1
|
Kulhankova L, Bindels E, Kayser M, Mulugeta E. Deconvoluting multi-person biological mixtures and accurate characterization and identification of separated contributors using non-targeted single-cell DNA sequencing. Forensic Sci Int Genet 2024; 71:103030. [PMID: 38513339 DOI: 10.1016/j.fsigen.2024.103030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/16/2024] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
The genetic characterization and identification of individuals who contributed to biological mixtures are complex and mostly unresolved tasks. These tasks are relevant in various fields, particularly in forensic investigations, which frequently encounters crime scene stains generated by more than one person. Currently, forensic mixture deconvolution is mostly performed subsequent to forensic DNA profiling at the level of the mixed DNA profiles, which comes with several limitations. Some previous studies attempted at separating single cells prior to forensic DNA profiling. However, these approaches are biased at selection of the cells and, due to their targeted DNA analysis on low template DNA, provide incomplete and unreliable forensic DNA profiles. We recently demonstrated the feasibility of performing mixture deconvolution prior to forensic DNA profiling through the utilization of a non-targeted single-cell transcriptome sequencing (scRNA-seq). In addition to individual-specific mixture deconvolution, this approach also allowed accurate characterisation of biological sex, biogeographic ancestry and individual identification of the separated mixture contributors. However, RNA has the forensic disadvantage of being prone to degradation, and sequencing RNA - focussing on coding regions - limits the number of single nucleotide polymorphisms (SNPs) utilized for genetic mixture deconvolution, characterization, and identification. These limitations can be overcome by performing single-cell sequencing on the level of DNA instead of RNA. Here, for the first time, we applied non-targeted single-cell DNA sequencing (scDNA-seq) by applying the scATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) technique to address the challenges of mixture deconvolution in the forensic context. We demonstrated that scATAC-seq, together with our recently developed De-goulash data analysis pipeline, is capable of deconvoluting complex blood mixtures of five individuals from both sexes with varying biogeographic ancestries. We further showed that our approach achieved correct genetic characterization of the biological sex and the biogeographic ancestry of each of the separated mixture contributors and established their identity. Furthermore, by analysing in-silico generated scATAC-seq data mixtures, we demonstrated successful individual-specific mixture deconvolution of i) highly complex mixtures of 11 individuals, ii) balanced mixtures containing as few as 20 cells (10 per each individual), and iii) imbalanced mixtures with a ratio as low as 1:80. Overall, our proof-of-principle study demonstrates the general feasibility of scDNA-seq in general, and scATAC-seq in particular, for mixture deconvolution, genetic characterization and individual identification of the separated mixture contributors. Furthermore, it shows that compared to scRNA-seq, scDNA-seq detects more SNPs from fewer cells, providing higher sensitivity, that is valuable in forensic genetics.
Collapse
Affiliation(s)
- Lucie Kulhankova
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Eric Bindels
- Department of Haematology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Eskeatnaf Mulugeta
- Department of Cell Biology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| |
Collapse
|
2
|
Schneider MP, Cullen AE, Pangonyte J, Skelton J, Major H, Van Oudenhove E, Garcia MJ, Chaves Urbano B, Piskorz AM, Brenton JD, Macintyre G, Markowetz F. scAbsolute: measuring single-cell ploidy and replication status. Genome Biol 2024; 25:62. [PMID: 38438920 PMCID: PMC10910719 DOI: 10.1186/s13059-024-03204-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024] Open
Abstract
Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute's capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.
Collapse
Affiliation(s)
- Michael P Schneider
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Amy E Cullen
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Justina Pangonyte
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Jason Skelton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Harvey Major
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Elke Van Oudenhove
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Maria J Garcia
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Anna M Piskorz
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - James D Brenton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Geoff Macintyre
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Florian Markowetz
- University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK.
| |
Collapse
|
3
|
Liu F, Shi F, Yu Z. Inferring single-cell copy number profiles through cross-cell segmentation of read counts. BMC Genomics 2024; 25:25. [PMID: 38166601 PMCID: PMC10762977 DOI: 10.1186/s12864-023-09901-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.
