Utility of a ready-to-use PCR system for neuroendocrine tumor diagnosis

Clinical Sensitivity and Specificity of the PROSTest in an American Cohort

INTRODUCTION

Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the United States, following skin cancer, with an incidence rate of 112.7 per 100,000 men per year. The need for a reliable, non-invasive diagnostic tool for early PCa detection (screening, biochemical residual disease) remains unmet due to the limitations of PSA testing, which often leads to overdiagnosis and overtreatment. The PROSTest is a novel, blood-based qPCR assay that assesses gene expression to diagnose PCa and predict patient outcomes to different treatments. This study aimed to validate the sensitivity and specificity of the PROSTest in a diverse cohort of US-based PCa patients compared to healthy controls.

MATERIALS AND METHODS

This prospective study included 143 PCa patients and 92 healthy controls. Blood samples were collected, and the PROSTest was conducted following RNA isolation and cDNA production, using a predefined 27-gene algorithm to provide a binary output. The assay's sensitivity and specificity were evaluated using receiver operating characteristic (ROC) analysis, with a 50% score cut-off distinguishing PCa from non-PCa patients. Analytical reproducibility was assessed with intra- and inter-assay comparisons of Ct values and PROSTest scores.

RESULTS

The PROSTest demonstrated a sensitivity of 94% (95% CI 89-98%) and a specificity of 88% (95% CI 80-94%) in distinguishing PCa patients from controls, with an area under the ROC curve (AUROC) of 0.97. The false positive rate among controls was 12%. Intra- and inter-assay reproducibility was confirmed with no significant differences in Ct values or PROSTest scores between operators or assays. PROSTest scores were significantly higher in PCa patients compared to controls and in those undergoing treatment versus untreated patients.

CONCLUSION

This validation study confirms the high sensitivity and specificity of the PROSTest in detecting PCa in a diverse USA cohort. The assay's robustness and reproducibility support its potential as a reliable diagnostic tool for PCa detection and monitoring. Further studies are warranted to evaluate its utility across broader populations and treatment settings.

NETest® 2.0-A decade of innovation in neuroendocrine tumor diagnostics

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are challenging to diagnose and manage. Because there is a critical need for a reliable biomarker, we previously developed the NETest, a liquid biopsy test that quantifies the expression of 51 neuroendocrine tumor (NET)-specific genes in blood using real-time PCR (NETest 1.0). In this study, we have leveraged our well-established laboratory approach (blood collection, RNA isolation, qPCR) with contemporary supervised machine learning methods and expanded training and testing sets to improve the discrimination and calibration of the NETest algorithm (NETest 2.0). qPCR measurements of RNA-stabilized blood-derived gene expression of 51 NET markers were used to train two supervised classifiers. The first classifier trained on 78 Controls and 162 NETs, distinguishing NETs from controls; the second, trained on 134 stable disease samples, 61 progressive disease samples, differentiated stable from progressive NET disease. In all cases, 80% of data was retained for model training, while remaining 20% were used for performance evaluation. The predictive performance of the AI system was assessed using sensitivity, specificity, and Area under Received Operating Characteristic curves (AUROC). The algorithm with the highest performance was retained for validation in two independent validation sets. Validation Cohort #I consisted of 277 patients and 186 healthy controls from the United States, Latin America, Europe, Africa and Asia, while Validation Cohort #II comprised 291 European patients from the Swiss NET Registry. A specificity cohort of 147 gastrointestinal, pancreatic and lung malignancies (non-NETs) was also evaluated. NETest 2.0 Algorithm #1 (Random Forest/gene expression normalized to ATG4B) achieved an AUROC of 0.91 for distinguishing NETs from controls (Validation Cohort #I), with a sensitivity of 95% and specificity of 81%. In Validation Cohort #II, 92% of NETs with image-positive disease were detected. The AUROC for differentiating NETs from other malignancies was 0.95; the sensitivity was 92% and specificity 90%. NETest 2.0 Algorithm #2 (Random Forest/gene expression normalized to ALG9) demonstrated an AUROC of 0.81 in Validation Cohort #I and 0.82 in Validation Cohort #II for differentiating stable from progressive disease, with specificities of 81% and 82%, respectively. Model performance was not affected by gender, ethnicity or age. Substantial improvements in performance for both algorithms were identified in head-to-head comparisons with NETest 1.0 (diagnostic: p = 1.73 × 10-9; prognostic: p = 1.02 × 10-10). NETest 2.0 exhibited improved diagnostic and prognostic capabilities over NETest 1.0. The assay also demonstrated improved sensitivity for differentiating NETs from other gastrointestinal, pancreatic and lung malignancies. The validation of this tool in geographically diverse cohorts highlights their potential for widespread clinical use.

