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Zhang J, Griffin J, Roy K, Hoffmann A, Zangle TA. Tracking of lineage mass via quantitative phase imaging and confinement in low refractive index microwells. LAB ON A CHIP 2024; 24:4440-4449. [PMID: 39190401 PMCID: PMC11412070 DOI: 10.1039/d4lc00389f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Measurements of cell lineages are central to a variety of fundamental biological questions, ranging from developmental to cancer biology. However, accurate lineage tracing requires nearly perfect cell tracking, which can be challenging due to cell motion during imaging. Here we demonstrate the integration of microfabrication, imaging, and image processing approaches to demonstrate a platform for cell lineage tracing. We use quantitative phase imaging (QPI), a label-free imaging approach that quantifies cell mass. This gives an additional parameter, cell mass, that can be used to improve tracking accuracy. We confine lineages within microwells fabricated to reduce cell adhesion to sidewalls made of a low refractive index polymer. This also allows the microwells themselves to serve as references for QPI, enabling measurement of cell mass even in confluent microwells. We demonstrate application of this approach to immortalized adherent and nonadherent cell lines as well as stimulated primary B cells cultured ex vivo. Overall, our approach enables lineage tracking, or measurement of lineage mass, in a platform that can be customized to varied cell types.
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Affiliation(s)
- Jingzhou Zhang
- Department of Chemical Engineering, University of Utah, USA.
| | - Justin Griffin
- Department of Chemical Engineering, University of Utah, USA.
| | - Koushik Roy
- Division of Microbiology and Immunology, Department of Pathology, School of Medicine, University of Utah, USA
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Institute for Quantitative and Computational Biosciences, and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Thomas A Zangle
- Department of Chemical Engineering, University of Utah, USA.
- Huntsman Cancer Institute, University of Utah, USA
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2
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Wang HF, Chen Y, Cao B, Pei J. Potential Value of HSP90α in Prognosis of Triple-Negative Breast Cancer. Med Sci Monit 2024; 30:e943049. [PMID: 38553816 PMCID: PMC10989195 DOI: 10.12659/msm.943049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/08/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is a distinct subtype of breast cancer, accounting for 12-18% of all breast cancer cases. It exhibits high heterogeneity and aggressiveness, resulting in a poorer prognosis with a high risk of early recurrence and metastasis. Due to the lack of expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), as well as insensitivity to endocrine therapy, determining a standard treatment for TNBC is challenging. The identification of potential prognostic biomarkers is crucial for developing personalized treatment strategies for patients. MATERIAL AND METHODS Our study investigated the potential value of HSP90a in TNBC prognosis. A retrospective analysis was conducted on 127 TNBC patients and 127 Healthy controls from March 1, 2019 to July 31, 2022. Venous blood was collected and tested for HSP90alpha, CEA, CA199, and CA125, and we recorded the clinical characteristics of the patients, including age, BMI, alcohol consumption status, surgical history, CEA level, CA199 level, CA125 level, HSP90alpha level, tumor size, distant metastases, lymph node metastasis, and TNM stage. Univariate and multivariate methods were used to screen independent risk factors for progression-free survival (PFS) and overall survival (OS). RESULTS HSP90alpha is not only upregulated in TNBC but is also highly correlated with lymph node metastasis and TNM stage. The results of multivariate analysis showed that distant metastasis, TNM stage and HSP90a level were independent factors associated with PFS. BMI, tumor size, TNM stage, surgical history, and HSP90a level were independent factors influencing OS. CONCLUSIONS Our research findings demonstrate a significant association between high HSP90alpha expression and adverse clinical features, suggesting a poorer prognosis for TNBC patients.
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Affiliation(s)
- Han Fei Wang
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Ying Chen
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Bang Cao
- Department of Breast Surgery, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, PR China
| | - Jing Pei
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
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3
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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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4
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Witz A, Dardare J, Betz M, Gilson P, Merlin JL, Harlé A. Tumor-derived cell-free DNA and circulating tumor cells: partners or rivals in metastasis formation? Clin Exp Med 2024; 24:2. [PMID: 38231464 PMCID: PMC10794481 DOI: 10.1007/s10238-023-01278-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/20/2023] [Indexed: 01/18/2024]
Abstract
The origin of metastases is a topic that has sparked controversy. Despite recent advancements, metastatic disease continues to pose challenges. The first admitted model of how metastases develop revolves around cells breaking away from the primary tumor, known as circulating tumor cells (CTCs). These cells survive while circulating through the bloodstream and subsequently establish themselves in secondary organs, a process often referred to as the "metastatic cascade". This intricate and dynamic process involves various steps, but all the mechanisms behind metastatic dissemination are not yet comprehensively elucidated. The "seed and soil" theory has shed light on the phenomenon of metastatic organotropism and the existence of pre-metastatic niches. It is now established that these niches can be primed by factors secreted by the primary tumor before the arrival of CTCs. In particular, exosomes have been identified as important contributors to this priming. Another concept then emerged, i.e. the "genometastasis" theory, which challenged all other postulates. It emphasizes the intriguing but promising role of cell-free DNA (cfDNA) in metastasis formation through oncogenic formation of recipient cells. However, it cannot be ruled out that all these theories are intertwined. This review outlines the primary theories regarding the metastases formation that involve CTCs, and depicts cfDNA, a potential second player in the metastasis formation. We discuss the potential interrelationships between CTCs and cfDNA, and propose both in vitro and in vivo experimental strategies to explore all plausible theories.
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Affiliation(s)
- Andréa Witz
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France.
| | - Julie Dardare
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France
| | - Margaux Betz
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France
| | - Pauline Gilson
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France
| | - Jean-Louis Merlin
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France
| | - Alexandre Harlé
- Département de Biopathologie, Institut de Cancérologie de Lorraine, CNRS UMR 7039 CRAN-Université de Lorraine, 6 avenue de Bourgogne, 54519, Vandœuvre-lès-Nancy Cedex, France
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Hyug Choi J, Sook Jun M, Yong Jeon J, Kim HS, Kyung Kim Y, Ho Jeon C, Hwan Choi S, Sun Kim D, Han MH, Won Oh J. Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents. J Transl Int Med 2023; 11:410-422. [PMID: 38130632 PMCID: PMC10732492 DOI: 10.2478/jtim-2023-0118] [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] [Indexed: 12/23/2023] Open
Abstract
Objective The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts. Methods The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights. Results The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions. Conclusions The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.
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Affiliation(s)
- June Hyug Choi
- Department of Anatomy, BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Mee Sook Jun
- Department of Internal Medicine, Chungbuk National University, Cheongju, Yonsei University-Industry Foundation, Seoul, Republic of Korea
| | | | - Hae-Suk Kim
- Theragen Bio Co., Ltd., Seongnam-si, Republic of Korea
| | - Yu Kyung Kim
- Department of Clinical Pathology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Chang Ho Jeon
- Department of Laboratory Medicine, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Seock Hwan Choi
- Department of Urology, School of Medicine, BioMedical Research Institute, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea
| | - Dong Sun Kim
- Department of Anatomy, BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Man-Hoon Han
- Department of Pathology, School of Medicine, BioMedical Research Institute, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea
| | - Ji Won Oh
- Department of Anatomy, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
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Maugeri S, Sibbitts J, Privitera A, Cardaci V, Di Pietro L, Leggio L, Iraci N, Lunte SM, Caruso G. The Anti-Cancer Activity of the Naturally Occurring Dipeptide Carnosine: Potential for Breast Cancer. Cells 2023; 12:2592. [PMID: 37998326 PMCID: PMC10670273 DOI: 10.3390/cells12222592] [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: 09/25/2023] [Revised: 10/27/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
Carnosine is an endogenous dipeptide composed of β-alanine and L-histidine, possessing a multimodal pharmacodynamic profile that includes anti-inflammatory and anti-oxidant activities. Carnosine has also shown its ability to modulate cell proliferation, cell cycle arrest, apoptosis, and even glycolytic energy metabolism, all processes playing a key role in the context of cancer. Cancer is one of the most dreaded diseases of the 20th and 21st centuries. Among the different types of cancer, breast cancer represents the most common non-skin cancer among women, accounting for an estimated 15% of all cancer-related deaths in women. The main aim of the present review was to provide an overview of studies on the anti-cancer activity of carnosine, and in particular its activity against breast cancer. We also highlighted the possible advantages and limitations involved in the use of this dipeptide. The first part of the review entailed a brief description of carnosine's biological activities and the pathophysiology of cancer, with a focus on breast cancer. The second part of the review described the anti-tumoral activity of carnosine, for which numerous studies have been carried out, especially at the preclinical level, showing promising results. However, only a few studies have investigated the therapeutic potential of this dipeptide for breast cancer prevention or treatment. In this context, carnosine has shown to be able to decrease the size of cancer cells and their viability. It also reduces the levels of vascular endothelial growth factor (VEGF), cyclin D1, NAD+, and ATP, as well as cytochrome c oxidase activity in vitro. When tested in mice with induced breast cancer, carnosine proved to be non-toxic to healthy cells and exhibited chemopreventive activity by reducing tumor growth. Some evidence has also been reported at the clinical level. A randomized phase III prospective placebo-controlled trial showed the ability of Zn-carnosine to prevent dysphagia in breast cancer patients undergoing adjuvant radiotherapy. Despite this evidence, more preclinical and clinical studies are needed to better understand carnosine's anti-tumoral activity, especially in the context of breast cancer.
