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Weber LL, Reiman D, Roddur MS, Qi Y, El-Kebir M, Khan AA. Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data. CELL GENOMICS 2024; 4:100637. [PMID: 39208795 DOI: 10.1016/j.xgen.2024.100637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/19/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.
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Affiliation(s)
- Leah L Weber
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Derek Reiman
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Mrinmoy S Roddur
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Yuanyuan Qi
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Aly A Khan
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA; Department of Pathology, University of Chicago, Chicago, IL 60637, USA; Chan Zuckerberg Biohub Chicago, Chicago, IL 60642, USA.
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2
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Vasei H, Foroughmand-Araabi MH, Daneshgar A. Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference. Bioinformatics 2024; 40:btae120. [PMID: 38984735 DOI: 10.1093/bioinformatics/btae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/19/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024] Open
Abstract
MOTIVATION Tumor trees, which depict the evolutionary process of cancer, provide a backbone for discovering recurring evolutionary processes in cancer. While they are not the primary information extracted from genomic data, they are valuable for this purpose. One such extraction method involves summarizing multiple trees into a single representative tree, such as consensus trees or supertrees. RESULTS We define the "weighted centroid tree problem" to find the centroid tree of a set of single-labeled rooted trees through the following steps: (i) mapping the given trees into the Euclidean space, (ii) computing the weighted centroid matrix of the mapped trees, and (iii) finding the nearest mapped tree (NMTP) to the centroid matrix. We show that this setup encompasses previously studied parent-child and ancestor-descendent metrics as well as the GraPhyC and TuELiP consensus tree algorithms. Moreover, we show that, while the NMTP problem is polynomial-time solvable for the adjacency embedding, it is NP-hard for ancestry and distance mappings. We introduce integer linear programs for NMTP in different setups where we also provide a new algorithm for the case of ancestry embedding called 2-AncL2, that uses a novel weighting scheme for ancestry signals. Our experimental results show that 2-AncL2 has a superior performance compared to available consensus tree algorithms. We also illustrate our setup's application on providing representative trees for a large real breast cancer dataset, deducing that the cluster centroid trees summarize reliable evolutionary information about the original dataset. AVAILABILITY AND IMPLEMENTATION https://github.com/vasei/WAncILP.
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Affiliation(s)
- Hamed Vasei
- Department of Mathematical Sciences, Sharif University of Technology, Tehran 111559415, Iran
| | | | - Amir Daneshgar
- Department of Mathematical Sciences, Sharif University of Technology, Tehran 111559415, Iran
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3
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Baciu-Drăgan MA, Beerenwinkel N. Oncotree2vec - a method for embedding and clustering of tumor mutation trees. Bioinformatics 2024; 40:i180-i188. [PMID: 38940124 PMCID: PMC11211817 DOI: 10.1093/bioinformatics/btae214] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis. RESULTS Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees. AVAILABILITY AND IMPLEMENTATION https://github.com/cbg-ethz/oncotree2vec.
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Affiliation(s)
- Monica-Andreea Baciu-Drăgan
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, Basel 4056, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, Basel 4056, Switzerland
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4
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Zhang X, Lan R, Liu Y, Pillarisetty VG, Li D, Zhao CL, Sarkar SA, Liu W, Hanna I, Gupta M, Hajdu C, Melamed J, Shusterman M, Widmer J, Allendorf J, Liu YZ. Enhanced Complement Expression in the Tumor Microenvironment Following Neoadjuvant Therapy: Implications for Immunomodulation and Survival in Pancreatic Ductal Adenocarcinoma. RESEARCH SQUARE 2024:rs.3.rs-4104258. [PMID: 38798691 PMCID: PMC11118688 DOI: 10.21203/rs.3.rs-4104258/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Neoadjuvant therapy (NAT) is increasingly being used for pancreatic ductal adenocarcinoma (PDAC) treatment. However, its specific effects on carcinoma cells and the tumor microenvironment (TME) are not fully understood. This study aims to investigate how NAT differentially impacts PDAC's carcinoma cells and TME. Methods Spatial transcriptomics was used to compare gene expression profiles in carcinoma cells and the TME between 23 NAT-treated and 13 NAT-naïve PDAC patients, correlating with their clinicopathologic features. Analysis of an online single-nucleus RNA sequencing (snRNA-seq) dataset was performed for validation of the specific cell types responsible for NAT-induced gene expression alterations. Results NAT not only induces apoptosis and inhibits proliferation in carcinoma cells but also significantly remodels the TME. Notably, NAT induces a coordinated upregulation of multiple key complement genes (C3, C1S, C1R, C4B and C7) in the TME, making the complement pathway one of the most significantly affected pathways by NAT. Patients with higher TME complement expression following NAT exhibit improved overall survival. These patients also exhibit increased immunomodulatory and neurotrophic cancer-associated fibroblasts (CAFs); more CD4+ T cells, monocytes, and mast cells; and reduced immune exhaustion gene expression. snRNA-seq analysis demonstrates C3 complement was specifically upregulated in CAFs but not in other stroma cell types. Conclusions NAT can enhance complement production and signaling within the TME, which is associated with reduced immunosuppression in PDAC. These findings suggest that local complement dynamics could serve as a novel biomarker for prognosis, evaluating treatment response and resistance, and guiding therapeutic strategies in NAT-treated PDAC patients.