A blood-based liquid biopsy analyzing soluble immune checkpoints and cytokines identifies distinct neuroendocrine tumors

BACKGROUND

Neuroendocrine neoplasms (NENs) comprise a group of rare tumors originating from neuroendocrine cells, which are present in both endocrine glands and scattered throughout the body. Due to their scarcity and absence of specific markers, diagnosing NENs remains a complex challenge. Therefore, new biomarkers are required, ideally, in easy-to-obtain blood samples.

METHODS

A panel of blood soluble immune checkpoints (sPD-L1, sPD-L2, sPD-1, sCD25, sTIM3, sLAG3, Galectin-9, sCD27, sB7.2 and sSIGLEC5) and cytokines (IL4, IL6, IP10 and MCP1) was quantified in a cohort of 139 NENs, including 29 pituitary NENs, 46 pheochromocytomas and paragangliomas, and 67 gastroenteropancreatic and pulmonary (GEPP) NENs, as well as in 64 healthy volunteers (HVs). The potential of these circulating immunological parameters to distinguish NENs from HVs, differentiate among various NENs subtypes, and predict their prognosis was evaluated using mathematical regression models. These immunological factors-based models generated scores that were evaluated by Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) analyses. Correlations between these scores and clinical data were performed. From these analyses, a minimal signature emerged, comprising the five shared immunological factors across the models: sCD25, sPD-L2, sTIM3, sLAG3, and Galectin-9. This refined signature was evaluated, validated, and checked for specificity against non-neuroendocrine tumors, demonstrating its potential as a clinically relevant tool for identifying distinct NENs.

RESULTS

Most of the immunological factors analyzed showed specific expression patterns among different NENs. Scores based on signatures of these factors identified NENs with high efficiency, showing AUCs ranging between 0.948 and 0.993 depending on the comparison, and accuracies between 92.52% and 95.74%. These scores illustrated biological features of NENs including the similarity between pheochromocytomas and paragangliomas, the divergence between gastrointestinal and pulmonary NENs, and correlated with clinical features. Furthermore, the models demonstrated strong performance in distinguishing metastatic and exitus GEPP NENs, achieving sensitivities and specificities ranging from 80.95% to 88.89%. Additionally, an easy-to-implement minimal signature successfully identified all analyzed NENs with AUC values exceeding 0.900, and accuracies between 84.11% and 93.12%, which was internally validated by a discovery and validation randomization strategy. These findings highlight the effectiveness of the models and minimal signature in accurately diagnosing and differentiating NENs.

CONCLUSIONS

The analysis of soluble immunological factors in blood presents a promising liquid biopsy approach for identifying NENs, delivering critical insights for both prognosis and diagnosis. This study serves as a proof-of-concept for an innovative clinical tool that holds the potential to transform the management of these rare malignancies, providing a non-invasive and effective method for early detection and disease monitoring.