Collapse
Affiliation(s)
- Furui Liu
- 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
| | - 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.
| |
Collapse
|
4
|
Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol 2023; 24:272. [PMID: 38037115 PMCID: PMC10688497 DOI: 10.1186/s13059-023-03106-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
Collapse
Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
| | | |
Collapse
|
5
|
Bahaj W, Kewan T, Gurnari C, Durmaz A, Ponvilawan B, Pandit I, Kubota Y, Ogbue OD, Zawit M, Madanat Y, Bat T, Balasubramanian SK, Awada H, Ahmed R, Mori M, Meggendorfer M, Haferlach T, Visconte V, Maciejewski JP. Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia. J Hematol Oncol 2023; 16:91. [PMID: 37537667 PMCID: PMC10401750 DOI: 10.1186/s13045-023-01480-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 07/14/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. METHODS We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. RESULTS Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. CONCLUSION Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.
Collapse
Affiliation(s)
- Waled Bahaj
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
- Division of Medical Oncology & Hematology, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Tariq Kewan
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
- Division of Hematology & Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Carmelo Gurnari
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
- Department of Biomedicine and Prevention, Ph.D. in Immunology, Molecular Medicine and Applied Biotechnology, University of Rome Tor Vergata, Rome, Italy
| | - Arda Durmaz
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Ben Ponvilawan
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Ishani Pandit
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Yasuo Kubota
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Olisaemeka D Ogbue
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Misam Zawit
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Yazan Madanat
- Department of Internal Medicine, Division of Hematology and Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Taha Bat
- Department of Internal Medicine, Division of Hematology and Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Hussein Awada
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Ramsha Ahmed
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | - Minako Mori
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA
| | | | | | - Valeria Visconte
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA.
| | - Jaroslaw P Maciejewski
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9620 Carnegie Ave N Building, Building NE6-250, Cleveland, OH, 44106, USA.
| |
Collapse
|
6
|
Kang S, Borgsmüller N, Valecha M, Kuipers J, Alves JM, Prado-López S, Chantada D, Beerenwinkel N, Posada D, Szczurek E. SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data. Genome Biol 2022; 23:248. [PMID: 36451239 PMCID: PMC9714196 DOI: 10.1186/s13059-022-02813-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 11/08/2022] [Indexed: 12/02/2022] Open
Abstract
We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.
Collapse
Affiliation(s)
- Senbai Kang
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Joao M. Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Sonia Prado-López
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Institute of Solid State Electronics E362, Technische Universität Wien, Vienna, Austria
| | - Débora Chantada
- Department of Pathology, Hospital Álvaro Cunqueiro, Vigo, Spain
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| |
Collapse
|
7
|
Abstract
BACKGROUND Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. RESULTS To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. CONCLUSION PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
Collapse
|
8
|
Wang R, Jiang Y. Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE. Methods Mol Biol 2022; 2493:279-288. [PMID: 35751822 DOI: 10.1007/978-1-0716-2293-3_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.
Collapse
Affiliation(s)
- Rujin Wang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| |
Collapse
|
9
|
Bahonar S, Montazeri H. Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV. Methods Mol Biol 2022; 2493:267-277. [PMID: 35751821 DOI: 10.1007/978-1-0716-2293-3_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
SCAN-SNV is a recent computational tool for somatic single-nucleotide variant (SNV) identification from the single-cell DNA sequencing data. The workflow of the SCAN-SNV package is as follows. First, candidate somatic SNVs and credible heterozygous single-nucleotide polymorphisms (hSNP) are obtained by analyzing single-cell and matched bulk sequencing data, respectively. Subsequently, SCAN-SNV estimates genome-wide allele-specific amplification balance (AB) at any position of DNA sequencing data using a probabilistic spatial statistical model. Finally, candidate somatic SNVs that are likely artifacts according to the AB predictions are further removed to obtain putative mutations. This chapter provides a step-by-step practical guide of the package by explaining how to install and use the variance caller in a real-world example.
Collapse
Affiliation(s)
- Sajedeh Bahonar
- Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Hesam Montazeri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
| |
Collapse
|
10
|
Weber LL, El-Kebir M. Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors. Algorithms Mol Biol 2021; 16:14. [PMID: 34229713 PMCID: PMC8259357 DOI: 10.1186/s13015-021-00194-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/22/2021] [Indexed: 01/24/2023] Open
Abstract
Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.
