Intraplatform Reproducibility and Technical Precision of Gene Expression Profiling in 4 Laboratories Investigating 160 Leukemia Samples: The DACH Study

Abstract

BACKGROUND:Gene expression profiling has the potential to offer consistent, objective diagnostic test results once a standardized protocol has been established. We investigated the robustness, precision, and reproducibility of microarray technology.

Gene expression profiling in MDS and AML: potential and future avenues

“…8,9 Although in the early days it was sometimes doubted whether GEP could produce reproducible results, there is now strong evidence from numerous AML studies that GEP technology can easily be standardized for diagnostics, in as much distinct gene expression patterns are quite robust with respect to both high intra-and inter-platform comparability. [10][11][12][13][14] Thus, GEP has been successfully applied to improve the molecular classification of AML. For example, genomics-based prediction of known leukemia classes (class prediction) has been shown to be feasible for well-defined cytogenetic AML subclasses of the World Health Organization classification guidelines such as AML with translocation t(8;21), inversion inv(16), t(15;17) and various translocations involving MLL (these are referred to as AML with t(11q23)/MLL rearrangements and include: AML with t(9;11)(p22;q23)/MLLT3-MLL; t(6;11)(q27;q23)/MLLT4-MLL; t(11;19) (q23;p13.3)/MLL-MLLT1; t(11;19)(q23;p13.1)/MLL-ELL; and t(10;11)(p12;q23)/MLLT10-MLL), thereby providing a powerful novel diagnostic tool.…”

Gene expression profiling in AML with normal karyotype can predict mutations for molecular markers and allows novel insights into perturbed biological pathways

“…Many of these genes reflected an intermediate-to-mature erythroid developmental stage, and were in general accordance with recent genome-wide analyses performed on a variety of in vitro erythroid differentiation systems. [6][7][8] Out of this extensive number of potential target genes, a list of 27 genes corresponding to well-characterized erythroid-affiliated genes was used for cluster analysis of samples using the Euclidean distance as a measure of similarity ( Figure 1b). As shown in Figure 1b, the NFI-A cells displayed considerable induction of erythroid cell membrane molecules, including CDH1, the Rh antigen family (RHAG; RHC/RHD), SLC4A1, AQP1, ADD3 and SPTB; molecules involved in the hemoglobin biosynthetic pathway, including ALAS2 and globin chains HBB, HBA and HBD; growth factors and growth factor receptors, including INHBA, EFBN2 and IGF2; signal transduction molecules, including JAK2, AKT2 and GAB1; transcription factors or DNA-binding proteins, including JUNB, HOXB8, KLF2, SSBP3 and TRIM10;…”

“…Microarray analyses were performed following a standardized assay workflow as reported earlier (Supplementary Figure S1). 7,8 To visualize gene expression patterns, we applied hierarchical clustering and principal component analyses (see Supplementary Information). Gene expression data were analyzed using GeneMaths XT Version 2.1 (Applied Maths, St-Martens-Latem, Belgium) and Partek Genomics Suite Version 6.4 (Partek Inc., St Louis, MO, USA).…”

RNA-Stabilized Whole Blood Samples but Not Peripheral Blood Mononuclear Cells Can Be Stored for Prolonged Time Periods Prior to Transcriptome Analysis

“…GEP of blood-based leukemia samples revealed reproducible detection of robust leukemia-associated signatures almost irrespective of preanalytical conditions 6 and of involvement of different laboratories. 7 However, when blood is used as surrogate tissue, the blood-based GEP result is significantly influenced by sample handling and storage. 8,9 This might be of particular importance when the surrogate bloodbased GEP reflects only subtle changes with respect to the disease under investigation.…”