Development of a Clinically Applicable NanoString-Based Gene Expression Classifier for Muscle-Invasive Bladder Cancer Molecular Stratification

SIMPLE SUMMARY: Molecular subtyping of muscle-invasive bladder cancer (MIBC) via gene expression can improve therapeutic decision-making and disease prognosis. However, the currently used molecular classification tools are based on complex transcriptomic profiling methodology that hinders timely translation to clinical practice. In this study, we evaluated the NanoString nCounter platform and conventional GATA3-CK5/6 immunohistochemistry for the molecular classification of MIBC in primary care settings. The methodologies were highly concordant and allowed us to explore differences in clinicopathologic parameters and prognosis between intrinsic MIBC molecular subtypes in a cohort of 138 MIBCs. ABSTRACT: Transcriptional profiling of muscle-invasive bladder cancer (MIBC) using RNA sequencing (RNA-seq) technology has demonstrated the existence of intrinsic basal and luminal molecular subtypes that vary in their prognosis and response to therapy. However, routine use of RNA-seq in a clinical setting is restricted by cost and technical difficulties. Herein, we provide a single-sample NanoString-based seven-gene (KRT5, KRT6C, SERPINB13, UPK1A, UPK2, UPK3A and KRT20) MIBC molecular classifier that assigns a luminal and basal molecular subtype. The classifier was developed in a series of 138 chemotherapy naïve MIBCs split into training (70%) and testing (30%) datasets. Further, we validated the previously published CK5/6 and GATA3 immunohistochemical classifier which showed high concordance of 96.9% with the NanoString-based gene expression classifier. Immunohistochemistry-based molecular subtypes significantly correlated with recurrence-free survival (RFS) and disease-specific survival (DSS) in univariable (p = 0.006 and p = 0.011, respectively) and multivariate cox regression analysis for DSS (p = 0.032). Used sequentially, the immunohistochemical- and NanoString-based classifiers provide faster turnaround time, lower cost per sample and simpler data analysis for ease of clinical implementation in routine diagnostics.