The clinical utility of circulating neuroendocrine gene transcript analysis in well-differentiated paragangliomas and pheochromocytomas

Initial validation of the clinical significance of the NETest in Japanese gastroenteropancreatic neuroendocrine tumor patients

As novel biomarkers for gastroenteropancreatic neuroendocrine tumors (GEPNET) are in demand, we aimed to validate the clinical value of the NETest in Japanese patients. Between 2021 and 2023, blood and clinical data were collected from patients with GEPNET. Among 35 patients (median age: 59 [49-66] years), 27 cases originated from the pancreas and eight from the gastrointestinal tract. Of 69 samples sent to the laboratory, 56 (81.2%) underwent NETest. The diagnostic sensitivity was 97.1%. Among three patients who underwent R0 resection and four treated with peptide receptor radionuclide therapy, the changes in NETest scores closely correlated with disease progression. The NETest demonstrated high diagnostic efficacy and accurate therapeutic monitoring capabilities in a Japanese population.

Biochemical Markers for Neuroendocrine Tumors: Traditional Circulating Markers and Recent Development-A Comprehensive Review

Neuroendocrine neoplasms (NENs) are a heterogeneous group of neoplasms presenting unique challenges in diagnosis and management. Traditional markers such as chromogranin A (CgA), pancreatic polypeptide (PP), and neuron-specific enolase (NSE) have limitations in terms of specificity and sensitivity. Specific circulating markers such as serotonin and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) and various gastrointestinal hormones such as gastrin, glucagon, somatostatin, and vasoactive intestinal peptide (VIP) have a role in identifying functional NENs. Recent advances in molecular and biochemical markers, also accounting for novel genomic and proteomic markers, have significantly improved the landscape for the diagnosis and monitoring of NENs. This review discusses these developments, focusing on both traditional markers such as CgA and NSE, as well as specific hormones like gastrin, insulin, somatostatin, glucagon, and VIP. Additionally, it covers emerging genomic and proteomic markers that are shaping current research. The clinical applicability of these markers is highlighted, and their role in improving diagnostic accuracy, predicting surgical outcomes, and monitoring response to treatment is demonstrated. The review also highlights the need for further research, including validation of these markers in larger studies, development of standardized assays, and integration with imaging techniques. The evolving field of biochemical markers holds promise for improving patient outcomes in the treatment of NENs, although challenges in standardization and validation remain.

Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas

In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data.

Biochemical Assessment of Pheochromocytoma and Paraganglioma

Pheochromocytoma and paraganglioma (PPGL) require prompt consideration and efficient diagnosis and treatment to minimize associated morbidity and mortality. Once considered, appropriate biochemical testing is key to diagnosis. Advances in understanding catecholamine metabolism have clarified why measurements of the O-methylated catecholamine metabolites rather than the catecholamines themselves are important for effective diagnosis. These metabolites, normetanephrine and metanephrine, produced respectively from norepinephrine and epinephrine, can be measured in plasma or urine, with choice according to available methods or presentation of patients. For patients with signs and symptoms of catecholamine excess, either test will invariably establish the diagnosis, whereas the plasma test provides higher sensitivity than urinary metanephrines for patients screened due to an incidentaloma or genetic predisposition, particularly for small tumors or in patients with an asymptomatic presentation. Additional measurements of plasma methoxytyramine can be important for some tumors, such as paragangliomas, and for surveillance of patients at risk of metastatic disease. Avoidance of false-positive test results is best achieved by plasma measurements with appropriate reference intervals and preanalytical precautions, including sampling blood in the fully supine position. Follow-up of positive results, including optimization of preanalytics for repeat tests or whether to proceed directly to anatomic imaging or confirmatory clonidine tests, depends on the test results, which can also suggest likely size, adrenal vs extra-adrenal location, underlying biology, or even metastatic involvement of a suspected tumor. Modern biochemical testing now makes diagnosis of PPGL relatively simple. Integration of artificial intelligence into the process should make it possible to fine-tune these advances.

