Prostate), featuring aggressive metastatic cancer to bone, liver, lungs, and lymph nodes. To define migration histories between primary and metastatic sites, we used our EvoTraceR pipeline to track distinct tumor clones containing recordable barcodes. We detected widespread intratumoral heterogeneity from the primary tumor in metastatic seeding, with few clonal populations instigating most migration. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within the tissue. Migration patterns in our model were congruent with human prostate cancer seeding topologies. Our findings support the view of metastatic prostate cancer as a systemic disease driven by waves of aggressive clones expanding their niche, infrequently overcoming constraints that otherwise keep them confined in the primary or metastatic site. Significance: Defining the kinetics of prostate cancer metastasis is critical for developing novel therapeutic strategies. This study uses CRISPR/Cas9-based barcoding technology to accurately define tumor clonal patterns and routes of migration in a novel somatically engineered mouse model (EvoCaP) that recapitulates human prostate cancer using an in-house developed analytical pipeline (EvoTraceR).
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Affiliation(s)
- Ryan N Serio
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Armin Scheben
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Billy Lu
- Department of Pharmacology, Weill Cornell Medicine, New York, New York
| | | | - Lucrezia Patruno
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | | | - Ryan J Chaffee
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | | | | | - Stephen J Staklinski
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Rebecca Hassett
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Lise M Brault
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Daniele Ramazzotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Christopher E Barbieri
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Department of Urology, Weill Cornell Medicine, New York, New York
| | - Adam C Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Dawid G Nowak
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Department of Pharmacology, Weill Cornell Medicine, New York, New York
- Division of Hematology and Medical Oncology, Department of Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York
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2
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Schiffman JS, D'Avino AR, Prieto T, Pang Y, Fan Y, Rajagopalan S, Potenski C, Hara T, Suvà ML, Gawad C, Landau DA. Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution. Nat Genet 2024:10.1038/s41588-024-01920-6. [PMID: 39317739 DOI: 10.1038/s41588-024-01920-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 08/21/2024] [Indexed: 09/26/2024]
Abstract
Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution.
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Affiliation(s)
- Joshua S Schiffman
- New York Genome Center, New York, NY, USA.
- Weill Cornell Medicine, New York, NY, USA.
| | - Andrew R D'Avino
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional MD-PhD Program, Weill Cornell Medicine, Rockefeller University, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tamara Prieto
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | | | - Yilin Fan
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Srinivas Rajagopalan
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Catherine Potenski
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Toshiro Hara
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mario L Suvà
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Charles Gawad
- Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Dan A Landau
- New York Genome Center, New York, NY, USA.
- Weill Cornell Medicine, New York, NY, USA.
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3
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Weber LL, Reiman D, Roddur MS, Qi Y, El-Kebir M, Khan AA. Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data. CELL GENOMICS 2024; 4:100637. [PMID: 39208795 DOI: 10.1016/j.xgen.2024.100637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/19/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.
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Affiliation(s)
- Leah L Weber
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Derek Reiman
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Mrinmoy S Roddur
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Yuanyuan Qi
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Aly A Khan
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA; Department of Pathology, University of Chicago, Chicago, IL 60637, USA; Chan Zuckerberg Biohub Chicago, Chicago, IL 60642, USA.
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4
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Koyyalagunta D, Ganesh K, Morris Q. Inferring cancer type-specific patterns of metastatic spread. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602790. [PMID: 39282311 PMCID: PMC11398359 DOI: 10.1101/2024.07.09.602790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
The metastatic spread of a cancer can be reconstructed from DNA sequencing of primary and metastatic tumours, but doing so requires solving a challenging combinatorial optimization problem. This problem often has multiple solutions that cannot be distinguished based on current maximum parsimony principles alone. Current algorithms use ad hoc criteria to select among these solutions, and decide, a priori, what patterns of metastatic spread are more likely, which is itself a key question posed by studies of metastasis seeking to use these tools. Here we introduce Metient, a freely available open-source tool which proposes multiple possible hypotheses of metastatic spread in a cohort of patients and rescores these hypotheses using independent data on genetic distance of metastasizing clones and organotropism. Metient is more accurate and is up to 50x faster than current state-of-the-art. Given a cohort of patients, Metient can calibrate its parsimony criteria, thereby identifying shared patterns of metastatic dissemination in the cohort. Reanalyzing metastasis in 169 patients based on 490 tumors, Metient automatically identifies cancer type-specific trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Metient's reconstructions usually agree with semi-manual expert analysis, however, in many patients, Metient identifies more plausible migration histories than experts, and further finds that polyclonal seeding of metastases is more common than previously reported. By removing the need for hard constraints on what patterns of metastatic spread are most likely, Metient introduces a way to further our understanding of cancer type-specific metastatic spread.
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5
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Roddur MS, Snir S, El-Kebir M. Enforcing Temporal Consistency in Migration History Inference. J Comput Biol 2024; 31:396-415. [PMID: 38754138 DOI: 10.1089/cmb.2023.0352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024] Open
Abstract
In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.
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Affiliation(s)
- Mrinmoy Saha Roddur
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Sagi Snir
- Department of Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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6
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Karras P, Black JRM, McGranahan N, Marine JC. Decoding the interplay between genetic and non-genetic drivers of metastasis. Nature 2024; 629:543-554. [PMID: 38750233 DOI: 10.1038/s41586-024-07302-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.
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Affiliation(s)
- Panagiotis Karras
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - James R M Black
- Cancer Genome Evolution Research Group, UCL Cancer Institute, London, UK
| | | | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
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7
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Mai U, Chu G, Raphael BJ. Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583638. [PMID: 38496496 PMCID: PMC10942411 DOI: 10.1101/2024.03.05.583638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.
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Affiliation(s)
| | | | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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8
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Rogava M, Aprati TJ, Chi WY, Melms JC, Hug C, Davis SH, Earlie EM, Chung C, Deshmukh SK, Wu S, Sledge G, Tang S, Ho P, Amin AD, Caprio L, Gurjao C, Tagore S, Ngo B, Lee MJ, Zanetti G, Wang Y, Chen S, Ge W, Melo LMN, Allies G, Rösler J, Gibney GT, Schmitz OJ, Sykes M, Creusot RJ, Tüting T, Schadendorf D, Röcken M, Eigentler TK, Molotkov A, Mintz A, Bakhoum SF, Beyaz S, Cantley LC, Sorger PK, Meckelmann SW, Tasdogan A, Liu D, Laughney AM, Izar B. Loss of Pip4k2c confers liver-metastatic organotropism through insulin-dependent PI3K-AKT pathway activation. NATURE CANCER 2024; 5:433-447. [PMID: 38286827 PMCID: PMC11175596 DOI: 10.1038/s43018-023-00704-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/08/2023] [Indexed: 01/31/2024]
Abstract
Liver metastasis (LM) confers poor survival and therapy resistance across cancer types, but the mechanisms of liver-metastatic organotropism remain unknown. Here, through in vivo CRISPR-Cas9 screens, we found that Pip4k2c loss conferred LM but had no impact on lung metastasis or primary tumor growth. Pip4k2c-deficient cells were hypersensitized to insulin-mediated PI3K/AKT signaling and exploited the insulin-rich liver milieu for organ-specific metastasis. We observed concordant changes in PIP4K2C expression and distinct metabolic changes in 3,511 patient melanomas, including primary tumors, LMs and lung metastases. We found that systemic PI3K inhibition exacerbated LM burden in mice injected with Pip4k2c-deficient cancer cells through host-mediated increase in hepatic insulin levels; however, this circuit could be broken by concurrent administration of an SGLT2 inhibitor or feeding of a ketogenic diet. Thus, this work demonstrates a rare example of metastatic organotropism through co-optation of physiological metabolic cues and proposes therapeutic avenues to counteract these mechanisms.