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Affiliation(s)
- Xiaofei Zhang
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Ruoxin Lan
- Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Yongjun Liu
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
| | - Venu G Pillarisetty
- Department of Surgery, University of Washington School of Medicine, Seattle, WA
| | - Danting Li
- Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Chaohui L Zhao
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Suparna A. Sarkar
- Department of Pathology and Laboratory Medicine, New York University Grossman School of Medicine, New York, NY
| | - Weiguo Liu
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Iman Hanna
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Mala Gupta
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Cristina Hajdu
- Department of Pathology and Laboratory Medicine, New York University Grossman School of Medicine, New York, NY
| | - Jonathan Melamed
- Department of Pathology and Laboratory Medicine, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Michael Shusterman
- Department of Oncology, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Jessica Widmer
- Department of Gastroenterology, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - John Allendorf
- Department of Surgery, New York University Grossman Long Island School of Medicine, Long Island, NY
| | - Yao-Zhong Liu
- Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
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5
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Li R, Shi F, Song L, Yu Z. scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data. BMC Genomics 2024; 25:393. [PMID: 38649804 PMCID: PMC11034052 DOI: 10.1186/s12864-024-10319-w] [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: 11/09/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.
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Affiliation(s)
- Ruixiang Li
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China
| | - Lijuan Song
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China.
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China.
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6
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Liu F, Shi F, Du F, Cao X, Yu Z. CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data. Brief Bioinform 2024; 25:bbae187. [PMID: 38670159 PMCID: PMC11052634 DOI: 10.1093/bib/bbae187] [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: 12/06/2023] [Revised: 03/08/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.
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Affiliation(s)
- Furui Liu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Fang Du
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Xiangmei Cao
- Basic Medical School, Ningxia Medical University, 750001, Ningxia, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
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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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Alaseem AM. Advancements in MDM2 inhibition: Clinical and pre-clinical investigations of combination therapeutic regimens. Saudi Pharm J 2023; 31:101790. [PMID: 37818252 PMCID: PMC10561124 DOI: 10.1016/j.jsps.2023.101790] [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: 07/26/2023] [Accepted: 09/12/2023] [Indexed: 10/12/2023] Open
Abstract
Cancer cells often depend on multiple pathways for their growth and survival, resulting in therapeutic resistance and the limited effectiveness of treatments. Combination therapy has emerged as a favorable approach to enhance treatment efficacy and minimize acquired resistance and harmful side effects. The murine double minute 2 (MDM2) protein regulates cellular proliferation and promotes cancer-related activities by negatively regulating the tumor suppressor protein p53. MDM2 aberrations have been reported in a variety of human cancers, making it an appealing target for cancer therapy. As a result, several small-molecule MDM2 inhibitors have been developed and are currently being investigated in clinical studies. Nevertheless, it has been shown that the inhibition of MDM2 alone is inadequate to achieve long-term suppression of tumor growth, thus prompting the need for further investigation into combination therapeutic strategies. In this review, possible clinical and preclinical MDM2 combination inhibitor regimens are thoroughly analyzed and discussed. It provides a rationale for combining MDM2 inhibitors with other therapeutic approaches in the management of cancer, taking into consideration ongoing clinical trials that evaluate the combination of MDM2 inhibitors. The review explores the current status of MDM2 inhibitors in combination with chemotherapy or targeted therapy, as well as promising approach of combining MDM2 inhibitors with immunotherapy. In addition, it investigates the function of PROTACs as MDM2 degraders in cancer treatment. A comprehensive examination of these combination regimens highlights the potential for advancing MDM2-inhibitor therapy and improving clinical outcomes for cancer patients and establishes the foundation for future research and development in this promising area of study.
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Affiliation(s)
- Ali M. Alaseem
- Department of Pharmacology, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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9
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [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: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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10
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Sadee W, Wang D, Hartmann K, Toland AE. Pharmacogenomics: Driving Personalized Medicine. Pharmacol Rev 2023; 75:789-814. [PMID: 36927888 PMCID: PMC10289244 DOI: 10.1124/pharmrev.122.000810] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.
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Affiliation(s)
- Wolfgang Sadee
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Danxin Wang
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Katherine Hartmann
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Amanda Ewart Toland
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
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11
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Luo XG, Kuipers J, Beerenwinkel N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat Commun 2023; 14:3676. [PMID: 37344522 DOI: 10.1038/s41467-023-39400-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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Affiliation(s)
- Xiang Ge Luo
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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12
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Lu B, Curtius K, Graham TA, Yang Z, Barnes CP. CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples. Genome Biol 2023; 24:144. [PMID: 37340508 DOI: 10.1186/s13059-023-02983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/08/2023] [Indexed: 06/22/2023] Open
Abstract
Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.
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Affiliation(s)
- Bingxin Lu
- Department of Cell and Developmental Biology, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
| | - Kit Curtius
- Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trevor A Graham
- Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Ziheng Yang
- Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
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13
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Jun SH, Toosi H, Mold J, Engblom C, Chen X, O'Flanagan C, Hagemann-Jensen M, Sandberg R, Aparicio S, Hartman J, Roth A, Lagergren J. Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nat Commun 2023; 14:982. [PMID: 36813776 PMCID: PMC9946941 DOI: 10.1038/s41467-023-36202-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/20/2023] [Indexed: 02/24/2023] Open
Abstract
Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.
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Affiliation(s)
- Seong-Hwan Jun
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA
| | - Hosein Toosi
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Xinsong Chen
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.,Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. .,Department of Computer Science, University of British Columbia, Vancouver, Canada.
| | - Jens Lagergren
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
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14
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Yan J, Ma M, Yu Z. bmVAE: a variational autoencoder method for clustering single-cell mutation data. Bioinformatics 2022; 39:6881080. [PMID: 36478203 PMCID: PMC9825778 DOI: 10.1093/bioinformatics/btac790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/26/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. RESULTS We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiaqian Yan
- School of Information Engineering, Ningxia University, Yinchuan 750021, China
| | - Ming Ma
- School of Information Engineering, Ningxia University, Yinchuan 750021, China
| | - Zhenhua Yu
- To whom correspondence should be addressed.
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15
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Zhang L, Qin Q, Xu C, Zhang N, Zhao T. Identification of immune cell function in breast cancer by integrating multiple single-cell data. Front Immunol 2022; 13:1058239. [PMID: 36479102 PMCID: PMC9719918 DOI: 10.3389/fimmu.2022.1058239] [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: 09/30/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022] Open
Abstract
Breast cancer has now become the most commonly diagnosed cancer worldwide. It is a highly complex and heterogeneous disease that comprises distinct histological features and treatment response. With the development of molecular biology and immunology, immunotherapy has become a new field of breast cancer treatment. Identifying cell-type-specific genes critical to the immune microenvironment contributes to breast cancer treatment. Single-cell RNA sequencing (scRNA-seq) technology could serve as a powerful tool to analyze cellular genetic information at single-cell resolution and to uncover the gene expression status of each cell, thus allowing comprehensive assessment of intercellular heterogeneity. Because of the influence of sample size and sequencing depth, the specificity of genes in different cell types for breast cancer cannot be fully revealed. Therefore, the present study integrated two public breast cancer scRNA-seq datasets aiming to investigate the functions of different type of immune cells in tumor microenvironment. We identified total five significant differential expressed genes of B cells, T cells and macrophage and explored their functions and immune mechanisms in breast cancer. Finally, we performed functional annotation analyses using the top fifteen differentially expressed genes in each immune cell type to discover the immune-related pathways and gene ontology (GO) terms.
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Affiliation(s)
- Liyuan Zhang
- Department of Computer Science, Harbin Institute of Technology, Harbin, China
| | - Qiyuan Qin
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chen Xu
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Ningyi Zhang
- Department of Computer Science, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin, China,*Correspondence: Tianyi Zhao,
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16
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Ke M, Elshenawy B, Sheldon H, Arora A, Buffa FM. Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity. Bioessays 2022; 44:e2200084. [PMID: 36068142 DOI: 10.1002/bies.202200084] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
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Affiliation(s)
- May Ke
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Badran Elshenawy
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Helen Sheldon
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Anjali Arora
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Francesca M Buffa
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.,Department of Computing Sciences, Bocconi University, Milano, Italy.,Institute for Data Science and Analytics, Bocconi University, Milano, Italy
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17
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Kuipers J, Singer J, Beerenwinkel N. Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence. Bioinformatics 2022; 38:4713-4719. [PMID: 36000873 PMCID: PMC9563700 DOI: 10.1093/bioinformatics/btac577] [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: 02/01/2022] [Revised: 07/08/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient’s tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process. Results By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data. Availability and implementation SCIΦN is available at https://github.com/cbg-ethz/SCIPhIN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jochen Singer
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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18
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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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19
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Rozhoňová H, Danciu D, Stark S, Rätsch G, Kahles A, Lehmann KV. SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinformatics 2022; 38:4293-4300. [PMID: 35900151 PMCID: PMC9477524 DOI: 10.1093/bioinformatics/btac510] [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: 01/12/2022] [Revised: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Several recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. RESULTS We developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. AVAILABILITY AND IMPLEMENTATION SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Stefan Stark
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland,Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
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20
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Ahmadinejad N, Troftgruben S, Wang J, Chandrashekar PB, Dinu V, Maley C, Liu L. Accurate Identification of Subclones in Tumor Genomes. Mol Biol Evol 2022; 39:msac136. [PMID: 35749590 PMCID: PMC9260306 DOI: 10.1093/molbev/msac136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding intratumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300× depth) to reliably infer subclones. Here, we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30×. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30× and 200×, whereas the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single-nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).
