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, P<0.05). Correlation analysis of matched blood/tumor was highly significant (R2=0.7, P<0.0001), and 58% of genes defined nine omic clusters (SSTRome, proliferome, signalome, metabolome, secretome, epigenome, plurome, and apoptome). Group 3: six clusters (SSTRome, proliferome, metabolome, secretome, epigenome, and plurome) differentiated SD fromPD(areaunder thecurve (AUC)=0.81). Integrationwith blood-algorithmamplified theAUC to 0.92±0.02 for differentiating PD and SD. The CIA defined a significantly lower SD score (34.1± 2.6%) than in PD (84±2.8%, P<0.0001). In conclusion, circulating transcripts measurements reflect NET tissue values. Integration of biologically relevant GC differentiate SD from PD. Combination of GC data with the blood-algorithm predicted disease status in >92%. Blood transcript measurement predicts NET activity.