Dataset Information

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing.

ABSTRACT: Clear-cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. VHL and PBRM1 are the top two significantly mutated genes in ccRCC specimens, while the genetic mechanism of the VHL/PBRM1-negative ccRCC remains to be elucidated. Here we carried out a comprehensive analysis of single-cell genomic copy number variations (CNVs) in VHL/PBRM1-negative ccRCC. Genomic CNVs were identified at the single-cell level, and the tumor cells showed widespread amplification and deletion across the whole genome. Functional enrichment analysis indicated that the amplified genes are significantly enriched in cancer-related signaling transduction pathways. Besides, receptor protein tyrosine kinase (RTK) genes also showed widespread copy number variations in cancer cells. Our studies indicated that the genomic CNVs in RTK genes and downstream signaling transduction pathways may be involved in VHL/PBRM1-negative ccRCC pathogenesis and progression, and highlighted the role of the comprehensive investigation of genomic CNVs at the single-cell level in both clarifying pathogenic mechanism and identifying potential therapeutic targets in cancers.

SUBMITTER: Zhou W

PROVIDER: S-EPMC6989475 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing.

Zhou Wenyang W Yang Fan F Xu Zhaochun Z Luo Meng M Wang Pingping P Guo Yu Y Nie Huan H Yao Lifen L Jiang Qinghua Q

Frontiers in genetics 20200123

Clear-cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. VHL and PBRM1 are the top two significantly mutated genes in ccRCC specimens, while the genetic mechanism of the VHL/PBRM1-negative ccRCC remains to be elucidated. Here we carried out a comprehensive analysis of single-cell genomic copy number variations (CNVs) in VHL/PBRM1-negative ccRCC. Genomic CNVs were identified at the single-cell level, and the tumor cells showed wid ...[more]

PMID: 32038722

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Project description:BackgroundReproducibility is receiving increased attention across many domains of science and genomics is no exception. Efforts to identify copy number variations (CNVs) from exome sequence (ES) data have been increasing. Many algorithms have been published to discover CNVs from exomes and a major challenge is the reproducibility in other datasets. Here we test exome CNV calling reproducibility under three conditions: data generated by different sequencing centers; varying sample sizes; and varying capture methodology.MethodsFour CNV tools were tested: eXome Hidden Markov Model (XHMM), Copy Number Inference From Exome Reads (CoNIFER), EXCAVATOR, and Copy Number Analysis for Targeted Resequencing (CONTRA). To examine the reproducibility, we ran the callers on four datasets, varying sample sizes of N = 10, 30, 75, 100, 300, and data with different capture methodology. We examined the false negative (FN) calls and false positive (FP) calls for potential limitations of the CNV callers. The positive predictive value (PPV) was measured by checking the CNV call concordance against single nucleotide polymorphism array.ResultsUsing independently generated datasets, we examined the PPV for each dataset and observed wide range of PPVs. The PPV values were highly data dependent (p <0.001). For the sample sizes and capture method analyses, we tested the callers in triplicates. Both analyses resulted in wide ranges of PPVs, even for the same test. Interestingly, negative correlations between the PPV and the sample sizes were observed for CoNIFER (ρ = -0.80). Further examination of FN calls showed that 44 % of these were missed by all callers and were attributed to the CNV size (46 % spanned ≤3 exons). Overlap of the FP calls showed that FPs were unique to each caller, indicative of algorithm dependency.ConclusionsOur results demonstrate that further improvements in CNV callers are necessary to improve reproducibility and to include wider spectrum of CNVs (including the small CNVs). These CNV callers should be evaluated on multiple independent, heterogeneously generated datasets of varying size to increase robustness and utility. These approaches to the evaluation of exome CNV are essential to support wide utility and applicability of CNV discovery in exome studies.

Dataset Information

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing.

Publications

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing.

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