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Background: The NETest is a multigene assay comprising 51 circulating neuroendocrine tumor (NET)-specific transcripts. The quotient of the 51-gene assay is based upon an ensemble of machine learning algorithms. Eight cancer hallmarks or "omes" (apoptome, epigenome, growth factor signalome, metabolome, proliferome, plurome, secretome, SSTRome) represent 29 genes. The NETest is an accurate diagnostic (>90%) test, but its prognostic utility has not been assessed. In this study, we describe the expansion of the NETest omic cluster components and demonstrate that integration amplifies NETest prognostic accuracy., Methods: Group 1: n = 222; including stable disease (SD, n = 146), progressive disease (PD, n = 76), and controls (n = 139). Group 2: NET Registry NCT02270567; n = 88; prospective samples (SD, n = 54; PD, n = 34) with up to 24 months follow-up. We used PubMed literature review, interactomic analysis, nonparametric testing, Kaplan-Meier survival curves, and χ2 analyses to inform and define the prognostic significance of NET genomic "hallmarks.", Results: 2020 analyses: In-depth analyses of 47 -NETest genes identified a further six omes: fibrosome, inflammasome, metastasome, NEDome, neurome, and TFome. Group 1 analysis: Twelve omes, excluding the inflammasome and apoptome, were significantly (p < 0.05, 2.1- to 8.2-fold) elevated compared to controls. In the PD group, seven omes (proliferome, NEDome, epigenome, SSTRome, neurome, metastasome, and fibrosome) were elevated (both expression levels and fold change >2) versus SD. Group 2 analysis: All these seven omes were upregulated. In PD, they were significantly more elevated (p < 0.02) than in SD. The septet omic expression exhibited a 69% prognostic accuracy. The NETest alone was 70.5% accurate. A low NETest (≤40) integrated with epigenome/metastasome levels was an accurate prognostic for PD (90%). A high NETest (>40) including the fibrosome/NEDome predicted PD development within 3 months (100%). Using decision tree analysis to integrate the four omes (epigenome, metastasome, fibrosome, and NEDome) with the NETest score generated an overall prognostic accuracy of 93%., Conclusions: Examination of NETest omic gene cluster analysis identified five additional clinically relevant cancer hallmarks. Identification of seven omic clusters (septet) provides a molecular pathological signature of disease progression. The integration of the quartet (epigenome, fibrosome, metastasome, NEDome) and the NETest score yielded a 93% accuracy in the prediction of future disease status., (© 2020 S. Karger AG, Basel.)

Purpose: There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest). The NETest represents a transcriptomic signature of neuroendocrine tumor (NETs) that captures tumor biology and disease activity. The data have direct clinical application in terms of identifying residual disease, disease progress and the efficacy of treatment. In this overview we assess the available published information on the metrics and clinical efficacy of the NETest., Material and Methods: Published data on the NETest have been collated and analyzed to understand the clinical application of this multianalyte biomarker in NETs., Results: NETest assay has been validated as a standardized and reproducible clinical laboratory measurement. It is not affected by demographic characteristics, or acid suppressive medication. Clinical utility of the NETest has been documented in gastroenteropancreatic, bronchopulmonary NETs, in paragangliomas and pheochromocytomas. The test facilitates accurate diagnosis of a NET disease, and real-time monitoring of the disease status (stable/progressive disease). It predicts aggressive tumor behavior, identifies operative tumor resection, and efficacy of the medical treatment (e.g. somatostatin analogues), or peptide receptor radionuclide therapy (PRRT). NETest metrics and clinical applications out-perform standard biomarkers like chromogranin A., Conclusions: The NETest exhibits clinically competent metrics as an effective biomarker for neuroendocrine tumors. Measurement of NET transcripts in blood is a significant advance in neuroendocrine tumor management and demonstrates that blood provides a viable source to identify and monitor tumor status., Competing Interests: Declaration of competing interest Mark Kidd is an employee of Wren Laboratories. Somer Matar formerly worked at Wren Laboratories. Irvin M. Modlin and Ignat Drozdov are consultants to Wren