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Affiliation(s)
- Salvatore Maugeri
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
| | - Jay Sibbitts
- Ralph N. Adams Institute for Bioanalytical Chemistry, University of Kansas, Lawrence, KS 66047, USA
- Department of Chemistry, University of Kansas, Lawrence, KS 66047, USA
| | - Anna Privitera
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Vincenzo Cardaci
- Scuola Superiore di Catania, University of Catania, 95123 Catania, Italy
- Vita-Salute San Raffaele University, 20132 Milano, Italy
| | - Lucia Di Pietro
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
- Scuola Superiore di Catania, University of Catania, 95123 Catania, Italy
| | - Loredana Leggio
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Nunzio Iraci
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Susan M. Lunte
- Ralph N. Adams Institute for Bioanalytical Chemistry, University of Kansas, Lawrence, KS 66047, USA
- Department of Chemistry, University of Kansas, Lawrence, KS 66047, USA
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, USA
| | - Giuseppe Caruso
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
- Unit of Neuropharmacology and Translational Neurosciences, Oasi Research Institute-IRCCS, 94018 Troina, Italy
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7
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Nurminen A, Jaatinen S, Taavitsainen S, Högnäs G, Lesluyes T, Ansari-Pour N, Tolonen T, Haase K, Koskenalho A, Kankainen M, Jasu J, Rauhala H, Kesäniemi J, Nikupaavola T, Kujala P, Rinta-Kiikka I, Riikonen J, Kaipia A, Murtola T, Tammela TL, Visakorpi T, Nykter M, Wedge DC, Van Loo P, Bova GS. Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine. Genome Med 2023; 15:82. [PMID: 37828555 PMCID: PMC10571458 DOI: 10.1186/s13073-023-01242-y] [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] [Received: 04/13/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question. METHODS We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones. RESULTS In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases. CONCLUSIONS PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.
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Affiliation(s)
- Anssi Nurminen
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Serafiina Jaatinen
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Sinja Taavitsainen
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Gunilla Högnäs
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Tom Lesluyes
- The Francis Crick Institute, London, NW1 1AT, UK
| | - Naser Ansari-Pour
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Teemu Tolonen
- Fimlab Laboratories, Department of Pathology, Tampere University Hospital, Tampere, Finland
| | - Kerstin Haase
- The Francis Crick Institute, London, NW1 1AT, UK
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität Zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany
| | - Antti Koskenalho
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Matti Kankainen
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00290, Finland
| | - Juho Jasu
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Hanna Rauhala
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Jenni Kesäniemi
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Tiia Nikupaavola
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - Paula Kujala
- Fimlab Laboratories, Department of Pathology, Tampere University Hospital, Tampere, Finland
| | - Irina Rinta-Kiikka
- Imaging Centre, Department of Radiology, Tampere University Hospital, Tampere, Finland
| | - Jarno Riikonen
- Department of Urology, TAYS Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Antti Kaipia
- Department of Urology, TAYS Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Teemu Murtola
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
- Department of Urology, TAYS Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Teuvo L Tammela
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
- Department of Urology, TAYS Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Tapio Visakorpi
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
- Fimlab Laboratories, Department of Pathology, Tampere University Hospital, Tampere, Finland
| | - Matti Nykter
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland
| | - David C Wedge
- Manchester Cancer Research Centre, Division of Cancer Sciences, University of Manchester, Manchester, M20 4GJ, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, NW1 1AT, UK
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - G Steven Bova
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University and Tays Cancer Center, PO Box 100, 33014, Tampere, Finland.
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Ceresa D, Alessandrini F, Lucchini S, Marubbi D, Piaggio F, Mena Vera JM, Ceccherini I, Reverberi D, Appolloni I, Malatesta P. Early clonal extinction in glioblastoma progression revealed by genetic barcoding. Cancer Cell 2023; 41:1466-1479.e9. [PMID: 37541243 DOI: 10.1016/j.ccell.2023.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2023] [Accepted: 07/07/2023] [Indexed: 08/06/2023]
Abstract
Glioblastoma progression in its early stages remains poorly understood. Here, we transfer PDGFB and genetic barcodes in mouse brain to initiate gliomagenesis and enable direct tracing of glioblastoma evolution from its earliest possible stage. Unexpectedly, we observe a high incidence of clonal extinction events and progressive divergence in clonal sizes, even after the acquisition of malignant phenotype. Computational modeling suggests these dynamics result from clonal-based cell-cell competition. Through bulk and single-cell transcriptome analyses, coupled with lineage tracing, we reveal that Myc transcriptional targets have the strongest correlation with clonal size imbalances. Moreover, we show that the downregulation of Myc expression is sufficient to drive competitive dynamics in intracranially transplanted gliomas. Our findings provide insights into glioblastoma evolution that are inaccessible using conventional retrospective approaches, highlighting the potential of combining clonal tracing and transcriptomic analyses in this field.
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Affiliation(s)
- Davide Ceresa
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy
| | - Francesco Alessandrini
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy
| | - Sara Lucchini
- Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy
| | - Daniela Marubbi
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy
| | | | - Jorge Miguel Mena Vera
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy
| | - Isabella Ceccherini
- Laboratory of Genetics and Genomics of Rare Diseases, IRCCS Istituto Giannina Gaslini, 16147 Genova, Italy
| | | | - Irene Appolloni
- Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy
| | - Paolo Malatesta
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; Department of Experimental Medicine (DIMES), University of Genova, 16132 Genova, Italy.
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9
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Połeć A, Ekstrøm PO, Fougner C, Sørlie T, Norum JH. Rapid assessment of 3-dimensional intra-tumor heterogeneity through cycling temperature capillary electrophoresis. BMC Res Notes 2023; 16:167. [PMID: 37568187 PMCID: PMC10416412 DOI: 10.1186/s13104-023-06437-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
OBJECTIVE Tumors are heterogeneous three-dimensional masses populated by numerous cell types, including distinct sub-clones of cancerous cells. Various sub-clones within the same tumor mass may respond differently to cancer treatment, and intra-tumor heterogeneity contributes to acquired therapeutic resistance. Thus, one tissue biopsy will in most cases not be representative of the entire genetic landscape of a tumor mass. In this study, we aimed to establish an easily accessible, low cost method to address intra-tumor heterogeneity in three dimensions, for a limited number of DNA alterations. RESULTS This study includes analyses of the three-dimensional (3D) distribution of DNA mutations in human colon cancer and mouse mammary gland tumor tissue samples. We used laser capture microdissection for the unbiased collection of tissue in several XY-planes throughout the tumor masses. Cycling temperature capillary electrophoresis was used to determine mutant allele frequency. High-resolution distribution maps of KRAS and Trp53 mutations were generated for each XY-plane in human and mouse tumor samples, respectively. To provide a holistic interpretation of the mutation distribution, we generated interactive 3D heatmaps giving an easily interpretable understanding of the spatial distribution of the analyzed mutations. The method described herein provides an accessible way of describing intra-tumor heterogeneity for a limited number of mutations.
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Affiliation(s)
- Anna Połeć
- Department of Cancer Genetics, Institute for Cancer Research, Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Per Olaf Ekstrøm
- Department of Tumor Biology, Institute for Cancer Research, Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Christian Fougner
- Department of Cancer Genetics, Institute for Cancer Research, Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Radium Hospital, Oslo University Hospital, Oslo, Norway
- Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jens Henrik Norum
- Department of Cancer Genetics, Institute for Cancer Research, Radium Hospital, Oslo University Hospital, Oslo, Norway.
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Powell CL, Saddoughi SA, Wigle DA. Progress in genome-inspired treatment decisions for multifocal lung adenocarcinoma. Expert Rev Respir Med 2023; 17:1009-1021. [PMID: 37982734 DOI: 10.1080/17476348.2023.2286277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/17/2023] [Indexed: 11/21/2023]
Abstract
INTRODUCTION Multifocal lung adenocarcinoma (MFLA) is becoming increasingly recognized as a distinct subset of lung cancer, with unique biology, disease course, and treatment outcomes. While definitions remain controversial, MFLA is characterized by the development and concurrent presence of multiple independent (non-metastatic) lesions on the lung adenocarcinoma spectrum. Disease progression typically follows an indolent course measured in years, with a lower propensity for nodal and distant metastases than other more common forms of non-small cell lung cancer. AREAS COVERED Traditional imaging and histopathological analyses of tumor biopsies are frequently unable to fully characterize the disease, prompting interest in molecular diagnosis. We highlight some of the key questions in the field, including accurate definitions to identify and stage MLFA, molecular tests to stratify patients and treatment decisions, and the lack of clinical trial data to delineate best management for this poorly understood subset of lung cancer patients. We review the existing literature and progress toward a genomic diagnosis for this unique disease entity. EXPERT OPINION Multifocal lung adenocarcinoma behaves differently than other forms of non-small cell lung cancer. Progress in molecular diagnosis may enhance potential for accurate definition, diagnosis, and optimizing treatment approach.
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Affiliation(s)
- Chelsea L Powell
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Sahar A Saddoughi
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Dennis A Wigle
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
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11
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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12
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Yang G, Lu T, Weisenberger DJ, Liang G. The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets. Genes (Basel) 2022; 13:1555. [PMID: 36140723 PMCID: PMC9498783 DOI: 10.3390/genes13091555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/08/2022] [Accepted: 08/23/2022] [Indexed: 12/02/2022] Open
Abstract
Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.
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Affiliation(s)
- Guang Yang
- School of Sciences, China Pharmaceutical University, Nanjing 211121, China
- China Grand Enterprises, Beijing 100101, China
| | - Tao Lu
- School of Sciences, China Pharmaceutical University, Nanjing 211121, China
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211121, China
| | - Daniel J. Weisenberger
- Department of Biochemistry and Molecular Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Gangning Liang
- Department of Urology, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
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13
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Evolution of intra-tumoral heterogeneity across different pathological stages in papillary thyroid carcinoma. Cancer Cell Int 2022; 22:263. [PMID: 35996174 PMCID: PMC9394008 DOI: 10.1186/s12935-022-02680-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Intra-tumor heterogeneity (ITH) results from the continuous accumulation of mutations during disease progression, thus impacting patients' clinical outcome. How the ITH evolves across papillary thyroid carcinoma (PTC) different tumor stages is lacking. METHODS We used the whole-exome sequencing data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort to track the ITH and assessed its relationship with clinical features through different stages of the PTC progression. We further assayed the expression levels of the specific genes in papillary thyroid cancer cell lines compared to an immortalized normal thyroid epithelial cell line by qRT-PCR. RESULTS We revealed the timing of mutational processes and the dynamics of the temporal acquisition of somatic events during the lifetime of the PTC. ITH significantly influences the PTC patient's survival rate and, as genetic heterogeneity increases, the prognosis gets worse in advanced tumor stages. ITH also affects the mutational architecture of each clinical stage which is subject to periodic fluctuations. Different mutational processes may cooperate to shape a stage-specific mutational spectrum during the progression from early to advanced tumor stages. Moreover, different evolutionary paths characterize PTC progression across pathological stages due to both mutations recurrently occurring in all stages in hotspot positions and distinct codon changes dominating in different stages. A different expression level of specific genes also exists in different thyroid cancer cell lines. CONCLUSIONS Our findings suggest ITH as a potential unfavorable prognostic factor in PTC and highlight the dynamic changes in different clinical stages of PTC, providing some clues for the precision medicine and suggesting different diagnostic decisions depending on the clinical stages of patients. Finally, complete clear guidelines to define risk stratification of PTC patients are lacking; thus, this work could contribute to defining patients who need more aggressive treatments and, in turn, could reduce the social burden of this cancer.
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14
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Liu Y, Chen L, Yu J, Ye L, Hu H, Wang J, Wu B. Advances in Single-Cell Toxicogenomics in Environmental Toxicology. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11132-11145. [PMID: 35881918 DOI: 10.1021/acs.est.2c01098] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The toxicity evaluation system of environmental pollutants has undergone numerous changes due to the application of new technologies. Single-cell toxicogenomics is rapidly changing our view on environmental toxicology by increasing the resolution of our analysis to the level of a single cell. Applications of this technology in environmental toxicology have begun to emerge and are rapidly expanding the portfolio of existing technologies and applications. Here, we first summarized different methods involved in single-cell isolation and amplification in single-cell sequencing process, compared the advantages and disadvantages of different methods, and analyzed their development trends. Then, we reviewed the main advances of single-cell toxicogenomics in environmental toxicology, emphatically analyzed the application prospects of this technology in identifying the target cells of pollutants in early embryos, clarifying the heterogeneous response of cell subtypes to pollutants, and finding pathogenic bacteria in unknown microbes, and highlighted the unique characteristics of this approach with high resolution, high throughput, and high specificity by examples. We also offered a prediction of the further application of this technology and the revolution it brings in environmental toxicology. Overall, these advances will provide practical solutions for controlling or mitigating exogenous toxicological effects that threaten human and ecosystem health, contribute to improving our understanding of the physiological processes affected by pollutants, and lead to the emergence of new methods of pollution control.
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Affiliation(s)
- Yuxuan Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Ling Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jing Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Haidong Hu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jinfeng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
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15
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Guo Q, Spasic M, Maynard AG, Goreczny GJ, Bizuayehu A, Olive JF, van Galen P, McAllister SS. Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research. Nat Commun 2022; 13:3837. [PMID: 35788590 PMCID: PMC9252988 DOI: 10.1038/s41467-022-31536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 06/21/2022] [Indexed: 11/27/2022] Open
Abstract
Single-cell analysis methods are valuable tools; however, current approaches do not easily enable live cell retrieval. That is a particular issue when further study of cells that were eliminated during experimentation could provide critical information. We report a clonal molecular barcoding method, called SunCatcher, that enables longitudinal tracking and live cell functional analysis. From complex cell populations, we generate single cell-derived clonal populations, infect each with a unique molecular barcode, and retain stocks of individual barcoded clones (BCs). We develop quantitative PCR-based and next-generation sequencing methods that we employ to identify and quantify BCs in vitro and in vivo. We apply SunCatcher to various breast cancer cell lines and combine respective BCs to create versions of the original cell lines. While the heterogeneous BC pools reproduce their original parental cell line proliferation and tumor progression rates, individual BCs are phenotypically and functionally diverse. Early spontaneous metastases can also be identified and quantified. SunCatcher thus provides a rapid and sensitive approach for studying live single-cell clones and clonal evolution, and performing functional analyses.
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Affiliation(s)
- Qiuchen Guo
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Milos Spasic
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Adam G Maynard
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Gregory J Goreczny
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Amanuel Bizuayehu
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Jessica F Olive
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter van Galen
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Sandra S McAllister
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
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16
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Hagemeijer YP, Guryev V, Horvatovich P. Accurate Prediction of Protein Sequences for Proteogenomics Data Integration. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2420:233-260. [PMID: 34905178 DOI: 10.1007/978-1-0716-1936-0_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This book chapter discusses proteogenomics data integration and provides an overview into the different omics layer involved in defining the proteome of a living organism. Various aspects of genome variability affecting either the sequence or abundance level of proteins are discussed in this book chapter, such as the effect of single-nucleotide variants or larger genomic structural variants on the proteome. Next, various sequencing technologies are introduced and discussed from a proteogenomics data integration perspective such as those providing short- and long-read sequencing and listing their respective advantages and shortcomings for accurate protein variant prediction using genomic/transcriptomics sequencing data. Finally, the various bioinformatics tools used to process and analyze DNA/RNA sequencing data are discussed with the ultimate goal of obtaining accurately predicted sample-specific protein sequences that can be used as a drop-in replacement in existing approaches for peptide and protein identification using popular database search engines such as MSFragger, SearchGUI/PeptideShaker.
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Affiliation(s)
- Yanick Paco Hagemeijer
- Department of Analytical Biochemistry, University of Groningen, Groningen Research Institute of Pharmacy, Groningen, The Netherlands.,European Research Institute for the Biology of Ageing, University Medical Center Groningen, Groningen, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter Horvatovich
- Department of Analytical Biochemistry, University of Groningen, Groningen Research Institute of Pharmacy, Groningen, The Netherlands.
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17
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Yu T, Gao X, Zheng Z, Zhao X, Zhang S, Li C, Liu G. Intratumor Heterogeneity as a Prognostic Factor in Solid Tumors: A Systematic Review and Meta-Analysis. Front Oncol 2021; 11:744064. [PMID: 34722299 PMCID: PMC8554141 DOI: 10.3389/fonc.2021.744064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/17/2021] [Indexed: 11/13/2022] Open
Abstract
Background The landscape of intratumor heterogeneity (ITH) is present from the tumor evolution. ITH is a promising clinical indicator, but the association between ITH and prognosis remains controversial. Therefore, a meta-analysis was performed to explore whether ITH can serve as a valuable prognostic indicator in solid tumors. Methods All included studies were from PubMed, Embase, Cochrane, and Web of Science databases up to October 10, 2020. Studies based on ITH with available prognostic information were included. Three researchers independently completed study selection and data extraction following PRISMA guidelines. The random-effect model was used for synthesis. Hazard ratio (HR) and 95% confidence intervals (CI) were used with the endpoint defined by overall survival (OS), disease-specific survival (DFS), and progression-free survival (PFS). Results A total of 9,804 solid tumor patients from 21 studies were included. Analysis of specific cancers in the TCGA database showed similar results based on different ITH assessment methods, which provided the logical support for data consolidation. Available evidence revealed a negative relationship between ITH and prognosis for a specific cancer (such as lung cancer). However, the OS results from 14 tumor types showed that high ITH associated with shorter survival time [HR 1.65 (95% CI, 1.42-1.91)]. PFS and DFS analyses showed similar results [HR 1.89 (95% CI, 1.41-2.54) and HR 1.87 (95% CI, 1.15-3.04)] in general. The status of tumor metastasis and sampling models were not the confounding factors. Conclusions High ITH is associated with worse prognosis in many solid tumors in general although this association was absent for some cancers. ITH is expected to be a promising clinical prognostic factor for the improvement of assessment, treatment, and surveillance strategy.
