Global copy number profiling of cancer genomes

Single B-Cell Genomic Analyses Differentiate Vitreoretinal Lymphoma from Chronic Inflammation

PURPOSE

To test whether analyzing DEPArray (Menarini Silicon Biosystems) isolated single B cells from the vitreous fluid can reveal crucial genomic and clinicopathological features to distinguish patients with vitreoretinal lymphoma (VRL) from those with chronic inflammation using immunoglobulin heavy chain (IGH), disease biomarker myeloid differentiation primary response 88 (MYD88)^L265P mutation, and copy number profiling.

DESIGN

A single-center, retrospective study.

PARTICIPANTS

Remnant vitreous biopsies from 7 patients with VRL and 4 patients with chronic inflammation were acquired for molecular analysis.

METHODS

Vitreous fluid samples were prefixed in PreservCyt (Hologic) and underwent cytologic analysis and immunohistochemistry examination. Single cells were isolated using the DEPArray NxT system, followed by downstream genomic analysis.

MAIN OUTCOME MEASURES

The frequencies of the dominant IGH and MYD88^L265P mutation and the genome-wide copy number aberration (CNA) profiles of individual vitreous-isolated B cells were characterized.

RESULTS

An average of 10 to 13 vitreous B cells were used in the single-cell IGH and MYD88 analyses. Higher frequencies of dominant IGH (88.8% ± 13.2%) and MYD88^L265P mutations (35.0% ± 31.3%) were detected in patients with VRL than in patients with chronic inflammation (65.9% ± 13.4% and 1.5% ± 2.6% for IGH and MYD88^L265P, respectively). In a cytology-proven VRL case, all 15 vitreous isolated B cells were derived from the same clone with 100% paired IGH: immunoglobulin light chain (IGK) sequences. Genome-wide copy number profiling revealed a high degree of similarity between B cells from the same patient with VRL, with extensive gains and losses at the same areas across the whole genome. In addition, 14 of 15 B cells showed a BCL2/JH t(14;18) translocation, confirming cellular malignancy with a clonal origin. Clustering analysis of the copy number profiles revealed that malignant B cells derived from different patients with VRL had no common genome-wide signatures.

CONCLUSIONS

Single B-cell genomic characterization of the IGH, MYD88^L265P mutation, and copy number profile enables VRL diagnosis. Because our study involved only a small cohort, these meaningful proof-of-concept data now warrant further investigation in a larger patient cohort.

DNA copy number profiling using single-cell sequencing

Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.

Overexpression of Long Non-Coding RNA MEG3 Inhibits Proliferation of Hepatocellular Carcinoma Huh7 Cells via Negative Modulation of miRNA-664

Evidence is accumulating that long non-coding RNAs (lncRNAs) are involved in human tumorigenesis and dysregulated in many cancers, including hepatocellular carcinoma (HCC). Because lncRNAs can regulate essential pathways that contribute to tumor initiation and progression with their tissue specificity, lncRNAs are valuable biomarkers and therapeutic targets. Maternally expressed gene 3 (MEG3) is a lncRNA overexpressed in HCC cells that inhibits HCC progression, however, the mechanism remains largely unknown. Recently, a novel regulatory mechanism has been proposed in which RNAs can cross-talk with each other via competing for shared microRNAs (miRNAs). The proposed competitive endogenous RNAs could mediate the bioavailability of miRNAs on their targets, thus imposing another level of post-transcriptional regulation. In the current study, we demonstrated that MEG3 is down-regulated in HCC tissues. MEG3 over-expression imposes another level of post-transcriptional regulation, whereas MEG3 overexpression increase the expression of the miR-664 target gene, ADH4, through competitive "sponging" miR-664. In addition, NF-κB may affect transcription of MEG3 by directly binding to the promoter region. Our data revealed that NF-κB may affect the transcript of MEG3. MEG3 overexpression inhibited the proliferation of HCC cells, at least in part by affecting miR-664mediated regulation of ADH4. Together, these results suggest that MEG3 is a suppressor of tumor which acts in part through "sponging" miR-664. J. Cell. Biochem. 118: 3713-3721, 2017. © 2017 Wiley Periodicals, Inc.

PureCN: copy number calling and SNV classification using targeted short read sequencing

Background

Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data.

Results

We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data.

Conclusions

Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

Electronic supplementary material

The online version of this article (doi:10.1186/s13029-016-0060-z) contains supplementary material, which is available to authorized users.