Decentral gene expression analysis: analytical validation of the Endopredict genomic multianalyte breast cancer prognosis test

Abstract

Background: EndoPredict (EP) is a clinically validated multianalyte gene expression test to predict distant metastasis in ER-positive, HER2-negative breast cancer treated with endocrine therapy alone. The test is based on the combined analysis of 12 genes in formalin-fixed, paraffin-embedded (FFPE) tissue by reverse transcription-quantitative real-time PCR (RT-qPCR). Recently, it was shown that EP is feasible for reliable decentralized assessment of gene expression. The aim of this study was the analytical validation of the performance characteristics of the assay and its verification in a molecular-pathological routine laboratory.

Methods: Gene expression values to calculate the EP score were assayed by one-step RT-qPCR using RNA from FFPE tumor tissue. Limit of blank, limit of detection, linear range, and PCR efficiency were assessed for each of the 12 PCR assays using serial samples dilutions. Different breast cancer samples were used to evaluate RNA input range, precision and inter-laboratory variability.

Results: PCR assays were linear up to Cq values between 35.1 and 37.2. Amplification efficiencies ranged from 75% to 101%. The RNA input range without considerable change of the EP score was between 0.16 and 18.5 ng/μl. Analysis of precision (variation of day, day time, instrument, operator, reagent lots) resulted in a total noise (standard deviation) of 0.16 EP score units on a scale from 0 to 15. The major part of the total noise (SD 0.14) was caused by the replicate-to-replicate noise of the PCR assays (repeatability) and was not associated with different operating conditions (reproducibility). Performance characteristics established in the manufacturer's laboratory were verified in a routine molecular pathology laboratory. Comparison of 10 tumor samples analyzed in two different laboratories showed a Pearson coefficient of 0.995 and a mean deviation of 0.15 score units.

Conclusions: The EP test showed reproducible performance characteristics with good precision and negligible laboratory-to-laboratory variation. This study provides further evidence that the EP test is suitable for decentralized testing in specialized molecular pathological laboratories instead of a reference laboratory. This is a unique feature and a technical advance in comparison with existing RNA-based prognostic multigene expression tests.

Figures

Figure 1

Translation of the EndoPredict multigene expression test from research laboratory to clinical practice. Workflow of sequential discovery and clinical as well as analytical validation is shown.

Figure 2

RNA input range and reproducibility of EndoPredict. (A) EP scores depending on amount of input RNA. RNAs from 6 different FFPE samples were diluted and EP scores were assessed dependent on RNA input (Cq-ARG as surrogate marker). 95% confidence intervals (CI) of EP scores calculated from the noise model are indicated. (B) Correlation between Cq-ARG and total RNA concentration assessed by RIBOGREEN assay. Lower RNA input limit is indicated by dotted lines. (C) Reproducibility of 160 EP scores assessed in three different RNA samples (low risk, close to the decision point, high risk) over time (11 different working days distributed over 21 calendar days). Individual EP measurement results are indicated by dots.

Figure 3

Verification of performance data at Charité. (A) EP scores depending on amount of input RNA assessed as Cq-ARG. Pre-specified input limit is indicated by a dotted line. 95% CI are given. (B) Correlation of EP scores of 10 different breast cancer FFPE samples assessed at the molecular-pathological laboratory at the Charité compared to reference values assessed at Sividon. The cutoff value between low and high risk for distant metastasis is indicated by dotted lines.