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Affiliation(s)
- Tao Yu
- Department of Oncology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xin Gao
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Zicheng Zheng
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xinyu Zhao
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Shiyao Zhang
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunqiang Li
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Gang Liu
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Institute of General Surgery, Tianjin Medical University General Hospital, Tianjin, China
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18
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Viera M, Yip GWC, Shen HM, Baeg GH, Bay BH. Targeting CD82/KAI1 for Precision Therapeutics in Surmounting Metastatic Potential in Breast Cancer. Cancers (Basel) 2021; 13:4486. [PMID: 34503296 PMCID: PMC8431267 DOI: 10.3390/cancers13174486] [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: 07/29/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 11/16/2022] Open
Abstract
Metastasis is the main cause of mortality in breast cancer patients. There is an unmet need to develop therapies that can impede metastatic spread. Precision oncology has shown great promise for the treatment of cancers, as the therapeutic approach is tailored to a specific group of patients who are likely to benefit from the treatment, rather than the traditional approach of "one size fits all". CD82, also known as KAI1, a glycoprotein belonging to the tetraspanin family and an established metastasis suppressor, could potentially be exploited to hinder metastases in breast cancer. This review explores the prospect of targeting CD82 as an innovative therapeutic approach in precision medicine for breast cancer patients, with the goal of preventing cancer progression and metastasis. Such an approach would entail the selection of a subset of breast cancer patients with low levels of CD82, and instituting an appropriate treatment scheme tailored towards restoring the levels of CD82 in this group of patients. Proposed precision treatment regimens include current modalities of treating breast cancer, in combination with either clinically approved drugs that could restore the levels of CD82, CD82 peptide mimics or non-coding RNA-based therapeutics.
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Affiliation(s)
- Maximillian Viera
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore; (M.V.); (G.W.C.Y.)
| | - George Wai Cheong Yip
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore; (M.V.); (G.W.C.Y.)
| | - Han-Ming Shen
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore;
- Faculty of Health Sciences, University of Macau, Taipa, China
| | - Gyeong Hun Baeg
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore; (M.V.); (G.W.C.Y.)
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
| | - Boon Huat Bay
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore; (M.V.); (G.W.C.Y.)
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19
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Rashid S, Shah S, Bar-Joseph Z, Pandya R. Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data. Bioinformatics 2021; 37:1535-1543. [PMID: 30768159 PMCID: PMC11025345 DOI: 10.1093/bioinformatics/btz095] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 01/18/2019] [Accepted: 02/13/2019] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data. RESULTS Here we describe 'Dhaka', a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and six single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data. AVAILABILITY AND IMPLEMENTATION All the datasets used in the paper are publicly available and developed software package and supporting info is available on Github https://github.com/MicrosoftGenomics/Dhaka. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sabrina Rashid
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15232, USA
| | - Sohrab Shah
- Department of Computer Science
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC V5Z 4E6, Canada
| | - Ziv Bar-Joseph
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15232, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15232, USA
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20
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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: 138] [Impact Index Per Article: 46.0] [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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21
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Pérez-Velázquez J, Rejniak KA. Drug-Induced Resistance in Micrometastases: Analysis of Spatio-Temporal Cell Lineages. Front Physiol 2020; 11:319. [PMID: 32362836 PMCID: PMC7180185 DOI: 10.3389/fphys.2020.00319] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/20/2020] [Indexed: 12/16/2022] Open
Abstract
Resistance to anti-cancer drugs is a major cause of treatment failure. While several intracellular mechanisms of resistance have been postulated, the role of extrinsic factors in the development of resistance in individual tumor cells is still not fully understood. Here we used a hybrid agent-based model to investigate how sensitive tumor cells develop drug resistance in the heterogeneous tumor microenvironment. We characterized the spatio-temporal evolution of lineages of the resistant cells and examined how resistance at the single-cell level contributes to the overall tumor resistance. We also developed new methods to track tumor cell adaptation, to trace cell viability trajectories and to examine the three-dimensional spatio-temporal lineage trees. Our findings indicate that drug-induced resistance can result from cells adaptation to the changes in drug distribution. Two modes of cell adaptation were identified that coincide with microenvironmental niches—areas sheltered by cell micro-communities (protectorates) or regions with limited drug penetration (refuga or sanctuaries). We also recognized that certain cells gave rise to lineages of resistant cells (precursors of resistance) and pinpointed three temporal periods and spatial locations at which such cells emerged. This supports the hypothesis that tumor micrometastases do not need to harbor cell populations with pre-existing resistance, but that individual tumor cells can adapt and develop resistance induced by the drug during the treatment.
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Affiliation(s)
- Judith Pérez-Velázquez
- Mathematical Modeling of Biological Systems, Centre for Mathematical Science, Technical University of Munich, Garching, Germany
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Tampa, FL, United States
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22
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Sanders AD, Meiers S, Ghareghani M, Porubsky D, Jeong H, van Vliet MACC, Rausch T, Richter-Pechańska P, Kunz JB, Jenni S, Bolognini D, Longo GMC, Raeder B, Kinanen V, Zimmermann J, Benes V, Schrappe M, Mardin BR, Kulozik AE, Bornhauser B, Bourquin JP, Marschall T, Korbel JO. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 2020; 38:343-354. [PMID: 31873213 PMCID: PMC7612647 DOI: 10.1038/s41587-019-0366-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 11/20/2019] [Indexed: 02/07/2023]
Abstract
Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.
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Affiliation(s)
- Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sascha Meiers
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maryam Ghareghani
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
| | - David Porubsky
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Hyobin Jeong
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Tobias Rausch
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | - Paulina Richter-Pechańska
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Joachim B Kunz
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Silvia Jenni
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Davide Bolognini
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Gabriel M C Longo
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Benjamin Raeder
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Venla Kinanen
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Jürgen Zimmermann
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Vladimir Benes
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Martin Schrappe
- Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Balca R Mardin
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- BioMed X Innovation Center, Heidelberg, Germany
| | - Andreas E Kulozik
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Beat Bornhauser
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Jean-Pierre Bourquin
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
- Max Planck Institute for Informatics, Saarbrücken, Germany.
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.
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23
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Masoodi T, Siraj AK, Siraj S, Azam S, Qadri Z, Parvathareddy SK, Al-Sobhi SS, AlDawish M, Alkuraya FS, Al-Kuraya KS. Evolution and Impact of Subclonal Mutations in Papillary Thyroid Cancer. Am J Hum Genet 2019; 105:959-973. [PMID: 31668701 DOI: 10.1016/j.ajhg.2019.09.026] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 09/27/2019] [Indexed: 01/02/2023] Open
Abstract
Unlike many cancers, the pattern of tumor evolution in papillary thyroid cancer (PTC) and its potential role in relapse have not been elucidated. In this study, multi-region whole-exome sequencing (WES) was performed on early-stage PTC tumors (n = 257 tumor regions) from 79 individuals, including 17 who had developed relapse, to understand the temporal and spatial framework within which subclonal mutations catalyze tumor evolution and its potential clinical relevance. Paired primary-relapse tumor tissues were also available for a subset of individuals. The resulting catalog of variants was analyzed to explore evolutionary histories, define clonal and subclonal events, and assess the relationship between intra-tumor heterogeneity and relapse-free survival. The multi-region WES approach was key in correctly classifying subclonal mutations, 40% of which would have otherwise been erroneously considered clonal. We observed both linear and branching evolution patterns in our PTC cohort. A higher burden of subclonal mutations was significantly associated with increased risk of relapse. We conclude that relapse in PTC, while generally rare, does not follow a predictable evolutionary path and that subclonal mutation burden may serve as a prognostic factor. Larger studies utilizing multi-region sequencing in relapsed PTC case subjects with matching primary tissues are needed to confirm these observations.
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Affiliation(s)
- Tariq Masoodi
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Abdul K Siraj
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Sarah Siraj
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Saud Azam
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Zeeshan Qadri
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Sandeep K Parvathareddy
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Saif S Al-Sobhi
- Department of Surgery, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia
| | - Mohammed AlDawish
- Department of Endocrinology and Diabetes, Prince Sultan Military Medical City, PO Box 261370, Riyadh 11342, Saudi Arabia
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia; Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
| | - Khawla S Al-Kuraya
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, PO Box 3354, Riyadh 11211, Saudi Arabia.
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Cytogenetics and Cytogenomics Evaluation in Cancer. Int J Mol Sci 2019; 20:ijms20194711. [PMID: 31547595 PMCID: PMC6801775 DOI: 10.3390/ijms20194711] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/07/2023] Open
Abstract
The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype-phenotype.
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25
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Liang Y, Zhang H, Song X, Yang Q. Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets. Semin Cancer Biol 2019; 60:14-27. [PMID: 31421262 DOI: 10.1016/j.semcancer.2019.08.012] [Citation(s) in RCA: 455] [Impact Index Per Article: 91.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 08/11/2019] [Accepted: 08/12/2019] [Indexed: 02/08/2023]
Abstract
Breast cancer is one of the most common malignancies among women throughout the world and is the major cause of most cancer-related deaths. Several explanations account for the high rate of mortality of breast cancer, and metastasis to vital organs is identified as the principal cause. Over the past few years, intensive efforts have demonstrated that breast cancer exhibits metastatic heterogeneity with distinct metastatic precedence to various organs, giving rise to differences in prognoses and responses to therapy in breast cancer patients. Bone, lung, liver, and brain are generally accepted as the primary target sites of breast cancer metastasis. However, the underlying molecular mechanism of metastatic heterogeneity of breast cancer remains to be further elucidated. Recently, the advent of novel genomic and pathologic approaches as well as technological breakthroughs in imaging analysis and animal modelling have yielded an unprecedented change in our understanding of the heterogeneity of breast cancer metastasis and provided novel insight for establishing more effective therapeutics. This review summarizes recent molecular mechanisms and emerging concepts on the metastatic heterogeneity of breast cancer and discusses the potential of identifying specific molecules against tumor cells or tumor microenvironments to thwart the development of metastatic disease and improve the prognosis of breast cancer patients.
