Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine

Abstract

Studies on gene expression in response to therapy have led to the discovery of pharmacogenomics biomarkers and advances in precision medicine. Whole transcriptome sequencing (RNA-seq) is an emerging tool for profiling gene expression and has received wide adoption in the biomedical research community. However, its value in regulatory decision making requires rigorous assessment and consensus between various stakeholders, including the research community, regulatory agencies, and industry. The FDA-led SEquencing Quality Control (SEQC) consortium has made considerable progress in this direction, and is the subject of this review. Specifically, three RNA-seq platforms (Illumina HiSeq, Life Technologies SOLiD, and Roche 454) were extensively evaluated at multiple sites to assess cross-site and cross-platform reproducibility. The results demonstrated that relative gene expression measurements were consistently comparable across labs and platforms, but not so for the measurement of absolute expression levels. As part of the quality evaluation several studies were included to evaluate the utility of RNA-seq in clinical settings and safety assessment. The neuroblastoma study profiled tumor samples from 498 pediatric neuroblastoma patients by both microarray and RNA-seq. RNA-seq offers more utilities than microarray in determining the transcriptomic characteristics of cancer. However, RNA-seq and microarray-based models were comparable in clinical endpoint prediction, even when including additional features unique to RNA-seq beyond gene expression. The toxicogenomics study compared microarray and RNA-seq profiles of the liver samples from rats exposed to 27 different chemicals representing multiple toxicity modes of action. Cross-platform concordance was dependent on chemical treatment and transcript abundance. Though both RNA-seq and microarray are suitable for developing gene expression based predictive models with comparable prediction performance, RNA-seq offers advantages over microarray in profiling genes with low expression. The rat BodyMap study provided a comprehensive rat transcriptomic body map by performing RNA-Seq on 320 samples from 11 organs in either sex of juvenile, adolescent, adult and aged Fischer 344 rats. Lastly, the transferability study demonstrated that signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development using a comprehensive approach with two large clinical data sets. This result suggests continued usefulness of legacy microarray data in the coming RNA-seq era. In conclusion, the SEQC project enhances our understanding of RNA-seq and provides valuable guidelines for RNA-seq based clinical application and safety evaluation to advance precision medicine.

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“…The transcriptome, which is assessed by RNA-seq, is widely considered to be the most mature omics technology with a long measure and the greatest depth of coverage. 26,27 This approach is also advantageous when the protein products of genes are either recalcitrant to proteomic analysis or of low abundance, which can result in their under-representation in some conditions. 28 The proteomic analysis is widely used in the analyses of differential expression of proteins in order to understand the changes of protein expression between experimental and control group.…”

HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset

“…The training dataset consisted of 249 samples, and the other 249 samples were in the validation/test dataset. We used the sample distribution as defined in SEQC project [4][5][6].…”

“…In general, "multi-platform" refers to data from multiple technologies or from different sites/tissues/organs, which usually have close linkages or relationships between data platforms. For example, the Sequencing Quality Control (SEQC) project [5,6] studied a large neuroblastoma cohort with both Microarray (Agilent) and RNA-seq (Illumina Hi-seq) datasets.…”

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