Collapse
|
11
|
Chen Y, Li Y, Qi C, Zhang C, Liu D, Deng Y, Fu Y, Khadka VS, Wang DD, Tan S, Liu S, Peng Z, Gong J, Lin PP, Zhang X, Li J, Li Y, Shen L. Dysregulated KRAS gene-signaling axis and abnormal chromatin remodeling drive therapeutic resistance in heterogeneous-sized circulating tumor cells in gastric cancer patients. Cancer Lett 2021; 517:78-87. [PMID: 34126192 DOI: 10.1016/j.canlet.2021.06.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/03/2021] [Accepted: 06/07/2021] [Indexed: 01/25/2023]
Abstract
The mechanism by which heterogeneous-sized circulating tumor cells (CTCs) in gastric cancer (GC) patients are resistant to the targeted therapy and/or chemotherapy remains unclear. This study investigated prognostic value and genomic variations of size-heterogenous CTCs, in an attempt to unravel the molecular mechanisms underlying the therapeutic resistance, which is relevant to poor prognosis in GC. Aneuploid CTCs, detected in 111 advanced GC patients, were categorized into small (≤white blood cell [WBC], 25.54%) and large (>WBC, 74.46%) cells. Pre-treatment patients possessing ≥3 baseline small CTCs with trisomy 8 (SCTCstri) or ≥6 large multiploid CTCs (LCTCsmulti) showed an inferior median progression-free survival. Moreover, the cut-off value of ≥6 LCTCsmulti was also an effective prognosticator for poor median overall survival. Single cell-based DNA sequencing of 50 targeted CTCs indicated that SCTCstri and LCTCsmulti harbored distinct gene variations respectively. Mutations in the KRAS and Rap1 pathway were remarkably abundant in SCTCstri, whereas several unique mutations in the MET/PI3K/AKT pathway and SMARCB1 gene were identified in LCTCsmulti. Obtained results suggested that SCTCstri and LCTCsmulti exhibited different mechanisms to therapy resistance and correlated with patients' poor outcome.
Collapse
|
12
|
Yan X, Xie Y, Yang F, Hua Y, Zeng T, Sun C, Yang M, Huang X, Wu H, Fu Z, Li W, Jiao S, Yin Y. Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis. J Exp Clin Cancer Res 2021; 40:142. [PMID: 33906694 PMCID: PMC8077685 DOI: 10.1186/s13046-021-01949-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/15/2021] [Indexed: 02/07/2023]
Abstract
Breast cancer is a heterogeneous disease with a complex microenvironment consisting of tumor cells, immune cells, fibroblasts and vascular cells. These cancer-associated cells shape the tumor microenvironment (TME) and influence the progression of breast cancer and the therapeutic responses in patients. The exact composition of the intra-tumoral cells is mixed as the highly heterogeneous and dynamic nature of the TME. Recent advances in single-cell technologies such as single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq) and mass cytometry have provided new insights into the phenotypic and functional diversity of tumor-infiltrating cells in breast cancer. In this review, we have outlined the recent progress in single-cell characterization of breast tumor ecosystems, and summarized the phenotypic diversity of intra-tumoral cells and their potential prognostic relevance.
Collapse
Affiliation(s)
- Xueqi Yan
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yinghong Xie
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Fan Yang
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yijia Hua
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Tianyu Zeng
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Chunxiao Sun
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Mengzhu Yang
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xiang Huang
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hao Wu
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Ziyi Fu
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Wei Li
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Shiping Jiao
- Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210029, Jiangsu Province, China. .,Drum Tower Institute of clinical medicine, Nanjing University, Nanjing, 210029, Jiangsu Province, China.