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Affiliation(s)
- Navid Ahmadinejad
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Shayna Troftgruben
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
| | - Junwen Wang
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
- Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA
| | - Pramod B Chandrashekar
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Valentin Dinu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Carlo Maley
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
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21
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Wang M, Liang H, Chen X, Chen D, Wang J, Zhang Y, Chen J. Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells. BIOSENSORS 2022; 12:bios12070443. [PMID: 35884246 PMCID: PMC9313373 DOI: 10.3390/bios12070443] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.
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Affiliation(s)
- Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongyan Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
| | - Yuan Zhang
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
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22
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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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23
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Markowska M, Cąkała T, Miasojedow B, Aybey B, Juraeva D, Mazur J, Ross E, Staub E, Szczurek E. CONET: copy number event tree model of evolutionary tumor history for single-cell data. Genome Biol 2022; 23:128. [PMID: 35681161 PMCID: PMC9185904 DOI: 10.1186/s13059-022-02693-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.
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Affiliation(s)
- Magda Markowska
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
- Medical University of Warsaw, Postgraduate School of Molecular Medicine, Ks. Trojdena 2a Street, Warsaw, Poland
| | - Tomasz Cąkała
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - BłaŻej Miasojedow
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
| | - Bogac Aybey
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293 Germany
| | - Dilafruz Juraeva
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293 Germany
| | - Johanna Mazur
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293 Germany
| | - Edith Ross
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293 Germany
| | - Eike Staub
- Merck Healthcare KGaA, Translational Medicine, Oncology Bioinformatics, Frankfurter Str. 250, Darmstadt, 64293 Germany
| | - Ewa Szczurek
- University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, Warsaw, Poland
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24
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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: 44] [Impact Index Per Article: 22.0] [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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25
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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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26
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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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27
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Plana D, Palmer AC, Sorger PK. Independent Drug Action in Combination Therapy: Implications for Precision Oncology. Cancer Discov 2022; 12:606-624. [PMID: 34983746 DOI: 10.1158/2159-8290.cd-21-0212] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 09/02/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022]
Abstract
Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.
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Affiliation(s)
- Deborah Plana
- Laboratory of Systems Pharmacology and the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts
| | - Adam C Palmer
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Peter K Sorger
- Laboratory of Systems Pharmacology and the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
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28
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OUP accepted manuscript. Bioinformatics 2022; 38:i125-i133. [PMID: 35758777 PMCID: PMC9236577 DOI: 10.1093/bioinformatics/btac253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. RESULTS In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. AVAILABILITY AND IMPLEMENTATION https://github.com/CMUSchwartzLab/TUSV-ext. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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29
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Dong Z, Wang Y, Yin D, Hang X, Pu L, Zhang J, Geng J, Chang L. Advanced techniques for gene heterogeneity research: Single‐cell sequencing and on‐chip gene analysis systems. VIEW 2022. [DOI: 10.1002/viw.20210011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Affiliation(s)
- Zaizai Dong
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
| | - Yu Wang
- Department of Laboratory Medicine State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University/Collaborative Innovation Center Chengdu China
| | - Dedong Yin
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
| | - Xinxin Hang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
| | - Lei Pu
- Department of Laboratory Medicine State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University/Collaborative Innovation Center Chengdu China
| | - Jianfu Zhang
- Department of Laboratory Medicine State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University/Collaborative Innovation Center Chengdu China
| | - Jia Geng
- Department of Laboratory Medicine State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University/Collaborative Innovation Center Chengdu China
| | - Lingqian Chang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
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30
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Lei H, Guo XA, Tao Y, Ding K, Fu X, Oesterreich S, Lee AV, Schwartz R. OUP accepted manuscript. Bioinformatics 2022; 38:i386-i394. [PMID: 35758822 PMCID: PMC9235482 DOI: 10.1093/bioinformatics/btac262] [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] [Indexed: 11/25/2022] Open
Abstract
Motivation Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available. Results In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples. Availability and implementation Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.