NETest in advanced high-grade gastroenteropancreatic neuroendocrine neoplasms

Molecular blood biomarkers are lacking for high-grade (HG) gastroenteropancreatic (GEP) neuroendocrine neoplasms (NEN). To histologically distinguish between neuroendocrine carcinoma (NEC), neuroendocrine tumors G3 (NET G3), adenocarcinoma and MINEN is often challenging. The mRNA-based NETest has diagnostic, prognostic and predictive value in neuroendocrine tumors G1-2 but has not been studied in HG GEP-NEN. Patients with advanced HG GEP-NEN were prospectively included in an observational study. A blood sample was collected before the start of chemotherapy and pseudonymised before NETest was performed. NETest results are expressed as an activity index (NETest score) from 0 to 100. The normal score cut-off is 20. Histological sections were pseudonymised before centralized pathological re-evaluation. Samples from 60 patients were evaluable with the NETest. Main primary tumor sites were colon (14), rectum (12), pancreas (11) and esophagus (7). Re-classification: 30 NEC, 12 NET G3, 3 HG-NEN ambiguous morphology, 8 MiNEN, 3 adenocarcinomas with neuroendocrine differentiation (ADNE), 3 adenocarcinomas and 1 NET G2. Elevated NETest (>20) was seen in 38/45 (84%) HG GEP-NEN, all 17 large-cell NEC (100%), 11/13 (85%) small-cell NEC, all ambiguous cases and 7/12 (64%) NET G3. NETest was elevated in 5/8 (63%) MiNEN, 2/3 ADNE, however not in 3 adenocarcinomas. Median survival was 10.2 months (9.6-10.8 95%CI) for evaluable HG GEP-NEN treated with palliative chemotherapy (n = 39), and survival was significantly shorter in patients with NETest >60 with an OS of only 6.5 months. This is the first study to evaluate use of the NETest in advanced HG GEP-NEN. The NETest was almost always elevated in GEP-NEC and in all large-cell NEC. The NETest was also frequently elevated in NET G3 and MiNEN, however cases were limited. Baseline NETest was not predictive for benefit of chemotherapy, however a NETest >60 was prognostic with a shorter survival for patients receiving chemotherapy.

Evaluation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection and follow-up in a Caribbean population

INTRODUCTION

The PROSTest is a novel machine learning-based liquid biopsy assay that functions as a diagnostic and prognostic tool in prostate cancer (PCa). The algorithm outcome (scored 0-100) has a cutoff of >50 to indicate PCa. In this study, we evaluated the screening utility of the test in comparison with the commonly used PSA test.

MATERIALS AND METHODS

509 men (median age: 62 [40-88]), predominantly Afro-Caribbean (n=449, 88.2 %) were enrolled in this study at a referral centre for prostatic cancer screening and management. These included 417 symptomatic patients with urinary tract (UT) problems, of which 12 were diagnosed with PCa, 53 with benign prostatic hyperplasia (BPH) and 352 were free from prostatic diseases, prior to PROSTest (Cohort 1). The remaining 92 of the 509, with relatively young age (median 49 and range 40-75 years) were screened with the PROSTest first and included 28 with UT symptoms and 64 asymptomatic but with a family history of cancer (Cohort 2). PROSTest and PSA was performed on all subjects.

RESULTS

In Cohort 1, the PROSTest was positive in 11 (91.7 %) PCa, 9 (17.0 %) of BPH, and 29 (8.2 %) of subject with UT symptoms but no disease. In Cohort 2, PROSTest was positive in 4 of 28 (14.3 %) symptomatic patients and in 5 of 64 (7.8 %) asymptomatic subjects. A total of 111 individuals (from both cohorts) were identified as "at risk" (PSA >4 ng/mL). Sixteen of 111 (14.4 %) were PROSTest-positive. Thirty-nine percent (43/111) have completed a 1-year follow-up. Of the 43 with elevated PSA, 2 out of 2 (100 %) who were also PROSTest-positive, were diagnosed with PCa.

CONCLUSION

PROSTest is a more sensitive and specific biomarker than PSA in the diagnosis and, potentially, screening for PCa. The seemingly false positive PROSTest results in BPH or symptomatic UT require a longer follow-up of these patients.

Development of a multigenomic liquid biopsy (PROSTest) for prostate cancer in whole blood

INTRODUCTION

We describe the development of a molecular assay from publicly available tumor tissue mRNA databases using machine learning and present preliminary evidence of functionality as a diagnostic and monitoring tool for prostate cancer (PCa) in whole blood.

MATERIALS AND METHODS

We assessed 1055 PCas (public microarray data sets) to identify putative mRNA biomarkers. Specificity was confirmed against 32 different solid and hematological cancers from The Cancer Genome Atlas (n = 10,990). This defined a 27-gene panel which was validated by qPCR in 50 histologically confirmed PCa surgical specimens and matched blood. An ensemble classifier (Random Forest, Support Vector Machines, XGBoost) was trained in age-matched PCas (n = 294), and in 72 controls and 64 BPH. Classifier performance was validated in two independent sets (n = 263 PCas; n = 99 controls). We assessed the panel as a postoperative disease monitor in a radical prostatectomy cohort (RPC: n = 47).