Management of pheochromocytomas and paragangliomas: Review of current diagnosis and treatment options

Pheochromocytomas (PCCs) are rare neuroendocrine tumors derived from the chromaffin cells of the adrenal medulla. When these tumors have an extra-adrenal location, they are called paragangliomas (PGLs) and arise from sympathetic and parasympathetic ganglia, particularly of the para-aortic location. Up to 25% of PCCs/PGLs are associated with inherited genetic disorders. The majority of PCCs/PGLs exhibit indolent behavior. However, according to their affiliation to molecular clusters based on underlying genetic aberrations, their tumorigenesis, location, clinical symptomatology, and potential to metastasize are heterogenous. Thus, PCCs/PGLs are often associated with diagnostic difficulties. In recent years, extensive research revealed a broad genetic background and multiple signaling pathways leading to tumor development. Along with this, the diagnostic and therapeutic options were also expanded. In this review, we focus on the current knowledge and recent advancements in the diagnosis and treatment of PCCs/PGLs with respect to the underlying gene alterations while also discussing future perspectives in this field.

NETest is superior to chromogranin A in neuroendocrine neoplasia: a prospective ENETS CoE analysis

INTRODUCTION

The absence of a reliable, universal biomarker is a significant limitation in neuroendocrine neoplasia (NEN) management. We prospectively evaluated two CgA assays, (NEOLISA, EuroDiagnostica) and (CgA ELISA, Demeditec Diagnostics (DD)) and compared the results to the NETest.

METHODS

NEN cohort (n = 258): pancreatic, n = 67; small intestine, n = 40; appendiceal, n = 10; rectal, n = 45; duodenal, n = 9; gastric, n = 44; lung, n = 43. Image-positive disease (IPD) (n = 123), image & histology- negative (IND) (n = 106), and image-negative and histology positive (n = 29). CgA metrics: NEOLISA, ULN: 108 ng/mL, DD: ULN: 99 ng/mL. Data mean ± s.e.m. NETest: qRT-PCR - multianalyte analyses, ULN: 20. All samples de-identified and assessed blinded. Statistics: Mann-Whitney U-test, Pearson correlation and McNemar-test.

RESULTS

CgA positive in 53/258 (NEOLISA), 32 (DD) and NETest-positive in 157/258. In image- positive disease (IPD, n = 123), NEOLISA-positive: 33% and DD: 19%. NETest-positive: 122/123 (99%; McNemar's Chi2= 79-97, P < 0.0001). NEOLISA was more accurate than DD (P = 0.0003). In image- negative disease (IND), CgA was NEOLISA-positive (11%), DD (8%), P = NS, and NETest (33%). CgA assays could not distinguish progressive (PD) from stable disease (SD) or localized from metastatic disease (MD). NETest was significantly higher in PD (47 ± 5) than SD (29 ± 1, P = 0.0009). NETest levels in MD (35 ± 2) were elevated vs localized disease (24 ± 1.3, P = 0.008).

CONCLUSIONS

NETest, a multigenomic mRNA biomarker, was ~99% accurate in the identification of NEN disease. The CgA assays detected NEN disease in 19-33%. Multigenomic blood analysis using NETest is more accurate than CgA and should be considered the biomarker standard of care.

Prospective Evaluation of the NETest as a Liquid Biopsy for Gastroenteropancreatic and Bronchopulmonary Neuroendocrine Tumors: An ENETS Center of Excellence Experience

BACKGROUND

There is a substantial unmet clinical need for an accurate and effective blood biomarker for neuroendocrine neoplasms (NEN). We therefore evaluated, under real-world conditions in an ENETS Center of Excellence (CoE), the clinical utility of the NETest as a liquid biopsy and compared its utility with chromogranin A (CgA) measurement.