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Affiliation(s)
- Meri Rogava
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Tyler J Aprati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Wei-Yu Chi
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Johannes C Melms
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Clemens Hug
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Stephanie H Davis
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Ethan M Earlie
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Charlie Chung
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Sharon Wu
- Caris Life Sciences, Phoenix, AZ, USA
| | | | - Stephen Tang
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Patricia Ho
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Amit Dipak Amin
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lindsay Caprio
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Carino Gurjao
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Somnath Tagore
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Bryan Ngo
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Michael J Lee
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Giorgia Zanetti
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yiping Wang
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Sean Chen
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - William Ge
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Luiza Martins Nascentes Melo
- Department for Dermatology, Venerology and Allergology, University Hospital Essen, NCT West, Campus Essen, German Cancer Consortium, Partner Site Essen & University Alliance Ruhr, Research Center One Health, Essen, Germany
| | - Gabriele Allies
- Department for Dermatology, Venerology and Allergology, University Hospital Essen, NCT West, Campus Essen, German Cancer Consortium, Partner Site Essen & University Alliance Ruhr, Research Center One Health, Essen, Germany
| | - Jonas Rösler
- Department for Dermatology, Venerology and Allergology, University Hospital Essen, NCT West, Campus Essen, German Cancer Consortium, Partner Site Essen & University Alliance Ruhr, Research Center One Health, Essen, Germany
| | - Goeffrey T Gibney
- Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Oliver J Schmitz
- Applied Analytical Chemistry, University of Duisburg-Essen, Essen, Germany
| | - Megan Sykes
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Rémi J Creusot
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Thomas Tüting
- Laboratory for Experimental Dermatology, Department of Dermatology, University of Magdeburg, Magdeburg, Germany
| | - Dirk Schadendorf
- Department for Dermatology, Venerology and Allergology, University Hospital Essen, NCT West, Campus Essen, German Cancer Consortium, Partner Site Essen & University Alliance Ruhr, Research Center One Health, Essen, Germany
| | - Martin Röcken
- Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Thomas K Eigentler
- Department of Dermatology, Venerology and Allergology, Charité University Hospital, Berlin, Germany
| | - Andrei Molotkov
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Akiva Mintz
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - 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
| | - Semir Beyaz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Peter K Sorger
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Sven W Meckelmann
- Applied Analytical Chemistry, University of Duisburg-Essen, Essen, Germany
| | - Alpaslan Tasdogan
- Department for Dermatology, Venerology and Allergology, University Hospital Essen, NCT West, Campus Essen, German Cancer Consortium, Partner Site Essen & University Alliance Ruhr, Research Center One Health, Essen, Germany
| | - David Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ashley M Laughney
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin Izar
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos School of Physicians and Surgeons, New York, NY, USA.
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA.
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA.
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9
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Sashittal P, Schmidt H, Chan M, Raphael BJ. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Syst 2023; 14:1113-1121.e9. [PMID: 38128483 PMCID: PMC11257033 DOI: 10.1016/j.cels.2023.11.005] [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: 04/01/2023] [Revised: 10/31/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Michelle Chan
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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10
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Weber LL, Reiman D, Roddur MS, Qi Y, El-Kebir M, Khan AA. TRIBAL: Tree Inference of B cell Clonal Lineages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568874. [PMID: 38076836 PMCID: PMC10705245 DOI: 10.1101/2023.11.27.568874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies.
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Affiliation(s)
- Leah L. Weber
- Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Derek Reiman
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Mrinmoy S. Roddur
- Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Yuanyuan Qi
- Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Aly A. Khan
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
- Department of Pathology, University of Chicago, Chicago, IL 60637, USA
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11
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Semaan A, Bernard V, Wong J, Makino Y, Swartzlander DB, Rajapakshe KI, Lee JJ, Officer A, Schmidt CM, Wu HH, Scaife CL, Affolter KE, Nachmanson D, Firpo MA, Yip-Schneider M, Lowy AM, Harismendy O, Sen S, Maitra A, Jakubek YA, Guerrero PA. Integrated Molecular Characterization of Intraductal Papillary Mucinous Neoplasms: An NCI Cancer Moonshot Precancer Atlas Pilot Project. CANCER RESEARCH COMMUNICATIONS 2023; 3:2062-2073. [PMID: 37721516 PMCID: PMC10563795 DOI: 10.1158/2767-9764.crc-22-0419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/27/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
Intraductal papillary mucinous neoplasms (IPMN) are cystic precursor lesions to pancreatic ductal adenocarcinoma (PDAC). IPMNs undergo multistep progression from low-grade (LG) to high-grade (HG) dysplasia, culminating in invasive neoplasia. While patterns of IPMN progression have been analyzed using multiregion sequencing for somatic mutations, there is no integrated assessment of molecular events, including copy-number alterations (CNA) and transcriptional changes that accompany IPMN progression. We performed laser capture microdissection on surgically resected IPMNs of varying grades of histologic dysplasia obtained from 23 patients, followed by whole-exome and whole-transcriptome sequencing. Overall, HG IPMNs displayed a significantly greater aneuploidy score than LG lesions, with chromosome 1q amplification being associated with HG progression and with cases that harbored co-occurring PDAC. Furthermore, the combined assessment of single-nucleotide variants (SNV) and CNAs identified both linear and branched evolutionary trajectories, underscoring the heterogeneity in the progression of LG lesions to HG and PDAC. At the transcriptome level, upregulation of MYC-regulated targets and downregulation of transcripts associated with the MHC class I antigen presentation machinery as well as pathways related to glycosylation were a common feature of progression to HG. In addition, the established PDAC transcriptional subtypes (basal-like and classical) were readily apparent within IPMNs. Taken together, this work emphasizes the role of 1q copy-number amplification as a putative biomarker of high-risk IPMNs, underscores the importance of immune evasion even in noninvasive precursor lesions, and reinforces that evolutionary pathways in IPMNs are heterogenous, comprised of both SNV and CNA-driven events. SIGNIFICANCE Integrated molecular analysis of genomic and transcriptomic alterations in the multistep progression of IPMNs, which are bona fide precursors of pancreatic cancer, identifies features associated with progression of low-risk lesions to high-risk lesions and cancer, which might enable patient stratification and cancer interception strategies.
