Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip

Abstract EN

Background.

With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important.

A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization.

This “cancer in reverse” approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in “driver genes.” Objective.

To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms.

Methods and Results.

We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0.

The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples.

Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria.

Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R.

Pearson correlation showed reproducibility between sites for all 60 samples with r=0.995 for Affymetrix and r=0.999 for NanoString.

Correlation in multiple days and multiple users was for Affymetrix r=0.962−0.999 and for NanoString r=0.982−0.991.

Conclusion.

The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance.

Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay.