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Xiang Z, Liu Z, Dinh KN. Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data. Sci Rep 2024; 14:17699. [PMID: 39085295 PMCID: PMC11291923 DOI: 10.1038/s41598-024-67842-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.
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Affiliation(s)
- Zijin Xiang
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA
| | - Zhihan Liu
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA
| | - Khanh N Dinh
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA.
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2
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Chu X, Tian W, Ning J, Xiao G, Zhou Y, Wang Z, Zhai Z, Tanzhu G, Yang J, Zhou R. Cancer stem cells: advances in knowledge and implications for cancer therapy. Signal Transduct Target Ther 2024; 9:170. [PMID: 38965243 PMCID: PMC11224386 DOI: 10.1038/s41392-024-01851-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/27/2024] [Accepted: 04/28/2024] [Indexed: 07/06/2024] Open
Abstract
Cancer stem cells (CSCs), a small subset of cells in tumors that are characterized by self-renewal and continuous proliferation, lead to tumorigenesis, metastasis, and maintain tumor heterogeneity. Cancer continues to be a significant global disease burden. In the past, surgery, radiotherapy, and chemotherapy were the main cancer treatments. The technology of cancer treatments continues to develop and advance, and the emergence of targeted therapy, and immunotherapy provides more options for patients to a certain extent. However, the limitations of efficacy and treatment resistance are still inevitable. Our review begins with a brief introduction of the historical discoveries, original hypotheses, and pathways that regulate CSCs, such as WNT/β-Catenin, hedgehog, Notch, NF-κB, JAK/STAT, TGF-β, PI3K/AKT, PPAR pathway, and their crosstalk. We focus on the role of CSCs in various therapeutic outcomes and resistance, including how the treatments affect the content of CSCs and the alteration of related molecules, CSCs-mediated therapeutic resistance, and the clinical value of targeting CSCs in patients with refractory, progressed or advanced tumors. In summary, CSCs affect therapeutic efficacy, and the treatment method of targeting CSCs is still difficult to determine. Clarifying regulatory mechanisms and targeting biomarkers of CSCs is currently the mainstream idea.
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Affiliation(s)
- Xianjing Chu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Wentao Tian
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Jiaoyang Ning
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Gang Xiao
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yunqi Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Ziqi Wang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Zhuofan Zhai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Guilong Tanzhu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jie Yang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Rongrong Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, China.
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4
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Del Pino Herrera A, Ferrall-Fairbanks MC. A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies. Front Genet 2024; 15:1383676. [PMID: 38873108 PMCID: PMC11169904 DOI: 10.3389/fgene.2024.1383676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
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Affiliation(s)
- Adriana Del Pino Herrera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- University of Florida Health Cancer Center, University of Florida, Gainesville, FL, United States
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5
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Beigi YZ, Lanjanian H, Fayazi R, Salimi M, Hoseyni BHM, Noroozizadeh MH, Masoudi-Nejad A. Heterogeneity and molecular landscape of melanoma: implications for targeted therapy. MOLECULAR BIOMEDICINE 2024; 5:17. [PMID: 38724687 PMCID: PMC11082128 DOI: 10.1186/s43556-024-00182-2] [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: 11/19/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".
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Affiliation(s)
- Yasaman Zohrab Beigi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Hossein Lanjanian
- Software Engineering Department, Engineering Faculty, Istanbul Topkapi University, Istanbul, Turkey
| | - Reyhane Fayazi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Mahdieh Salimi
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Behnaz Haji Molla Hoseyni
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ali Masoudi-Nejad
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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6
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Ray SK, Mukherjee S. Molecular perspectives on systemic priming and concomitant immunity in colorectal carcinoma. J Egypt Natl Canc Inst 2024; 36:7. [PMID: 38462581 DOI: 10.1186/s43046-024-00211-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/15/2024] [Indexed: 03/12/2024] Open
Abstract
The progression of metastasis, a complex systemic disease, is facilitated by interactions between tumor cells and their isolated microenvironments. Over the past few decades, researchers have investigated the metastatic spread of cancer extensively, identifying multiple stages in the process, such as intravasation, extravasation, tumor latency, and the development of micrometastasis and macrometastasis. The premetastatic niche is established in target organs by the accumulation of aberrant immune cells and extracellular matrix proteins. The "seed and soil" idea, which has become widely known and accepted, is being used to this day to guide cancer studies. Changes in the local and systemic immune systems have a major impact on whether an infection spreads or not. The belief that the immune response may play a role in slowing tumor growth and may be beneficial against the metastatic disease underpins the responsiveness shown in the immunological landscape of metastasis. Various hypotheses on the phylogenesis of metastases have been proposed in the past. The primary tumor's secreting factors shape the intratumoral microenvironment and the immune landscape, allowing this progress to be made. Therefore, it is evident that among disseminated tumor cells, there are distinct phenotypes that either carry budding for metastasis or have the ability to obtain this potential or in systemic priming through contact with substantial metastatic niches that have implications for medicinal chemistry. Concurrent immunity signals that the main tumor induces an immune response that may not be strong enough to eradicate the tumor. Immunotherapy's success with some cancer patients shows that it is possible to effectively destroy even advanced-stage tumors by modifying the microenvironment and tumor-immune cell interactions. This review focuses on the metastasome in colorectal carcinoma and the therapeutic implications of site-specific metastasis, systemic priming, tumor spread, and the relationship between the immune system and metastasis.