METHODS

The cohorts were: gastroenteropancreatic NEN (GEP-NEN; n = 253), bronchopulmonary NEN (BPNEN; n = 64), thymic NEN (n = 1), colon cancer (n = 37), non-small-cell lung cancer (NSCLC; n = 63), benign lung disease (n = 59), and controls (n = 86). In the GEPNEN group, 164 (65%) had image-positive disease (IPD, n = 135) or were image-negative but resection-margin/biopsy-positive (n = 29), and were graded as G1 (n = 106), G2 (n = 49), G3 (n = 7), or no data (n = 2). The remainder (n = 71) had no evidence of disease (NED). In the BPNEN group, 43/64 (67%) had IPD. Histology revealed typical carcinoids (TC, n = 14), atypical carcinoids (AC, n = 14), small-cell lung cancer (SCLC, n = 11), and large-cell neuroendocrine carcinoma (LCNEC, n = 4). Disease status (stable or progressive) was evaluated according to RECIST v1.1. Blood sampling involved NETest (n = 563) and NETest/CgA analysis matched samples (n = 178). NETest was performed by PCR (on a scale of 0-100), with a score ≥20 reflecting a disease-positive status and >40 reflecting progressive disease. CgA positivity was determined by ELISA. Samples were deidentified and measurements blinded. The Kruskal-Wallis, Mann-Whitney U, and McNemar tests, and the area under the curve (AUC) of the receiver-operating characteristics (ROC) were used in the statistical analysis.

RESULTS

In the GEPNEN group, NETest was significantly higher (34.4 ± 1.8, p < 0.0001) in disease-positive patients than in patients with NED (10.5 ± 1, p < 0.0001), colon cancer patients (18 ± 4, p < 0.0004), and controls (7 ± 0.5, p < 0.0001). Sensitivity for detecting disease compared to controls was 89% and specificity was 94%. NETest levels were increased in G2 vs. G1 (39 ± 3 vs. 32 ± 2, p = 0.02) and correlated with stage (localized: 26 ± 2 vs. regional/distant: 40 ± 3, p = 0.0002) and progression (55 ± 5 vs. 34 ± 2 in stable disease, p = 0.0005). In the BPNEN group, diagnostic sensitivity was 100% and levels were significantly higher in patients with bronchopulmonary carcinoids (BPC; 30 ± 1.3) who had IPD than in controls (7 ± 0.5, p < 0.0001), patients with NED (24.1 ± 1.3, p < 0.005), and NSCLC patients (17 ± 3, p = 0.0001). NETest levels were higher in patients with poorly differentiated BPNEN (LCNEC + SCLC; 59 ± 7) than in those with BPC (30 ± 1.3, p = 0.0005) or progressive disease (57.8 ± 7), compared to those with stable disease (29.4 ± 1, p < 0.0001). The AUC for differentiating disease from controls was 0.87 in the GEPNEN group and 0.99 in BPC patients (p < 0.0001). Matched CgA analysis was performed in 178 patients. In the GEPNEN group (n = 135), NETest was significantly more accurate for detecting disease (99%) than CgA positivity (53%; McNemar test χ2 = 87, p < 0.0001). In the BPNEN group (n = 43), NETest was significantly more accurate for disease detection (100%) than CgA positivity (26%; McNemar's test χ2 = 30, p < 0.0001).

CONCLUSIONS

The NETest is an accurate diagnostic for GEPNEN and BPNEN. It exhibits tumor biology correlation with grading, staging, and progression. CgA as a biomarker is significantly less accurate than NETest. The NETest has substantial clinical utility that can facilitate patient management.

Neuroendocrine Tumor Omic Gene Cluster Analysis Amplifies the Prognostic Accuracy of the NETest

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.

Transcriptome Analysis of lncRNAs in Pheochromocytomas and Paragangliomas

CONTEXT

Pheochromocytomas and paragangliomas (PPGLs) are neuroendocrine tumors explained by germline or somatic mutations in about 70% of cases. Patients with SDHB mutations are at high risk of developing metastatic disease, yet no reliable tumor biomarkers are available to predict tumor aggressiveness.

