scholarly journals Precise identification of cancer cells from allelic imbalances in single cell transcriptomes

2021 ◽  
Author(s):  
Mi K Trinh ◽  
Clarissa N Pacyna ◽  
Gerda K Kildisiute ◽  
Nathaniel D Anderson ◽  
Eleonora Khabirova ◽  
...  

A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Patrick P. T. Leong ◽  
Aleksandar Mihajlović ◽  
Nadežda Bogdanović ◽  
Luka Breberina ◽  
Larry Xi

AbstractSingle-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.


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

2020 ◽  
Vol 27 (4) ◽  
pp. 565-598 ◽  
Author(s):  
Haoyun Lei ◽  
Bochuan Lyu ◽  
E. Michael Gertz ◽  
Alejandro A. Schäffer ◽  
Xulian Shi ◽  
...  

Author(s):  
Liam F Spurr ◽  
Mehdi Touat ◽  
Alison M Taylor ◽  
Adrian M Dubuc ◽  
Juliann Shih ◽  
...  

Abstract Summary The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data. Availability and implementation ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation. Supplementary information Supplementary data are available at Bioinformatics online.


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.


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):  
Akdes Serin Harmancı ◽  
Arif O. Harmanci ◽  
Xiaobo Zhou

AbstractRNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNV). Here, we propose CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER performs smoothing of the genome-wide RNA sequencing signal profiles in different multiscale resolutions, identifying CNV events at different length scales. CaSpER also employs a novel methodology for generation of genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it in multiscale fashion for correction of CNV calls. The shift in allelic signal is used to quantify the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER uses Hidden Markov Models (HMM) to assign copy number states to regions. The multiscale nature of CaSpER enables comprehensive analysis of focal and large-scale CNVs and LOH segments. CaSpER performs well in accuracy compared to gold standard SNP genotyping arrays. In particular, analysis of single cell Glioblastoma (GBM) RNA sequencing data with CaSpER reveals novel mutually exclusive and co-occurring CNV sub-clones at different length scales. Moreover, CaSpER discovers gene expression signatures of CNV sub-clones, performs gene ontology (GO) enrichment analysis and identifies potential therapeutic targets for the sub-clones. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data, especially in cancer research.


Author(s):  
Tian Lan ◽  
Gyorgy Hutvagner ◽  
Qing Lan ◽  
Tao Liu ◽  
Jinyan Li

Abstract Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.


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