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Affiliation(s)
- Suman Kumar Ray
- Independent Researcher, Bhopal, Madhya Pradesh, 462020, India
| | - Sukhes Mukherjee
- Department of Biochemistry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, 462020, India.
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Lynch A, Bradford S, Burkard ME. The reckoning of chromosomal instability: past, present, future. Chromosome Res 2024; 32:2. [PMID: 38367036 DOI: 10.1007/s10577-024-09746-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/11/2024] [Accepted: 01/27/2024] [Indexed: 02/19/2024]
Abstract
Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.
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Affiliation(s)
- Andrew Lynch
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Shermineh Bradford
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mark E Burkard
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA.
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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Zhang L, Bass HW, Irianto J, Mallory X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res 2023; 33:2002-2017. [PMID: 37993137 PMCID: PMC10760445 DOI: 10.1101/gr.277249.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Jerome Irianto
- College of Medicine, Florida State University, Tallahassee, Florida 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA;
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10
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol 2023; 24:272. [PMID: 38037115 PMCID: PMC10688497 DOI: 10.1186/s13059-023-03106-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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11
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Khan R, Mallory X. Assessing the performance of methods for cell clustering from single-cell DNA sequencing data. PLoS Comput Biol 2023; 19:e1010480. [PMID: 37824596 PMCID: PMC10597505 DOI: 10.1371/journal.pcbi.1010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. RESULTS In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. CONCLUSION From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.
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Affiliation(s)
- Rituparna Khan
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
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12
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徐 晨, 王 寅, 魏 东, 李 文, 钱 晔, 潘 新, 雷 大. [Advances of spatial omics in the individualized diagnosis and treatment of head and neck cancer]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2023; 37:729-733;739. [PMID: 37830120 PMCID: PMC10722126 DOI: 10.13201/j.issn.2096-7993.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Indexed: 10/14/2023]
Abstract
Spatialomics is another research hotspot of biotechnology after single-cell sequencing technology, which can make up for the defect that single-cell sequencing technology can not obtain cell spatial distribution information. Spatialomics mainly studies the relative position of cells in tissue samples to reveal the effect of cell spatial distribution on diseases. In recent years, spatialomics has made new progress in the pathogenesis, target exploration, drug development and many other aspects of head and neck tumors. This paper summarizes the latest progress of spatialomics in the diagnosis and treatment of head and neck cancer.
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Affiliation(s)
- 晨阳 徐
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 寅 王
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 东敏 魏
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 文明 李
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 晔 钱
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 新良 潘
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
| | - 大鹏 雷
- 山东大学齐鲁医院耳鼻咽喉科,国家卫生健康委员会耳鼻喉科学重点实验室(山东大学)(济南,250012)Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology[Shandong University], Jinan, 250012, China
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13
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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14
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Miura S, Dolker T, Sanderford M, Kumar S. Improving cellular phylogenies through the integrated use of mutation order and optimality principles. Comput Struct Biotechnol J 2023; 21:3894-3903. [PMID: 37602230 PMCID: PMC10432911 DOI: 10.1016/j.csbj.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tenzin Dolker
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
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15
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Guang Z, Smith-Erb M, Oesper L. A weighted distance-based approach for deriving consensus tumor evolutionary trees. Bioinformatics 2023; 39:i204-i212. [PMID: 37387177 DOI: 10.1093/bioinformatics/btad230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently. RESULTS On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified. AVAILABILITY An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.
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Affiliation(s)
- Ziyun Guang
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
| | - Matthew Smith-Erb
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
| | - Layla Oesper
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
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16
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Hessey S, Fessas P, Zaccaria S, Jamal-Hanjani M, Swanton C. Insights into the metastatic cascade through research autopsies. Trends Cancer 2023; 9:490-502. [PMID: 37059687 DOI: 10.1016/j.trecan.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 04/16/2023]
Abstract
Metastasis is a complex process and the leading cause of cancer-related death globally. Recent studies have demonstrated that genomic sequencing data from paired primary and metastatic tumours can be used to trace the evolutionary origins of cells responsible for metastasis. This approach has yielded new insights into the genomic alterations that engender metastatic potential, and the mechanisms by which cancer spreads. Given that the reliability of these approaches is contingent upon how representative the samples are of primary and metastatic tumour heterogeneity, we review insights from studies that have reconstructed the evolution of metastasis within the context of their cohorts and designs. We discuss the role of research autopsies in achieving the comprehensive sampling necessary to advance the current understanding of metastasis.
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Affiliation(s)
- Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Petros Fessas
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK; Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
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17
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Chen M, Jiang J, Hou J. Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications. Biomark Res 2023; 11:55. [PMID: 37259170 PMCID: PMC10234006 DOI: 10.1186/s40364-023-00502-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/12/2023] [Indexed: 06/02/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.
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Affiliation(s)
- Mengping Chen
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jinxing Jiang
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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18
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Moravec JC, Lanfear R, Spector DL, Diermeier SD, Gavryushkin A. Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data. J Comput Biol 2023; 30:518-537. [PMID: 36475926 PMCID: PMC10125402 DOI: 10.1089/cmb.2022.0357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.
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Affiliation(s)
- Jiří C. Moravec
- Department of Computer Science, University of Otago, Dunedin, New Zealand
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Robert Lanfear
- Division of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, Australia
| | | | | | - Alex Gavryushkin
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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19
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Lewinsohn MA, Bedford T, Müller NF, Feder AF. State-dependent evolutionary models reveal modes of solid tumour growth. Nat Ecol Evol 2023; 7:581-596. [PMID: 36894662 PMCID: PMC10089931 DOI: 10.1038/s41559-023-02000-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/26/2023] [Indexed: 03/11/2023]
Abstract
Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.
