scholarly journals Single-cell copy number calling and event history reconstruction

Author(s):  
Jack Kuipers ◽  
Mustafa Anıl Tuncel ◽  
Pedro Ferreira ◽  
Katharina Jahn ◽  
Niko Beerenwinkel

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to a xenograft breast cancer sample.

2018 ◽  
Author(s):  
Shweta Ramdas ◽  
Yanchao Pan ◽  
Jun Z Li

1AbstractDNA sequencing can discover not only single-base variants but also copy-number alterations (CNAs). In shotgun sequencing, regions of CNAs show step-wise changes in read depth when compared to adjacent “normal” regions, allowing their detection by parametric statistical tests that compare the mean coverage in suspected regions against that of a baseline distribution. Traditionally, the power of such a test depends on (1) the integer number of copy number change, (2) the overall sequencing depth, (3) the length of the CNA region, (4) the read length and (5) the variation of coverage along the genome, which depends on many experimental factors, including whether the chosen platform is whole-genome, whole-exome, or targeted-panel sequencing. In cases involving inadvertent sample mixing or genuine somatic mosaicism, power also depends on the mixing ratio. However, the analysis of statistical power that considers the interplay of all these factors has not been systematically developed. Here we present a general analytical framework and a series of simulations that explore situations from the simplest to the increasingly multifactorial. Specifically, we expand the expression of power to include not just the known factors but also one or both of two complications: (1) the dispersion of read depth around the mean beyond the independent sampling-by-sequencing assumption, and (2) the reduced fraction of the CNA-bearing sample (“purity”) as seen in studies of intratumor heterogeneity or in clinical monitoring of minimal residual disease. We describe the analytical formula and their simplifications in special cases, and share the extendable scripts for others to perform customized power analysis using study-specific parameters. As study designs vary and technologies continue to evolve, the input data and the noise characteristics will change depending on the practical situation. We present two use cases commonly encountered in cancer research: ultra-shallow whole-genome sequencing for detecting large, chromosome-scale events, and targeted ultra-deep sequencing for surveillance of known CNAs in rare tumor clones in the task of sensitive detection of cancer relapse or metastasis. We also present an online calculator at https://shiny.med.umich.edu/apps/hanyou/CNV_Detection_Power_Calculator/.


2021 ◽  
Author(s):  
Sandra Hui ◽  
Rasmus Nielsen

Copy number alterations are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, copy number alterations are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content, and do not account for the stochastic evolutionary process. We present a theoretical framework to use tumor evolutionary history to accurately call copy number alterations in a principled manner. In order to model the tumor evolutionary process and account for technical noise from low coverage single cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public datasets and simulations, we show SCONCE accurately decodes copy number profiles, with broader implications for understanding tumor evolution.


2020 ◽  
Vol 16 (7) ◽  
pp. e1008012 ◽  
Author(s):  
Xian F. Mallory ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14023
Author(s):  
Hatice Gulcin Ozer ◽  
Aisulu Usubalieva ◽  
Adrienne Dorrance ◽  
Ayse Selen Yilmaz ◽  
Michael Caligiuri ◽  
...  

The genome-wide discoveries such as detection of copy number alterations (CNA) from high-throughput whole-genome sequencing data enabled new developments in personalized medicine. The CNAs have been reported to be associated with various diseases and cancers including acute myeloid leukemia. However, there are multiple challenges to the use of current CNA detection tools that lead to high false-positive rates and thus impede widespread use of such tools in cancer research. In this paper, we discuss these issues and propose possible solutions. First, since the entire genome cannot be mapped due to some regions lacking sequence uniqueness, current methods cannot be appropriately adjusted to handle these regions in the analyses. Thus, detection of medium-sized CNAs is also being directly affected by these mappability problems. The requirement for matching control samples is also an important limitation because acquiring matching controls might not be possible or might not be cost efficient. Here we present an approach that addresses these issues and detects medium-sized CNAs in cancer genomes by (1) masking unmappable regions during the initial CNA detection phase, (2) using pool of a few normal samples as control, and (3) employing median filtering to adjust CNA ratios to its surrounding coverage and eliminate false positives.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1590-1590
Author(s):  
Mehmet K. Samur ◽  
Anil Aktas-Samur ◽  
Romain Lannes ◽  
Jill Corre ◽  
Anjan Thakurta ◽  
...  