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Affiliation(s)
- Yiran Liang
- Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, 250012, PR China
| | - Hanwen Zhang
- Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, 250012, PR China
| | - Xiaojin Song
- Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, 250012, PR China
| | - Qifeng Yang
- Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, 250012, PR China; Pathology Tissue Bank, Qilu Hospital, Shandong University, Jinan, Shandong, 250012, PR China.
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26
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Cox MC, Deng C, Naler LB, Lu C, Verbridge SS. Effects of culture condition on epigenomic profiles of brain tumor cells. ACS Biomater Sci Eng 2019; 5:1544-1552. [PMID: 31799379 PMCID: PMC6886720 DOI: 10.1021/acsbiomaterials.9b00161] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Personalized cancer medicine offers the promise of more effective treatments that are tailored to an individual's own dynamic cancer phenotype. Meanwhile, tissue-engineering approaches to modeling tumors may complement these advances by providing a powerful new approach to understanding the adaptation dynamics occurring during treatment. However, in both of these areas new tools will be required to gain a full picture of the genetic and epigenetic regulators of phenotype dynamics occurring in the small populations of cells that drive resistance. In this study, we perform epigenomic analysis of brain tumor cells that are collected from micro-engineered three-dimensional tumor models, overcoming the challenges associated with the small numbers of cells contained within these micro-tissue niches, in this case collecting ~1,000 cells per sample. Specifically, we use a high-resolution epigenomic analysis method known as microfluidic-oscillatory-washing-based chromatin immunoprecipitation with sequencing (MOWChIP-seq) to analyze histone methylation patterns (H3K4me3). We identified gene loci that are associated with the H3K4me3 modification, which is generally a mark of active transcription. We compared methylation patterns in standard 2D cultures and 3D cultures based on type I collagen hydrogels, under both normoxic and hypoxic conditions. We found that culture dimensionality drastically impacted the H3k4me3 profile and resulted in differential modifications in response to hypoxic stress. Differentially H3K4me3-marked regions under the culture conditions used in this study have important implications for gene expression differences that have been previously observed. In total, our work illustrates a direct connection between cell culture or tissue niche condition and genome-wide alterations in histone modifications, providing the first steps towards analyzing the spatiotemporal variations in epigenetic regulation of cancer cell phenotypes. This study, to our knowledge, also represents the first time broad-spectrum epigenomic analysis has been applied to small cell samples collected from engineered micro-tissues.
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Affiliation(s)
- Megan C. Cox
- School of Biomedical Engineering and Mechanics, Virginia Tech-Wake Forest University
| | - Chengyu Deng
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, United States
| | - Lynette B. Naler
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, United States
| | - Chang Lu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, United States
| | - Scott S. Verbridge
- School of Biomedical Engineering and Mechanics, Virginia Tech-Wake Forest University
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27
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Anderson JB, Bruhn JN, Kasimer D, Wang H, Rodrigue N, Smith ML. Clonal evolution and genome stability in a 2500-year-old fungal individual. Proc Biol Sci 2018; 285:20182233. [PMID: 30963893 PMCID: PMC6304041 DOI: 10.1098/rspb.2018.2233] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 11/29/2018] [Indexed: 12/20/2022] Open
Abstract
Individuals of the basidiomycete fungus Armillaria are well known for their ability to spread from woody substrate to substrate on the forest floor through the growth of rhizomorphs. Here, we made 248 collections of A. gallica in one locality in Michigan's Upper Peninsula. To identify individuals, we genotyped collections with molecular markers and somatic compatibility testing. We found several different individuals in proximity to one another, but one genetic individual stood out as exceptionally large, covering hundreds of tree root systems over approximately 75 hectares of the forest floor. Based on observed growth rates of the fungus, we estimate the minimum age of the large individual as 2500 years. With whole-genome sequencing and variant discovery, we also found that mutation had occurred within the somatic cells of the individual, reflecting its historical pattern of growth from a single point. The overall rate of mutation over the 90 mb genome, however, was extremely low. This same individual was first discovered in the late 1980s, but its full spatial extent and internal mutation dynamic was unknown at that time. The large individual of A. gallica has been remarkably resistant to genomic change as it has persisted in place.
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Affiliation(s)
- James B. Anderson
- Department of Biology, University of Toronto, Mississauga, Ontario, CanadaL5 L 1C6
| | - Johann N. Bruhn
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Dahlia Kasimer
- Department of Biology, University of Toronto, Mississauga, Ontario, CanadaL5 L 1C6
| | - Hao Wang
- Department of Biology, Carleton University, Ottawa, Ontario, CanadaK1S 5B6
- School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, CanadaK1S 5B6
| | - Nicolas Rodrigue
- Department of Biology, Carleton University, Ottawa, Ontario, CanadaK1S 5B6
- School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, CanadaK1S 5B6
| | - Myron L. Smith
- Department of Biology, Carleton University, Ottawa, Ontario, CanadaK1S 5B6
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28
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Nieboer MM, Dorssers LCJ, Straver R, Looijenga LHJ, de Ridder J. TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors. PLoS One 2018; 13:e0208002. [PMID: 30496231 PMCID: PMC6264523 DOI: 10.1371/journal.pone.0208002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 11/11/2018] [Indexed: 11/18/2022] Open
Abstract
Most tumors are composed of a heterogeneous population of subclones. A more detailed insight into the subclonal evolution of these tumors can be helpful to study progression and treatment response. Problematically, tumor samples are typically very heterogeneous, making deconvolving individual tumor subclones a major challenge. To overcome this limitation, reducing heterogeneity, such as by means of microdissections, coupled with targeted sequencing, is a viable approach. However, computational methods that enable reconstruction of the evolutionary relationships require unbiased read depth measurements, which are commonly challenging to obtain in this setting. We introduce TargetClone, a novel method to reconstruct the subclonal evolution tree of tumors from single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant measurements. Furthermore, our method infers copy numbers, alleles and the fraction of the tumor component in each sample. TargetClone was specifically designed for targeted sequencing data obtained from microdissected samples. We demonstrate that our method obtains low error rates on simulated data. Additionally, we show that our method is able to reconstruct expected trees in a testicular germ cell cancer and ovarian cancer dataset. The TargetClone package including tree visualization is written in Python and is publicly available at https://github.com/UMCUGenetics/targetclone.
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Affiliation(s)
- Marleen M. Nieboer
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lambert C. J. Dorssers
- Department of Pathology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Roy Straver
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Leendert H. J. Looijenga
- Department of Pathology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
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29
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Abstract
Exposure to pathogen infection, and occupational and environmental agents, contributes to induction of most types of cancer through different mechanisms. Cancer is defined and characterized by accumulation of mutations and epimutations that lead to changes in the cellular genome and epigenome. According to a recent Bad Luck Hypothesis, random error mutations during DNA replication in a small population of stem cells may be implicated in two-thirds of variation of cancer risk in 25 organs and tissues. What determines stem cell vulnerability and risk of malignancy across the spectrum of organs, such as the brain, bone marrow, skeletal muscles, skin, and liver? Have stem cells pooled in particular tissues or organs evolved some critical ability to deal with DNA damage in the presence of extrinsic environmental factors? This paper describes how the complex replication and repair DNA systems control mutational events. In addition, recent advances on cancer epigenomic signatures and epigenetic mechanisms are discussed, which will guide future investigation of the origin of cancer initiating cells in tissue and organs in a clinical setting.
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30
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Patient-derived conditionally reprogrammed cells maintain intra-tumor genetic heterogeneity. Sci Rep 2018; 8:4097. [PMID: 29511269 PMCID: PMC5840339 DOI: 10.1038/s41598-018-22427-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 02/22/2018] [Indexed: 02/05/2023] Open
Abstract
Preclinical in vitro models provide an essential tool to study cancer cell biology as well as aid in translational research, including drug target identification and drug discovery efforts. For any model to be clinically relevant, it needs to recapitulate the biology and cell heterogeneity of the primary tumor. We recently developed and described a conditional reprogramming (CR) cell technology that addresses many of these needs and avoids the deficiencies of most current cancer cell lines, which are usually clonal in origin. Here, we used the CR cell method to generate a collection of patient-derived cell cultures from non-small cell lung cancers (NSCLC). Whole exome sequencing and copy number variations are used for the first time to address the capability of CR cells to keep their tumor-derived heterogeneity. Our results indicated that these primary cultures largely maintained the molecular characteristics of the original tumors. Using a mutant-allele tumor heterogeneity (MATH) score, we showed that CR cells are able to keep and maintain most of the intra-tumoral heterogeneity, suggesting oligoclonality of these cultures. CR cultures therefore represent a pre-clinical lung cancer model for future basic and translational studies.