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Affiliation(s)
- Maya A Lewinsohn
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Trevor Bedford
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Nicola F Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Alison F Feder
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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20
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Yamamoto A, Doak AE, Cheung KJ. Orchestration of Collective Migration and Metastasis by Tumor Cell Clusters. ANNUAL REVIEW OF PATHOLOGY 2023; 18:231-256. [PMID: 36207009 DOI: 10.1146/annurev-pathmechdis-031521-023557] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Metastatic dissemination has lethal consequences for cancer patients. Accruing evidence supports the hypothesis that tumor cells can migrate and metastasize as clusters of cells while maintaining contacts with one another. Collective metastasis enables tumor cells to colonize secondary sites more efficiently, resist cell death, and evade the immune system. On the other hand, tumor cell clusters face unique challenges for dissemination particularly during systemic dissemination. Here, we review recent progress toward understanding how tumor cell clusters overcome these disadvantages as well as mechanisms they utilize to gain advantages throughout the metastatic process. We consider useful models for studying collective metastasis and reflect on how the study of collective metastasis suggests new opportunities for eradicating and preventing metastatic disease.
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Affiliation(s)
- Ami Yamamoto
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , , .,Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, USA
| | - Andrea E Doak
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , , .,Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, USA
| | - Kevin J Cheung
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA; , ,
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21
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Leighton J, Hu M, Sei E, Meric-Bernstam F, Navin NE. Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing. CELL GENOMICS 2023; 3:100215. [PMID: 36777188 PMCID: PMC9903705 DOI: 10.1016/j.xgen.2022.100215] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/21/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.
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Affiliation(s)
- Jake Leighton
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Min Hu
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Emi Sei
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Meric-Bernstam
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Precision Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nicholas E. Navin
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
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22
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522408. [PMID: 36711528 PMCID: PMC9882003 DOI: 10.1101/2023.01.05.522408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study. Code availability Software is available at https://github.com/raphael-group/constrained-Dollo.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A. Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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23
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Gao Y, Gaither J, Chifman J, Kubatko L. A phylogenetic approach to inferring the order in which mutations arise during cancer progression. PLoS Comput Biol 2022; 18:e1010560. [PMID: 36459515 DOI: 10.1371/journal.pcbi.1010560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 12/14/2022] [Accepted: 09/12/2022] [Indexed: 12/05/2022] Open
Abstract
Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.
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Affiliation(s)
- Yuan Gao
- Division of Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Gaither
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, United States of America
| | - Julia Chifman
- Dept of Mathematics and Statistics, American University, Washington D. C., United States of America
| | - Laura Kubatko
- Dept of Statistics, The Ohio State University, Columbus, Ohio, United States of America
- Dept of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
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24
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Niu Y, Yang W, Qian H, Sun Y. Intracellular and extracellular factors of colorectal cancer liver metastasis: a pivotal perplex to be fully elucidated. Cancer Cell Int 2022; 22:341. [DOI: 10.1186/s12935-022-02766-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractMetastasis is the leading cause of death in colorectal cancer (CRC) patients, and the liver is the most common site of metastasis. Tumor cell metastasis can be thought of as an invasion-metastasis cascade and metastatic organotropism is thought to be a process that relies on the intrinsic properties of tumor cells and their interactions with molecules and cells in the microenvironment. Many studies have provided new insights into the molecular mechanism and contributing factors involved in CRC liver metastasis for a better understanding of the organ-specific metastasis process. The purpose of this review is to summarize the theories that explain CRC liver metastasis at multiple molecular dimensions (including genetic and non-genetic factors), as well as the main factors that cause CRC liver metastasis. Many findings suggest that metastasis may occur earlier than expected and with specific organ-anchoring property. The emergence of potential metastatic clones, the timing of dissemination, and the distinct routes of metastasis have been explained by genomic studies. The main force of CRC liver metastasis is also thought to be epigenetic alterations and dynamic phenotypic traits. Furthermore, we review key extrinsic factors that influence CRC cell metastasis and liver tropisms, such as pre-niches, tumor stromal cells, adhesion molecules, and immune/inflammatory responses in the tumor microenvironment. In addition, biomarkers associated with early diagnosis, prognosis, and recurrence of liver metastasis from CRC are summarized to enlighten potential clinical practice, including some markers that can be used as therapeutic targets to provide new perspectives for the treatment strategies of CRC liver metastasis.