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Affiliation(s)
- Alexander Semaan
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Vincent Bernard
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Justin Wong
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yuki Makino
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daniel B. Swartzlander
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kimal I. Rajapakshe
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jaewon J. Lee
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program and Moores Cancer Center, University of California San Diego School of Medicine, San Diego, California
| | | | - Howard H. Wu
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | | | | | - Daniela Nachmanson
- Bioinformatics and Systems Biology Graduate Program and Moores Cancer Center, University of California San Diego School of Medicine, San Diego, California
| | | | | | - Andrew M. Lowy
- Department of Surgery, Division of Surgical Oncology, University of California San Diego, San Diego, California
| | - Olivier Harismendy
- Bioinformatics and Systems Biology Graduate Program and Moores Cancer Center, University of California San Diego School of Medicine, San Diego, California
| | - Subrata Sen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anirban Maitra
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yasminka A. Jakubek
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Paola A. Guerrero
- Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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12
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Lee JE, Kim KT, Shin SJ, Cheong JH, Choi YY. Genomic and evolutionary characteristics of metastatic gastric cancer by routes. Br J Cancer 2023; 129:672-682. [PMID: 37422528 PMCID: PMC10421927 DOI: 10.1038/s41416-023-02338-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/07/2023] [Accepted: 06/19/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND In gastric cancer (GC) patients, metastatic progression through the lymphatic, hematogenous, peritoneal, and ovarian routes, is the ultimate cause of death. However, the genomic and evolutionary characteristics of metastatic GC have not been widely evaluated. METHODS Whole-exome sequencing data were analyzed for 99 primary and paired metastatic gastric cancers from 15 patients who underwent gastrectomy and metastasectomy. RESULTS Hematogenous metastatic tumors were associated with increased chromosomal instability and de novo gain/amplification in cancer driver genes, whereas peritoneal/ovarian metastasis was linked to sustained chromosomal stability and de novo somatic mutations in driver genes. The genomic distance of the hematogenous and peritoneal metastatic tumors was found to be closer to the primary tumors than lymph node (LN) metastasis, while ovarian metastasis was closer to LN and peritoneal metastasis than the primary tumor. Two migration patterns for metastatic GCs were identified; branched and diaspora. Both molecular subtypes of the metastatic tumors, rather than the primary tumor, and their migration patterns were related to patient survival. CONCLUSIONS Genomic characteristics of metastatic gastric cancer is distinctive by routes and associated with patients' prognosis along with genomic evolution pattenrs, indicating that both primary and metastatic gastric cancers require genomic evaluation.
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Affiliation(s)
- Jae Eun Lee
- Portrai Inc., Seoul, Korea
- Department of Surgery, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
| | - Ki Tae Kim
- Department of Molecular Genetics & Dental Pharmacology, School of Dentistry, Seoul National University, Seoul, South Korea
- Dental Research Institute and Dental Multi-omics Center, Seoul National University, Seoul, South Korea
| | - Su-Jin Shin
- Department of Pathology, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
| | - Jae-Ho Cheong
- Department of Surgery, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea.
| | - Yoon Young Choi
- Department of Surgery, Soonchunhyang Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, South Korea.
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13
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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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14
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Hessey S, Fessas P, Zaccaria S, Jamal-Hanjani M, Swanton C. Insights into the metastatic cascade through research autopsies. Trends Cancer 2023; 9:490-502. [PMID: 37059687 DOI: 10.1016/j.trecan.2023.03.002] [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: 02/02/2023] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 04/16/2023]
Abstract
Metastasis is a complex process and the leading cause of cancer-related death globally. Recent studies have demonstrated that genomic sequencing data from paired primary and metastatic tumours can be used to trace the evolutionary origins of cells responsible for metastasis. This approach has yielded new insights into the genomic alterations that engender metastatic potential, and the mechanisms by which cancer spreads. Given that the reliability of these approaches is contingent upon how representative the samples are of primary and metastatic tumour heterogeneity, we review insights from studies that have reconstructed the evolution of metastasis within the context of their cohorts and designs. We discuss the role of research autopsies in achieving the comprehensive sampling necessary to advance the current understanding of metastasis.
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Affiliation(s)
- Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Petros Fessas
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK; Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
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15
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Huzar J, Shenoy M, Sanderford MD, Kumar S, Miura S. Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data. FRONTIERS IN BIOINFORMATICS 2023; 3:1090730. [PMID: 37261293 PMCID: PMC10228696 DOI: 10.3389/fbinf.2023.1090730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 03/28/2023] [Indexed: 06/02/2023] Open
Abstract
Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.
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Affiliation(s)
- Jared Huzar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Madelyn Shenoy
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
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16
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Al Bakir M, Huebner A, Martínez-Ruiz C, Grigoriadis K, Watkins TBK, Pich O, Moore DA, Veeriah S, Ward S, Laycock J, Johnson D, Rowan A, Razaq M, Akther M, Naceur-Lombardelli C, Prymas P, Toncheva A, Hessey S, Dietzen M, Colliver E, Frankell AM, Bunkum A, Lim EL, Karasaki T, Abbosh C, Hiley CT, Hill MS, Cook DE, Wilson GA, Salgado R, Nye E, Stone RK, Fennell DA, Price G, Kerr KM, Naidu B, Middleton G, Summers Y, Lindsay CR, Blackhall FH, Cave J, Blyth KG, Nair A, Ahmed A, Taylor MN, Procter AJ, Falzon M, Lawrence D, Navani N, Thakrar RM, Janes SM, Papadatos-Pastos D, Forster MD, Lee SM, Ahmad T, Quezada SA, Peggs KS, Van Loo P, Dive C, Hackshaw A, Birkbak NJ, Zaccaria S, Jamal-Hanjani M, McGranahan N, Swanton C. The evolution of non-small cell lung cancer metastases in TRACERx. Nature 2023; 616:534-542. [PMID: 37046095 PMCID: PMC10115651 DOI: 10.1038/s41586-023-05729-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/12/2023] [Indexed: 04/14/2023]
Abstract
Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse.
