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Article ; Online: Delineating cancer evolution with single-cell sequencing.

Navin, Nicholas E

2015 Volume 7, Issue 296, Page(s) 296fs29

Abstract: Single-cell sequencing methods are revolutionizing cancer research and medicine by providing powerful tools to investigate intratumor heterogeneity and rare subpopulations. ...

Abstract	Single-cell sequencing methods are revolutionizing cancer research and medicine by providing powerful tools to investigate intratumor heterogeneity and rare subpopulations.
MeSH term(s)	Breast Neoplasms/genetics ; Breast Neoplasms/pathology ; DNA Copy Number Variations ; Disease Progression ; Female ; Gene Dosage ; Genetic Heterogeneity ; Genomics ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Mutation ; Phenotype ; Sequence Analysis, DNA ; Single-Cell Analysis ; Translational Medical Research
Language	English
Publishing date	2015-07-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2518854-9
ISSN	1946-6242 ; 1946-6234
ISSN (online)	1946-6242
ISSN	1946-6234
DOI	10.1126/scitranslmed.aac8319
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Article ; Online: The first five years of single-cell cancer genomics and beyond.

Navin, Nicholas E

Genome research

2015 Volume 25, Issue 10, Page(s) 1499–1507

Abstract: Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been ... ...

Abstract	Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been difficult to address with bulk tumor measurements. Over the past five years, there has been extraordinary progress in technological developments and research applications, but these efforts represent only the tip of the iceberg. In the coming years, SCS will greatly improve our understanding of invasion, metastasis, and therapy resistance during cancer progression. These tools will also have direct translational applications in the clinic, in areas such as early detection, noninvasive monitoring, and guiding targeted therapy. In this perspective, I discuss the progress that has been made and the myriad of unexplored applications that still lie ahead in cancer research and medicine.
MeSH term(s)	Animals ; Cytological Techniques ; Genetic Research ; Genomics ; Humans ; Neoplasms/genetics
Language	English
Publishing date	2015-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.191098.115
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Article ; Online: Cancer genomics: one cell at a time.

Navin, Nicholas E

Genome biology

2014 Volume 15, Issue 8, Page(s) 452

Abstract: The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to ...

Abstract	The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to study complex biological processes in human cancers.
MeSH term(s)	Cell-Free System ; Clonal Evolution ; Genome, Human ; Genomics/methods ; Humans ; Neoplasms/genetics
Language	English
Publishing date	2014-08-30
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-014-0452-9
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Article ; Online: Tumor evolution in response to chemotherapy: phenotype versus genotype.

Navin, Nicholas E

Cell reports

2014 Volume 6, Issue 3, Page(s) 417–419

Abstract: In this issue of Cell Reports, Almendro et al. report one of the first comprehensive studies on the intratumor heterogeneity of cell phenotypes and genotypes before and after chemotherapy in breast cancer. These data challenge the concept of genetic ... ...

Abstract	In this issue of Cell Reports, Almendro et al. report one of the first comprehensive studies on the intratumor heterogeneity of cell phenotypes and genotypes before and after chemotherapy in breast cancer. These data challenge the concept of genetic population bottlenecks and suggest that cellular phenotypes play an important role in developing resistance to therapy.
MeSH term(s)	Antineoplastic Agents/therapeutic use ; Breast Neoplasms/drug therapy ; Breast Neoplasms/genetics ; Female ; Genetic Variation ; Humans
Chemical Substances	Antineoplastic Agents
Language	English
Publishing date	2014-02-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Comment
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2014.01.035
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Article ; Online: Advancing Cancer Research and Medicine with Single-Cell Genomics.

Lim, Bora / Lin, Yiyun / Navin, Nicholas

Cancer cell

2020 Volume 37, Issue 4, Page(s) 456–470

Abstract: Single-cell sequencing (SCS) has impacted many areas of cancer research and improved our understanding of intratumor heterogeneity, the tumor microenvironment, metastasis, and therapeutic resistance. The development and refinement of SCS technologies has ...

Abstract	Single-cell sequencing (SCS) has impacted many areas of cancer research and improved our understanding of intratumor heterogeneity, the tumor microenvironment, metastasis, and therapeutic resistance. The development and refinement of SCS technologies has led to massive reductions in costs, increased cell throughput, and improved reproducibility, paving the way for clinical applications. However, before translational applications can be realized, there are a number of logistical and technical challenges that must be overcome. This review discusses past cancer research studies, emerging technologies, and future clinical applications that are bound to transform cancer medicine.
MeSH term(s)	Biomedical Research ; Genetic Heterogeneity ; Genomics/methods ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Neoplasms/diagnosis ; Neoplasms/genetics ; Neoplasms/therapy ; Single-Cell Analysis/methods ; Tumor Microenvironment
Language	English
Publishing date	2020-04-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2078448-X
ISSN	1878-3686 ; 1535-6108
ISSN (online)	1878-3686
ISSN	1535-6108
DOI	10.1016/j.ccell.2020.03.008
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Zs.A 5650: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing.

Leighton, Jake / Hu, Min / Sei, Emi / Meric-Bernstam, Funda / Navin, Nicholas E

Cell genomics

2022 Volume 3, Issue 1, Page(s) 100215

Abstract: Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) ... ...

Abstract	Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early
Language	English
Publishing date	2022-11-09
Publishing country	United States
Document type	Journal Article
ISSN	2666-979X
ISSN (online)	2666-979X
DOI	10.1016/j.xgen.2022.100215
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Article ; Online: SCOPIT: sample size calculations for single-cell sequencing experiments.

Davis, Alexander / Gao, Ruli / Navin, Nicholas E

BMC bioinformatics

2019 Volume 20, Issue 1, Page(s) 566

Abstract: Background: In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be ... ...

Abstract	Background: In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results: We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions: Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.
MeSH term(s)	Humans ; Retrospective Studies ; Sample Size ; Sequence Analysis, DNA/methods ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software
Language	English
Publishing date	2019-11-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-019-3167-9
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Article ; Online: SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data.

Zafar, Hamim / Navin, Nicholas / Chen, Ken / Nakhleh, Luay

Genome research

2019 Volume 29, Issue 11, Page(s) 1847–1859

Abstract: Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) ... ...

Abstract	Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.
MeSH term(s)	Bayes Theorem ; Clone Cells ; Genotype ; Humans ; Neoplasms/genetics ; Phylogeny ; Point Mutation ; Single-Cell Analysis/methods
Language	English
Publishing date	2019-10-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.243121.118
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Article ; Online: Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.

Mallory, Xian F / Edrisi, Mohammadamin / Navin, Nicholas / Nakhleh, Luay

PLoS computational biology

2020 Volume 16, Issue 7, Page(s) e1008012

Abstract: Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide ... ...

Abstract	Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.
MeSH term(s)	Algorithms ; Chromosome Aberrations ; Computational Biology ; Computer Simulation ; DNA Copy Number Variations ; Gene Dosage ; Genome, Human ; Humans ; Mutation ; Neoplasms/genetics ; Ploidies ; Poisson Distribution ; ROC Curve ; Reproducibility of Results ; Sequence Analysis, DNA/methods ; Single-Cell Analysis/methods ; Software
Language	English
Publishing date	2020-07-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008012
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Article ; Online: Methods for copy number aberration detection from single-cell DNA-sequencing data.

Mallory, Xian F / Edrisi, Mohammadamin / Navin, Nicholas / Nakhleh, Luay

Genome biology

2020 Volume 21, Issue 1, Page(s) 208

Abstract: Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In ... ...

Abstract	Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.
MeSH term(s)	Base Sequence ; Chromosome Aberrations ; Computational Biology/methods ; DNA ; DNA Copy Number Variations ; High-Throughput Nucleotide Sequencing ; Humans ; Neoplasms/genetics ; Sequence Analysis, DNA/methods
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2020-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-020-02119-8
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