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25
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Liu Z, Li H, Dang Q, Weng S, Duo M, Lv J, Han X. Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy. Cell Mol Life Sci 2022; 79:577. [DOI: 10.1007/s00018-022-04608-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022]
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26
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Zeng Y, Jin RU. Molecular pathogenesis, targeted therapies, and future perspectives for gastric cancer. Semin Cancer Biol 2022; 86:566-582. [PMID: 34933124 DOI: 10.1016/j.semcancer.2021.12.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/29/2021] [Accepted: 12/11/2021] [Indexed: 01/27/2023]
Abstract
Gastric cancer is a major source of global cancer mortality with limited treatment options and poor patient survival. As our molecular understanding of gastric cancer improves, we are now beginning to recognize that these cancers are a heterogeneous group of diseases with incredibly unique pathogeneses and active oncogenic pathways. It is this molecular diversity and oftentimes lack of common oncogenic driver mutations that bestow the poor treatment responses that oncologists often face when treating gastric cancer. In this review, we will examine the treatments for gastric cancer including up-to-date molecularly targeted therapies and immunotherapies. We will then review the molecular subtypes of gastric cancer to highlight the diversity seen in this disease. We will then shift our discussion to basic science and gastric cancer mouse models as tools to study gastric cancer molecular heterogeneity. Furthermore, we will elaborate on a molecular process termed paligenosis and the cyclical hit model as key events during gastric cancer initiation that impart nondividing mature differentiated cells the ability to re-enter the cell cycle and accumulate disparate genomic mutations during years of chronic inflammation and injury. As our basic science understanding of gastric cancer advances, so too must our translational and clinical efforts. We will end with a discussion regarding single-cell molecular analyses and cancer organoid technologies as future translational avenues to advance our understanding of gastric cancer heterogeneity and to design precision-based gastric cancer treatments. Elucidation of interpatient and intratumor heterogeneity is the only way to advance future cancer prevention, diagnoses and treatment.
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Affiliation(s)
- Yongji Zeng
- Section of Gastroenterology, Department of Medicine, Baylor College of Medicine, Houston, USA
| | - Ramon U Jin
- Section of Hematology/Oncology, Department of Medicine, Baylor College of Medicine, Houston, USA.
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27
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Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development. Commun Biol 2022; 5:937. [PMID: 36085309 PMCID: PMC9463147 DOI: 10.1038/s42003-022-03884-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractColorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.
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28
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Su X, Bai S, Xie G, Shi Y, Zhao L, Yang G, Tian F, He KY, Wang L, Li X, Long Q, Han ZG. Accurate tumor clonal structures require single-cell analysis. Ann N Y Acad Sci 2022; 1517:213-224. [PMID: 36081327 DOI: 10.1111/nyas.14897] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.
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Affiliation(s)
- Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shihao Bai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gangcai Xie
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Linan Zhao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guoliang Yang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Futong Tian
- Department of Design, Politecnico di Milano, Milan, Italy
| | - Kun-Yan He
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lan Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolin Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Long
- Joint School of Life Sciences, Guangzhou Medical University & Guangzhou Institutes of Biomedicine and Health-Chinese Academy of Sciences, Guangzhou, China
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
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29
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Wang Y, Song W, Yu S, Liu Y, Chen YG. Intestinal cellular heterogeneity and disease development revealed by single-cell technology. CELL REGENERATION 2022; 11:26. [PMID: 36045190 PMCID: PMC9433512 DOI: 10.1186/s13619-022-00127-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/15/2022] [Indexed: 11/10/2022]
Abstract
The intestinal epithelium is responsible for food digestion and nutrient absorption and plays a critical role in hormone secretion, microorganism defense, and immune response. These functions depend on the integral single-layered intestinal epithelium, which shows diversified cell constitution and rapid self-renewal and presents powerful regeneration plasticity after injury. Derailment of homeostasis of the intestine epithelium leads to the development of diseases, most commonly including enteritis and colorectal cancer. Therefore, it is important to understand the cellular characterization of the intestinal epithelium at the molecular level and the mechanisms underlying its homeostatic maintenance. Single-cell technologies allow us to gain molecular insights at the single-cell level. In this review, we summarize the single-cell RNA sequencing applications to understand intestinal cell characteristics, spatiotemporal evolution, and intestinal disease development.
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30
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Mattila TT, Patankar M, Väyrynen JP, Klintrup K, Mäkelä J, Tuomisto A, Nieminen P, Mäkinen MJ, Karttunen TJ. Putative anoikis resistant subpopulations are enriched in lymph node metastases and indicate adverse prognosis in colorectal carcinoma. Clin Exp Metastasis 2022; 39:883-898. [PMID: 36018456 PMCID: PMC9637608 DOI: 10.1007/s10585-022-10184-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/10/2022] [Indexed: 11/29/2022]
Abstract
Anoikis refers to apoptosis induced by the loss of contact with the extracellular matrix. Anoikis resistance is essential for metastasis. We have recently shown that it is possible to quantitatively evaluate putative anoikis resistant (AR) subpopulations in colorectal carcinoma (CRC). Abundance of these multi-cell structures is an independent marker of adverse prognosis. Here, we have quantified putative AR subpopulations in lymph node (LN) metastases of CRC and evaluated their prognostic value and relationship with the characteristics of primary tumors. A case series included 137 unselected CRC patients, 54 with LN metastases. Areal densities (structures/mm2) of putative AR structures in primary tumors had been analyzed previously and now were determined from all nodal metastases (n = 183). Areal density of putative AR structures was higher in LN metastases than in primary tumors. Variation of the areal density within different LN metastases of a single patient was lower than between metastases of different patients. Abundance of putative AR structures in LN metastases was associated with shorter cancer specific survival (p = 0.013), and this association was independent of T and N stages. Abundance of putative AR structures in primary tumors and LN metastases had a cumulative adverse effect on prognosis. Enrichment of putative AR subpopulations in LN metastases suggest that in metastasis formation, there is a selection favoring cells capable of forming these structures. Higher intra-case constancy relative to inter-case variation suggests that such selection is stable in metastasis development. Our findings indirectly support the biological validity of our concept of putative AR structures.