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Affiliation(s)
- Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Oriol Pich
- 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
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Joanne Laycock
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Diana Johnson
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Andrew Rowan
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Maryam Razaq
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Mita Akther
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | - Paulina Prymas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Abigail Bunkum
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Emilia L Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Crispin T Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Emma Nye
- Experimental Histopathology, The Francis Crick Institute, London, UK
| | | | - Dean A Fennell
- University of Leicester, Leicester, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Gillian Price
- Department of Medical Oncology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
- University of Aberdeen, Aberdeen, UK
| | - Keith M Kerr
- University of Aberdeen, Aberdeen, UK
- Department of Pathology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
| | - Babu Naidu
- Birmingham Acute Care Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Gary Middleton
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Yvonne Summers
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Colin R Lindsay
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Fiona H Blackhall
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Judith Cave
- Department of Oncology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kevin G Blyth
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Glasgow, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | - Arjun Nair
- Department of Radiology, University College London Hospitals, London, UK
- UCL Respiratory, Department of Medicine, University College London, London, UK
| | - Asia Ahmed
- Department of Radiology, University College London Hospitals, London, UK
| | - Magali N Taylor
- Department of Radiology, University College London Hospitals, London, UK
| | | | - Mary Falzon
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - David Lawrence
- Department of Thoracic Surgery, University College London Hospital NHS Trust, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
- Department of Thoracic Medicine, University College London Hospitals, London, UK
| | - Ricky M Thakrar
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
- Department of Thoracic Medicine, University College London Hospitals, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | | | - Martin D Forster
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Siow Ming Lee
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Tanya Ahmad
- Department of Oncology, University College London Hospitals, London, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Immune Regulation and Tumour Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Karl S Peggs
- Department of Haematology, University College London Hospitals, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Peter Van Loo
- Cancer Genomics Laboratory, 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
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Allan Hackshaw
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | - Nicolai J Birkbak
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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17
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Moravec JC, Lanfear R, Spector DL, Diermeier SD, Gavryushkin A. Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data. J Comput Biol 2023; 30:518-537. [PMID: 36475926 PMCID: PMC10125402 DOI: 10.1089/cmb.2022.0357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.
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Affiliation(s)
- Jiří C. Moravec
- Department of Computer Science, University of Otago, Dunedin, New Zealand
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Robert Lanfear
- Division of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, Australia
| | | | | | - Alex Gavryushkin
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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18
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Yamamoto A, Doak AE, Cheung KJ. Orchestration of Collective Migration and Metastasis by Tumor Cell Clusters. ANNUAL REVIEW OF PATHOLOGY 2023; 18:231-256. [PMID: 36207009 DOI: 10.1146/annurev-pathmechdis-031521-023557] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Metastatic dissemination has lethal consequences for cancer patients. Accruing evidence supports the hypothesis that tumor cells can migrate and metastasize as clusters of cells while maintaining contacts with one another. Collective metastasis enables tumor cells to colonize secondary sites more efficiently, resist cell death, and evade the immune system. On the other hand, tumor cell clusters face unique challenges for dissemination particularly during systemic dissemination. Here, we review recent progress toward understanding how tumor cell clusters overcome these disadvantages as well as mechanisms they utilize to gain advantages throughout the metastatic process. We consider useful models for studying collective metastasis and reflect on how the study of collective metastasis suggests new opportunities for eradicating and preventing metastatic disease.
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Affiliation(s)
- Ami Yamamoto
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , , .,Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, USA
| | - Andrea E Doak
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , , .,Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, USA
| | - Kevin J Cheung
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , ,
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19
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Wang X, Liu C, Chen J, Chen L, Ren X, Hou M, Cui X, Jiang Y, Liu E, Zong Y, Duan A, Fu X, Yu W, Zhao X, Yang Z, Zhang Y, Fu J, Wang H. Single-cell dissection of remodeled inflammatory ecosystem in primary and metastatic gallbladder carcinoma. Cell Discov 2022; 8:101. [PMID: 36198671 PMCID: PMC9534837 DOI: 10.1038/s41421-022-00445-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 07/09/2022] [Indexed: 11/09/2022] Open
Abstract
Gallbladder carcinoma (GBC) is the most common biliary tract malignancy with the lowest survival rate, primarily arising from chronic inflammation. To better characterize the progression from inflammation to cancer to metastasis, we performed single-cell RNA sequencing across samples of 6 chronic cholecystitis, 12 treatment-naive GBCs, and 6 matched metastases. Benign epithelial cells from inflamed gallbladders displayed resting, immune-regulating, and gastrointestinal metaplastic phenotypes. A small amount of PLA2G2A+ epithelial cells with copy number variation were identified from a histologically benign sample. We validated significant overexpression of PLA2G2A across in situ GBCs, together with increased proliferation and cancer stemness in PLA2G2A-overexpressing GBC cells, indicating an important role for PLA2G2A during early carcinogenesis. Malignant epithelial cells displayed pervasive cancer hallmarks and cellular plasticity, differentiating into metaplastic, inflammatory, and mesenchymal subtypes with distinct transcriptomic, genomic, and prognostic patterns. Chronic cholecystitis led to an adapted microenvironment characterized by MDSC-like macrophages, CD8+ TRM cells, and CCL2+ immunity-regulating fibroblasts. By contrast, GBC instigated an aggressive and immunosuppressive microenvironment, featured by tumor-associated macrophages, Treg cells, CD8+ TEX cells, and STMN1+ tumor-promoting fibroblasts. Single-cell and bulk RNA-seq profiles consistently showed a more suppressive immune milieu for GBCs with inflammatory epithelial signatures, coupled with strengthened epithelial-immune crosstalk. We further pinpointed a subset of senescence-like fibroblasts (FN1+TGM2+) preferentially enriched in metastatic lesions, which promoted GBC migration and invasion via their secretory phenotype. Collectively, this study provides comprehensive insights into epithelial and microenvironmental reprogramming throughout cholecystitis-propelled carcinogenesis and metastasis, laying a new foundation for the precision therapy of GBC.
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Affiliation(s)
- Xiang Wang
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.,Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Chunliang Liu
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jianan Chen
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Lei Chen
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Xianwen Ren
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Minghui Hou
- Research Center for Organoids, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiuliang Cui
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Youhai Jiang
- Cancer Research Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Erdong Liu
- School of Life Sciences, Fudan University, Shanghai, China
| | - Yali Zong
- School of Life Sciences, Fudan University, Shanghai, China
| | - Anqi Duan
- Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Xiaohui Fu
- Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Wenlong Yu
- Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Xiaofang Zhao
- Research Center for Organoids, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhao Yang
- Second Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yongjie Zhang
- Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
| | - Jing Fu
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai Key Laboratory of Hepato-biliary Tumor Biology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
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20
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Kulman E, Wintersinger J, Morris Q. Reconstructing cancer phylogenies using Pairtree, a clone tree reconstruction algorithm. STAR Protoc 2022; 3:101706. [PMID: 36129821 PMCID: PMC9494285 DOI: 10.1016/j.xpro.2022.101706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/21/2022] [Accepted: 08/22/2022] [Indexed: 01/25/2023] Open
Abstract
Pairtree is a clone tree reconstruction algorithm that uses somatic point mutations to build clone trees describing the evolutionary history of individual cancers. Using the Pairtree software package, we describe steps to preprocess somatic mutation data, cluster mutations into subclones, search for clone trees, and visualize clone trees. Pairtree builds clone trees using up to 100 samples from a single cancer with at least 30 subclonal populations. For complete details on the use and execution of this protocol, please refer to Wintersinger et al. (2022).