Laboratories., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: The management of bronchopulmonary neuroendocrine tumours (BPNETs) is difficult, since imaging, histology and biomarkers have a limited value in diagnosis, predicting outcome and defining therapeutic efficacy. We evaluated a NET multigene blood test (NETest) to diagnose BPNETs, assess disease status and evaluate surgical resection., Methods: (i) Diagnostic cohort: BP carcinoids (n = 118)-typical carcinoid, n = 67 and atypical carcinoid, n = 51; other lung NEN (large-cell neuroendocrine carcinoma and small-cell lung carcinoma, n = 13); adenocarcinoma, (n = 26); squamous cell carcinoma (n = 23); controls (n = 90) and chronic obstructive pulmonary disease (n = 18). (ii) Surgical cohort, n = 28: BP carcinoids (n = 16: typical carcinoid 12; atypical carcinoid 4); large-cell neuroendocrine carcinoma, n = 3; lung adenocarcinoma, n = 8 and squamous cell carcinoma, n = 1. Blood sampling was performed presurgery and 30 days post-surgery. Transcript levels measured by quantitative polymerase chain reaction were calculated as activity scores (0-100% scale: normal < 14%) and compared with chromogranin A (enzyme-linked immunosorbent assay; normal <109 ng/ml)., Results: NETest was significantly elevated in carcinoids (48.7 ± 27%) versus controls (6 ± 6%, P < 0.001) with metrics: sensitivity 93%, specificity 89%, positive predictive value 92% and negative predictive value 91%. NETest differentiated progressive disease (73 ± 22%) from stable disease (36 ± 19%, P < 0.001) and R0 resections (10 ± 5%, P < 0.001, area under the curve: 0.98). Levels in chronic obstructive pulmonary disease and lung cancers were 18-24% while elevated in small-cell lung carcinoma/large-cell neuroendocrine carcinoma (59 ± 10%). In BPNETs on postoperative Day 30, NETest decreased by 60% (P < 0.001). Chromogranin A was elevated in only 40% of carcinoids and not altered by surgery., Conclusions: Blood NET gene levels accurately identified BPNETs (100%) and differentiated these from controls, benign and malignant lung disease. Progressive disease could be identified and surgical resection verified. Chromogranin A had no clinical utility. Monitoring NET transcript levels in blood will facilitate management by detecting residual tumour and identifying progressive disease., (© The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Background/aims: A key issue in gastroenteropancreatic neuroendocrine tumors (GEP-NETs) is early identification and prediction of disease progression. Clinical evaluation and imaging are limited due to the lack of sensitivity and disease indolence. We assessed the NETest as a predictive and prognostic marker of progression in a long-term follow-up study., Methods: GEP-NETs (n = 34) followed for a median 4 years (2.2-5.4) were evaluated. WHO tumor grade/stage grade 1: n = 17, grade 2: n = 14, grade 3: n = 1 (for 2, no grade was available); 31 (91%) were stage IV. Baseline and longitudinal imaging and blood biomarkers were available in all, and progression was defined per standard clinical protocols (RECIST 1.0). The NETest was measured by quantitative PCR of blood and multianalyte algorithmic analysis (disease activity scaled 0-100% with low <40% and high activity risk cutoffs >80%); chromogranin A (CgA) was measured by radioimmunoassay (normal <150 µg/l); progression-free survival (PFS) was analyzed by Cox proportional-hazard regression and Kaplan-Meier analysis., Results: At baseline, 100% were NETest positive, and CgA was elevated in 50%. The only baseline variable (Cox modeling) associated with PFS was NETest (hazard ratio = 1.022, 95% confidence interval = 1.005-1.04; p < 0.012). Using Kaplan-Meier analyses, the baseline NETest (>80%) was significantly associated (p = 0.01) with disease progression (median PFS 0.68 vs. 2.78 years with <40% levels). The NETest was more informative (96%) than CgA changes (>25%) in consistently predicting disease alterations (40%, p < 2 × 10-5, χ2 = 18). The NETest had an earlier time point change than imaging (1.02 ± 0.15 years). Baseline NETest levels >40% in stable disease were 100% prognostic of disease progression versus CgA (χ2 = 5, p < 0.03). Baseline NETest values <40% accurately (100%) predicted stability over 5 years (p = 0.05, χ2 = 3.8 vs. CgA)., Conclusion: The NETest correlated with a well-differentiated GEP-NET clinical status. The NETest has predictive and prognostic utility for GEP-NETs identifying clinically actionable alterations ∼1 year before image-based evidence of progression., (© 2016 S. Karger AG, Basel.)