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Affiliation(s)
- Taneli T Mattila
- Department of Pathology, Cancer and Translational Medicine Research Unit, University of Oulu, POB 5000, 90014, Oulu, Finland.,Department of Pathology, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland
| | - Madhura Patankar
- Division of Gastroenterology and Hepatology, Department of Medicine, Keck School of Medicine of USC, Los Angeles, CA, 90089-0110, USA
| | - Juha P Väyrynen
- Department of Pathology, Cancer and Translational Medicine Research Unit, University of Oulu, POB 5000, 90014, Oulu, Finland.,Department of Pathology, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland
| | - Kai Klintrup
- Department of Surgery, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland.,Department of Surgery, Research Unit of Surgery, Anesthesia and Intensive Care, University of Oulu, POB 5000, 90014, Oulu, Finland
| | - Jyrki Mäkelä
- Department of Surgery, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland.,Department of Surgery, Research Unit of Surgery, Anesthesia and Intensive Care, University of Oulu, POB 5000, 90014, Oulu, Finland
| | - Anne Tuomisto
- Department of Pathology, Cancer and Translational Medicine Research Unit, University of Oulu, POB 5000, 90014, Oulu, Finland.,Department of Pathology, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland
| | - Pentti Nieminen
- Medical Informatics and Data Analysis Research Group, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Markus J Mäkinen
- Department of Pathology, Cancer and Translational Medicine Research Unit, University of Oulu, POB 5000, 90014, Oulu, Finland.,Department of Pathology, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland
| | - Tuomo J Karttunen
- Department of Pathology, Cancer and Translational Medicine Research Unit, University of Oulu, POB 5000, 90014, Oulu, Finland. .,Department of Pathology, Oulu University Hospital and Medical Research Center Oulu, POB 21, 90029, Oulu, Finland.
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31
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Wang R, Li J, Zhou X, Mao Y, Wang W, Gao S, Wang W, Gao Y, Chen K, Yu S, Wu X, Wen L, Ge H, Fu W, Tang F. Single-cell genomic and transcriptomic landscapes of primary and metastatic colorectal cancer tumors. Genome Med 2022; 14:93. [PMID: 35974387 PMCID: PMC9380328 DOI: 10.1186/s13073-022-01093-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 07/15/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Colorectal cancer (CRC) ranks as the second-leading cause of cancer-related death worldwide with metastases being the main cause of cancer-related death. Here, we investigated the genomic and transcriptomic alterations in matching adjacent normal tissues, primary tumors, and metastatic tumors of CRC patients. METHODS We performed whole genome sequencing (WGS), multi-region whole exome sequencing (WES), simultaneous single-cell RNA-Seq, and single-cell targeted cDNA Sanger sequencing on matching adjacent normal tissues, primary tumors, and metastatic tumors from 12 metastatic colorectal cancer patients (n=84 for genomes, n=81 for exomes, n=9120 for single cells). Patient-derived tumor organoids were used to estimate the anti-tumor effects of a PPAR inhibitor, and self-renewal and differentiation ability of stem cell-like tumor cells. RESULTS We found that the PPAR signaling pathway was prevalently and aberrantly activated in CRC tumors. Blocking of PPAR pathway both suppressed the growth and promoted the apoptosis of CRC organoids in vitro, indicating that aberrant activation of the PPAR signaling pathway plays a critical role in CRC tumorigenesis. Using matched samples from the same patient, distinct origins of the metastasized tumors between lymph node and liver were revealed, which was further verified by both copy number variation and mitochondrial mutation profiles at single-cell resolution. By combining single-cell RNA-Seq and single-cell point mutation identification by targeted cDNA Sanger sequencing, we revealed important phenotypic differences between cancer cells with and without critical point mutations (KRAS and TP53) in the same patient in vivo at single-cell resolution. CONCLUSIONS Our data provides deep insights into how driver mutations interfere with the transcriptomic state of cancer cells in vivo at a single-cell resolution. Our findings offer novel knowledge on metastatic mechanisms as well as potential markers and therapeutic targets for CRC diagnosis and therapy. The high-precision single-cell RNA-seq dataset of matched adjacent normal tissues, primary tumors, and metastases from CRCs may serve as a rich resource for further studies.
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Affiliation(s)
- Rui Wang
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- Beijing Advanced Innovation Center for Genomics & Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, People's Republic of China
| | - Jingyun Li
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, People's Republic of China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, People's Republic of China
| | - Xin Zhou
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- Peking University Third Hospital Cancer Center, Beijing, 100193, China
| | - Yunuo Mao
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- Beijing Advanced Innovation Center for Genomics & Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, 100871, People's Republic of China
| | - Wendong Wang
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Shuai Gao
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wei Wang
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Yuan Gao
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Kexuan Chen
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Shuntai Yu
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Xinglong Wu
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Lu Wen
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
| | - Hao Ge
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China
- Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, People's Republic of China
| | - Wei Fu
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China.
- Peking University Third Hospital Cancer Center, Beijing, 100193, China.
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center, Department of General Surgery, School of Life Sciences, Third Hospital, Peking University, Beijing, 100871, People's Republic of China.
- Beijing Advanced Innovation Center for Genomics & Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, 100871, People's Republic of China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, People's Republic of China.
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32
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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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Foroughmand-Araabi MH, Goliaei S, McHardy AC. Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm. PLoS Comput Biol 2022; 18:e1009100. [PMID: 35951662 PMCID: PMC9426887 DOI: 10.1371/journal.pcbi.1009100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/30/2022] [Accepted: 06/23/2022] [Indexed: 11/19/2022] Open
Abstract
Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms—BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit—on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.
