Consistency of predictive signature genes and classifiers generated using different microarray platforms

Abstract

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

“…[63][64][65] However, inclusion of genomic studies in formal drug safety assessment remains limited, 66 with relatively few reports describing significant insights from expression profiling that complement traditional nonclinical studies. 67,68 This may be due in part to a focus on gene signatures as chemical classifiers and a poor understanding of how selected genes from case-by-case analyses fit into the larger context of organ injury.…”

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

“…In the past decade, microarrays have been a principal technology for analyzing transcriptomes to support drug development and safety evaluation 2 . The FDA launched the community-wide MicroArray Quality Control (MAQC) consortium to investigate the reliability and utility of microarrays in identifying differentially expressed genes (DEGs) and predicting patient/toxicity outcomes based on gene-expression data in the first (MAQC-I) 3, 4 and second (MAQC-II) 5, 6 phases of the project, respectively. MAQC-I and MAQC-II demonstrated the critical roles of a comprehensive study design and crowd sourcing model to reach community-wide consensus on the fit-for-purpose use of emerging technologies.…”

“…Furthermore, the question has not been adequately addressed about whether predicting toxicity outcomes based on gene-expression data could be enhanced with RNA-seq over microarray. Under the umbrella of the third phase of the MAQC consortium 3–6 , also known as the SEquencing Quality Control (SEQC) project, we conducted a comprehensive study to evaluate RNA-seq in its differences and similarities to microarrays in terms of identifying DEGs and developing predictive models. In contrast to data generated as part of the SEQC project using reference RNA samples 25 , our study design provides a comparison of the transcription response for rat livers that each platform detects in terms of extensive chemical treatments, biologic replication and breath of shared mode of action (MOA) of the chemicals beyond simply monitoring performance metrics.…”

Biomarker Applications in the Pharmaceutical Industry

“…With the advent of 'omic' technologies allowing the simultaneous measurements of tens to thousands of endpoints, multivariate analysis has led to the concept of the 'signature', wherein a specific algorithmic evaluation of a defined, multiple endpoint becomes the biomarker [46]. While the use of signatures was initially controversial [47], and research into their optimal derivation continues [48][49][50][51][52][53], signatures based on mRNA expression levels have been shown to be consistent across methodologies [54] and have a functional basis to their collection [55,56]. Several mRNA-based signature assays for prognostic applications in oncology are currently commercially available, with two (MammaPrint and Oncotype Dx) having convincing data supporting their clinical benefit in predicting recurrence and aggressiveness of breast cancers [57].…”