| | - Yongmei Yin
- Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
| |
Collapse
|
13
|
Yu J, Gemenetzis G, Kinny-Köster B, Habib JR, Groot VP, Teinor J, Yin L, Pu N, Hasanain A, van Oosten F, Javed AA, Weiss MJ, Burkhart RA, Burns WR, Goggins M, He J, Wolfgang CL. Pancreatic circulating tumor cell detection by targeted single-cell next-generation sequencing. Cancer Lett 2020; 493:245-253. [PMID: 32896616 DOI: 10.1016/j.canlet.2020.08.043] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/07/2020] [Accepted: 08/28/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND AIMS Single-cell next-generation sequencing (scNGS) technology has been widely used in genomic profiling, which relies on whole-genome amplification (WGA). However, WGA introduces errors and is especially less accurate when applied to single nucleotide variant (SNV) analysis. Targeted scNGS for SNV without WGA has not been described. We aimed to develop a method to detect circulating tumor cells (CTCs) with DNA SNVs. METHODS We tested this targeted scNGS method with three driver mutant genes (KRAS/TP53/SMAD4) on one pancreatic cancer cell line AsPC-1 and then applied it to patients with metastatic PDAC for the validation. RESULTS All single-cell of AsPC-1 and spiked-in AsPC-1 cells in healthy donor blood, which were isolated by the filtration with size or by flow cytometry, were detected by targeted scNGS method. All blood samples from six patients with metastatic PDAC, for the validation of target scNGS method, showed CTCs with SNVs of KRAS/TP53/SMAD4 and the positive confirmation of immunofluorescent stainings with Pan-CK/Vimentin/CD45. Four patients with early stage disease, one patient with benign pancreatic cyst and a healthy control sample all showed concordant results between targeted scNGS and CTC enumeration. CONCLUSIONS The novel technique of targeted scNGS for SNV analysis, without pre-amplification, is a promising method for identifying and characterizing circulating tumor cells.
Collapse
Affiliation(s)
- Jun Yu
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Georgios Gemenetzis
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benedict Kinny-Köster
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph R Habib
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Vincent P Groot
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jonathan Teinor
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lingdi Yin
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ning Pu
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alina Hasanain
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Floortje van Oosten
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ammar A Javed
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew J Weiss
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard A Burkhart
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Oncology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - William R Burns
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Oncology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Goggins
- Departments of Oncology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Medicine, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jin He
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Oncology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Christopher L Wolfgang
- Departments of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Oncology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
14
|
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.
Collapse
Affiliation(s)
- Xian F. Mallory
- Department of Computer Science, Rice University, Houston, TX USA
- Department of Computer Science, Florida State University, Tallahassee, FL USA
| | | | - Nicholas Navin
- Department of Genetics, the University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, TX USA
| |
Collapse
|
15
|
Giguere C, Dubey HV, Sarsani VK, Saddiki H, He S, Flaherty P. SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data. BMC Bioinformatics 2020; 21:215. [PMID: 32456609 PMCID: PMC7249349 DOI: 10.1186/s12859-020-03550-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/18/2020] [Indexed: 11/21/2022] Open
Abstract
Background Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. Results We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. Conclusions The DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.
Collapse
Affiliation(s)
- Collin Giguere
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Harsh Vardhan Dubey
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Vishal Kumar Sarsani
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Hachem Saddiki
- School of Public Health, University of Massachusetts Amherst, Amherst, 01003, USA
| | - Shai He
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Patrick Flaherty
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA.
| |
Collapse
|
16
|
Hård J, Al Hakim E, Kindblom M, Björklund ÅK, Sennblad B, Demirci I, Paterlini M, Reu P, Borgström E, Ståhl PL, Michaelsson J, Mold JE, Frisén J. Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing. Genome Biol 2019; 20:68. [PMID: 30935387 PMCID: PMC6444814 DOI: 10.1186/s13059-019-1673-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 03/12/2019] [Indexed: 01/04/2023] Open
Abstract
Accurate variant calling and genotyping represent major limiting factors for downstream applications of single-cell genomics. Here, we report Conbase for the identification of somatic mutations in single-cell DNA sequencing data. Conbase leverages phased read data from multiple samples in a dataset to achieve increased confidence in somatic variant calls and genotype predictions. Comparing the performance of Conbase to three other methods, we find that Conbase performs best in terms of false discovery rate and specificity and provides superior robustness on simulated data, in vitro expanded fibroblasts and clonal lymphocyte populations isolated directly from a healthy human donor.
Collapse
Affiliation(s)
- Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.
| | - Ezeddin Al Hakim
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Marie Kindblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Åsa K Björklund
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Scilifelab, Uppsala University, Uppsala, Sweden
| | - Bengt Sennblad
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Scilifelab, Uppsala University, Uppsala, Sweden
| | - Ilke Demirci
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Marta Paterlini
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Pedro Reu
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Erik Borgström
- Division of Gene Technology, Scilifelab, KTH Royal Institute of Technology, Solna, Sweden
| | - Patrik L Ståhl
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Jakob Michaelsson
- Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.
| |
Collapse
|