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

Irvin M. Modlin, Ignat Drozdov, Mark Kidd

We developed a multi-transcript molecular signature for PCR-based blood analysis. NEN transcripts were identified by computational analysis of 3 microarray datasets and examined in 130 blood samples. Gene-based classifiers detected NENs in independent sets with high sensitivity (85-98%), specificity (93-97%), PPV (95-96%) and NPV (87-98%). 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.

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Gut Neuroendocrine Tumor Blood qPCR Fingerprint Assay: Characteristics and Reproducibility

Irvin M. Modlin, Ignat Drozdov, Mark Kidd

We have developed a PCR-based tool that measures a 51-gene panel for identification of gastro- enteropancreatic (GEP) neuroendocrine neoplasms (NENs) in peripheral blood. This manuscript assesses the robustness (performance metrics) of this tool with a specific focus on the effects of individual parameters including collection, storage, acid suppressive medica-tion (proton pump inhibitor (PPI)), age, sex, race and food on accuracy.

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Biomarkers in Neuroendocrine Tumors

Marvin Duque, Irvin M Modlin, Anumeha Gupta, Muhammad Wasif Saif

Neuroendocrine tumors are a heterogeneous group of tumors with cells of neuroendocrine differentiation that arise from diverse anatomic sites with varying morphologic and clinical features. Since the natural history and prognosis varies widely between individual neuroendocrine tumor types, there is a critical need to identify accurate prognostic and predictive biomarkers and markers predictive of therapeutic efficacy. To date, plasma chromogranin-A levels have generally been accepted as the most useful biomarker, despite the fact that there are substantial concerns in sensitivity and discrepancies in measurement techniques. As a consequence, considerable attention has been focused upon the development of novel biomarkers that can be utilized with more clinical efficacy than chromogranin-A. In addition to amplifying the diagnostic/prognostic landscape, the need to calibrate the efficacy of biological targeted therapy has further accelerated the development of molecular biomarkers. At the 2013 American Society of Clinical Oncology (ASCO) Annual Meeting, Chou et al. (Abstract #e15151) presented data that chromogranin A levels can be monitored during treatment to predict clinical outcome. Modlin et al. (Abstract #4137), demonstrated a promising novel biomarker, serum multi-transcript molecular signature. Grande et al. (Abstract #4140), Heetfield et al. (Abstract #e15071) and Casanovas et al. (Abstract #4139) described sVEGFR2, p-mTOR and IGF1R as molecular markers with potential for use in targeted therapy trials. The authors review and summarize these abstracts in this article.

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Molecular Pathology and Genetics of Gastrointestinal Neuroendocrine Tumours

Mark A Lewis and James C. Yao

Neuroendocrine tumours (NETs) of the luminal gastrointestinal tract and pancreas are increasing in incidence and prevalence. This review highlights the most contemporary genetic and molecular insights into gastroenteropancreatic NETs. Recent findings demonstrate that biomarkers such as neuron-specific enolase or chromogranin A could be supplemented or supplanted by PCR-based analysis of Neuroendocrine Tumor (NET) genes detectable in the blood transcriptome. After a long-standing paucity of options, the comprehension and treatment of gastroenteropancreatic NETs has been enriched by advancements in taxonomy, molecular pathology and genetic/epigenetic testing.

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Comparative curves for NETest (green and red) and chromogranin A (blue) demonstrating the NETest is significantly superior.


NETest gene expression histogram: demonstrates stable values after eating. Fasting is therefore not required for the test.


Gene score is significantly elevated in patients that do not respond to therapy or have progressive disease (red bar) compared to those that are surgically cured (blue bar) or have stable disease (orange bar).


Functional gene clusters in a small-intestinal neuroendocrine tumor (A). Frequency distribution of this network (B), non-randomness of the network (C) and stability of network following removal of key elements (D)..