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31
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Wang F, Dohogne Z, Yang J, Liu Y, Soibam B. Predictors of breast cancer cell types and their prognostic power in breast cancer patients. BMC Genomics 2018; 19:137. [PMID: 29433432 PMCID: PMC5809864 DOI: 10.1186/s12864-018-4527-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 02/05/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Comprehensive understanding of intratumor heterogeneity requires identification of molecular markers, which are capable of differentiating different subpopulations and which also have clinical significance. One important tool that has been addressing this issue is single cell RNA-Sequencing (scRNASeq) that allows the quantification of expression profiles of transcripts in individual cells in a population of cancer cells. Using the expression profiles from scRNASeq, current studies conduct analysis to group cells into different subpopulations using clustering algorithms. In this study, we explore scRNASeq cancer data from a different perspective. We focus on scRNASeq data originating from cancer cells pertaining to a particular cancer type, where the cell type or the subpopulation to which each cell belongs is known. We investigate if the "cell type" of a cancer cell can be predicted based on the expression profiles of a small set of transcripts. RESULTS We outline a predictive analytics pipeline to accurately predict 6 breast cancer cell types using single cell gene expression profiles. Instead of building predictive models using the complete human transcripts, the pipeline first eliminates predictors with low expression and low variance. A multinomial penalized logistic regression further reduces the size of the predictors to only 308, out of which 34 are long non-coding RNAs. Tuning of predictive models shows support vector machines and neural networks as the most accurate models achieving close to 98% prediction accuracies. We also find that mixture of protein coding genes and long non-coding RNAs are better predictors compared to when the two sets of transcripts are treated separately. A signature risk score originating from 65 protein coding genes and 5 lncRNA predictors is associated with prognostic survival of TCGA breast cancer patients. This association was maintained when the risk scores were generated using 65 PCGs and 5 lncRNA separately. We further show that predictors restricted to a particular cell type serve as better prognostic markers for the respective patient subtype. CONCLUSION Our results show that in general, the breast cancer cell type predictors are also associated with patient survivability and hence have clinical significance.
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Affiliation(s)
- Fan Wang
- Department of Oncology, the First Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi Province, 710061, People's Republic of China.,Department of Biology and Biochemistry, University of Houston, Houston, TX, 77204, USA
| | - Zachariah Dohogne
- Computer Science and Engineering Technology, University of Houston-Downtown, Houston, TX, 77002, USA
| | - Jin Yang
- Department of Oncology, the First Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi Province, 710061, People's Republic of China
| | - Yu Liu
- Department of Biology and Biochemistry, University of Houston, Houston, TX, 77204, USA
| | - Benjamin Soibam
- Computer Science and Engineering Technology, University of Houston-Downtown, Houston, TX, 77002, USA.
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32
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López JI, Angulo JC. Pathological Bases and Clinical Impact of Intratumor Heterogeneity in Clear Cell Renal Cell Carcinoma. Curr Urol Rep 2018; 19:3. [PMID: 29374850 DOI: 10.1007/s11934-018-0754-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW Intratumor heterogeneity is an inherent event in tumor development that is receiving much attention in the last years since it is responsible for most failures of current targeted therapies. The purpose of this review is to offer clinicians an updated insight of the multiple manifestations of a complex event that impacts significantly patient's life. RECENT FINDINGS Clear cell renal cell carcinoma is the most common renal tumor and a paradigmatic example of a heterogeneous neoplasm. Next-generation sequencing has demonstrated that intratumor heterogeneity encompasses genetic, epigenetic, and microenvironmental variability. Currently accepted protocols of tumor sampling seem insufficient in unveiling intratumor heterogeneity with reliability and need to be updated. This variability challenges the precise morphological diagnosis, its molecular characterization, and the selection of optimal personalized therapies in clear cell renal cell carcinoma, a neoplasm traditionally considered chemo- and radio-resistant. We review the state of the art of the different approaches to intratumor heterogeneity in clear cell renal cell carcinomas, from the simple morphology to the most sophisticated massive sequencing tools.
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Affiliation(s)
- José I López
- Department of Pathology, Cruces University Hospital, Biocruces Research Institute, University of the Basque Country (UPV/EHU), 48903, Barakaldo, Spain.
| | - Javier C Angulo
- Clinical Department, Urology, Hospital Universitario de Getafe, Universidad Europea de Madrid, 28905, Madrid, Spain
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Kim M, Druliner BR, Vasmatzis N, Bae T, Chia N, Abyzov A, Boardman LA. Inferring modes of evolution from colorectal cancer with residual polyp of origin. Oncotarget 2018; 9:6780-6792. [PMID: 29467928 PMCID: PMC5805514 DOI: 10.18632/oncotarget.23687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/20/2017] [Indexed: 12/31/2022] Open
Abstract
Besides the classical evolutionary model of colorectal cancer (CRC) defined by the stepwise accumulation of mutations in which normal epithelium transforms through an intermediary polyp stage to cancer, a few studies have proposed alternative modes of evolution (MOE): early eruptive subclonal expansion, branching of the subclones in parallel evolution, and neutral evolution. However, frequencies of MOEs and their connection to mutational characteristics of cancer remain elusive. In this study, we analyzed patterns of somatic single nucleotide variations (SNVs) and copy number aberrations (CNAs) in CRC with residual polyp of origin from 13 patients in order to determine this relationship. For each MOE we defined an expected pattern with characteristic features of allele frequency distributions for SNVs in cancers and their matching adenomas. From these distinct patterns, we then assigned an MOE to each CRC case and found that stepwise progression was the most common (70% of cases). We found that CRC with the same MOE may exhibit different mutational spectra, suggesting that different mutational mechanisms can result in the same MOE. Inversely, cancers with different MOEs can have the same mutational spectrum, suggesting that the same mutational mechanism can lead to different MOEs. The types of somatic substitutions, distribution of CNAs across genome, and mutated pathways did not correlate with MOEs. As this could be due to small sample size, these relations warrant further investigation. Our study paves the way to connect MOE with clinical and mutational characteristics not only in CRC but also to neoplastic transformation in other cancers.
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Affiliation(s)
- Minsoo Kim
- Program in Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA.,Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Brooke R Druliner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Nikolaos Vasmatzis
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Taejeong Bae
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Alexej Abyzov
- Program in Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA.,Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Lisa A Boardman
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
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Sutton PA, Jithesh PV, Jones RP, Evans JP, Vimalachandran D, Malik HZ, Park BK, Goldring CE, Palmer DH, Kitteringham NR. Exome sequencing of synchronously resected primary colorectal tumours and colorectal liver metastases to inform oncosurgical management. Eur J Surg Oncol 2017; 44:115-121. [PMID: 29174709 DOI: 10.1016/j.ejso.2017.10.211] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 10/06/2017] [Accepted: 10/08/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Next generation sequencing technology has facilitated mapping of the colorectal cancer genotype and furthered our understanding of metastogenesis. The aim of this study was to investigate for conserved and different mutations in the exomes of synchronously resected primary colorectal tumour and liver metastases. This information could potentially be utilised to guide the treatment of advanced disease with the help of biological information from the primary tumour. METHODS We performed exome sequencing of synchronously resected primary colorectal cancer and colorectal liver metastases as well as normal colonic mucosa and liver parenchyma, from four patients who had received neo-adjuvant chemotherapy, at a depth of 50X using the Ion Proton platform. Raw data was mapped to the reference genome prior to variant calling, annotation and downstream analysis. RESULTS Exome sequencing identified 585 non-synonymous missense single nucleotide variants (SNVs), of which 215 (36.8%) were unique to the primary tumour, 226 (38.6%) unique to the metastasis and 81 (13.8%) present in patient matched pairs. SNVs identified in the ErbB pathway appear to be concordant between primary and metastatic tumours. CONCLUSION Only 13.8% of the metastatic exome can be predicted by the genotype of the primary tumour. We have demonstrated concordance of a number of SNVs in the ErbB pathway, which may inform selection of therapeutic agents in advanced colorectal cancer.
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Affiliation(s)
- P A Sutton
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK.
| | - P V Jithesh
- Sidra Medical and Research Centre, Doha, Qatar
| | - R P Jones
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
| | - J P Evans
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
| | - D Vimalachandran
- Countess of Chester NHS Foundation Trust, Liverpool Road, Chester, CH2 1UL, UK
| | - H Z Malik
- Aintree University Hospital NHS Foundation Trust, Longmoor Lane, Liverpool, L9 7AL, UK
| | - B K Park
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
| | - C E Goldring
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
| | - D H Palmer
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
| | - N R Kitteringham
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GE, UK
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35
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A branching process model of heterogeneous DNA damages caused by radiotherapy in in vitro cell cultures. Math Biosci 2017; 294:100-109. [PMID: 29054768 DOI: 10.1016/j.mbs.2017.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 07/21/2017] [Accepted: 09/23/2017] [Indexed: 11/22/2022]
Abstract
This paper deals with the dynamic modeling and simulation of cell damage heterogeneity and associated mutant cell phenotypes in the therapeutic responses of cancer cell populations submitted to a radiotherapy session during in vitro assays. Each cell is described by a finite number of phenotypic states with possible transitions between them. The population dynamics is then given by an age-dependent multi-type branching process. From this representation, we obtain formulas for the average size of the global survival population as well as the one of subpopulations associated with 10 mutation phenotypes. The proposed model has been implemented into Matlab© and the numerical results corroborate the ability of the model to reproduce four major types of cell responses: delayed growth, anti-proliferative, cytostatic and cytotoxic.
