Blood and tissue neuroendocrine tumor gene cluster analysis correlate, define hallmarks and predict disease status

Abstract

A multianalyte algorithmic assay (MAAA) identifies circulating neuroendocrine tumor (NET) transcripts (n=51) with a sensitivity/specificity of 98%/97%. We evaluated whether blood measurements correlated with tumor tissue transcript analysis. The latter were segregated into gene clusters (GC) that defined clinical ‘hallmarks’ of neoplasia. A MAAA/cluster integrated algorithm (CIA) was developed as a predictive activity index to define tumor behavior and outcome. We evaluated three groups. Group 1: publically available NET transcriptome databases (n=15; GeneProfiler). Group 2: prospectively collected tumors and matched blood samples (n=22; qRT-PCR). Group 3: prospective clinical blood samples,n=159: stable disease (SD):n=111 and progressive disease (PD):n=48. Regulatory network analysis, linear modeling, principal component analysis (PCA), and receiver operating characteristic analyses were used to delineate neoplasia ‘hallmarks’ and assess GC predictive utility. Our results demonstrated: group 1: NET transcriptomes identified (92%) genes elevated. Group 2: 98% genes elevated by qPCR (fold change >2,PR²=0.7,PP92%. Blood transcript measurement predicts NET activity.