OBJECTIVE

We aimed at identifying long noncoding RNAs (lncRNAs) specific for PPGL molecular groups and metastatic progression.

DESIGN AND METHODS

To analyze the expression of lncRNAs, we used a mining approach of transcriptome data from a well-characterized series of 187 tumor tissues. Clustering consensus analysis was performed to determine a lncRNA-based classification, and informative transcripts were validated in an independent series of 51 PPGLs. The expression of metastasis-related lncRNAs was confirmed by RT-qPCR. Receiver operating characteristic (ROC) curve analysis was used to estimate the predictive accuracy of potential markers.

MAIN OUTCOME MEASURE

Univariate/multivariate and metastasis-free survival (MFS) analyses were carried out for the assessment of risk factors and clinical outcomes.

RESULTS

Four lncRNA-based subtypes strongly correlated with mRNA expression clusters (chi-square P-values from 1.38 × 10-32 to 1.07 × 10-67). We identified one putative lncRNA (GenBank: BC063866) that accurately discriminates metastatic from benign tumors in patients with SDHx mutations (area under the curve 0.95; P = 4.59 × 10-05). Moreover, this transcript appeared as an independent risk factor associated with poor clinical outcome of SDHx carriers (log-rank test P = 2.29 × 10-05).

CONCLUSION

Our findings extend the spectrum of transcriptional dysregulations in PPGL to lncRNAs and provide a novel biomarker that could be useful to identify potentially metastatic tumors in patients carrying SDHx mutations.

The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors

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.

An Assessment of Circulating Chromogranin A as a Biomarker of Bronchopulmonary Neuroendocrine Neoplasia: A Systematic Review and Meta-Analysis

BACKGROUND

Management of bronchopulmonary neuroendocrine neoplasia (NEN; pulmonary carcinoids [PCs], small-cell lung cancer [SCLC], and large cell neuroendocrine carcinoma) is hampered by the paucity of biomarkers. Chromogranin A (CgA), the default neuroendocrine tumor biomarker, has undergone wide assessment in gastroenteropancreatic neuroendocrine tumors.

OBJECTIVES

To evaluate CgA in lung NEN, define its clinical utility as a biomarker, assess its diagnostic, prognostic, and predictive efficacy, as well as its accuracy in the identification of disease recurrence.

METHODS

A systematic review of PubMed was undertaken using the preferred reporting items for systematic reviews and meta-analyses guidelines. No language restrictions were applied. Overall, 33 original scientific papers and 3 case reports, which met inclusion criteria, were included in qualitative analysis, and meta-analysis thereafter. All studies, except 2, were retrospective. Meta-analysis statistical assessment by generic inverse variance methodology.

RESULTS

Ten different CgA assay types were reported, without consistency in the upper limit of normal (ULN). For PCs (n = 16 studies; median patient inclusion 21 [range 1-200, total: 591 patients]), the CgA diagnostic sensitivity was 34.5 ± 2.7% with a specificity of 93.8 ± 4.7. CgA metrics were not available separately for typical or atypical carcinoids. CgA >100 ng/mL (2.7 × ULN) and >600 ng/mL (ULN unspecified) were anecdotally prognostic for overall survival (n = 2 retrospective studies). No evidence was presented for predicting treatment response or identifying post-surgery residual disease. For SCLC (n = 19 studies; median patient inclusion 23 [range 5-251, total: 1,241 patients]), the mean diagnostic sensitivity was 59.9 ± 6.8% and specificity 79.4 ± 3.1. Extensive disease typically exhibited higher CgA levels (diagnostic accuracy: 61 ± 2.5%). An elevated CgA was prognostic for overall survival (n = 4 retrospective studies). No prospective studies evaluating predictive benefit or prognostic utility were identified.