RESULTS

A PCa-specific 27-gene panel was identified. Matched blood and tumor gene expression levels were concordant (r = 0.72, p < 0.0001). The ensemble classifier ("PROSTest") was scaled 0%-100% and the industry-standard operating point of ≥50% used to define a PCa. Using this, the PROSTest exhibited an 85% sensitivity and 95% specificity for PCa versus controls. In two independent sets, the metrics were 92%-95% sensitivity and 100% specificity. In the RPCs (n = 47), PROSTest scores decreased from 72% ± 7% to 33% ± 16% (p < 0.0001, Mann-Whitney test). PROSTest was 26% ± 8% in 37 with normal postoperative PSA levels (<0.1 ng/mL). In 10 with elevated postoperative PSA, PROSTest was 60% ± 4%.

CONCLUSION

A 27-gene whole blood signature for PCa is concordant with tissue mRNA levels. Measuring blood expression provides a minimally invasive genomic tool that may facilitate prostate cancer management.

Circulating Neuroendocrine Tumor Biomarkers: Past, Present and Future

Neuroendocrine tumors are a heterogeneous group of neoplasms originating from the diffuse endocrine system. Depending on primary location and hormonal status, they range in terms of clinical presentation, prognosis and treatment. Functional tumors often develop symptoms indicating an excess of hormones produced by the neoplasm (exempli gratia insulinoma, glucagonoma and VIPoma) and can be diagnosed using monoanalytes. For non-functional tumors (inactive or producing insignificant amounts of hormones), universal biomarkers have not been established. The matter remains an important unmet need in the field of neuroendocrine tumors. Substances researched over the years, such as chromogranin A and neuron-specific enolase, lack the desired sensitivity and specificity. In recent years, the potential use of Circulating Tumor Cells or multianalytes such as a circulating microRNA and NETest have been widely discussed. They offer superior diagnostic parameters in comparison to traditional biomarkers and depict disease status in a more comprehensive way. Despite a lot of promise, no international standards have yet been developed regarding their routine use and clinical application. In this literature review, we describe the analytes used over the years and cover novel biomarkers that could find a use in the future. We discuss their pros and cons while showcasing recent advances in the field of neuroendocrine tumor biomarkers.

Predictive Factors for Resistant Disease with Medical/Radiologic/Liver-Directed Anti-Tumor Treatments in Patients with Advanced Pancreatic Neuroendocrine Neoplasms: Recent Advances and Controversies

Purpose: Recent advances in the diagnosis, management and nonsurgical treatment of patients with advanced pancreatic neuroendocrine neoplasms (panNENs) have led to an emerging need for sensitive and useful prognostic factors for predicting responses/survival. Areas covered: The predictive value of a number of reported prognostic factors including clinically-related factors (clinical/laboratory/imaging/treatment-related factors), pathological factors (histological/classification/grading), and molecular factors, on therapeutic outcomes of anti-tumor medical therapies with molecular targeting agents (everolimus/sunitinib/somatostatin analogues), chemotherapy, radiological therapy with peptide receptor radionuclide therapy, or liver-directed therapies (embolization/chemoembolization/radio-embolization (SIRTs)) are reviewed. Recent findings in each of these areas, as well as remaining controversies and uncertainties, are discussed in detail, particularly from the viewpoint of treatment sequencing. Conclusions: The recent increase in the number of available therapeutic agents for the nonsurgical treatment of patients with advanced panNENs have raised the importance of prognostic factors predictive for therapeutic outcomes of each treatment option. The establishment of sensitive and useful prognostic markers will have a significant impact on optimal treatment selection, as well as in tailoring the therapeutic sequence, and for maximizing the survival benefit of each individual patient. In the paper, the progress in this area, as well as the controversies/uncertainties, are reviewed.