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Affiliation(s)
- Ethan Kulman
- Memorial Sloan Kettering Cancer Center, New York, NY, USA,Corresponding author
| | | | - Quaid Morris
- Memorial Sloan Kettering Cancer Center, New York, NY, USA,Corresponding author
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21
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Chroni A, Miura S, Hamilton L, Vu T, Gaffney SG, Aly V, Karim S, Sanderford M, Townsend JP, Kumar S. Clone Phylogenetics Reveals Metastatic Tumor Migrations, Maps, and Models. Cancers (Basel) 2022; 14:cancers14174326. [PMID: 36077861 PMCID: PMC9454754 DOI: 10.3390/cancers14174326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Metastasis is the spread of cancer cells across organs and is a major cause of cancer mortality. Analysis of tumor sequencing data provides a means toward the reconstruction of routes of metastatic cell migrations. Our reconstructions demonstrated that many metastases were likely seeded from pre-existing metastasis of primary tumors. Additionally, multiple clone exchanges between tumor sites were common. In conclusion, the pattern of cancer cell migrations is often complex and is highly variable among patients. Abstract Dispersal routes of metastatic cells are not medically detected or even visible. A molecular evolutionary analysis of tumor variation provides a way to retrospectively infer metastatic migration histories and answer questions such as whether the majority of metastases are seeded from clones within primary tumors or seeded from clones within pre-existing metastases, as well as whether the evolution of metastases is generally consistent with any proposed models. We seek answers to these fundamental questions through a systematic patient-centric retrospective analysis that maps the dynamic evolutionary history of tumor cell migrations in many cancers. We analyzed tumor genetic heterogeneity in 51 cancer patients and found that most metastatic migration histories were best described by a hybrid of models of metastatic tumor evolution. Synthesizing across metastatic migration histories, we found new tumor seedings arising from clones of pre-existing metastases as often as they arose from clones from primary tumors. There were also many clone exchanges between the source and recipient tumors. Therefore, a molecular phylogenetic analysis of tumor variation provides a retrospective glimpse into general patterns of metastatic migration histories in cancer patients.
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Affiliation(s)
- Antonia Chroni
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Lauren Hamilton
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | | | - Vivian Aly
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sajjad Karim
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 22252, Saudi Arabia
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Jeffrey P. Townsend
- Department of Biostatistics, Yale University, New Haven, CT 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06525, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 22252, Saudi Arabia
- Correspondence:
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22
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Li Q, Xu B, Shen Z. Editorial: The Molecular Basis of Somatic Evolution. Front Oncol 2022; 12:906068. [PMID: 35734593 PMCID: PMC9207474 DOI: 10.3389/fonc.2022.906068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/26/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Qiyuan Li
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Bing Xu
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, China.,Department of Hematology, Key Laboratory of Xiamen for Diagnosis and Treatment of Hematological Malignancy, Xiamen, China
| | - Zhanlong Shen
- Laboratory of Surgical Oncology, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Beijing, China
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23
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A phylogenetic approach to study the evolution of somatic mutational processes in cancer. Commun Biol 2022; 5:617. [PMID: 35732905 PMCID: PMC9217972 DOI: 10.1038/s42003-022-03560-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/07/2022] [Indexed: 11/09/2022] Open
Abstract
Cancer cell genomes change continuously due to mutations, and mutational processes change over time in patients, leaving dynamic signatures in the accumulated genomic variation in tumors. Many computational methods detect the relative activities of known mutation signatures. However, these methods may produce erroneous signatures when applied to individual branches in cancer cell phylogenies. Here, we show that the inference of branch-specific mutational signatures can be improved through a joint analysis of the collections of mutations mapped on proximal branches of the cancer cell phylogeny. This approach reduces the false-positive discovery rate of branch-specific signatures and can sometimes detect faint signatures. An analysis of empirical data from 61 lung cancer patients supports trends based on computer-simulated datasets for which the correct signatures are known. In lung cancer somatic variation, we detect a decreasing trend of smoking-related mutational processes over time and an increasing influence of APOBEC mutational processes as the tumor evolution progresses. These analyses also reveal patterns of conservation and divergence of mutational processes in cell lineages within patients.
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24
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Vitos N, Gerlee P. Model-based inference of metastatic seeding rates in de novo metastatic breast cancer reveals the impact of secondary seeding and molecular subtype. Sci Rep 2022; 12:9455. [PMID: 35676303 PMCID: PMC9177582 DOI: 10.1038/s41598-022-12500-1] [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: 09/09/2021] [Accepted: 03/08/2022] [Indexed: 11/25/2022] Open
Abstract
We present a stochastic network model of metastasis spread for de novo metastatic breast cancer, composed of tumor to metastasis (primary seeding) and metastasis to metastasis spread (secondary seeding), parameterized using the SEER (Surveillance, Epidemiology, and End Results) database. The model provides a quantification of tumor cell dissemination rates between the tumor and metastasis sites. These rates were used to estimate the probability of developing a metastasis for untreated patients. The model was validated using tenfold cross-validation. We also investigated the effect of HER2 (Human Epidermal Growth Factor Receptor 2) status, estrogen receptor (ER) status and progesterone receptor (PR) status on the probability of metastatic spread. We found that dissemination rate through secondary seeding is up to 300 times higher than through primary seeding. Hormone receptor positivity promotes seeding to the bone and reduces seeding to the lungs and primary seeding to the liver, while HER2 expression increases dissemination to the bone, lungs and primary seeding to the liver. Secondary seeding from the lungs to the liver seems to be hormone receptor-independent, while that from the lungs to the brain appears HER2-independent.
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Affiliation(s)
- Noemi Vitos
- Bla Kustens Halsocentral, 57251, Oskarshamn, Sweden
| | - Philip Gerlee
- Mathematical Sciences, Chalmers University of Technology, 41296, Gothenburg, Sweden. .,Mathematical Sciences, University of Gothenburg, 41296, Gothenburg, Sweden.
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25
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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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26
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Mallory XF, Nakhleh L. SimSCSnTree: a simulator of single-cell DNA sequencing data. Bioinformatics 2022; 38:2912-2914. [PMID: 35561189 DOI: 10.1093/bioinformatics/btac169] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/15/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY We report on a new single-cell DNA sequence simulator, SimSCSnTree, which generates an evolutionary tree of cells and evolves single nucleotide variants (SNVs) and copy number aberrations (CNAs) along its branches. Data generated by the simulator can be used to benchmark tools for single-cell genomic analyses, particularly in cancer where SNVs and CNAs are ubiquitous. AVAILABILITY AND IMPLEMENTATION SimSCSnTree is now on BioConda and also is freely available for download at https://github.com/compbiofan/SimSCSnTree.git with detailed documentation.