Background: Peptide receptor radionuclide therapy (PRRT) is an effective method for treating neuroendocrine tumors (NETs). It is limited, however, in the prediction of individual tumor response and the precise and early identification of changes in tumor size. Currently, response prediction is based on somatostatin receptor expression and efficacy by morphological imaging and/or chromogranin A (CgA) measurement. The aim of this study was to assess the accuracy of circulating NET transcripts as a measure of PRRT efficacy, and moreover to identify prognostic gene clusters in pretreatment blood that could be interpolated with relevant clinical features in order to define a biological index for the tumor and a predictive quotient for PRRT efficacy., Methods: NET patients (n = 54), M: F 37:17, median age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with (177)Lu-based-PRRT (cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low-grade (G1/G2; bronchial typical/atypical), 31/49 (18)FDG positive and 39/54 progressive. Disease status was assessed by RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription PCR (qRT-PCR) and multianalyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analyses., Statistical Analyses: chi-square, non-parametric measurements, multiple regression, receiver operating characteristic and Kaplan-Meier survival., Results: The disease control rate was 72 %. Median PFS was not achieved (follow-up: 1-33 months, median: 16). Only grading was associated with response (p < 0.01). At baseline, 94 % of patients were NETest-positive, while CgA was elevated in 59 %. NETest accurately (89 %, χ(2) = 27.4; p = 1.2 × 10(-7)) correlated with treatment response, while CgA was 24 % accurate. Gene cluster expression (growth-factor signalome and metabolome) had an AUC of 0.74 ± 0.08 (z-statistic = 2.92, p < 0.004) for predicting response (76 % accuracy). Combination with grading reached an AUC: 0.90 ± 0.07, irrespective of tumor origin. Circulating transcripts correlated accurately (94 %) with PRRT responders (SD+PR+CR; 97 %) vs. non-responders (91 %)., Conclusions: Blood NET transcript levels and the predictive quotient (circulating gene clusters+grading) accurately predicted PRRT efficacy. CgA was non-informative.

Background: Surgery is the only curative treatment for gastroenteropancreatic neuroendocrine tumors (GEP-NETs), but the prediction of residual disease/recurrence is limited in the absence of optimal biomarkers. We examined whether a blood-based multianalyte neuroendocrine gene transcript assay (NETest) would define tumor cytoreduction and therapeutic efficacy., Methods: The NETest is a polymerase chain reaction-based analysis of 51 genes. Disease activity is scaled 0-100%; minimal <14%, low 14-47%, and high >47%. A total of 35 GEP-NETs in 2 groups were evaluated. I: after surgery (R0, n = 15; residual, n = 12); II: nonsurgery (n = 8: embolization with gel-foam alone [bland: n = 3]), transarterial chemoembolization (n = 2), and radiofrequency embolization (n = 3). Measurement (quantitative real-time-polymerase chain reaction) and chromogranin A (CgA; enzyme-linked immunosorbent assay) were undertaken preoperatively and 1 month after treatment., Results: NETest score was increased in 35 (100%) preoperatively; 14 (40%) had increased CgA (χ(2) = 30, P < 2 × 10(-8)). Resection reduced NETest from 80 ± 5% to 29% ± 5, (P < .0001). CgA decrease was insignificant (14.3 ± 1.6 U/L to 12.2 ± 1.7 U/L). NETest decreases correlated with diminished tumor volume (R(2) = 0.29, P = .03). Cytoreduction significantly reduced NETest from 82 ± 3% to 41% ± 6, P < .0001). CgA was not decreased (21.4 ± 5.5 U/L to 18.4 ± 10.1 U/L). Four (36%) of 11 R0s with increased NETest at 1 month developed positive imaging (sensitivity 100%, specificity 20%). One hundred percent (ablated group) were transcript- and image-positive., Conclusion: Blood NET transcripts delineate surgical resection/cytoreduction and facilitate identification