Genetic Engineering News by Richard Stein – Two technologies used to interrogate gene expression, RNA-Seq and microarray analysis, often return strongly correlated results. These technologies, however, have not been evaluated for their concordance at the isoform level.
To understand the correlation between RNA-Seq and exon-array platforms in detecting isoforms, Dr. Davuluri and colleagues compared gene- and isoform-level expression for glioblastoma multiforme transcripts from The Cancer Genome Atlas (TCGA). Glioblastoma multiforme is one of the three malignancies for which TCGA contains both RNA-Seq and exon-array data.
The investigation revealed that only about 36% of the differentially expressed isoforms identified by RNA-Seq were also classified as differentially expressed by exon arrays, and that about 70% of the ones classified as differentially expressed by exon arrays were also classified as such by RNA-Seq, indicating that isoform-level expression may be masked by gene-expression estimates.
“Gene-expression arrays and RNA-Seq will be used in a complementary manner,” asserts Dr. Davuluri. “And if the costs of sequencing drop further, people will use sequencing more and more.”
While microarrays are more cost-effective, RNA-Seq provides several advantages, including single-nucleotide resolution and the possibility of performing analyses without prior knowledge about the targeted sequences.
To quantitatively compare gene-expression measurements between different analytical platforms and allow signatures to be transferred across them, Dr. Davuluri and colleagues made use of the PIGExClass (platform-independent isoform-level gene-expression-based classification) system. Using this computational tool, the investigators performed the first isoform-level assay for the molecular stratification of cancer.
Dr. Davuluri’s group examined exon-array and RNA-Seq isoform-level profiles for glioblastoma multiforme samples, and it illustrated the possibility of stratifying patients into one of the four molecular subgroups. As a result of the isoform-level analysis, the subgroup classification changed for 19% of the samples, leading to a different prognostic classification, a finding of critical therapeutic and prognostic relevance.
“The technology for the data-generating platforms moves fast,” comments Dr. Davuluri. “But the data that comes out from the platforms cannot be understood without informatics.”