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36
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Rübben A, Araujo A. Cancer heterogeneity: converting a limitation into a source of biologic information. J Transl Med 2017; 15:190. [PMID: 28886708 PMCID: PMC5591523 DOI: 10.1186/s12967-017-1290-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 08/29/2017] [Indexed: 01/12/2023] Open
Abstract
Analysis of spatial and temporal genetic heterogeneity in human cancers has revealed that somatic cancer evolution in most cancers is not a simple linear process composed of a few sequential steps of mutation acquisitions and clonal expansions. Parallel evolution has been observed in many early human cancers resulting in genetic heterogeneity as well as multilineage progression. Moreover, aneuploidy as well as structural chromosomal aberrations seems to be acquired in a non-linear, punctuated mode where most aberrations occur at early stages of somatic cancer evolution. At later stages, the cancer genomes seem to get stabilized and acquire only few additional rearrangements. While parallel evolution suggests positive selection of driver mutations at early stages of somatic cancer evolution, stabilization of structural aberrations at later stages suggests that negative selection takes effect when cancer cells progressively lose their tolerance towards additional mutation acquisition. Mixing of genetically heterogeneous subclones in cancer samples reduces sensitivity of mutation detection. Moreover, driver mutations present only in a fraction of cancer cells are more likely to be mistaken for passenger mutations. Therefore, genetic heterogeneity may be considered a limitation negatively affecting detection sensitivity of driver mutations. On the other hand, identification of subclones and subclone lineages in human cancers may lead to a more profound understanding of the selective forces which shape somatic cancer evolution in human cancers. Identification of parallel evolution by analyzing spatial heterogeneity may hint to driver mutations which might represent additional therapeutic targets besides driver mutations present in a monoclonal state. Likewise, stabilization of cancer genomes which can be identified by analyzing temporal genetic heterogeneity might hint to genes and pathways which have become essential for survival of cancer cell lineages at later stages of cancer evolution. These genes and pathways might also constitute patient specific therapeutic targets.
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Affiliation(s)
- Albert Rübben
- Department of Dermatology, Euregio Skin Cancer Center, University Hospital of the RWTH Aachen, Aachen, Germany.
| | - Arturo Araujo
- Integrated Mathematical Oncology Department, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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37
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38
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Chang AY, Marshall WF. Organelles - understanding noise and heterogeneity in cell biology at an intermediate scale. J Cell Sci 2017; 130:819-826. [PMID: 28183729 DOI: 10.1242/jcs.181024] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Many studies over the years have shown that non-genetic mechanisms for producing cell-to-cell variation can lead to highly variable behaviors across genetically identical populations of cells. Most work to date has focused on gene expression noise as the primary source of phenotypic heterogeneity, yet other sources may also contribute. In this Commentary, we explore organelle-level heterogeneity as a potential secondary source of cellular 'noise' that contributes to phenotypic heterogeneity. We explore mechanisms for generating organelle heterogeneity and present evidence of functional links between organelle morphology and cellular behavior. Given the many instances in which molecular-level heterogeneity has been linked to phenotypic heterogeneity, we posit that organelle heterogeneity may similarly contribute to overall phenotypic heterogeneity and underline the importance of studying organelle heterogeneity to develop a more comprehensive understanding of phenotypic heterogeneity. Finally, we conclude with a discussion of the medical challenges associated with phenotypic heterogeneity and outline how improved methods for characterizing and controlling this heterogeneity may lead to improved therapeutic strategies and outcomes for patients.
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Affiliation(s)
- Amy Y Chang
- Department of Biochemistry and Biophysics, Center for Cellular Construction, University of California, San Francisco, CA 94158, USA
| | - Wallace F Marshall
- Department of Biochemistry and Biophysics, Center for Cellular Construction, University of California, San Francisco, CA 94158, USA
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39
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Davis A, Gao R, Navin N. Tumor evolution: Linear, branching, neutral or punctuated? Biochim Biophys Acta Rev Cancer 2017; 1867:151-161. [PMID: 28110020 DOI: 10.1016/j.bbcan.2017.01.003] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/14/2017] [Accepted: 01/16/2017] [Indexed: 02/08/2023]
Abstract
Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Alexander Davis
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruli Gao
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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40
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Hlady RA, Zhou D, Puszyk W, Roberts LR, Liu C, Robertson KD. Initiation of aberrant DNA methylation patterns and heterogeneity in precancerous lesions of human hepatocellular cancer. Epigenetics 2017; 12:215-225. [PMID: 28059585 DOI: 10.1080/15592294.2016.1277297] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
While intratumor heterogeneity contributes to disease progression, metastasis, and resistance to chemotherapy, it also provides a route to understanding the evolution and drivers of disease. Defects in epigenetic landscapes are intimately linked to pathogenesis of a variety of human diseases, with epigenetic deregulation promoting tumorigenesis. Understanding epigenetic heterogeneity is crucial in hepatocellular carcinoma (HCC), where epigenetic alterations are frequent, early, and pathogenic events. We determined genome-wide DNA methylation and copy number variation leveraging the Infinium 450K in a series of regenerative nodules from within single patient livers. Bioinformatics strategies were used to ascertain within-patient heterogeneity, link epigenetic changes to clinical features, and determine their relevance to disease pathogenesis. Our data demonstrate that DNA methylation and copy number alterations evolve during the pre-neoplastic phase of HCC and independently segregate regenerative nodules into distinct clusters. Regenerative nodules with a high frequency of epigenetic changes have significantly lower copy number variation, suggesting that individual nodules have differential enrichment of epigenetic and genetic components, with both contributing to disease progression. Regenerative nodules were scored based on 'epigenetic progression' with higher scores associated with increased proliferation measured by Ki67 staining. Early events observed in epigenetically 'aggressive' nodules are enriched for genes involved in liver cancer. Our study demonstrates that marked epigenetic and genetic heterogeneity exists in early pre-neoplastic liver tissue within individual patients, emphasizing the potential contributions of each mechanism to driving liver disease progression, and it unveils strategies for identifying epigenetic drivers of hepatocellular carcinoma.
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Affiliation(s)
- Ryan A Hlady
- a Department of Molecular Pharmacology and Experimental Therapeutics , Mayo Clinic , Rochester , MN , USA
| | - Dan Zhou
- a Department of Molecular Pharmacology and Experimental Therapeutics , Mayo Clinic , Rochester , MN , USA
| | - William Puszyk
- b Shands Cancer Center, University of Florida , Gainesville , FL , USA
| | - Lewis R Roberts
- c Division of Gastroenterology and Hepatology , Mayo Clinic , Rochester , MN , USA
| | - Chen Liu
- d Department of Pathology and Laboratory Medicine , Rutgers University , Newark , NJ , USA
| | - Keith D Robertson
- a Department of Molecular Pharmacology and Experimental Therapeutics , Mayo Clinic , Rochester , MN , USA.,e Center for Individualized Medicine , Mayo Clinic , Rochester , MN , USA
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Abstract
Metastatic relapse in patients with solid tumors is the consequence of cancer cells that disseminated to distant sites, adapted to the new microenvironment, and escaped systemic adjuvant therapy. There is increasing evidence that hematogeneous dissemination starts at an early stage of cancer progression with single tumor cells or cell clusters leaving the primary site and entering the blood circulation. These circulating tumor cells (CTCs) can extravasate into secondary tissues where they become disseminated tumor cells (DTCs). Patients might relapse years after initial resection of the primary tumor when DTCs become overt metastases. Current diagnostic strategies for stratification of therapies against metastatic cells focus on the primary tumor tissue. This approach is based on the availability of stored primary tumors obtained at primary surgery, but it ignores that the DTCs might have evolved over years, which can affect the antimetastatic drug response. However, taking biopsies from metastatic tissues is an invasive procedure, and multiple metastases located at different sites in an individual patient show marked genomic heterogeneity. Thus, capturing CTCs from the peripheral blood as a "liquid biopsy" has obvious advantages in particular when repeated sampling is required for monitoring therapies in cancer patients. However, the biology behind tumor cell dissemination and its contribution to metastatic progression in cancer patients is still subject to controversial discussions. This manuscript reviews current theories on the genetic traits behind the spread of CTCs and progression of DTCs into overt metastases.
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Affiliation(s)
- Simon A Joosse
- Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
| | - Klaus Pantel
- Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
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42
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Casasent AK, Edgerton M, Navin NE. Genome evolution in ductal carcinoma in situ: invasion of the clones. J Pathol 2016; 241:208-218. [PMID: 27861897 DOI: 10.1002/path.4840] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 10/21/2016] [Accepted: 10/26/2016] [Indexed: 12/21/2022]
Abstract
Ductal carcinoma in situ (DCIS) is the most frequently diagnosed early-stage breast cancer. Only a subset of patients progress to invasive ductal carcinoma (IDC), and this presents a formidable clinical challenge for determining which patients to treat aggressively and which patients to monitor without therapeutic intervention. Understanding the molecular and genomic basis of invasion has been difficult to study in DCIS cancers due to several technical obstacles, including low tumour cellularity, lack of fresh-frozen tissues, and intratumour heterogeneity. In this review, we discuss the role of intratumour heterogeneity in the progression of DCIS to IDC in the context of three evolutionary models: independent lineages, evolutionary bottlenecks, and multiclonal invasion. We examine the evidence in support of these models and their relevance to the diagnosis and treatment of patients with DCIS. We also discuss how emerging technologies, such as single-cell sequencing, STAR-FISH, and imaging mass spectrometry, are likely to provide new insights into the evolution of this enigmatic disease. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Anna K Casasent
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mary Edgerton
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicholas E Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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43
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Qian M, Wang DC, Chen H, Cheng Y. Detection of single cell heterogeneity in cancer. Semin Cell Dev Biol 2016; 64:143-149. [PMID: 27619166 DOI: 10.1016/j.semcdb.2016.09.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Accepted: 09/08/2016] [Indexed: 11/19/2022]
Abstract
Single cell heterogeneity has already been highlighted in cancer classification, diagnosis, and treatment. Recent advanced technologies have gained more ability to reveal the heterogeneity on single cell level. In this review, we listed various detection targets applied in single cell study, including tumor tissue cells, circulating tumor cells (CTCs), disseminated tumor cells (DTCs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), and cancer stem cells (CSCs). We further discussed and compared detection methods using these detection targets in different fields to reveal single cell heterogeneity in cancer. We focused not only on the methods that have already been established and validated, but also on newly developed methods. In morphology and phenotype, the methods mainly included cell imaging and immune-staining. In genomics and proteomics, the main methods were single cell sequencing and single cell western blotting. Collectively, from using these methods, we can have a better understanding of the single cell variation, as well as what kind of variation it is and how the variation works. Our observations imply that study on single cell heterogeneity in cancer is an important step to precision medicine. The development of technologies in detection of single cell heterogeneity will be sure to improve the diagnosis and treatment in cancer.
