Dataset Information

SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.

ABSTRACT: Whole-genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy-number profiles at the cellular level. We propose SCOPE, a normalization and copy-number estimation method for the noisy scDNA-seq data. SCOPE's main features include the following: (1) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using in silico identified negative control cells; (2) an expectation-maximization algorithm embedded in the normalization step, which accounts for the aberrant copy-number changes and allows direct ploidy estimation without the need for post hoc adjustment; and (3) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background. We evaluate SCOPE on a diverse set of scDNA-seq data in cancer genomics and show that SCOPE offers accurate copy-number estimates and successfully reconstructs subclonal structure. A record of this paper's transparent peer review process is included in the Supplemental Information.

SUBMITTER: Wang R

PROVIDER: S-EPMC7250054 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.

Wang Rujin R Lin Dan-Yu DY Jiang Yuchao Y

Cell systems 20200501 5

Whole-genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy-number profiles at the cellular level. We propose SCOPE, a normalization and copy-number estimation method for the noisy scDNA-seq data. SCOPE's main features include the following: (1) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using in silico identified negative control cells; (2) an expectation-maximization algorithm embedded in the no ...[more]

PMID: 32437686

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Project description:Genomic instability is a hallmark of cancer often associated with poor patient outcome and resistance to targeted therapy. Assessment of genomic instability in bulk tumor or biopsy can be complicated due to sample availability, surrounding tissue contamination, or tumor heterogeneity. The Epic Sciences circulating tumor cell (CTC) platform utilizes a non-enrichment based approach for the detection and characterization of rare tumor cells in clinical blood samples. Genomic profiling of individual CTCs could provide a portrait of cancer heterogeneity, identify clonal and sub-clonal drivers, and monitor disease progression. To that end, we developed a single cell Copy Number Variation (CNV) Assay to evaluate genomic instability and CNVs in patient CTCs. For proof of concept, prostate cancer cell lines, LNCaP, PC3 and VCaP, were spiked into healthy donor blood to create mock patient-like samples for downstream single cell genomic analysis. In addition, samples from seven metastatic castration resistant prostate cancer (mCRPC) patients were included to evaluate clinical feasibility. CTCs were enumerated and characterized using the Epic Sciences CTC Platform. Identified single CTCs were recovered, whole genome amplified, and sequenced using an Illumina NextSeq 500. CTCs were then analyzed for genome-wide copy number variations, followed by genomic instability analyses. Large-scale state transitions (LSTs) were measured as surrogates of genomic instability. Genomic instability scores were determined reproducibly for LNCaP, PC3, and VCaP, and were higher than white blood cell (WBC) controls from healthy donors. A wide range of LST scores were observed within and among the seven mCRPC patient samples. On the gene level, loss of the PTEN tumor suppressor was observed in PC3 and 5/7 (71%) patients. Amplification of the androgen receptor (AR) gene was observed in VCaP cells and 5/7 (71%) mCRPC patients. Using an in silico down-sampling approach, we determined that DNA copy number and genomic instability can be detected with as few as 350K sequencing reads. The data shown here demonstrate the feasibility of detecting genomic instabilities at the single cell level using the Epic Sciences CTC Platform. Understanding CTC heterogeneity has great potential for patient stratification prior to treatment with targeted therapies and for monitoring disease evolution during treatment.

Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Dataset Information

SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.

Publications

SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.

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