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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.

Many management strategies exist for neuroendocrine liver metastases. These strategies range from surgery to ablation with various interventional radiology procedures, and include both regional and systemic therapy with diverse biological, cytotoxic, or targeted agents. A paucity of biological, molecular, and genomic information and an absence of data from rigorous trials limit the validity of many publications detailing management. This Review represents the views from an international conference, for which 15 expert working groups prepared evidence-based assessments addressing specific questions, and from which an independent jury derived final recommendations. The aim of the conference was to review the existing approaches to neuroendocrine liver metastases, assess the evidence on which management decisions were based, develop internationally acceptable recommendations for clinical practice (when evidence was available), and make recommendations for clinical and research endeavours. This report represents the final clinical statements and proposals for future research.

Peptide receptor radionuclide therapy (PRRT) is a type of radiotherapy that targets peptide receptors and is typically used for neuroendocrine tumours (NETs). Some of the key challenges in its use are the prediction of efficacy and toxicity, patient selection, and response optimisation. In this Review, we assess current knowledge on the molecular profile of NETs and the strategies and tools used to predict, monitor, and assess the toxicity of PRRT. The few mutations in tumour genes that can be evaluated (eg, ATM and DAXX) are limited to pancreatic NETs and are most likely not informative. Assays that are transcriptomic or based on genes are effective in the prediction of radiotherapy response in other cancers. A blood-based assay for eight genes (the PRRT prediction quotient [PPQ]) has an overall accuracy of 95% for predicting responses to PRRT in NETs. No molecular markers exist that can predict the toxicity of PRRT. Candidate molecular targets include seven single nucleotide polymorphisms (SNPs) that are susceptible to radiation. Transcriptomic evaluations of blood and a combination of gene expression and specific SNPs, assessed by machine learning with algorithms that are tumour-specific, might yield molecular tools to enhance the efficacy and safety of PRRT.

Multigene-based PCR tests are time-consuming and limiting aspects of the protocol include increased risk of operator-based variation. In addition, such protocols are complex to transfer and reproduce between laboratories. Evaluate the clinical utility of a pre-spotted PCR plate (PSP) for a novel multigene (n = 51) blood-based gene expression diagnostic assay for neuroendocrine tumors (NETs). A pilot study (n = 44; 8 controls and 36 NETs) was undertaken to compare CQ, normalized gene expression and algorithm-based output (NETest score). Gene expression was then evaluated between matched blood:tumor tissue samples (n = 7). Thereafter, two prospective sets (diagnostic: n = 167; clinical validation: n = 48, respectively) were evaluated for diagnostic and clinical utility value. Two independent molecular diagnostics facilities were used to assess assay reproducibility and inter-laboratory metrics. Samples were collected (per CLIA protocol) processed to mRNA and cDNA and then either run per standard assay (liquid primers) or on PSPs. Separately, matching plasma samples were analyzed for chromogranin A (CgA). Statistics included non-parametric testing, Pearson-concordance, Predictive Modeling and AUROC analyses. In the pilot study (n = 44), CQ values were highly concordant (r: 0.82, p<0.0001) and normalized gene expression data significantly related (p<0.0001) (Pearson-pairwise correlation). NETest values were not different (49.7±33 standard vs. 48.5±31.5 PSP) and the overall concordance in output 96%. Predictive modelling confirmed this concordance (F1 score = 0.95). Gene expression levels were highly correlated between blood and tumor tissue (R: 0.71-0.83). In the diagnostic cohort (n = 30 controls, n = 87 non-NET controls, n = 50 NET), NETest was significantly lower (p<0.0001) in controls (11±6.5) and non-NET controls (13±18) than NETs (61±31). The AUROCs were 0.93-0.97 and the diagnostic accuracy was 90-97.5%. As a diagnostic, the PSP-NETest was significantly better than CgA (accuracy: 56%, p<0.0001). For clinical samples, the PSP generated robust and accurate (>96%) scores and was significantly better (p<0.0001) than CgA. The assay protocol was consistent (r: 0.97) and reproducible (co-efficient of variation: 1.3-4.2%) across the two facilities. The PSP protocol for the NETest has been established and prospectively tested in clinical samples. It is highly reproducible, has similar metrics (CV, categorization by control or NET) to the standard PCR assay and generates clinically concordant (>96%) NETest results. Moreover, it functions significantly more accurately than CgA.