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Affiliation(s)
- Mohammad-Hadi Foroughmand-Araabi
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Sama Goliaei
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Alice C. McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
- * E-mail:
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34
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Fimereli D, Venet D, Rediti M, Boeckx B, Maetens M, Majjaj S, Rouas G, Marchio C, Bertucci F, Mariani O, Capra M, Bonizzi G, Contaldo F, Galant C, Van den Eynden G, Salgado R, Biganzoli E, Vincent-Salomon A, Pruneri G, Larsimont D, Lambrechts D, Desmedt C, Brown DN, Rothé F, Sotiriou C. Timing evolution of lobular breast cancer through phylogenetic analysis. EBioMedicine 2022; 82:104169. [PMID: 35882101 PMCID: PMC9309404 DOI: 10.1016/j.ebiom.2022.104169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/22/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Danai Fimereli
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - David Venet
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Mattia Rediti
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Bram Boeckx
- Laboratory of Translational Genetics, VIB Center for Cancer Biology, Leuven, Belgium; Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Marion Maetens
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium; Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Samira Majjaj
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Ghizlane Rouas
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Caterina Marchio
- Department of Medical Sciences, University of Turin, Turin, Italy; FPO-IRCCS Candiolo Cancer Institute, Candiolo, Italy
| | - Francois Bertucci
- Predictive Oncology Laboratory, Institut Paoli-Calmettes, CRCM, INSERM U1068, CNRS UMR7258, Aix-Marseille Université Marseille, France
| | - Odette Mariani
- Department of Pathology, Institut Curie, Paris Sciences Lettres Research University, Paris, France
| | - Maria Capra
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuseppina Bonizzi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Federica Contaldo
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Christine Galant
- Department of Pathology, Cliniques Universitaires Saint Luc, Brussels, Belgium; IREC, Université Catholique de Louvain, Brussels, Belgium
| | | | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium; Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Elia Biganzoli
- Department of Biomedical and Clinical Sciences (DIBIC) "L. Sacco" & DSRC, LITA Vialba campus, University of Milan, Milan, Italy
| | - Anne Vincent-Salomon
- Department of Pathology, Institut Curie, Paris Sciences Lettres Research University, Paris, France
| | - Giancarlo Pruneri
- Division of Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; School of Medicine, University of Milan, Milano, Milan, Italy
| | - Denis Larsimont
- Department of Pathology, Institut Jules Bordet, Brussels, Belgium
| | - Diether Lambrechts
- Laboratory of Translational Genetics, VIB Center for Cancer Biology, Leuven, Belgium; Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Christine Desmedt
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
| | - David N Brown
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Françoise Rothé
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Christos Sotiriou
- J.-C. Heuson Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium.
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35
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Zheng Y, Yang X. Application and prospect of single-cell sequencing in cancer metastasis. Future Oncol 2022; 18:2723-2736. [PMID: 35686493 DOI: 10.2217/fon-2022-0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Cancer metastasis is a complicated process driven by internal genetic variations and developed through interactions with the external environment. This process usually causes therapeutic resistance and results in a low survival rate. In recent years, single-cell sequencing has become a popular method for revealing the tumor evolutionary genetic lineage, intra-tumoral heterogeneity and tumor microenvironment of the metastasis process. So as to find more therapeutic targets for clinical application, the spatial transcriptomics method has become a new rising field of cancer studies, which promotes the combination between clinical medicine and molecular biology. In future prospects, more accurate and personalized treatment models will come into reality.
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Affiliation(s)
- Yue Zheng
- Department of Biochemistry & Molecular Biology, Basic Medical College, Shanxi Medical University, Taiyuan City, Shanxi Province, 030000, China
| | - Xiaofeng Yang
- Department of Urology, First Hospital of Shanxi Medical University,Taiyuan City, Shanxi Province, 030000, China
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36
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Chen K, Moravec JÍC, Gavryushkin A, Welch D, Drummond AJ. Accounting for errors in data improves divergence time estimates in single-cell cancer evolution. Mol Biol Evol 2022; 39:6613463. [PMID: 35733333 PMCID: PMC9356729 DOI: 10.1093/molbev/msac143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data is more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.
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Affiliation(s)
- Kylie Chen
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Jiř Í C Moravec
- Department of Computer Science, University of Otago, Dunedin, New Zealand.,School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Alex Gavryushkin
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - David Welch
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Alexei J Drummond
- School of Computer Science, University of Auckland, Auckland, New Zealand.,School of Biological Sciences, University of Auckland, Auckland, New Zealand
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37
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Xie Z, Li J, Huang P, Zhang Y, Yang J, Liu K, Jiang Y. Applications and Achievements of Single-Cell Sequencing in Gastrointestinal Cancer. Front Oncol 2022; 12:905571. [PMID: 35785171 PMCID: PMC9245065 DOI: 10.3389/fonc.2022.905571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/13/2022] [Indexed: 12/04/2022] Open
Abstract
Gastrointestinal cancer represents a public health concern that seriously endangers human health. The emerging single-cell sequencing (SCS) technologies are different from the large-scale sequencing technologies which provide inaccurate data. SCS is a powerful tool for deciphering the single-cell resolutions of cellular and molecular landscapes, revealing the features of single-cell genomes, transcriptomes, and epigenomes. Recently, SCS has been applied in the field of gastrointestinal cancer research for clarifying the origin and heterogeneity of gastrointestinal cancer, acquiring micro-environmental information, and improving diagnostic and treatment methods. This review outlines the applications of SCS in gastrointestinal cancer research and summarizes the most recent advances in the field.