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Affiliation(s)
- Xian Fan Mallory
- Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, TX 77025, USA
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27
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Pich O, Bailey C, Watkins TBK, Zaccaria S, Jamal-Hanjani M, Swanton C. The translational challenges of precision oncology. Cancer Cell 2022; 40:458-478. [PMID: 35487215 DOI: 10.1016/j.ccell.2022.04.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/16/2022] [Accepted: 04/05/2022] [Indexed: 12/11/2022]
Abstract
The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.
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Affiliation(s)
- Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Chris Bailey
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Department of Medical Oncology, University College London Hospitals, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
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28
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Rogiers A, Lobon I, Spain L, Turajlic S. The Genetic Evolution of Metastasis. Cancer Res 2022; 82:1849-1857. [PMID: 35476646 DOI: 10.1158/0008-5472.can-21-3863] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 11/16/2022]
Abstract
Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.
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Affiliation(s)
- Aljosja Rogiers
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Renal and Skin Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Irene Lobon
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Lavinia Spain
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Medical Oncology Department, Peter MacCallum Cancer Centre, Melbourne, Australia.,Medical Oncology Department, Eastern Health, Melbourne Australia.,Eastern Health Clinical School, Monash University, Box Hill, Australia
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Renal and Skin Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom.,Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, United Kingdom
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29
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Farswan A, Gupta R, Gupta A. ARCANE-ROG: Algorithm for Reconstruction of Cancer Evolution from single-cell data using Robust Graph Learning. J Biomed Inform 2022; 129:104055. [PMID: 35337943 DOI: 10.1016/j.jbi.2022.104055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/17/2022] [Accepted: 03/12/2022] [Indexed: 11/27/2022]
Abstract
Tumor heterogeneity, marked by the presence of divergent clonal subpopulations of tumor cells, impedes the treatment response in cancer patients. Single-cell sequencing technology provides substantial prospects to gain an in-depth understanding of the cellular phenotypic variability driving tumor progression. A comprehensive insight into the intra-tumor heterogeneity may further assist in dealing with the treatment-resistant clones in cancer patients, thereby improving their overall survival. However, this task is hampered due to the challenges associated with the single-cell data, such as false positives, false negatives and missing bases, and the increase in their size. As a result, the computational cost of the existing methods increases, thereby limiting their usage. In this work, we propose a robust graph learning-based method, ARCANE-ROG (Algorithm for Reconstruction of CANcer Evolution via RObust Graph learning), for inferring clonal evolution from single-cell datasets. The first step of the proposed method is a joint framework of denoising with data imputation for the noisy and incomplete matrix while simultaneously learning an adjacency graph. Both the operations in the joint framework boost each other such that the overall performance of the denoising algorithm is improved. In the second step, an optimal number of clusters are identified via the Leiden method. In the last step, clonal evolution trees are inferred via a minimum spanning tree algorithm. The method has been benchmarked against a state-of-the-art method, RobustClone, using simulated datasets of varying sizes and five real datasets. The performance of our proposed method is found to be significantly superior (p-value < 0.05) in terms of reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error and V-measure compared to the other method. Overall, the proposed method is an improvement over the existing methods as it enhances cluster assignment and inference on clonal hierarchies.
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Affiliation(s)
- Akanksha Farswan
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, AIIMS, New Delhi, India.
| | - Anubha Gupta
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India.
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30
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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: 48] [Impact Index Per Article: 24.0] [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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31
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OUP accepted manuscript. Bioinformatics 2022; 38:i125-i133. [PMID: 35758777 PMCID: PMC9236577 DOI: 10.1093/bioinformatics/btac253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. RESULTS In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. AVAILABILITY AND IMPLEMENTATION https://github.com/CMUSchwartzLab/TUSV-ext. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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32
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Govek K, Sikes C, Zhou Y, Oesper L. GraPhyC: Using Consensus to Infer Tumor Evolution. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:465-478. [PMID: 33031032 DOI: 10.1109/tcbb.2020.3029689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We consider the problem of finding a consensus tumor evolution tree from a set of conflicting input trees. In contrast to traditional phylogenetic trees, the tumor trees we consider do not have the same set of labels applied to the leaves of each tree. We describe several distance measures between these tumor trees. Our GraPhyC algorithm solves the consensus problem using a weighted directed graph where vertices are sets of mutations and edges are weighted based on the number of times a parental relationship is observed between their constituent mutations in the input trees. We find a minimum weight spanning arborescence in this graph and prove that it minimizes the total distance to all input trees for one of our distance measures. We also describe several extensions of our GraPhyC approach. On simulated data we show that GraPhyC outperforms a baseline method and demonstrate that GraPhyC can be an effective means of computing centroids in k-medians clustering. We analyze two real sequencing datasets and find that GraPhyC is able to identify a tree not included in the set of input trees, but that contains characteristics supported by other reported evolutionary reconstructions of this tumor.
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33
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Gui P, Bivona TG. Evolution of metastasis: new tools and insights. Trends Cancer 2021; 8:98-109. [PMID: 34872888 DOI: 10.1016/j.trecan.2021.11.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 02/07/2023]
Abstract
Metastasis is an evolutionary process occurring across multiple organs and timescales. Due to its continuous and dynamic nature, this multifaceted process has been challenging to investigate and remains incompletely understood, in part due to the lack of tools capable of probing genomic evolution at high enough resolution. However, technological advances in genetic sequencing and editing have provided new and powerful methods to refine our understanding of the complex series of events that lead to metastatic dissemination. In this review, we summarize the latest genetic and lineage-tracing approaches developed to unravel the genetic evolution of metastasis. The findings that have emerged have enhanced our comprehension of the mechanistic trajectories and timescales of metastasis and could provide new strategies for therapy.
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Affiliation(s)
- Philippe Gui
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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34
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Abstract
Integration of ecological and evolutionary features has begun to understand the interplay of tumor heterogeneity, microenvironment, and metastatic potential. Developing a theoretical framework is intrinsic to deciphering tumors' tremendous spatial and longitudinal genetic variation patterns in patients. Here, we propose that tumors can be considered evolutionary island-like ecosystems, that is, isolated systems that undergo evolutionary and spatiotemporal dynamic processes that shape tumor microenvironments and drive the migration of cancer cells. We examine attributes of insular systems and causes of insularity, such as physical distance and connectivity. These properties modulate migration rates of cancer cells through processes causing spatial and temporal isolation of the organs and tissues functioning as a supply of cancer cells for new colonizations. We discuss hypotheses, predictions, and limitations of tumors as islands analogy. We present emerging evidence of tumor insularity in different cancer types and discuss their relevance to the islands model. We suggest that the engagement of tumor insularity into conceptual and mathematical models holds promise to illuminate cancer evolution, tumor heterogeneity, and metastatic potential of cells.
