The Identification of Gut Neuroendocrine Tumor Disease by Multiple Synchronous Transcript Analysis in Blood

• Published in: PloS one Vol. 8; no. 5; p. e63364
• Main Authors: Modlin, Irvin M., Drozdov, Ignat, Kidd, Mark
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.05.2013; Public Library of Science (PLoS)
• Subjects: Adenocarcinoma; Algorithms; Area Under Curve; Arrays; Biomarkers; Blood; Blood tests; Classifiers; Computational neuroscience; Data processing; Datasets; Development and progression; Diagnosis; DNA microarrays; Effectiveness; Enzyme-linked immunosorbent assay; Gastrointestinal Neoplasms - blood; Gastrointestinal Neoplasms - genetics; Gene expression; Genes; Genetic Markers; Humans; Identification; Lesions; Medical diagnosis; Medical prognosis; Medical treatment; Medicine; Melanoma; Metastases; Metastasis; Neoplasia; Neoplasms; Neuroendocrine tumors; Neuroendocrine Tumors - blood; Neuroendocrine Tumors - genetics; Pancreas; Peripheral blood; Polymerase chain reaction; Prostate; Real-Time Polymerase Chain Reaction; RNA, Messenger - blood; Sensitivity; Skin cancer; Statistical analysis; Statistical methods; Surgery; Transcription; Tumors; United States > US; Connecticut
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0063364

Gastroenteropancreatic (GEP) neuroendocrine neoplasms (NENs) are increasing in both incidence and prevalence. A delay in correct diagnosis is common for these lesions. This reflects the absence of specific blood biomarkers to detect NENs. Measurement of the neuroendocrine secretory peptide Chromogranin A (CgA) is used, but is a single value, is non-specific and assay data are highly variable. To facilitate tumor detection, we developed a multi-transcript molecular signature for PCR-based blood analysis. NEN transcripts were identified by computational analysis of 3 microarray datasets: NEN tissue (n = 15), NEN peripheral blood (n = 7), and adenocarcinoma (n = 363 tumors). The candidate gene signature was examined in 130 blood samples (NENs: n = 63) and validated in two independent sets (Set 1 [n = 115, NENs: n = 72]; Set 2 [n = 120, NENs: n = 58]). Comparison with CgA (ELISA) was undertaken in 176 samples (NENs: n = 81). 51 significantly elevated transcript markers were identified. Gene-based classifiers detected NENs in independent sets with high sensitivity (85-98%), specificity (93-97%), PPV (95-96%) and NPV (87-98%). The AUC for the NEN gene-based classifiers was 0.95-0.98 compared to 0.64 for CgA (Z-statistic 6.97-11.42, p<0.0001). Overall, the gene-based classifier was significantly (χ(2) = 12.3, p<0.0005) more accurate than CgA. In a sub-analysis, pancreatic NENs and gastrointestinal NENs could be identified with similar efficacy (79-88% sensitivity, 94% specificity), as could metastases (85%). In patients with low CgA, 91% exhibited elevated transcript markers. A panel of 51 marker genes differentiates NENs from controls with a high PPV and NPV (>90%), identifies pancreatic and gastrointestinal NENs with similar efficacy, and confirms GEP-NENs when CgA levels are low. The panel is significantly more accurate than the CgA assay. This reflects its utility to identify multiple diverse biological components of NENs. Application of this sensitive and specific PCR-based blood test to NENs will allow accurate detection of disease, and potentially define disease progress enabling monitoring of treatment efficacy.