Development and validation of a NanoString BASE47 bladder cancer gene classifier

Jordan Kardos, Tracy L. Rose, Ujjawal Manocha, Sara E. Wobker, Jeffrey S. Damrauer, Trinity J. Bivalaqua, Max Kates, Kristin J. Moore, Joel S. Parker, William Y. Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Background Recent molecular characterization of urothelial cancer (UC) has suggested potential pathways in which to direct treatment, leading to a host of targeted therapies in development for UC. In parallel, gene expression profiling has demonstrated that high-grade UC is a heterogeneous disease. Prognostic basal-like and luminal-like subtypes have been identified and an accurate transcriptome BASE47 classifier has been developed. However, these phenotypes cannot be broadly investigated due to the lack of a clinically viable diagnostic assay. We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens. Methods Tumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n = 30). The training and validation datasets accurately classified 87% and 93% of samples, respectively. Results Here we have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier. Conclusions The NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.

Original languageEnglish (US)
Article numbere0243935
JournalPloS one
Volume15
Issue number12 December
DOIs
StatePublished - Dec 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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