Update on Epidemiology, Diagnosis, and Biomarkers in Gastroenteropancreatic Neuroendocrine Neoplasms

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a heterogeneous group of malignancies that originate from the diffuse neuroendocrine cell system of the pancreas and gastrointestinal tract and have increasingly increased in number over the decades. GEP-NENs are roughly classified into well-differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas; it is essential to understand the pathological classification according to the mitotic count and Ki67 proliferation index. In addition, with the advent of molecular-targeted drugs and somatostatin analogs and advances in endoscopic and surgical treatments, the multidisciplinary treatment of GEP-NENs has made great progress. In the management of GEP-NENs, accurate diagnosis is key for the proper selection among these diversified treatment methods. The evaluation of hormone-producing ability, diagnostic imaging, and histological diagnosis is central. Advances in the study of the genetic landscape have led to deeper understanding of tumor biology; it has also become possible to identify druggable mutations and predict therapeutic effects. Liquid biopsy, based on blood mRNA expression for GEP-NENs, has been developed, and is useful not only for early detection but also for assessing minimal residual disease after surgery and prediction of therapeutic effects. This review outlines the updates and future prospects of the epidemiology, diagnosis, and management of GEP-NENs.

A novel liquid biopsy (NETest) identifies paragangliomas and pheochromocytomas with high accuracy

Pheochromocytomas and paragangliomas (PHEOs/PGLs) represent diagnostically challenging and complex neuroendocrine tumors (NETs). Current biomarker tests for PHEOs/PGLs are technically complex or limited. We assessed the diagnostic utility of a NET-specific 51-marker gene blood assay (NETest) in patients with PHEOs/PGLs (n = 81), including ten pediatric patients, and age-/gender-matched controls (n = 142) using a prospective case:control (1:2) analysis. mRNA was measured (qPCR), and results were scaled from 0 to 100 (upper limit of normal < 20). Receiver operating curve (ROC) and non-parametric (Mann-Whitney) tests were used for analyses (two-tailed). All data are presented as mean ± s.e.m. NETest accuracy for PHEO/PGL diagnosis was 100%. PHEO/PGL scores were 70 ± 3 vs 8.5 ± 1 in controls (P < 0.0001), and ROC analysis was 0.99 ± 0.004 (P < 0.0001). Diagnostic metrics were 94% accurate, 100% sensitive, and 92% specific. Imaging correlation with 68Ga-PET-SSA was 100%. NETest levels in PHEOs (n = 26) were significantly (P < 0.0001) elevated (83 ± 4) vs 66 ± 4 in PGLs (n = 40) and mixed PHEOs/PGLs (n = 5: 37 ± 3). Adrenal-derived tumors (n = 30) exhibited higher scores (76 ± 5) than extra-adrenal-derived tumors (66 ± 4, P < 0.05). Cluster 2 tumors exhibited significantly (P = 0.034) elevated NETest levels (n = 4: 92 ± 2) vs cluster 1 tumors (n = 35: 69 ± 4). Regulatory pathway analysis identified elevated RAS-RAF, metastatic, pluripotential, neural and secretory gene cluster levels (P < 0.05) in PHEOs compared to PGLs. Cluster 2 PPGLs exhibited elevated (P = 0.046) levels of growth factor signaling genes compared to cluster 1. The PHEOs/PGLs in the pediatric cohort (n = 10) were all NETest-positive (81 ± 8) and exhibited a gene expression profile spectrum analogous to adults. Circulating NET transcript analysis identifies PHEOs/PGLs with 100% efficacy and is likely to have clinical utility in the diagnosis and management of PHEO/PGL patients.

Molecular Genomic Assessment Using a Blood-based mRNA Signature (NETest) is Cost-effective and Predicts Neuroendocrine Tumor Recurrence With 94% Accuracy

INTRODUCTION

Identification of residual disease after neuroendocrine tumor (NET) resection is critical for management. Post-surgery imaging is insensitive, expensive, and current biomarkers ineffective. We evaluated whether the NETest, a multigene liquid biopsy blood biomarker, correlated with surgical resection and could predict recurrence.

METHODS

Multicenter evaluation of NET resections over 24 months (n = 103): 47 pancreas, 26 small bowel, 26 lung, 2 appendix, 1 duodenum, 1 stomach. Surgery: R0 (83), R1/R2 (20). One millilitre of blood was collected at D0 and posroperative day (POD) 30. Transcript quantification by polymerase chain reaction (normal: ≤20), CgA by NEOLISA (normal ≤108 ng/mL). Standard-of-care (SoC) follow-up costs were calculated and compared to POD30 NETest-stratification approach. Analyses: Wilcoxon-paired test, Chi-square test.