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Affiliation(s)
- Zhenliang Xie
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Jincheng Li
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Pu Huang
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Ye Zhang
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Jingkuan Yang
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Kangdong Liu
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
- China-US (Henan) Hormel Cancer Institute, Zhengzhou, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, China
- Basic Medicine Sciences Research Center, Zhengzhou University, Zhengzhou, China
- Provincial Cooperative Innovation Center for Cancer Chemoprevention, Zhengzhou University, Zhengzhou, China
- Cancer Chemoprevention International Collaboration Laboratory, Zhengzhou, China
| | - Yanan Jiang
- The Pathophysiology Department, School of Basic Medical Sciences, College of Medicine, Zhengzhou University, Zhengzhou, China
- China-US (Henan) Hormel Cancer Institute, Zhengzhou, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, China
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38
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Lendahl U, Muhl L, Betsholtz C. Identification, discrimination and heterogeneity of fibroblasts. Nat Commun 2022; 13:3409. [PMID: 35701396 PMCID: PMC9192344 DOI: 10.1038/s41467-022-30633-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 05/04/2022] [Indexed: 12/14/2022] Open
Abstract
Fibroblasts, the principal cell type of connective tissue, secrete extracellular matrix components during tissue development, homeostasis, repair and disease. Despite this crucial role, the identification and distinction of fibroblasts from other cell types are challenging and laden with caveats. Rapid progress in single-cell transcriptomics now yields detailed molecular portraits of fibroblasts and other cell types in our bodies, which complement and enrich classical histological and immunological descriptions, improve cell class definitions and guide further studies on the functional heterogeneity of cell subtypes and states, origins and fates in physiological and pathological processes. In this review, we summarize and discuss recent advances in the understanding of fibroblast identification and heterogeneity and how they discriminate from other cell types. In this review, the authors look at how recent progress in single-cell transcriptomics complement and enrich the classical, largely morphological, portraits of fibroblasts. The detailed molecular information now available provides new insights into fibroblast identity, heterogeneity and function.
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Affiliation(s)
- Urban Lendahl
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Department of Neurobiology, Care sciences and Society, Karolinska Institutet, SE-14183, Huddinge, Sweden
| | - Lars Muhl
- Department of Medicine, Huddinge, Karolinska Institutet, Blickagången 16, SE-141 57, Huddinge, Sweden
| | - Christer Betsholtz
- Department of Medicine, Huddinge, Karolinska Institutet, Blickagången 16, SE-141 57, Huddinge, Sweden. .,Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Dag Hammarskjölds väg 20, SE-751 85, Uppsala, Sweden.
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39
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Valecha M, Posada D. Somatic variant calling from single-cell DNA sequencing data. Comput Struct Biotechnol J 2022; 20:2978-2985. [PMID: 35782734 PMCID: PMC9218383 DOI: 10.1016/j.csbj.2022.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/03/2022] Open
Abstract
Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.
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Key Words
- ADO, allelic dropout
- Allele dropout
- Amplification error
- CNV, copy number variant
- Indel, short insertion or deletion
- LDO, locus dropout
- SNV, single nucleotide variant
- SV, structural variant
- Single-cell genomics
- Somatic variants
- VAF, variant allele frequency
- Variant calling
- hSNP, heterozygous single-nucleotide polymorphism
- scATAC-seq, single-cell sequencing assay for transposase-accessible chromatin
- scDNA-seq, single-cell DNA sequencing
- scHi-C, single-cell Hi-C sequencing
- scMethyl-seq, single-cell Methylation sequencing
- scRNA-seq, single-cell RNA sequencing
- scWGA, single-cell whole-genome amplification
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Affiliation(s)
- Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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40
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Velu PD, Cushman-Vokoun A, Ewalt MD, Feilotter H, Gastier-Foster JM, Goswami RS, Laudadio J, Olsen RJ, Johnson R, Schlinsog A, Douglas A, Sandersfeld T, Kaul KL. Alignment of Fellowship Training with Practice Patterns for Molecular Pathologists: A Report of the Association for Molecular Pathology Training and Education Committee. J Mol Diagn 2022; 24:825-840. [PMID: 35690309 DOI: 10.1016/j.jmoldx.2022.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/12/2022] [Accepted: 04/26/2022] [Indexed: 12/19/2022] Open
Abstract
In the two decades since Accreditation Council for Graduate Medical Education-accredited Molecular Genetic Pathology fellowships began, the field of clinical molecular pathology has evolved considerably. The American Board of Pathology gathered data from board-certified molecular genetic pathologists assessing the alignment of skills and knowledge gained during fellowship with current needs on the job. The Association of Molecular Pathology conducted a parallel survey of program directors, and included questions on how various topics were taught during fellowship, as well as ranking their importance. Both surveys showed that most training aligned well with the practice needs of former trainees. Genomic profiling of tumors by next-generation sequencing, bioinformatics, laboratory management, and regulatory issues were topics thought to require increased emphasis in training. Topics related to clinical genetics and microbiology were deemed less important by those in practice, perhaps reflecting the increasing subspecialization of molecular pathologists. Program directors still viewed these topics as important to provide foundational knowledge. Parentage, identity, and human leukocyte antigen testing were less important to both survey audiences. These data may be helpful in guiding future adjustments to the Molecular Genetic Pathology curriculum and Accreditation Council for Graduate Medical Education program requirements.
