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Immuno‐oncology gene expression profiling of formalin‐fixed and paraffin‐embedded clear cell renal cell carcinoma: Performance comparison of the NanoString nCounter technology with targeted RNA sequencing
Author(s) -
Talla Suranand B.,
Rempel Eugen,
Endris Volker,
Jenzer Maximilian,
Allgäuer Michael,
Schwab Constantin,
Kazdal Daniel,
Stögbauer Fabian,
Volckmar AnnaLena,
Kocsmar Ildiko,
Neumann Olaf,
Schirmacher Peter,
Zschäbitz Stefanie,
Duensing Stefan,
Budczies Jan,
Stenzinger Albrecht,
Kirchner Martina
Publication year - 2020
Publication title -
genes, chromosomes and cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.754
H-Index - 119
eISSN - 1098-2264
pISSN - 1045-2257
DOI - 10.1002/gcc.22843
Subject(s) - gene expression , gene expression profiling , computational biology , cancer research , dna microarray , biology , gene , transcriptome , genetics
Abstract Inflammatory gene signatures are currently being explored as predictive biomarkers for immune checkpoint blockade, and particularly for the treatment of renal cell cancers. From a diagnostic point of view, the nCounter analysis platform and targeted RNA sequencing are emerging alternatives to microarrays and comprehensive transcriptome sequencing in assessing formalin‐fixed and paraffin‐embedded (FFPE) cancer samples. So far, no systematic study has analyzed and compared the technical performance metrics of these two approaches. Filling this gap, we performed a head‐to‐head comparison of two commercially available immune gene expression assays, using clear cell renal cell cancer FFPE specimens. We compared the nCounter system that utilizes a direct hybridization technology without amplification with an NGS assay that is based on targeted RNA‐sequencing with preamplification. We found that both platforms displayed high technical reproducibility and accuracy (Pearson coefficient: ≥0.96, concordance correlation coefficient [CCC]: ≥0.93). A density plot for normalized expression of shared genes on both platforms showed a comparable bi‐modal distribution and dynamic range. RNA‐Seq demonstrated relatively larger signaling intensity whereas the nCounter system displayed higher inter‐sample variability. Estimated fold changes for all shared genes showed high correlation (Spearman coefficient: 0.73). This agreement is even better when only significantly differentially expressed genes were compared. Composite gene expression profiles, such as an interferon gamma (IFNg) signature, can be reliably inferred by both assays. In summary, our study demonstrates that focused transcript read‐outs can reliably be achieved by both technologies and that both approaches achieve comparable results despite their intrinsic technical differences.