An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase

Abstract

SummaryGene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r 2 correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability.

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).…”

A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic

“…Interestingly, the broad classes of hematological cancers can be traced back to the specific differentiation lineages of the cells they affect. The vast amount of gene expression data sets available for these cancers (Kohlmann et al, 2008;Mullighan et al, 2008;Mills et al, 2009), and the fact that carefully sorted cell types were systematically profiled (Novershtern et al, 2011), opens exciting prospects for our method.…”