RESULTS

D BIOMARKERS

NETest: 103 of 103 (100%)-positive, whereas 23 of 103 (22%) were CgA-positive (Chi-square = 78, P < 0.0001).In the R0 group, the NETest decreased 59 ± 28 to 26 ± 23 (P < 0.0001); 36% (30/83) remained elevated. No significant decrease was evident for CgA. In the R1/R2 group the NETest decreased but 100% remained elevated. CgA levels did not decrease.An elevated POD30 NETest was present in R0 and 25 (83%) developed radiological recurrences. Normal score R0 s (n = 53) did not develop recurrence (Chi-square = 56, P < 0.0001). Recurrence prediction was 94% accurate with the NETest.

COST EVALUATION

Using the NETest to stratify postoperative imaging resulted in a cost-savings of 42%.

CONCLUSION

NETest diagnosis is more accurate than CgA (100% vs 22%). Surgery significantly decreased NETest. An elevated POD30 NETest predicted recurrence with 94% accuracy and post-surgical POD30 NETest follow-up stratification decreased costs by 42%. CgA had no surgical utility. Further studies would define the accuracy and cost-effectiveness of the NETest in the detection of postoperative recurrent disease.

Early Identification of Residual Disease After Neuroendocrine Tumor Resection Using a Liquid Biopsy Multigenomic mRNA Signature (NETest)

INTRODUCTION

Surgery 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.

METHODS

This 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.

RESULTS

The 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).

CONCLUSION

The 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.

Multiple endocrine neoplasia type 1 (MEN-1) and neuroendocrine neoplasms (NENs)

Neuroendocrine neoplasms (NENs) are relatively rare neoplasms with 6.4-times increasing age-adjusted annual incidence during the last four decades. NENs arise from neuroendocrine cells, which release hormones in response to neuronal stimuli and they are distributed into organs and tissues. The presentation and biological behaviour of the NENs are highly heterogeneous, depending on the organ. The increased incidence is mainly due to increased awareness and improved detection methods both in the majority of sporadic NENs (non-inherited), but also the inherited groups of neoplasms appearing in at least ten genetic syndromes. The most important one is multiple endocrine neoplasia type 1 (MEN-1), caused by mutations in the tumour suppressor gene MEN1. MEN-1 has been associated with different tumour manifestations of NENs e.g. pancreas, gastrointestinal tract, lungs, thymus and pituitary. Pancreatic NENs tend to be less aggressive when arising in the setting of MEN-1 compared to sporadic pancreatic NENs. There have been very important improvements over the past years in both genotyping, genetic counselling and family screening, introduction and validation of various relevant biomarkers, as well as newer imaging modalities. Alongside this development, both medical, surgical and radionuclide treatments have also advanced and improved morbidity, quality of life and mortality in many of these patients. Despite this progress, there is still space for improving insight into the genetic and epigenetic factors in relation to the biological mechanisms determining NENs as part of MEN-1. This review gives a comprehensive update of current evidence for co-occurrence, diagnosis and treatment of MEN-1 and neuroendocrine neoplasms and highlight the important progress now finding its way to international guidelines in order to improve the global management of these patients.

The Potential Role of Liquid Biopsies in Advancing the Understanding of Neuroendocrine Neoplasms

Tumour tissue as a source for molecular profiling and for in vivo models has limitations (e.g., difficult access, limited availability, single time point, potential heterogeneity between primary and metastatic sites). Conversely, liquid biopsies provide an easily accessible approach, enabling timely and longitudinal interrogation of the tumour molecular makeup, with increased ability to capture spatial and temporal intra-tumour heterogeneity compared to tumour tissue. Blood-borne biomarker assays (e.g., circulating tumour cells (CTCs), circulating free/tumour DNA (cf/ctDNA)) pose unique opportunities for aiding in the molecular characterisation and phenotypic subtyping of neuroendocrine neoplasms and will be discussed in this article.

Sensitivity and Specificity of the NETest: A Validation Study

BACKGROUND

Secretory tumor markers traditionally measured in patients with neuroendocrine tumors (NET) are lacking sensitivity and specificity, and consequently they are of limited clinical utility. The NETest, a novel blood multigene RNA transcript assay, has been found to be highly sensitive and specific. We sought to validate the sensitivity of the NETest in a population of metastatic well-differentiated NETs of gastroenteropancreatic and lung origin and to evaluate NETest specificity in a mixed population of metastatic non-NET gastrointestinal (GI) malignancies and healthy individuals.