CONCLUSION

The available data are scarce. An assessment of all published data showed that CgA exhibits major limitations as an effective and accurate biomarker for either PC or SCLC. Its utility especially for localized PC/limited SCLC (when surgery is potentially curative), is limited. The clinical value of CgA remains to be determined. This requires validated, well-constructed, multicenter, prospective, randomized studies. An assessment of all published data indicates that CgA does not exhibit the minimum required metrics to function as a clinically useful biomarker for lung NENs.

Genetic and epigenetic differences of benign and malignant pheochromocytomas and paragangliomas (PPGLs)

Pheochromocytomas and paragangliomas (PPGLs) are tumors arising from the adrenal medulla and sympathetic/parasympathetic paraganglia, respectively. According to Th e Cancer Genome Atlas (TCGA), approximately 40% of PPGLs are due to germ line mutations in one of 16 susceptibility genes, and a further 30% are due to somatic alterations in at least seven main genes (VHL, EPAS1, CSDE1, MAX, HRAS, NF1, RET, and possibly KIF1B). Th e diagnosis of malignant PPGL was straight forward in most cases as it was defined as presence of PPGL in non-chromaffin tissues. Accordingly, there is an extreme need for new diagnostic marker(s) to identify tumors with malignant prospective. Th e aim of this study was to review all suggested genetic and epigenetic alterations that are remarkably different between benign and malignant PPGLs. It seems that more than two genetic mutation clusters in PPGLs and other genetic and methylation biomarkers could be targeted for malignancy discrimination in different studies.

Emerging molecular markers of metastatic pheochromocytomas and paragangliomas

Metastatic pheochromocytoma/paraganglioma (PPGL) represents a major clinical challenge due to limitations in accurate diagnostic tools and effective treatments. Currently, patients classified at high-risk by means of clinical, biochemical and genetic criteria, require a lifelong monitoring, while it remains difficult to determine the metastatic potential of PPGL only on the basis of histopathological features. Thus, tumor molecular markers that improve the risk stratification of these patients are needed. In the past few years, we have witnessed an unprecedented molecular characterization of PPGL, which led to the emergence of promising candidate biomarkers predictive of metastatic behavior. Here, we briefly discuss these breakthroughs and provide some insights for the prospective implementation of molecular markers of metastatic PPGL in the clinical setting in years to come.

The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors

The neuroendocrine neoplasms test (NETest) is a multianalyte liquid biopsy that measures neuroendocrine tumor gene expression in blood. This unique signature precisely defines the biological activity of an individual tumor in real time. The assay meets the 3 critical requirements of an optimal biomarker: diagnostic accuracy, prognostic value, and predictive therapeutic assessment. NETest performance metrics are sensitivity and specificity and in head-to-head comparison are 4-fold to 10-fold more accurate than chromogranin A. NETest accurately identifies completeness of surgery and response to somatostatin analogs. Clinical registry data demonstrate significant clinical utility in watch/wait programs.

When should genetic testing be performed in patients with neuroendocrine tumours?

Neuroendocrine tumours (NETs) are a heterogenous group of tumours arising from neuroendocrine cells in several sites around the body. They include tumours of the gastroenteropancreatic system, phaeochromocytoma and paraganglioma and medullary thyroid cancer. In recent years, it has become increasingly apparent that a number of these tumours arise as a result of germline genetic mutations and are inherited in an autosomal dominant pattern. The number of genes implicated is increasing rapidly. Identifying which patients are likely to have a germline mutation enables clinicians to counsel patients adequately about their future disease risk, and allows for earlier detection of at-risk patients through family screening. The institution of screening and surveillance programmes may in turn lead to a major shift in presentation patterns for some of these tumours. In this review, we examine the features which may lead a clinician to suspect that a patient may have an inherited cause of a NET and we outline which underlying conditions should be suspected. We also discuss what type of screening may be appropriate in a variety of situations.