Resolving clonal substructure from single cell genomic data using CopyKit

Navin, Nicholas; Minussi, Darlan Conterno; Sei, Emi; Wang, Junke; Schalck, Aislyn; Yan, Yun; Davis, Ashley N.; Wu, Hua‐Jun; Bai, Shanshan; Peng, Cheng; Hu, Min; Casasent, Anna; Contreras, Alejandro; Chen, Hui; Hui, David S.; Damodaran, Senthil; Edgerton, Mary E.; Kopetz, Scott

doi:10.1101/2022.03.09.483497

Cited by 5 publications

(4 citation statements)

References 62 publications

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“…We also investigated whether CNV calls are supported by another algorithm for scWGS CNV calling, Copykit , 37 also based on circular binary segmentation, which uses hg38 as default ( Supplementary Table 2 ). The SNCA triplication was detected in all three fibroblasts, although the copy number was given as 3 (rather than 4) in the one that failed confidence score.…”

Section: Resultsmentioning

confidence: 99%

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Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

Kalef-Ezra,

Turan,

Perez-Rodriguez

et al. 2023

Preprint

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The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. We compared PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrated different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we performed CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We found that 38% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

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Section: Resultsmentioning

confidence: 99%

“…The newly developed algorithm, Copykit 37 was used for comparison against filtered Ginkgo CNV calls in all cells passing QC. For consistency with Ginkgo parameters used, Alpha was adjusted to 0.01, and the bin size used for PicoPLEX and PTA amplified cells was 220 kb (as 250 kb is not available) and 500 kb, respectively.…”

Section: Bioinformatic Analysesmentioning

confidence: 99%

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

Kalef-Ezra,

Turan,

Perez-Rodriguez

et al. 2023

Preprint

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“…A variety of genomic bioinformatic tools are available to describe mutations, including copy number alterations (CNAs), which refer to changes in the copy number of genomic regions, such as amplifications and deletions, and copy number variations (CNVs), which are more comprehensive and encompass a broader range of structural alterations in the genome, including CNAs as well as duplications and complex rearrangements across bulk and single-cell data. These tools enable the identification of specific genomic drivers of cancer [267,269,271,272,301]. Importantly, an integrated multiomics approach would be needed to associate these genomic drivers with lineage plasticity.…”

Section: Genomicsmentioning

confidence: 99%

“…CopyKit is an R package designed to preprocess and analyze single-cell CNV genomic data in advance of the detection and visualization of CNVs, including those that occur at low allele frequencies and in subclonal populations [269]. CopyKit enables the analysis of the copy number substructures of tumor samples, as well as in furthering the investigation into the intratumoral heterogeneity that is frequently seen in PCa [307].…”

Section: Genomicsmentioning

confidence: 99%

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated “Omics” Approaches to Explore Measurable Metrics

Logotheti,

Papadaki,

Zolota

et al. 2023

Cancers

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Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

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