DESIGN AND METHODS

Forty-nine patients with metastatic NETs, 21 patients with other metastatic GI cancers, and 26 healthy individuals were enrolled in the study. Samples were sent in a blinded fashion to a central laboratory, and an NETest value of 0-13% was considered normal.

RESULTS

Using 13% as the upper limit of normal, the sensitivity of the NETest was 98% (95% CI 89-100%). The overall specificity was 66% (95% CI 51-79%), with 16 false-positive results. Specificity was 81% (95% CI 62-92%) among 26 healthy individuals and 48% (95% CI 26-70%) among patients with other GI malignancies. Using an updated normal range of 0-20%, sensitivity was unchanged, but specificity improved to 100% among healthy participants and to 67% among patients with other cancers.

CONCLUSIONS

The sensitivity of the NETest is exceptionally high (>95%) in a population of metastatic, well-differentiated NETs. Specificity within a healthy population of patients is exceptionally high when using a normal range of 0-20% but relatively low when evaluating patients with other GI malignancies.

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.

Evaluation of circulating transcript analysis (NETest) in small intestinal neuroendocrine neoplasms after surgical resection

PURPOSE

Surgical resection is the only effective curative strategy for small intestinal neuroendocrine neoplasms (SINENs). Nevertheless, the evaluation of residual disease and prediction of disease recurrence/progression remains a problematic issue.

METHODS

We evaluated 13 SINENs that underwent surgical resection of the primary tumour and/or mesenteric mass. Patients were divided in three groups: (a) Group 1: SINENs that underwent resection with curative intent, (b) Group 2: SINENs treated with resection in the setting of metastatic disease, which remained stable and (c) Group 3: SINENs treated with resection in the setting of metastatic disease, with evidence of any progression at follow-up. NETest and chromogranin A were measured pre-operatively and post-operatively during a 22-month median follow-up period and compared with imaging studies. NETest score <20% was determined as normal, 20-40% low, 41-79% intermediate and ≥80% high score.

RESULTS

NETest score was raised in all (100%) SINENs pre-operatively. Surgery with curative intent resulted in NETest score reduction from 78.25 ± 15.32 to 25.25 ± 1.75 (p < 0.05). Low NETest scores post-operatively were evident in all cases without clinical evidence of residual disease (Group 1). However, the low disease activity score suggested the presence of microscopic residual disease. In three cases (75%) with stable disease (Group 2) the NETest score was low consistent with indolent disease. In the progressive disease group (Group 3), a high NETest score was present in three cases (60%) and an intermediate NETest score in the remainder (40%).

CONCLUSIONS

Blood NETest scores accurately identified SINENs and were significantly decreased by curative surgery. Monitoring NETest post-operatively may facilitate management by identifying the presence of residual/progressive disease.

Prognostic and predictive factors on overall survival and surgical outcomes in pancreatic neuroendocrine tumors: recent advances and controversies

Introduction: Recent advances in diagnostic modalities and therapeutic agents have raised the importance of prognostic factors in predicting overall survival, as well as predictive factors for surgical outcomes, in tailoring therapeutic strategies of patients with pancreatic neuroendocrine neoplasms (panNENs).Areas covered: Numerous recent studies of panNEN patients report the prognostic values of a number of clinically related factors (clinical, laboratory, imaging, treatment-related factors), pathological factors (histological, classification, grading) and molecular factors on long-term survival. In addition, an increasing number of studies showed the usefulness of various factors, specifically biomarkers and molecular makers, in predicting recurrence and mortality related to surgical treatment. Recent findings (from the last 3 years) in each of these areas, as well as recent controversies, are reviewed.Expert commentary: The clinical importance of prognostic and predictive factors for panNENs is markedly increased for both overall outcome and post resection, as a result of recent advances in all aspects of the diagnosis, management and treatment of panNENs. Despite the proven prognostic utility of routinely used tumor grading/classification and staging systems, further studies are required to establish these novel prognostic factors to support their routine clinical use.