Management of neuroendocrine neoplasia represents a clinical challenge because of its late presentation, lack of treatment options, and limitations in present imaging modalities and biomarkers to guide management. Monoanalyte biomarkers have poor sensitivity, specificity, and predictive ability. A National Cancer Institute summit, held in 2007, on neuroendocrine tumours noted biomarker limitations to be a crucial unmet need in the management of neuroendocrine tumours. A multinational consensus meeting of multidisciplinary experts in neuroendocrine tumours assessed the use of current biomarkers and defined the perquisites for novel biomarkers via the Delphi method. Consensus (at >75%) was achieved for 88 (82%) of 107 assessment questions. The panel concluded that circulating multianalyte biomarkers provide the highest sensitivity and specificity necessary for minimum disease detection and that this type of biomarker had sufficient information to predict treatment effectiveness and prognosis. The panel also concluded that no monoanalyte biomarker of neuroendocrine tumours has yet fulfilled these criteria and there is insufficient information to support the clinical use of miRNA or circulating tumour cells as useful prognostic markers for this disease. The panel considered that trials measuring multianalytes (eg, neuroendocrine gene transcripts) should also identify how such information can optimise the management of patients with neuroendocrine tumours.

Diagnostic imaging plays a pivotal role in the diagnosis, staging, treatment selection and follow-up for neuroendocrine tumors. The available diagnostic strategies are morphologic imaging, including computed tomography, magnetic resonance imaging (MRI) and ultrasound techniques, and molecular imaging, including scintigraphy with 111 In-pentetreotide and positron emission tomography with 68 Ga-DOTA-peptides, 18 F-DOPA and 11 C-5-HTP. A combination of anatomic and functional techniques is routinely performed to optimize sensitivity and specificity. The introduction of diffusion-weighted MRI and dynamic contrast-enhanced techniques represents a promising advance in radiologic imaging, whereas new receptor-binding peptides, including somatostatin agonists and antagonists, represent the recent most favorable innovation in molecular imaging. Future development includes the short-term validation of these techniques, but in extension also a more comprehensive multilevel integration of biologic information pertaining to a specific tumor and patient, possibly encompassing genomic considerations, currently evolving as a new entity denoted ‘precision medicine'. The ideal is a diagnostic sequence that captures the global status of an individual's tumor and encompasses a multidimensional characterization of tumor location, metabolic performance and target identification. To date, advances in imagery have focused on increasing resolution, discrimination and functional characterization. In the future, the fusion of imagery with the parallel analysis of biological and genomic information has the potential to considerably amplify diagnosis.

Gastroenteropancreatic (GEP) neuroendocrine tumours (NETs) are fairly rare neoplasms that present many clinical challenges. They secrete peptides and neuroamines that cause distinct clinical syndromes, including carcinoid syndrome. However, many are clinically silent until late presentation with mass effects. Investigation and management should be highly individualised for a patient, taking into consideration the likely natural history of the tumour and general health of the patient. Management strategies include surgery for cure (which is achieved rarely) or for cytoreduction, radiological intervention (by chemoembolisation and radiofrequency ablation), chemotherapy, and somatostatin analogues to control symptoms that result from release of peptides and neuroamines. New biological agents and somatostatin-tagged radionuclides are under investigation. The complexity, heterogeneity, and rarity of GEP NETs have contributed to a paucity of relevant randomised trials and little or no survival increase over the past 30 years. To improve outcome from GEP NETs, a better understanding of their biology is needed, with emphasis on molecular genetics and disease modeling. More-reliable serum markers, better tumour localisation and identification of small lesions, and histological grading systems and classifications with prognostic application are needed. Comparison between treatments is currently very difficult. Progress is unlikely to occur without development of centers of excellence, with dedicated combined clinical teams to coordinate multicentre studies, maintain clinical and tissue databases, and refine molecularly targeted therapeutics.

Background Neuroendocrine tumors (NETs) are a form of cancer that differ from other neoplasia in that they synthesize, store, and secrete peptides, e.g., chromogranin A (CgA) and amines. A critical issue is late diagnosis due to failure to identify symptoms or to establish the biochemical diagnosis. We review here the utility of CgA measurement in NETs and describe its biological role and the clinical value of its measurement. Methods Literature review and analysis of the utility of plasma/serum CgA measurements in NETs and other diseases. Results CgA is a member of the chromogranin family; its transcription and peptide processing are well characterized, but its precise function remains unknown. Levels are detectable in the circulation but vary substantially (~25%) depending on which assay is used. Serum and plasma measurements are concordant. CgA is elevated in ~90% of gut NETs and correlates with tumor burden and recurrence. Highest values are noted in ileal NETs and gastrointestinal NETs associated with multiple endocrine neoplasia type 1. Both functioning and nonfunctioning pancreatic NETs have elevated values. CgA is more frequently elevated in well-differentiated tumors compared to poorly differentiated NETs. Effective treatment is often associated with decrease in CgA levels. Proton pump inhibitors falsely increase CgA, but levels normalize with therapy cessation. Conclusions CgA is currently the best available biomarker for the diagnosis of NETs. It is critical to establish diagnosis and has some utility in predicting disease recurrence, outcome, and efficacy of therapy. Measurement of plasma CgA is mandatory for the effective diagnosis and management of NET disease.