of residual disease., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objectives: Current monoanalyte blood-based biomarkers for the diagnosis and follow-up of neuroendocrine tumors (NETs) do not achieve satisfactory metrics of sensitivity and specificity. We report the sensitivity and selectivity of the PCR-based test, the NETest, to detect tumors with reference to other benign and malignant gastrointestinal diseases., Methods: A total of 179 cases (gastrointestinal tumors: n=81; pancreatic disease: n=98) were prospectively collected and assessed using the NETest or chromogranin A (CgA) to determine metrics for detecting small intestinal and pancreatic NETs., Results: For intestinal carcinoids, the accuracy of the NETest was 93% (all NETs positive and 3 (12%) colorectal tumors were positive). CgA was positive in 80%, but 29% (n=7) of colorectal cancers were CgA positive. For pancreatic disease, the NETest accuracy was 94% (96% NETs positive, 2 (6%) of intraductal papillary mucinous neoplasms (IPMNs) were positive). The accuracy of CgA was 56% (29% of pancreatic NETs were CgA positive). Overall, the NETest was significantly more sensitive than CgA for the detection of small intestinal (area under the curve 0.98 vs. 0.75 P<0.0001) and pancreatic NETs (0.94 vs. 0.52, P<0.0001). NETest scores were elevated (P<0.05) in extensive disease and were more accurate (76-80%) than CgA levels (20-32%). The metrics of the multianalyte NETest met the performance criteria proposed by the National Institutes of Health for biomarkers, whereas CgA measurement did not., Conclusions: This study demonstrates that a blood-based multianalyte NET gene transcript measurement of well-differentiated small intestinal and pancreatic neuroendocrine tumor disease is sensitive and specific and outperforms the current monoanalyte diagnostic strategy of plasma CgA measurement.

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 (R(2)=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 from PD (area under the curve (AUC)=0.81). Integration with blood-algorithm amplified the AUC 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., (© 2015 Society for Endocrinology.)

A critical requirement in neuroendocrine tumor (NET) management is a sensitive, specific and reproducible blood biomarker test. We evaluated a PCR-based 51 transcript signature (NETest) and compared it to chromogranin A (CgA), pancreastatin (PST) and neurokinin A (NKA). The multigene signature was evaluated in two groups: i) a validation set of 40 NETs and controls and ii) a prospectively collected group of NETs (n=41, 61% small intestinal, 50% metastatic, 44% currently treated and 41 age-sex matched controls). Samples were analyzed by a two-step PCR (51 marker genes) protocol and ELISAs for CgA, PST and NKA. Sensitivity comparisons included χ(2), non-parametric measurements, ROC curves and predictive feature importance (PFAI) analyses. NETest identified 38 of 41 NETs. Performance metrics were: sensitivity 92.8%, specificity 92.8%, positive predictive value 92.8% and negative predictive value 92.8%. Single analyte ELISA metrics were: CgA 76, 59, 65, and 71%; PST 63, 56, 59, and 61% and NKA 39, 93, 84, and 60%. The AUCs (ROC analysis) were: NETest: 0.96±0.025, CgA: 0.67±0.06, PST 0.56±0.06, NKA: 0.66±0.06. NETest significantly outperformed single analyte tests (area differences: 0.284-0.403, Z-statistic 4.85-5.9, P<0.0001). PFAI analysis determined NETest had most value (69%) in diagnosis (CgA (13%), PST (9%), and NKA (9%)). Test data were consistent with the validation set (NETest >95% sensitivity and specificity, AUC =0.98 vs single analytes: 59-67% sensitivity, AUCs: 0.58-0.63). The NETest is significantly more sensitive and efficient (>93%) than single analyte assays (CgA, PST or NKA) in NET diagnosis. Blood-based multigene analytic measurement will facilitate early detection of disease recurrence and can predict therapeutic efficacy., (©  2014 Society for Endocrinology.)

