MP66-06 OPTIMIZATION OF NANOSTRING PLATFORM FOR EVALUATION OF PROSTATE CANCER BIOMARKERS AND THERAPEUTIC TARGETS IN FFPE SPECIMENS

Abstract

INTRODUCTION AND OBJECTIVES: Identification of prognostic indicators of disease progression is one of the highest priorities in prostate cancer (CaP) research. The majority of CaP tissue specimens available for biomarker discovery and validation are formalin fixed paraffin embedded (FFPE) specimens. The RNA quality of these tissues for traditional gene expression analysis approaches is not adequate, especially of cases with long termfollow up necessary for prognostic marker studies. We systematically evaluated and optimized the NanoString platform for CaP gene expression analysis in FFPE whole mounted prostate specimens with up to 15 years follow up. METHODS: NanoString readouts were compared between a series of input total RNA prepared through different tissue dissection methods. We then performed NanoString analysis of 151-CaP probe set on 63 archived CaP samples to evaluate this modified protocol. ERG gene expression was validated by immunohistochemistry (IHC) assay using CPDR ERG-MAb (9FY). RESULTS: We have performed NanoString analysis of CPDR designed 151CaP gene panel on whole-mounted prostate tissue specimens using our optimized protocol. The prostate epithelial cell markers KLK3, MSMB and PAP had the highest signals in all samples reflecting the prostate epithelial origin of the specimens. Established CaP specific genes, such as AMACR, PCA3 and PSGR were most highly over-expressed in tumors compared to matched benign prostate epithelium. ERG positive tumors had strong expression of both TMPRSS2-ERG fusion probes and probes for several ERG splice forms (ERG 1,2,3 and 8), with no signals in the matched benign samples. The ERG gene expression status was consistent with blinded ERG IHC results. CONCLUSIONS: The feasibility of NanoString profiling in archived whole mounted FFPE prostate specimens was demonstrated. This approach is well suited for a medium throughput evaluation and validation of prognostic biomarker and therapeutic target candidates in CaP.