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Affiliation(s)
- Priya D Velu
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York
| | - Allison Cushman-Vokoun
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Mark D Ewalt
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Harriet Feilotter
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Julie M Gastier-Foster
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Departments of Pediatrics and Pathology and Immunology, Baylor College of Medicine, Houston, Texas; Department of Pathology, Ohio State University College of Medicine, Columbus, Ohio
| | - Rashmi S Goswami
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Laboratory Medicine and Molecular Diagnostics/Department of Laboratory Medicine and Pathobiology, Sunnybrook Health Sciences Centre/University of Toronto, Toronto, Ontario, Canada
| | - Jennifer Laudadio
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Randall J Olsen
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York; Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | | | | | | | | | - Karen L Kaul
- Molecular Genetic Pathology Curriculum Update Working Group of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; NorthShore University HealthSystem, University of Chicago Pritzker School of Medicine, Evanston, Illinois.
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41
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Giannikopoulos P, Parham DM. Pediatric Sarcomas: The Next Generation of Molecular Studies. Cancers (Basel) 2022; 14:2515. [PMID: 35626119 PMCID: PMC9139929 DOI: 10.3390/cancers14102515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.
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Affiliation(s)
| | - David M. Parham
- Department of Anatomic Pathology, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
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42
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Caligola S, De Sanctis F, Canè S, Ugel S. Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies. Front Genet 2022; 13:867880. [PMID: 35651929 PMCID: PMC9149246 DOI: 10.3389/fgene.2022.867880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/27/2022] [Indexed: 12/31/2022] Open
Abstract
Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.
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Affiliation(s)
| | | | | | - Stefano Ugel
- Immunology Section, Department of Medicine, University of Verona, Verona, Italy
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43
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Han Y, Wang D, Peng L, Huang T, He X, Wang J, Ou C. Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis. J Hematol Oncol 2022; 15:59. [PMID: 35549970 PMCID: PMC9096771 DOI: 10.1186/s13045-022-01280-w] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/28/2022] [Indexed: 02/08/2023] Open
Abstract
Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.
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Affiliation(s)
- Yingying Han
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Dan Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Lushan Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Tao Huang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xiaoyun He
- Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Junpu Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,Department of Pathology, School of Basic Medicine, Central South University, Changsha, 410031, Hunan, China. .,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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44
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Rogiers A, Lobon I, Spain L, Turajlic S. The Genetic Evolution of Metastasis. Cancer Res 2022; 82:1849-1857. [PMID: 35476646 DOI: 10.1158/0008-5472.can-21-3863] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 11/16/2022]
Abstract
Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.
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Affiliation(s)
- Aljosja Rogiers
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Renal and Skin Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Irene Lobon
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Lavinia Spain
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Medical Oncology Department, Peter MacCallum Cancer Centre, Melbourne, Australia.,Medical Oncology Department, Eastern Health, Melbourne Australia.,Eastern Health Clinical School, Monash University, Box Hill, Australia
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom.,Renal and Skin Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom.,Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, United Kingdom
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45
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Tian B, Li Q. Single-Cell Sequencing and Its Applications in Liver Cancer. Front Oncol 2022; 12:857037. [PMID: 35574365 PMCID: PMC9097917 DOI: 10.3389/fonc.2022.857037] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/24/2022] [Indexed: 02/06/2023] Open
Abstract
As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.
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46
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Chen Z, Gong F, Wan L, Ma L. BiTSC
2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data. Brief Bioinform 2022; 23:6562684. [PMID: 35368055 PMCID: PMC9116244 DOI: 10.1093/bib/bbac092] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/29/2022] [Accepted: 02/23/2022] [Indexed: 12/14/2022] Open
Abstract
Abstract
The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.
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Affiliation(s)
- Ziwei Chen
- Institute of Zoology, Chinese Academy of Sciences, Beichen West Road, 100101, Beijing, Country
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Fuzhou Gong
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Lin Wan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Liang Ma
- Institute of Zoology, Chinese Academy of Sciences, Beichen West Road, 100101, Beijing, Country
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
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47
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Sashittal P, Zaccaria S, El-Kebir M. Parsimonious Clone Tree Integration in cancer. Algorithms Mol Biol 2022; 17:3. [PMID: 35282838 PMCID: PMC8919608 DOI: 10.1186/s13015-022-00209-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/25/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. RESULTS To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. CONCLUSION PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
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48
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Yu Z, Du F, Song L. SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data. Front Genet 2022; 13:823941. [PMID: 35154282 PMCID: PMC8830741 DOI: 10.3389/fgene.2022.823941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/04/2022] [Indexed: 12/11/2022] Open
Abstract
Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells and is especially useful for deciphering the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false-positive errors, and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show that it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.
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Affiliation(s)
- Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Fang Du
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Lijuan Song
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
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49
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Kozlov A, Alves JM, Stamatakis A, Posada D. CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data. Genome Biol 2022; 23:37. [PMID: 35081992 PMCID: PMC8790911 DOI: 10.1186/s13059-021-02583-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 12/20/2021] [Indexed: 01/15/2023] Open
Abstract
We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .
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Affiliation(s)
- Alexey Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany
| | - Joao M. Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
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50
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Chen WW, Liu W, Li Y, Wang J, Ren Y, Wang G, Chen C, Li H. Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology. Front Oncol 2022; 11:716042. [PMID: 35047383 PMCID: PMC8761635 DOI: 10.3389/fonc.2021.716042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/08/2021] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.
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Affiliation(s)
- Wei-Wei Chen
- Department of Clinical Oncology, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wei Liu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingze Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guangsuo Wang
- Department of Thoracic Surgery, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hanjie Li
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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