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Affiliation(s)
- Antonia Chroni
- Institute for Genomics and Evolutionary Medicine, Temple University, USA
- Department of Biology, Temple University, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, USA
- Department of Biology, Temple University, USA
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
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35
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Migrations of cancer cells through the lens of phylogenetic biogeography. Sci Rep 2021; 11:17184. [PMID: 34433859 PMCID: PMC8387374 DOI: 10.1038/s41598-021-96215-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/06/2021] [Indexed: 12/02/2022] Open
Abstract
Malignant cells leave their initial tumor of growth and disperse to other tissues to form metastases. Dispersals also occur in nature when individuals in a population migrate from their area of origin to colonize other habitats. In cancer, phylogenetic biogeography is concerned with the source and trajectory of cell movements. We examine the suitability of primary features of organismal biogeography, including genetic diversification, dispersal, extinction, vicariance, and founder effects, to describe and reconstruct clone migration events among tumors. We used computer-simulated data to compare fits of seven biogeographic models and evaluate models’ performance in clone migration reconstruction. Models considering founder effects and dispersals were often better fit for the clone phylogenetic patterns, especially for polyclonal seeding and reseeding of metastases. However, simpler biogeographic models produced more accurate estimates of cell migration histories. Analyses of empirical datasets of basal-like breast cancer had model fits consistent with the patterns seen in the analysis of computer-simulated datasets. Our analyses reveal the powers and pitfalls of biogeographic models for modeling and inferring clone migration histories using tumor genome variation data. We conclude that the principles of molecular evolution and organismal biogeography are useful in these endeavors but that the available models and methods need to be applied judiciously.
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36
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Dasari K, Somarelli JA, Kumar S, Townsend JP. The somatic molecular evolution of cancer: Mutation, selection, and epistasis. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:56-65. [PMID: 34364910 DOI: 10.1016/j.pbiomolbio.2021.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 12/17/2022]
Abstract
Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixation occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal deconvolution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.
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Affiliation(s)
| | | | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, and Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jeffrey P Townsend
- Yale College, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Yale Cancer Center, Yale University, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
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37
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Abstract
The observation and analysis of intra-tumour heterogeneity (ITH), particularly in genomic studies, has advanced our understanding of the evolutionary forces that shape cancer growth and development. However, only a subset of the variation observed in a single tumour will have an impact on cancer evolution, highlighting the need to distinguish between functional and non-functional ITH. Emerging studies highlight a role for the cancer epigenome, transcriptome and immune microenvironment in functional ITH. Here, we consider the importance of both genetic and non-genetic ITH and their role in tumour evolution, and present the rationale for a broad research focus beyond the cancer genome. Systems-biology analytical approaches will be necessary to outline the scale and importance of functional ITH. By allowing a deeper understanding of tumour evolution this will, in time, encourage development of novel therapies and improve outcomes for patients.
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Affiliation(s)
- James R M Black
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Center of Excellence, University College London Cancer Institute, London, UK
| | - Nicholas McGranahan
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK.
- Cancer Research UK Lung Cancer Center of Excellence, University College London Cancer Institute, London, UK.
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38
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Biswas A, De S. Drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Am J Physiol Cell Physiol 2021; 320:C750-C760. [PMID: 33657326 PMCID: PMC8163571 DOI: 10.1152/ajpcell.00575.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/08/2021] [Accepted: 02/25/2021] [Indexed: 12/19/2022]
Abstract
Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.
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Affiliation(s)
- Antara Biswas
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Subhajyoti De
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
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39
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Kumar S, Chroni A, Tamura K, Sanderford M, Oladeinde O, Aly V, Vu T, Miura S. PathFinder: Bayesian inference of clone migration histories in cancer. Bioinformatics 2021; 36:i675-i683. [PMID: 33381835 DOI: 10.1093/bioinformatics/btaa795] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2020] [Indexed: 12/18/2022] Open
Abstract
SUMMARY Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes.In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. AVAILABILITY AND IMPLEMENTATION PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.
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Affiliation(s)
- Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA.,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Antonia Chroni
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Koichiro Tamura
- Research Center for Genomics and Bioinformatics.,Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo 192-039, Japan
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Olumide Oladeinde
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Vivian Aly
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine.,Department of Biology, Temple University, Philadelphia, PA 19122, USA
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40
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Sun R, Nikolakopoulos AN. Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors. PLoS Comput Biol 2021; 17:e1008838. [PMID: 33730105 PMCID: PMC8007046 DOI: 10.1371/journal.pcbi.1008838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/29/2021] [Accepted: 02/26/2021] [Indexed: 12/24/2022] Open
Abstract
Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters.
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Affiliation(s)
- Ruping Sun
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Athanasios N. Nikolakopoulos
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
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41
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Quinn JJ, Jones MG, Okimoto RA, Nanjo S, Chan MM, Yosef N, Bivona TG, Weissman JS. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 2021; 371:eabc1944. [PMID: 33479121 PMCID: PMC7983364 DOI: 10.1126/science.abc1944] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/23/2020] [Accepted: 12/17/2020] [Indexed: 12/11/2022]
Abstract
Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.
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Affiliation(s)
- Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Inscripta, Inc., Boulder, CO, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Ross A Okimoto
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Shigeki Nanjo
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michelle M Chan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
- Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA
| | - Trever G Bivona
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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42
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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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43
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Tsyvina V, Zelikovsky A, Snir S, Skums P. Inference of mutability landscapes of tumors from single cell sequencing data. PLoS Comput Biol 2020; 16:e1008454. [PMID: 33253159 PMCID: PMC7728263 DOI: 10.1371/journal.pcbi.1008454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 12/10/2020] [Accepted: 10/20/2020] [Indexed: 11/18/2022] Open
Abstract
One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.
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Affiliation(s)
- Viachaslau Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Sagi Snir
- Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
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44
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Chen Z, Gong F, Wan L, Ma L. RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data. Bioinformatics 2020; 36:3299-3306. [PMID: 32159762 DOI: 10.1093/bioinformatics/btaa172] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 02/10/2020] [Accepted: 03/06/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging. RESULTS To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. AVAILABILITY AND IMPLEMENTATION RobustClone software is available at https://github.com/ucasdp/RobustClone. CONTACT lwan@amss.ac.cn or maliang@ioz.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziwei Chen
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fuzhou Gong
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Wan
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
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45
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Woodcock DJ, Riabchenko E, Taavitsainen S, Kankainen M, Gundem G, Brewer DS, Ellonen P, Lepistö M, Golubeva YA, Warner AC, Tolonen T, Jasu J, Isaacs WB, Emmert-Buck MR, Nykter M, Visakorpi T, Bova GS, Wedge DC. Prostate cancer evolution from multilineage primary to single lineage metastases with implications for liquid biopsy. Nat Commun 2020; 11:5070. [PMID: 33033260 PMCID: PMC7545111 DOI: 10.1038/s41467-020-18843-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 09/01/2020] [Indexed: 02/08/2023] Open
Abstract
The evolutionary progression from primary to metastatic prostate cancer is largely uncharted, and the implications for liquid biopsy are unexplored. We infer detailed reconstructions of tumor phylogenies in ten prostate cancer patients with fatal disease, and investigate them in conjunction with histopathology and tumor DNA extracted from blood and cerebrospinal fluid. Substantial evolution occurs within the prostate, resulting in branching into multiple spatially intermixed lineages. One dominant lineage emerges that initiates and drives systemic metastasis, where polyclonal seeding between sites is common. Routes to metastasis differ between patients, and likely genetic drivers of metastasis distinguish the metastatic lineage from the lineage that remains confined to the prostate within each patient. Body fluids capture features of the dominant lineage, and subclonal expansions that occur in the metastatic phase are non-uniformly represented. Cerebrospinal fluid analysis reveals lineages not detected in blood-borne DNA, suggesting possible clinical utility.