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Affiliation(s)
| | | | - Yifeng Tao
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kai Ding
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
| | - Xuecong Fu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Steffi Oesterreich
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
| | - Adrian V Lee
- Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
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31
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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32
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Lähnemann D, Köster J, Fischer U, Borkhardt A, McHardy AC, Schönhuth A. Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo. Nat Commun 2021; 12:6744. [PMID: 34795237 PMCID: PMC8602313 DOI: 10.1038/s41467-021-26938-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 10/22/2021] [Indexed: 01/14/2023] Open
Abstract
Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.
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Affiliation(s)
- David Lähnemann
- Department for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, 38106, Braunschweig, Germany
- Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Algorithms for Reproducible Bioinformatics, Institute of Human Genetics, University of Duisburg-Essen, 45147, Essen, Germany
| | - Johannes Köster
- Algorithms for Reproducible Bioinformatics, Institute of Human Genetics, University of Duisburg-Essen, 45147, Essen, Germany
- Genome Data Science, Life Sciences Group, Centrum Wiskunde & Informatica, 1098 XG, Amsterdam, The Netherlands
| | - Ute Fischer
- Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Arndt Borkhardt
- Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Alice C McHardy
- Department for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124, Braunschweig, Germany.
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, 38106, Braunschweig, Germany.
- Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany.
| | - Alexander Schönhuth
- Genome Data Science, Life Sciences Group, Centrum Wiskunde & Informatica, 1098 XG, Amsterdam, The Netherlands.
- Genome Data Science, Faculty of Technology, Bielefeld University, 33615, Bielefeld, Germany.
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33
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Chen L, Qing Y, Li R, Li C, Li H, Feng X, Li SC. Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics. Brief Bioinform 2021; 23:6406714. [PMID: 34671807 DOI: 10.1093/bib/bbab452] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/01/2021] [Accepted: 10/01/2021] [Indexed: 11/15/2022] Open
Abstract
The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.
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Affiliation(s)
- Lingxi Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Yuhao Qing
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Ruikang Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Chaohui Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Hechen Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China.,School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332, USA
| | - Xikang Feng
- School of Software, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
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34
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Heslop KA, Milesi V, Maldonado EN. VDAC Modulation of Cancer Metabolism: Advances and Therapeutic Challenges. Front Physiol 2021; 12:742839. [PMID: 34658929 PMCID: PMC8511398 DOI: 10.3389/fphys.2021.742839] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 08/25/2021] [Indexed: 12/16/2022] Open
Abstract
Most anionic metabolites including respiratory substrates, glycolytic adenosine triphosphate (ATP), and small cations that enter mitochondria, and mitochondrial ATP moving to the cytosol, cross the outer mitochondrial membrane (OMM) through voltage dependent anion channels (VDAC). The closed states of VDAC block the passage of anionic metabolites, and increase the flux of small cations, including calcium. Consequently, physiological or pharmacological regulation of VDAC opening, by conditioning the magnitude of both anion and cation fluxes, is a major contributor to mitochondrial metabolism. Tumor cells display a pro-proliferative Warburg phenotype characterized by enhanced aerobic glycolysis in the presence of partial suppression of mitochondrial metabolism. The heterogeneous and flexible metabolic traits of most human tumors render cells able to adapt to the constantly changing energetic and biosynthetic demands by switching between predominantly glycolytic or oxidative phenotypes. Here, we describe the biological consequences of changes in the conformational state of VDAC for cancer metabolism, the mechanisms by which VDAC-openers promote cancer cell death, and the advantages of VDAC opening as a valuable pharmacological target. Particular emphasis is given to the endogenous regulation of VDAC by free tubulin and the effects of VDAC-tubulin antagonists in cancer cells. Because of its function and location, VDAC operates as a switch to turn-off mitochondrial metabolism (closed state) and increase aerobic glycolysis (pro-Warburg), or to turn-on mitochondrial metabolism (open state) and decrease glycolysis (anti-Warburg). A better understanding of the role of VDAC regulation in tumor progression is relevant both for cancer biology and for developing novel cancer chemotherapies.
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Affiliation(s)
- Kareem A Heslop
- Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Veronica Milesi
- Facultad de Ciencias Exactas, Instituto de Estudios Inmunológicos y Fisiopatológicos (IIFP), UNLP, CONICET, CIC PBA, La Plata, Argentina
| | - Eduardo N Maldonado
- Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, SC, United States.,Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States
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35
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Baghaarabani L, Goliaei S, Foroughmand-Araabi MH, Shariatpanahi SP, Goliaei B. Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data. BMC Bioinformatics 2021; 22:416. [PMID: 34461827 PMCID: PMC8404257 DOI: 10.1186/s12859-021-04338-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 08/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. RESULT In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. CONCLUSIONS In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.
