Dataset Information

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

SUBMITTER: Xu X

PROVIDER: S-EPMC7458411 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Xu Xun X Hou Yong Y Yin Xuyang X Bao Li L Tang Aifa A Song Luting L Li Fuqiang F Tsang Shirley S Wu Kui K Wu Hanjie H He Weiming W Zeng Liang L Xing Manjie M Wu Renhua R Jiang Hui H Liu Xiao X Cao Dandan D Guo Guangwu G Hu Xueda X Gui Yaoting Y Li Zesong Z Xie Wenyue W Sun Xiaojuan X Shi Min M Cai Zhiming Z Wang Bin B Zhong Meiming M Li Jingxiang J Lu Zuhong Z Gu Ning N Zhang Xiuqing X Goodman Laurie L Bolund Lars L Wang Jian J Yang Huanming H Kristiansen Karsten K Dean Michael M Li Yingrui Y Wang Jun J

Cell 20120301 5

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any si ...[more]

PMID: 22385958

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Project description:BackgroundThe targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.ResultsUsing down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give "power estimates" for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5-15% of heterozygous and 1-4% of homozygous SNVs in the targeted regions will be missed.ConclusionsNon-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the "missing heritability" of quantitative traits.

Dataset Information

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Publications

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

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