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Affiliation(s)
- D J Woodcock
- Big Data Institute, University of Oxford, Old Road Campus, Headington, Oxford, UK
| | - E Riabchenko
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
| | - S Taavitsainen
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
| | - M Kankainen
- Medical and Clinical Genetics and Hematology Research Unit Helsinki, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - G Gundem
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - D S Brewer
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - P Ellonen
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, FIN-00290, Helsinki, Finland
| | - M Lepistö
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, FIN-00290, Helsinki, Finland
| | - Y A Golubeva
- Cancer Genomic Research Laboratory (CGR), Division of Cancer Epidemiology and Genetics, NCI, FNLCR, Leidos Biomedical Research, Inc, Gaithersburg, MD, USA
| | - A C Warner
- Molecular Histopathology Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - T Tolonen
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
- Fimlab Laboratories, Department of Pathology, Tampere University Hospital, Tampere, Finland
| | - J Jasu
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
| | - W B Isaacs
- Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - M R Emmert-Buck
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Avoneaux Medical Institute, Baltimore, MD, USA
| | - M Nykter
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
| | - T Visakorpi
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland
- Fimlab Laboratories, Department of Pathology, Tampere University Hospital, Tampere, Finland
| | - G S Bova
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, FI, 33014, Finland.
| | - D C Wedge
- Big Data Institute, University of Oxford, Old Road Campus, Headington, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Oxford, UK.
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK.
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46
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Weber LL, Aguse N, Chia N, El-Kebir M. PhyDOSE: Design of follow-up single-cell sequencing experiments of tumors. PLoS Comput Biol 2020; 16:e1008240. [PMID: 33001973 PMCID: PMC7553321 DOI: 10.1371/journal.pcbi.1008240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 10/13/2020] [Accepted: 08/12/2020] [Indexed: 01/07/2023] Open
Abstract
The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.
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Affiliation(s)
- Leah L Weber
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nuraini Aguse
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Mohammed El-Kebir
- Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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47
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Zhang AW, Campbell KR. Computational modelling in single-cell cancer genomics: methods and future directions. Phys Biol 2020; 17:061001. [DOI: 10.1088/1478-3975/abacfe] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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48
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Aganezov S, Raphael BJ. Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples. Genome Res 2020; 30:1274-1290. [PMID: 32887685 PMCID: PMC7545144 DOI: 10.1101/gr.256701.119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 08/07/2020] [Indexed: 12/25/2022]
Abstract
Many cancer genomes are extensively rearranged with aberrant chromosomal karyotypes. Deriving these karyotypes from high-throughput DNA sequencing of bulk tumor samples is complicated because most tumors are a heterogeneous mixture of normal cells and subpopulations of cancer cells, or clones, that harbor distinct somatic mutations. We introduce a new algorithm, Reconstructing Cancer Karyotypes (RCK), to reconstruct haplotype-specific karyotypes of one or more rearranged cancer genomes from DNA sequencing data from a bulk tumor sample. RCK leverages evolutionary constraints on the somatic mutational process in cancer to reduce ambiguity in the deconvolution of admixed sequencing data into multiple haplotype-specific cancer karyotypes. RCK models mixtures containing an arbitrary number of derived genomes and allows the incorporation of information both from short-read and long-read DNA sequencing technologies. We compare RCK to existing approaches on 17 primary and metastatic prostate cancer samples. We find that RCK infers cancer karyotypes that better explain the DNA sequencing data and conform to a reasonable evolutionary model. RCK's reconstructions of clone- and haplotype-specific karyotypes will aid further studies of the role of intra-tumor heterogeneity in cancer development and response to treatment. RCK is freely available as open source software.
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Affiliation(s)
- Sergey Aganezov
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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49
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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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50
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Rabbie R, Ansari-Pour N, Cast O, Lau D, Scott F, Welsh SJ, Parkinson C, Khoja L, Moore L, Tullett M, Wong K, Ferreira I, Gómez JMM, Levesque M, Gallagher FA, Jiménez-Sánchez A, Riva L, Miller ML, Allinson K, Campbell PJ, Corrie P, Wedge DC, Adams DJ. Multi-site clonality analysis uncovers pervasive heterogeneity across melanoma metastases. Nat Commun 2020; 11:4306. [PMID: 32855398 PMCID: PMC7453196 DOI: 10.1038/s41467-020-18060-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 07/27/2020] [Indexed: 01/06/2023] Open
Abstract
Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal evidence of diversification among metastatic lineages. UV-induced mutations dominate the trunk, whereas APOBEC-associated mutations are found in the branches of the evolutionary tree. Multi-sample analyses from a further seven patients confirmed that lineage diversification was pervasive, representing an important mode of melanoma dissemination. Our analyses demonstrate that joint analysis of cancer cell fraction estimates across multiple metastases can uncover previously unrecognised levels of tumour heterogeneity and highlight the limitations of inferring heterogeneity from a single biopsy.
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Affiliation(s)
- Roy Rabbie
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Naser Ansari-Pour
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Oliver Cast
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK
| | - Doreen Lau
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, UK
| | - Francis Scott
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, UK
| | - Sarah J Welsh
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Christine Parkinson
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Leila Khoja
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, Vincent Drive, University of Birmingham, Birmingham, UK
| | - Luiza Moore
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Mark Tullett
- St Richard's Hospital, Spitalfield Lane, Chichester, UK
| | - Kim Wong
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Ingrid Ferreira
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Julia M Martínez Gómez
- Department of Dermatology, University of Zurich, University of Zurich Hospital, Gloriastrasse 31, CH-8091, Zurich, Switzerland
| | - Mitchell Levesque
- Department of Dermatology, University of Zurich, University of Zurich Hospital, Gloriastrasse 31, CH-8091, Zurich, Switzerland
| | - Ferdia A Gallagher
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, UK
| | - Alejandro Jiménez-Sánchez
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK
| | - Laura Riva
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Martin L Miller
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK
| | - Kieren Allinson
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Peter J Campbell
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Pippa Corrie
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David C Wedge
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Oxford, UK.
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK.
| | - David J Adams
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK.
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