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Affiliation(s)
- Leila Baghaarabani
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sama Goliaei
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | | | | | - Bahram Goliaei
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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36
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Stiehl T, Marciniak-Czochra A. Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples. Front Physiol 2021; 12:596194. [PMID: 34497529 PMCID: PMC8419336 DOI: 10.3389/fphys.2021.596194] [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: 08/18/2020] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.
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Affiliation(s)
- Thomas Stiehl
- Institute for Computational Biomedicine – Disease Modeling, RWTH Aachen University, Aachen, Germany
- Institute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, Germany
| | - Anna Marciniak-Czochra
- Institute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, Germany
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37
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Weber LL, Sashittal P, El-Kebir M. doubletD: detecting doublets in single-cell DNA sequencing data. Bioinformatics 2021; 37:i214-i221. [PMID: 34252961 PMCID: PMC8275324 DOI: 10.1093/bioinformatics/btab266] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance. Results We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results. Availability and implementation https://github.com/elkebir-group/doubletD. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leah L Weber
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
| | - Palash Sashittal
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA.,Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
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38
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Nath A, Bild AH. Leveraging Single-Cell Approaches in Cancer Precision Medicine. Trends Cancer 2021; 7:359-372. [PMID: 33563578 PMCID: PMC7969443 DOI: 10.1016/j.trecan.2021.01.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 12/24/2022]
Abstract
Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk 'omic' data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.
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Affiliation(s)
- Aritro Nath
- Department of Medical Oncology and Therapeutics Research, City of Hope, Monrovia, CA 91016, USA.
| | - Andrea H Bild
- Department of Medical Oncology and Therapeutics Research, City of Hope, Monrovia, CA 91016, USA
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39
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Shafighi SD, Kiełbasa SM, Sepúlveda-Yáñez J, Monajemi R, Cats D, Mei H, Menafra R, Kloet S, Veelken H, van Bergen CAM, Szczurek E. CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells. Genome Med 2021; 13:45. [PMID: 33761980 PMCID: PMC7988935 DOI: 10.1186/s13073-021-00842-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 02/03/2021] [Indexed: 01/13/2023] Open
Abstract
Background Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. Methods Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. Results We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. Conclusions The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS). Supplementary Information The online version contains supplementary material available at (10.1186/s13073-021-00842-w).
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Affiliation(s)
- Shadi Darvish Shafighi
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, Warsaw, 02-097, Poland
| | - Szymon M Kiełbasa
- Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Julieta Sepúlveda-Yáñez
- Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | - Ramin Monajemi
- Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Davy Cats
- Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Hailiang Mei
- Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Roberta Menafra
- Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Susan Kloet
- Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
| | - Hendrik Veelken
- Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | - Cornelis A M van Bergen
- Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, Warsaw, 02-097, Poland
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40
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Sadeqi Azer E, Rashidi Mehrabadi F, Malikić S, Li XC, Bartok O, Litchfield K, Levy R, Samuels Y, Schäffer AA, Gertz EM, Day CP, Pérez-Guijarro E, Marie K, Lee MP, Merlino G, Ergun F, Sahinalp SC. PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem. Bioinformatics 2021; 36:i169-i176. [PMID: 32657358 DOI: 10.1093/bioinformatics/btaa464] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. RESULTS We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and bound algorithm to compute the most likely perfect phylogeny on an input genotype matrix extracted from an SCS dataset. PhISCS-BnB not only offers an optimality guarantee, but is also 10-100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma dataset derived from the sublineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in <4 h. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor. AVAILABILITY AND IMPLEMENTATION https://github.com/algo-cancer/PhISCS-BnB. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erfan Sadeqi Azer
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Farid Rashidi Mehrabadi
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA.,Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Salem Malikić
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Xuan Cindy Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.,Program in Computational Biology, Bioinformatics and Genomics, University of Maryland, College Park, MD 20742, USA
| | - Osnat Bartok
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Kevin Litchfield
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London NW1 1AT, UK.,Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ronen Levy
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - E Michael Gertz
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kerrie Marie
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Maxwell P Lee
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Funda Ergun
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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41
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Zhang Y, Wang D, Peng M, Tang L, Ouyang J, Xiong F, Guo C, Tang Y, Zhou Y, Liao Q, Wu X, Wang H, Yu J, Li Y, Li X, Li G, Zeng Z, Tan Y, Xiong W. Single-cell RNA sequencing in cancer research. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:81. [PMID: 33648534 PMCID: PMC7919320 DOI: 10.1186/s13046-021-01874-1] [Citation(s) in RCA: 133] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 02/08/2021] [Indexed: 02/06/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.