IntroductionSurgery is the only cure for neuroendocrine tumors (NETs), with R0 resection being critical for successful tumor removal. Early detection of residual disease is key for optimal management, but both imaging and current biomarkers are ineffective post-surgery. NETest, a multigene blood biomarker, identifies NETs with >90% accuracy. We hypothesized that surgery would decrease NETest levels and that elevated scores post-surgery would predict recurrence.MethodsThis was a multicenter evaluation of surgically treated primary NETs (n = 153). Blood sampling was performed at day 0 and postoperative day (POD) 30. Follow-up included computed tomography/magnetic resonance imaging (CT/MRI), and messenger RNA (mRNA) quantification was performed by polymerase chain reaction (PCR; NETest score: 0–100; normal ≤20). Statistical analyses were performed using the Mann–Whitney U-test, Chi-square test, Kaplan–Meier survival, and area under the receiver operating characteristic curve (AUROC), as appropriate. Data are presented as mean ± standard deviation.ResultsThe NET cohort (n = 153) included 57 patients with pancreatic cancer, 62 patients with small bowel cancer, 27 patients with lung cancer, 4 patients with duodenal cancer, and 3 patients with gastric cancer, while the surgical cohort comprised patients with R0 (n = 102) and R1 and R2 (n = 51) resection. The mean follow-up time was 14 months (range 3–68). The NETest was positive in 153/153 (100%) samples preoperatively (mean levels of 68 ± 28). In the R0 cohort, POD30 levels decreased from 62 ± 28 to 22 ± 20 (p < 0.0001), but remained elevated in 30% (31/102) of patients: 28% lung, 29% pancreas, 27% small bowel, and 33% gastric. By 18 months, 25/31 (81%) patients with a POD30 NETest >20 had image-identifiable recurrence. An NETest score of >20 predicted recurrence with 100% sensitivity and correlated with residual disease (Chi-square 17.1, p < 0.0001). AUROC analysis identified an AUC of 0.97 (p < 0.0001) for recurrence-prediction. In the R1 (n = 29) and R2 (n = 22) cohorts, the score decreased (R1: 74 ± 28 to 45 ± 24, p = 0.0012; R2: 72 ± 24 to 60 ± 28, p = non-significant). At POD30, 100% of NETest scores were elevated despite surgery (p < 0.0001).ConclusionThe preoperative NETest accurately identified all NETs (100%). All resections decreased NETest levels and a POD30 NETest score >20 predicted radiologically recurrent disease with 94% accuracy and 100% sensitivity. R0 resection appears to be ineffective in approximately 30% of patients. NET mRNA blood levels provide early objective genomic identification of residual disease and may facilitate management.

Background Tumor transcriptomes contain information of critical value to understanding the different capacities of a cell at both a physiological and pathological level. In terms of clinical relevance, they provide information regarding the cellular “toolbox” e.g., pathways associated with malignancy and metastasis or drug dependency. Exploration of this resource can therefore be leveraged as a translational tool to better manage and assess neoplastic behavior. The availability of public genome-wide expression datasets, provide an opportunity to reassess neuroendocrine tumors at a more fundamental level. We hypothesized that stringent analysis of expression profiles as well as regulatory networks of the neoplastic cell would provide novel information that facilitates further delineation of the genomic basis of small intestinal neuroendocrine tumors. Results We re-analyzed two publically available small intestinal tumor transcriptomes using stringent quality control parameters and network-based approaches and validated expression of core secretory regulatory elements e.g., CPE, PCSK1, secretogranins, including genes involved in depolarization e.g., SCN3A, as well as transcription factors associated with neurodevelopment (NKX2-2, NeuroD1, INSM1) and glucose homeostasis (APLP1). The candidate metastasis-associated transcription factor, ST18, was highly expressed (>14-fold, p  < 0.004). Genes previously associated with neoplasia, CEBPA and SDHD, were decreased in expression (-1.5 – -2, p  < 0.02). Genomic interrogation indicated that intestinal tumors may consist of two different subtypes, serotonin-producing neoplasms and serotonin/substance P/tachykinin lesions. QPCR validation in an independent dataset ( n  = 13 neuroendocrine tumors), confirmed up-regulated expression of 87% of genes (13/15). Conclusions An integrated cellular transcriptomic analysis of small intestinal neuroendocrine tumors identified that they are regulated at a developmental level, have key activation of hypoxic pathways (a known regulator of malignant stem cell phenotypes) as well as activation of genes involved in apoptosis and proliferation. Further refinement of these analyses by RNAseq studies of large-scale databases will enable definition of individual master regulators and facilitate the development of novel tissue and blood-based tools to better understand diagnose and treat tumors.