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Affiliation(s)
- Mengjia Qian
- Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China
| | - Diane C Wang
- Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China.
| | - Hao Chen
- Department of Cardiothoracic Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Yunfeng Cheng
- Department of Hematology, Zhongshan Hospital Fudan University, Shanghai 200032, China; Department of Hematology, Zhongshan Hospital Qingpu Branch, Fudan University, Shanghai, 201700, China.
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44
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Gao R, Davis A, McDonald TO, Sei E, Shi X, Wang Y, Tsai PC, Casasent A, Waters J, Zhang H, Meric-Bernstam F, Michor F, Navin NE. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat Genet 2016; 48:1119-30. [PMID: 27526321 PMCID: PMC5042845 DOI: 10.1038/ng.3641] [Citation(s) in RCA: 318] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 07/13/2016] [Indexed: 12/15/2022]
Abstract
Aneuploidy is a hallmark of breast cancer; however, our knowledge of how these complex genomic rearrangements evolve during tumorigenesis is limited. In this study we developed a highly multiplexed single-nucleus-sequencing method to investigate copy number evolution in triple-negative breast cancer patients. We sequenced 1000 single cells from 12 patients and identified 1–3 major clonal subpopulations in each tumor that shared a common evolutionary lineage. We also identified a minor subpopulation of non-clonal cells that were classified as: 1) metastable, 2) pseudo-diploid, or 3) chromazemic. Phylogenetic analysis and mathematical modeling suggest that these data are unlikely to be explained by the gradual accumulation of copy number events over time. In contrast, our data challenge the paradigm of gradual evolution, showing that the majority of copy number aberrations are acquired at the earliest stages of tumor evolution, in short punctuated bursts, followed by stable clonal expansions that form the tumor mass.
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Affiliation(s)
- Ruli Gao
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander Davis
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Thomas O McDonald
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Emi Sei
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xiuqing Shi
- Peking Union Medical College, Department of Medical Oncology, Cancer Hospital and Institute, Chinese Academy of Medical Sciences, Beijing, China
| | - Yong Wang
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Pei-Ching Tsai
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anna Casasent
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jill Waters
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hong Zhang
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Funda Meric-Bernstam
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Franziska Michor
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Nicholas E Navin
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Abstract
Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been difficult to address with bulk tumor measurements. Over the past five years, there has been extraordinary progress in technological developments and research applications, but these efforts represent only the tip of the iceberg. In the coming years, SCS will greatly improve our understanding of invasion, metastasis, and therapy resistance during cancer progression. These tools will also have direct translational applications in the clinic, in areas such as early detection, noninvasive monitoring, and guiding targeted therapy. In this perspective, I discuss the progress that has been made and the myriad of unexplored applications that still lie ahead in cancer research and medicine.
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Affiliation(s)
- Nicholas E Navin
- Department of Genetics, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, USA; Department of Bioinformatics and Computational Biology, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, USA; Graduate Program in Genes and Development, Graduate School of Biomedical Sciences, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
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46
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Chisholm RH, Lorenzi T, Clairambault J. Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation. Biochim Biophys Acta Gen Subj 2016; 1860:2627-45. [PMID: 27339473 DOI: 10.1016/j.bbagen.2016.06.009] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 05/25/2016] [Accepted: 06/05/2016] [Indexed: 12/14/2022]
Abstract
BACKGROUND Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. SCOPE OF REVIEW We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies. MAJOR CONCLUSIONS Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies. GENERAL SIGNIFICANCE Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- Rebecca H Chisholm
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
| | - Tommaso Lorenzi
- School of Mathematics and Statistics, University of St Andrews, North Haugh, KY16 9SS, St Andrews, Scotland, United Kingdom. http://www.tommasolorenzi.com
| | - Jean Clairambault
- INRIA Paris, MAMBA team, 2, rue Simone Iff, CS 42112, 75589 Paris Cedex 12, France; Sorbonne Universités, UPMC Univ. Paris 6, UMR 7598, Laboratoire Jacques-Louis Lions, Boîte courrier 187, 4 Place Jussieu, 75252 Paris Cedex 05, France.
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47
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Forte VA, Barrak DK, Elhodaky M, Tung L, Snow A, Lang JE. The potential for liquid biopsies in the precision medical treatment of breast cancer. Cancer Biol Med 2016; 13:19-40. [PMID: 27144060 PMCID: PMC4850125 DOI: 10.28092/j.issn.2095-3941.2016.0007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Currently the clinical management of breast cancer relies on relatively few prognostic/predictive clinical markers (estrogen receptor, progesterone receptor, HER2), based on primary tumor biology. Circulating biomarkers, such as circulating tumor DNA (ctDNA) or circulating tumor cells (CTCs) may enhance our treatment options by focusing on the very cells that are the direct precursors of distant metastatic disease, and probably inherently different than the primary tumor's biology. To shift the current clinical paradigm, assessing tumor biology in real time by molecularly profiling CTCs or ctDNA may serve to discover therapeutic targets, detect minimal residual disease and predict response to treatment. This review serves to elucidate the detection, characterization, and clinical application of CTCs and ctDNA with the goal of precision treatment of breast cancer.
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Affiliation(s)
- Victoria A Forte
- Department of Medicine, Division of Medical Oncology, University of Southern California (USC), Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Dany K Barrak
- USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA; Department of Surgery, Division of Breast, Endocrine and Soft Tissue Surgery, USC, Los Angeles, CA 90033, USA
| | - Mostafa Elhodaky
- USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA; Department of Stem Cell and Regenerative Medicine, USC, Los Angeles, CA 90033, USA
| | - Lily Tung
- USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA; Department of Surgery, Division of Breast, Endocrine and Soft Tissue Surgery, USC, Los Angeles, CA 90033, USA
| | - Anson Snow
- Department of Medicine, Division of Medical Oncology, University of Southern California (USC), Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Julie E Lang
- USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA; Department of Surgery, Division of Breast, Endocrine and Soft Tissue Surgery, USC, Los Angeles, CA 90033, USA
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Affiliation(s)
- Nicolai Juul Birkbak
- The Francis Crick Institute, London, United Kingdom; University College London Cancer Institute, London, United Kingdom
| | - Jesper B Andersen
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark.
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Roller E, Ivakhno S, Lee S, Royce T, Tanner S. Canvas: versatile and scalable detection of copy number variants. ACTA ACUST UNITED AC 2016; 32:2375-7. [PMID: 27153601 DOI: 10.1093/bioinformatics/btw163] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 03/21/2016] [Indexed: 12/26/2022]
Abstract
MOTIVATION Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. RESULTS We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. AVAILABILITY AND IMPLEMENTATION Canvas is distributed under an open source license and can be downloaded from https://github.com/Illumina/canvas CONTACT eroller@illumina.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Sergii Ivakhno
- Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK
| | - Steve Lee
- Illumina Inc, San Diego, CA 92122, USA
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Kadara H, Scheet P, Wistuba II, Spira AE. Early Events in the Molecular Pathogenesis of Lung Cancer. Cancer Prev Res (Phila) 2016; 9:518-27. [PMID: 27006378 DOI: 10.1158/1940-6207.capr-15-0400] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 03/01/2016] [Indexed: 11/16/2022]
Abstract
The majority of cancer-related deaths in the United States and worldwide are attributed to lung cancer. There are more than 90 million smokers in the United States who represent a significant population at elevated risk for lung malignancy. In other epithelial tumors, it has been shown that if neoplastic lesions can be detected and treated at their intraepithelial stage, patient prognosis is significantly improved. Thus, new strategies to detect and treat lung preinvasive lesions are urgently needed in order to decrease the overwhelming public health burden of lung cancer. Limiting these advances is a poor knowledge of the earliest events that underlie lung cancer development and that would constitute markers and targets for early detection and prevention. This review summarizes the state of knowledge of human lung cancer pathogenesis and the molecular pathology of premalignant lung lesions, with a focus on the molecular premalignant field that associates with lung cancer development. Lastly, we highlight new approaches and models to study genome-wide alterations in human lung premalignancy in order to facilitate the discovery of new markers for early detection and prevention of this fatal disease. Cancer Prev Res; 9(7); 518-27. ©2016 AACR.
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Affiliation(s)
- Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas. The University of Texas Graduate School of Biomedical Sciences, Houston, Texas.
| | - Paul Scheet
- The University of Texas Graduate School of Biomedical Sciences, Houston, Texas. Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Avrum E Spira
- Section of Computational Biomedicine, Boston University School of Medicine, Boston University, Boston, Massachusetts
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