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Affiliation(s)
- Yijie Zhang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Dan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Miao Peng
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Le Tang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Jiawei Ouyang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Fang Xiong
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Can Guo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Yanyan Tang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Yujuan Zhou
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Xu Wu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Hui Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Jianjun Yu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Xiaoling Li
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Guiyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yixin Tan
- Department of Dermatology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China. .,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
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Analysis of Intratumoral Heterogeneity in Myelodysplastic Syndromes with Isolated del(5q) Using a Single Cell Approach. Cancers (Basel) 2021; 13:cancers13040841. [PMID: 33671317 PMCID: PMC7922695 DOI: 10.3390/cancers13040841] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/09/2021] [Accepted: 02/14/2021] [Indexed: 01/10/2023] Open
Abstract
Simple Summary Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic stem cell malignancies characterized by ineffective differentiation of one or more bone marrow cell lineages. Only 50% of patients with de novo MDS will be found to have cytogenetic abnormalities, of which del(5q) is the most common. In 10% of MDS cases, del(5q) is found as a sole abnormality. In this work, a single cell approach was used to analyze intratumoral heterogeneity in four patients with MDS with isolated del(5q). We were able to observe that an ancestral event in one patient can appear as a secondary hit in another one, thus reflecting the high intratumoral heterogeneity in MDS with isolated del(5q) and the importance of patient-specific molecular characterization. Abstract Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Among them, the most well characterized subtype is MDS with isolated chromosome 5q deletion (MDS del(5q)), which is the only one defined by a cytogenetic abnormality that makes these patients candidates to be treated with lenalidomide. During the last decade, single cell (SC) analysis has emerged as a powerful tool to decipher clonal architecture and to further understand cancer and other diseases at higher resolution level compared to bulk sequencing techniques. In this study, a SC approach was used to analyze intratumoral heterogeneity in four patients with MDS del(5q). Single CD34+CD117+CD45+CD19- bone marrow hematopoietic stem progenitor cells were isolated using the C1 system (Fluidigm) from diagnosis or before receiving any treatment and from available follow-up samples. Selected somatic alterations were further analyzed in SC by high-throughput qPCR (Biomark HD, Fluidigm) using specific TaqMan assays. A median of 175 cells per sample were analyzed. Inferred clonal architectures were relatively simple and either linear or branching. Similar to previous studies based on bulk sequencing to infer clonal architecture, we were able to observe that an ancestral event in one patient can appear as a secondary hit in another one, thus reflecting the high intratumoral heterogeneity in MDS del(5q) and the importance of patient-specific molecular characterization.
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43
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Zhou T, Sengupta S, Müller P, Ji Y. RNDClone: Tumor subclone reconstruction based on integrating DNA and RNA sequence data. Ann Appl Stat 2020. [DOI: 10.1214/20-aoas1368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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44
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Tsyvina V, Zelikovsky A, Snir S, Skums P. Inference of mutability landscapes of tumors from single cell sequencing data. PLoS Comput Biol 2020; 16:e1008454. [PMID: 33253159 PMCID: PMC7728263 DOI: 10.1371/journal.pcbi.1008454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 12/10/2020] [Accepted: 10/20/2020] [Indexed: 11/18/2022] Open
Abstract
One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.
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Affiliation(s)
- Viachaslau Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Sagi Snir
- Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
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45
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Abstract
Metastatic dissemination occurs very early in the malignant progression of a cancer but the clinical manifestation of metastases often takes years. In recent decades, 5-year survival of patients with many solid cancers has increased due to earlier detection, local disease control and adjuvant therapies. As a consequence, we are confronted with an increase in late relapses as more antiproliferative cancer therapies prolong disease courses, raising questions about how cancer cells survive, evolve or stop growing and finally expand during periods of clinical latency. I argue here that the understanding of early metastasis formation, particularly of the currently invisible phase of metastatic colonization, will be essential for the next stage in adjuvant therapy development that reliably prevents metachronous metastasis.
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Affiliation(s)
- Christoph A Klein
- Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany.
- Division of Personalized Tumor Therapy, Fraunhofer Institute for Toxicology and Experimental Medicine, Regensburg, Germany.
