Abstract

BackgroundMalignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Recently, gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. However, the varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. This study provides a quantitative assessment and comparison of the ability of microarrays and tag-seq to detect mRNA transcripts from malignant B-cells. A model system was established by eight serial dilutions of the malignant B-cell lymphoma cell line, OCI-Ly8, into the embryonic kidney cell line, HEK293, prior to parallel analysis by exon microarrays and tag-seq.ResultsWe identified 123 and 117 differentially expressed genes between pure OCI-Ly8 and HEK293 cells by exon microarray and tag-seq, respectively. There were thirty genes in common, and of those, most were B-cell specific. Hierarchical clustering from all dilutions based on the differentially expressed genes showed that neither technology could distinguish between samples with less than 1% malignant B-cells from non-B-cells. A novel statistical concept was developed to assess the ability to detect single genes for both technologies, and used to demonstrate an inverse proportional relationship with the sample purity. Of the 30 common genes, the detection capability of a representative set of three B-cell specific genes - CD74, HLA-DRA, and BCL6 - was analysed. It was noticed that at least 5%, 13% and 22% sample purity respectively was required for detection of the three genes by exon microarray whereas at least 2%, 4% and 51% percent sample purity of malignant B-cells were required for tag-seq detection.ConclusionA sample purity-dependent loss of the ability to detect genes for both technologies was demonstrated. Taq-seq, in comparison to exon microarray, required slightly less malignant B-cells in the samples analysed in order to detect the two most abundantly expressed of the selected genes. The results show that malignant cell frequency is an important variable, with fundamental impact when interpreting GEPs from both technologies.

Highlights

  • Malignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity

  • MRNA expression levels of 17,816 RefSeq transcripts and full-length mRNA transcripts using the core probes of the exon microarray were determined, and the number of unique genes detected above background (DABG) was 11,112

  • Comparison of background level and instrument detection limit (IDL) Of the 123 genes differentially expressed between the 100% and 0% samples detected by exon microarray, we identified 79 B-cell specific genes that were expressed more highly in the 100% B-cell sample

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Summary

Introduction

Malignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. The varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. Malignant cells in tumour tissues of B-cell origin account for 0.1% to 98% of the total cell content depending on the specific disease entity [1,2]. There is a need to explore the technical limits of the exon microarray and tag-seq for identification and interpretation of mRNA transcript profiles, in particular malignant B-cells in a pool of non-malignant cells

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