New data on robustness of gene expression signatures in leukemia: comparison of three distinct total RNA preparation procedures

High expression of miR-125b-2 and SNORD116 noncoding RNA clusters characterize ERG-related B cell precursor acute lymphoblastic leukemia

ERG-related leukemia is a B cell precursor acute lymphoblastic leukemia (BCP ALL) subtype characterized by aberrant expression of DUX4 and ERG transcription factors, and highly recurrent ERG intragenic deletions. ERG-related patients have remarkably favorable outcome despite a high incidence of inauspicious IKZF1 aberrations.

We describe clinical and genomic features of the ERG-related cases in an unselected cohort of B-other BCP ALL pediatric patients enrolled in the AIEOP ALL 2000 therapeutic protocol. We report a small noncoding RNA signature specific of ERG-related group, with up-regulation of miR-125b-2 cluster on chromosome 21 and several snoRNAs in the Prader-Willi locus at 15q11.2, including the orphan SNORD116 cluster.

Sequential stopping for high-throughput experiments

In high-throughput experiments, the sample size is typically chosen informally. Most formal sample-size calculations depend critically on prior knowledge. We propose a sequential strategy that, by updating knowledge when new data are available, depends less critically on prior assumptions. Experiments are stopped or continued based on the potential benefits in obtaining additional data. The underlying decision-theoretic framework guarantees the design to proceed in a coherent fashion. We propose intuitively appealing, easy-to-implement utility functions. As in most sequential design problems, an exact solution is prohibitive. We propose a simulation-based approximation that uses decision boundaries. We apply the method to RNA-seq, microarray, and reverse-phase protein array studies and show its potential advantages. The approach has been added to the Bioconductor package gaga.

Introduction to miRNA profiling technologies and cross-platform comparison

MicroRNA analysis has been widely adopted for basic and applied science. The tools and technologies available for quantifying and analysing miRNAs are still maturing. Here, we give an introductory overview of the main tools and the challenges in their use. We also discuss the importance of basic experimental design, sample handling and analysis methods as the impact of these can be as profound as the choice of miRNA analysis platform. Whether the reader is interested in a gene-by-gene or genome-wide approach choosing the platform to use is not trivial. Careful thought given before starting an experiment will make the execution much easier.

RNA-stabilized whole blood samples but not peripheral blood mononuclear cells can be stored for prolonged time periods prior to transcriptome analysis

Microarray-based transcriptome analysis of peripheral blood as surrogate tissue has become an important approach in clinical implementations. However, application of gene expression profiling in routine clinical settings requires careful consideration of the influence of sample handling and RNA isolation methods on gene expression profile outcome. We evaluated the effect of different sample preservation strategies (eg, cryopreservation of peripheral blood mononuclear cells or freezing of PAXgene-stabilized whole blood samples) on gene expression profiles. Expression profiles obtained from cryopreserved peripheral blood mononuclear cells differed substantially from those of their nonfrozen counterpart samples. Furthermore, expression profiles in cryopreserved peripheral blood mononuclear cell samples were found to undergo significant alterations with increasing storage period, whereas long-term freezing of PAXgene RNA stabilized whole blood samples did not significantly affect stability of gene expression profiles. This report describes important technical aspects contributing toward the establishment of robust and reliable guidance for gene expression studies using peripheral blood and provides a promising strategy for reliable implementation in routine handling for diagnostic purposes.

Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group

PURPOSE

The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies.

METHODS

The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling-based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients.

RESULTS

On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods.

CONCLUSION

Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias.

Current status of gene expression profiling in the diagnosis and management of acute leukaemia

Gene expression profiling (GEP) enables the simultaneous investigation of the expression of tens of thousands of genes and was successfully introduced in leukaemia research a decade ago. Aiming to better understand the diversity of genetic aberrations in acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), pioneer studies investigated and confirmed the predictability of many cytogenetic and molecular subclasses in AML and ALL. In addition, GEP can define new prognostic subclasses within distinct leukaemia subgroups, as illustrated in AML with normal karyotype. Another approach is the development of treatment-specific sensitivity assays, which might contribute to targeted therapy studies. Finally, GEP might enable the detection of new molecular targets for therapy in patients with acute leukaemia. Meanwhile, large multicentre studies, e.g. the Microarray Innovations in LEukaemia (MILE) study, prepare for a standardised introduction of GEP in leukaemia diagnostic algorithms, aiming to translate this novel methodology into clinical routine for the benefit of patients with the complex disorders of AML and ALL.