The identification of accurate harbingers of disease status and therapeutic efficacy are critical requirements in precise diagnosis and effective management. Initially, tissue analysis was regarded as ideal but invasive strategies represent risk compared with peripheral blood sampling. Thus far, most biomarkers, whether in tissue or blood/urine, have been single analytes with varying degrees of sensitivity and specificity. Some analytes have not exhibited robust metrics or have lacked methodological rigor. Neuroendocrine disease represents an area of dire biomarker paucity since the individual biomarkers (gastrin, insulin, etc.) are not widely applicable to the diverse types of neuroendocrine neoplasia. Broad-spectrum markers such as chromogranin A have limitations in sensitivity, specificity and reproducibility. Monoanalytes cannot define the multiple variables (proliferation, metabolic activity, invasive potential, metastatic propensity) that constitute tumor growth. The restricted status of the neuroendocrine neoplasia field has resulted in a lack of comprehensive knowledge of the molecular and cellular biology of the disease, with tardy application of innovative technology. This overview examines limitations in current practice and describes contemporary viable strategies under evaluation, including the identification of novel analytes (gene transcripts, microRNA), circulating tumor cells and metabolic imaging agents that identify disease. Novel requirements are necessary to develop biomathematical algorithms for synchronous calibration of multiple molecular markers and predictive nomograms that interface biological variables to delineate disease progress or treatment efficacy. Optimally, the application of novel techniques and amalgamations of multianalyte assessment will provide a personalized molecular disease signature extrapolative of neuroendocrine neoplasia status and likelihood of progression and predictive of therapeutic opportunity.

Incidence of neuroendocrine tumors (NETs) is increasing (approximately 6%/year), but clinical presentation is nonspecific, resulting in delays in diagnosis (5-7 years; approximately 70% have metastases). This reflects absence of a sensitive plasma marker. The aim of this study is to investigate whether detection of circulating messenger RNA (mRNA) alone or in combination with circulating NET-related hormones and growth factors can detect gastrointestinal NET disease. The small intestinal (SI) NET cell line KRJ-I was used to define the sensitivity of real-time polymerase chain reaction (PCR) for mRNA detection in blood. NSE, Tph-1, and VMAT ( 2 ) transcripts were identified from one KRJ-I cell/ml blood. mRNA from the tissue and plasma of SI-NETs (n = 12) and gastric NETs (n = 7), and plasma from healthy controls (n = 9) was isolated and real-time PCR performed. Tph-1 was a specific marker of SI-NETs (58%, p < 0.03) whereas CgA transcripts did not differentiate tumors from controls. Patients with metastatic disease expressed more marker transcripts than localized tumors (75% versus 18%, p < 0.02). Plasma 5-hydroxytryptamine (5-HT), chromogranin A (CgA), ghrelin, and connective tissue growth factor (CTGF) fragments were measured, combined with mRNA levels, and a predictive mathematical model for NET diagnosis developed using decision trees. The sensitivity and specificity to diagnose SI-NETs and gastric NETs were 81.2% and 100%, and 71.4% and 55.6%, respectively. We conclude that mRNA from one NET cell/ml blood can be detected. Circulating plasma Tph-1 is a promising marker gene for SI-NET disease (specificity 100%) while an increased number of marker transcripts (>2) correlated with disease spread. Including NET-related circulating hormones and growth factors in the algorithm increased the sensitivity of detection of SI-NETs from 58 to 82%.