Abstract New generation immunotherapies in Multiple Myeloma (MM) targeting BCMA, have shown remarkable clinical benefits. However relapse still occurs due to tumor intrinsic and extrisic resistance mechanisms including antigen loss related to mutation, deletion and splicing pattern changes. Two recent case reports including ours highlighted biallelic loss of BCMA as a cause for resistance to anti-BCMA targeting therapy. In both studies BCMA locus at 16p was deleted bringing in focus importance of del16p. Here, we have evaluated 2883 MM patients at diagnosis and relapse to understand frequency characteristics of somatic events targeting BCMA. We first evaluated the frequency of deletion involving the BCMA locus (16p13.13) in MM patients from multiple studies using WGS sequencing data as well as using Affymetrix Cytoscan HD and SNP 6.0 arrays. We observed del16p in 8.58 % (7.6% to 14.6% in individual studies) of newly-diagnosed patients (n=2458). Similar frequency was observed in relapsed MM patients not previously exposed to BCMA targeting therapy. Next, we evaluated genome wide copy number alterations (CNAs) in all patients with loss of BCMA locus and observed similar frequency of loss in both hyperdiploid MM (HMM) and non-HMM suggesting its independence from cytogentic subtypes of MM. Overall copy number loss was significantly higher in patients with BCMA loss compared to rest of the MM patients. Patients with loss of BCMA locus have increased mutational load (8202 with 95% HDI 6921 and 9535) compared to those without BCMA locus loss (6975 with 95% HDI 6626 - 7343); probability of difference greater than 0 was 96.8% and difference of the means were 1222 [95% CI -112 - 2589] We next evaluated co-occurrence of BCMA loss with other high risk events and observed del1p and del17p as being significantly associated with loss of BCMA locus [Odds ratio 19.37 (13.13-25.80), FDR = 1.57e-65; and 8.8 (6.39-12.15), FDR = 5.57E-39, respectively)]. Furthermore, we observed that when both BCMA and TP53 loss are present, they have same log ratio (sequencing) or smoothed copy numbers (SNP array). Similarly, we used CDKN2C as a proxy to chromosome 1p loss and observed that when both BCMA and CKDN2C loss are present in the same patient they tend to show similar copy number values. These data suggested a possibility of co-occurrence of these events in the same cell. To further investigate this observation, we used single cell DNA sequencing data from patients with sub clonal and clonal BCMA locus loss. scDNA sequencing showed that almost all cells with BCMA deletion also had TP53 deletion (95%). Interestingly, almost all cells with BCMA loss also had p53 loss, while not all cells with p53 loss had BCMA loss suggesting that the chronology of this copy number alternation may suggest first p53 loss followed by BCMA loss. We further investigated whether a bi-allelic BCMA loss was observed after anti-BCMA targeted CAR-T cell therapy by imputing the copy number alterations using single cell RNA sequencing data. Our data from this case also indicated that BCMA loss tend to co-occur with TP53 deletions (OR=5.67 [95% CI 4.12-7.84], p value < 0.0001). Moreover, TP53 mutations were also more frequent in patients with del16p and del17p, compared to patients who only had del16p or del17p. In summary, our data from large scale copy number profiles at the diagnosis and relapse showed that monoallelic BCMA deletions are frequent events, patients with these events show increased aneuploidy, mostly deletions, potentially making these cells vulnerable for biallelic loss of genes, especially under the pressure of targeted therapy. Our results also highlight that BCMA expressions in bulk sample may not detect the presence or absence of cells with target loss and therefore combining strategies at bulk and single cell level are necessary to understand the disease status. These results suggest the need to study del16p in patients being targeted for BCMA-directed therapy and its association with other risk factors in MM. Disclosures Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Anderson: Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Munshi: Takeda: Consultancy; Adaptive Biotechnology: Consultancy; Amgen: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Novartis: Consultancy; Legend: Consultancy; Pfizer: Consultancy; Janssen: Consultancy; Bristol-Myers Squibb: Consultancy.


2021 ◽  
Author(s):  
Lu Liu ◽  
He Chen ◽  
Cheng Sun ◽  
Jianyun Zhang ◽  
Juncheng Wang ◽  
...  

Genomic-scale somatic copy number alterations in healthy humans are difficult to investigate because of low occurrence rates and the structural variations' stochastic natures. Using a Tn5-transposase assisted single-cell whole genome sequencing method, we sequenced over 20,000 single lymphocytes from 16 individuals. Then, with the scale increased to a few thousand single cells per individual, we found that about 7.5% of the cells had large-size copy number alterations. Trisomy 21 was the most prevalent aneuploid event among all autosomal copy number alterations, while monosomy X occurred most frequently in over-30-year-old females. In the monosomy X single cells from individuals with phased genomes and identified X- inactivation ratios in bulk, the inactive X Chromosomes were lost more often than were the active ones.


2019 ◽  
Author(s):  
Xian Fan ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh

AbstractSingle-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. Here we review the major steps that are followed by these methods when analyzing such data, and then review the strengths and limitations of the methods individually. In terms of segmenting the genome into regions of different copy numbers, we categorize the methods into three groups, select a representative method from each group that has been commonly used in this context, and benchmark them on simulated as well as real datasets. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, 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.


2021 ◽  
Author(s):  
Sanjana Rajan ◽  
Simone Zaccaria ◽  
Matthew V. Cannon ◽  
Maren Cam ◽  
Amy C. Gross ◽  
...  

AbstractOsteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNAs) are the genetic drivers of disease. Models around genomic instability conflict-it is unclear if osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in 12,019 tumor cells obtained from expanded patient tissues using single-cell DNA sequencing, in ways that were previously impossible with bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from whole-genome single-cell DNA sequencing data. Surprisingly, we found that, despite extensive genomic aberrations, cells within each tumor exhibit remarkably homogeneous SCNA profiles with little sub-clonal diversification. Longitudinal analysis between two pairs of patient samples obtained at distant time points (early detection, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the bulk of SCNAs was acquired early in the oncogenic process, with few new events arising in response to therapy or during adaptation to growth in distant tissues. These data suggest that early catastrophic events, rather than sustained genomic instability, drive formation of these extensively aberrant genomes. Overall, we demonstrate the power of combining single-cell DNA sequencing with an allele- and haplotype-specific SCNA inference algorithm to resolve longstanding questions regarding genetics of tumor initiation and progression, questioning the underlying assumptions of genomic instability inferred from bulk tumor data.


2016 ◽  
Vol 56 (1) ◽  
pp. 15.9.1-15.9.17 ◽  
Author(s):  
Keiran M. Raine ◽  
Peter Van Loo ◽  
David C. Wedge ◽  
David Jones ◽  
Andrew Menzies ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document