On reliable discovery of molecular signatures

Background

Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR) in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability.

Results

We demonstrate that with modern prediction methods, signatures that yield accurate predictions may still have a high FDR. Further, we show that even signatures with low FDR may fail to replicate in independent studies due to limited statistical power. Thus, neither stability nor predictive accuracy are relevant when FDR control is the primary goal. We therefore develop a general statistical hypothesis testing framework that for the first time provides FDR control for signature discovery. Our method is demonstrated to be correct in simulation studies. When applied to five cancer data sets, the method was able to discover molecular signatures with 5% FDR in three cases, while two data sets yielded no significant findings.

Conclusion

Our approach enables reliable discovery of molecular signatures from genome-wide data with current sample sizes. The statistical framework developed herein is potentially applicable to a wide range of prediction problems in bioinformatics.

Intraplatform reproducibility and technical precision of gene expression profiling in 4 laboratories investigating 160 leukemia samples: the DACH study

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.

METHODS

One hundred sixty individual patient samples representing 11 subtypes of acute and chronic leukemias, myelodysplastic syndromes, and nonleukemia as a control group were centrally collected and diagnosed as part of the daily routine in the Munich Leukemia Laboratory. The custom AmpliChip Leukemia research microarray was used for technical analyses of quadruplicate mononuclear cell lysates in 4 different laboratories in Germany (D), Austria (A), and Switzerland (CH) (the DACH study).

RESULTS

Total-RNA preparations were successfully performed in 637 (99.5%) of 640 cases. Mean differences between pairs of laboratories in the total-RNA yield from the same sample ranged from 0.02 mug to 1.03 mug. Further processing produced 622 successful in vitro transcription reactions (97.6%); the mean differences between laboratories in the cRNA yield from the same sample ranged from 0.40 mug to 6.18 mug. After hybridization to microarrays, a mean of 47.6%, 46.5%, 46.2%, and 46.4% of probe sets were detected as present for the 4 laboratories, with mean signal-intensity scaling factors of 3.1, 3.7, 4.0, and 4.2, respectively. In unsupervised hierarchical cluster and principal component analyses, replicates from the same patient always clustered closely together, with no indications of any association between gene expression profiles due to different operators or laboratories.

CONCLUSIONS

Microarray analysis can be performed with high interlaboratory reproducibility and with comparable quality and high technical precision across laboratories.

Technical variables in high-throughput miRNA expression profiling: much work remains to be done

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Optimization of minuscule samples for use with cDNA microarrays

The recent advent of microarray technology and RNA amplification allows us to compare the expression profiles of thousands of genes from small amounts of tissue or cells. We have compared and contrasted various methods of RNA preparation, RNA amplification, target labelling and array analysis in order to achieve a streamlined protocol for microarraying small samples. We have concluded that usage of the NIA 15K cDNA array set, in combination with RNA extraction using the Mini RNA Isolation kit (Zymo), amplification with the RiboAmp kit (Arcturus), followed by indirect labelling via the Atlas PowerScript Fluorescent Labelling kit (using a modified protocol), is optimal with a material derived from either very early stage mouse embryos or individually picked embryonic stem cell colonies. Normalisation using the analysis package Limma (Bioconductor) with data normalisation by print tip Loess, using the "normexp" function with an offset of 50 for background adjustment, and incorporating A-quantile between array normalisation was best with our results. Furthermore, RT-PCR confirmation of array results is achievable without amplification, thereby controlling for amplification bias. These methods will be of great utility in mapping the transcriptome of embryonic and other small samples.