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46
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Carretta C, Mallia S, Genovese E, Parenti S, Rontauroli S, Bianchi E, Fantini S, Sartini S, Tavernari L, Tagliafico E, Manfredini R. Genomic Analysis of Hematopoietic Stem Cell at the Single-Cell Level: Optimization of Cell Fixation and Whole Genome Amplification (WGA) Protocol. Int J Mol Sci 2020; 21:E7366. [PMID: 33036143 PMCID: PMC7582552 DOI: 10.3390/ijms21197366] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/30/2020] [Accepted: 10/03/2020] [Indexed: 12/15/2022] Open
Abstract
Single-cell genomics has become the method of choice for the study of heterogeneous cell populations and represents an elective application in defining the architecture and clonal evolution in hematological neoplasms. Reconstructing the clonal evolution of a neoplastic population therefore represents the main way to understand more deeply the pathogenesis of the neoplasm, but it is also a potential tool to understand the evolution of the tumor population with respect to its response to therapy. Pre-analytical phase for single-cell genomics analysis is crucial to obtain a cell population suitable for single-cell sorting, and whole genome amplification is required to obtain the necessary amount of DNA from a single cell in order to proceed with sequencing. Here, we evaluated the impact of different methods of cellular immunostaining, fixation and whole genome amplification on the efficiency and yield of single-cell sequencing.
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Affiliation(s)
- Chiara Carretta
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Selene Mallia
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Elena Genovese
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Sandra Parenti
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Sebastiano Rontauroli
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Elisa Bianchi
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Sebastian Fantini
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Stefano Sartini
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Lara Tavernari
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
| | - Enrico Tagliafico
- Center for Genome Research, University of Modena and Reggio Emilia, 41125 Modena, Italy;
- Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Rossella Manfredini
- Centre for Regenerative Medicine “S. Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy; (C.C.); (S.M.); (E.G.); (S.P.); (S.R.); (E.B.); (S.F.); (S.S.); (L.T.)
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47
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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48
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Feng S, Zhang L, Liu X, Li G, Zhang B, Wang Z, Zhang H, Ma H. Low levels of AMPK promote epithelial-mesenchymal transition in lung cancer primarily through HDAC4- and HDAC5-mediated metabolic reprogramming. J Cell Mol Med 2020; 24:7789-7801. [PMID: 32519437 PMCID: PMC7348170 DOI: 10.1111/jcmm.15410] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/15/2020] [Accepted: 05/03/2020] [Indexed: 12/11/2022] Open
Abstract
AMP-activated protein kinase (AMPK) serves as a "supermetabolic regulator" that helps maintain cellular energy homeostasis. However, the role of AMPK in glucose metabolism reprogramming in lung cancer remains unclear. Here, our study shows that low AMPK expression correlates with metastasis and clinicopathologic parameters of non-small-cell lung cancer. Low AMPK significantly enhances the Warburg effect in HBE and A549 cells, which in turn induces the expression of mesenchymal markers and enhances their invasion and migration. At the mechanistic level, low AMPK up-regulates HK2 expression and glycolysis levels through HDAC4 and HDAC5. Collectively, our findings demonstrate that low AMPK-induced metabolism can promote epithelial-mesenchymal transition progression in normal bronchial epithelial cells and lung cancer cells, and increase the risk for tumour metastasis.
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Affiliation(s)
- Shoujie Feng
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.,Department of Thoracic Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Thoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China
| | - Li Zhang
- Editorial Office of International Journal of Anesthesiology and Resuscitation, Xuzhou Medical University, Xuzhou, China
| | - Xiucheng Liu
- Department of Thoracic Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Thoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China
| | - Guangbin Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Biao Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ziwen Wang
- Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hao Zhang
- Department of Thoracic Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Thoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China
| | - Haitao Ma
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
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49
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Skums P, Tsyvina V, Zelikovsky A. Inference of clonal selection in cancer populations using single-cell sequencing data. Bioinformatics 2020; 35:i398-i407. [PMID: 31510696 PMCID: PMC6612866 DOI: 10.1093/bioinformatics/btz392] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Summary Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Availability and implementation Its source code is available at https://github.com/compbel/SCIFIL.
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Affiliation(s)
- Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Viachaslau Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.,The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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50
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Lei H, Lyu B, Gertz EM, Schäffer AA, Shi X, Wu K, Li G, Xu L, Hou Y, Dean M, Schwartz R. Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data. J Comput Biol 2020; 27:565-598. [PMID: 32181683 DOI: 10.1089/cmb.2019.0302] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.
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Affiliation(s)
- Haoyun Lei
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Bochuan Lyu
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, Indiana
| | - E Michael Gertz
- National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, Maryland.,Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, Maryland
| | - Alejandro A Schäffer
- National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, Maryland.,Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, Maryland
| | | | - Kui Wu
- BGI-Shenzhen, Shenzhen, China
| | | | | | | | - Michael Dean
- Laboratory of Translational Genomics, Division of Cancer Epidemiology & Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, Maryland
| | - Russell Schwartz
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania
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