Subclonal identification of driver mutations and copy number variations from single-cell DNA sequencing of tumors

通过对肿瘤进行单细胞DNA测序,鉴定驱动突变和拷贝数变异的亚克隆

阅读:1

Abstract

Background: Single-cell sequencing elucidates unique insights in understanding intratumor heterogeneity and clonal evolution. Both chromosomal structural change/copy number alteration/variation (CNA/CNV) and driver gene mutation events appear somatically at the early stages of oncogenesis and are critical in cancer initiation, tumor progression and therapy response. Previously, we have developed a high-throughput single-cell DNA analysis platform that leverages droplet microfluidics and a multiplex-PCR based targeted DNA sequencing approach. The platform demonstrates high sensitivity detection of single nucleotide variants (SNVs) and indels in the same cells and generation of high-resolution maps of clonal architecture based on mutational profiling. Methods: Here, we present a dynamic solution that we developed to simultaneously characterize point mutations, small indels and gene-level CNVs from the same single-cell. With improved biochemistry, we develop novel data analysis algorithms to detect amplification or loss of function in oncogenes and/or tumor suppressors reliably. Either using Loss of Heterozygosity (LOH) or the mutation profiles we generate a baseline control population and then estimate the ploidy by normalizing the read counts to the median of the normal population. We enable multiple visualizations of the copy number estimates in karyotype plots and line plots projected on snv clones. Results: We validated this method on clinical samples and admixture samples with cell lines mixed at known ratios. CNV alone confidently detects subclones while when combined with mutational analysis, rare subclones of ∼1% prevalence was detected. Integration of CNVs and SNVs facilitates more accurate reconstruction of tumor evolution to better understand cancer progression mechanisms as well for quality control of gene edited cells, to further advance cancer research and therapy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。