The traditionally promulgated perspectives of neuroendocrine neoplasms (NEN) as rare, indolent tumours are blunt and have been outdated for the last 2 decades. Clear increments in their incidence over the past decades render them increasingly clinically relevant, and at initial diagnosis many present with nodal and/or distant metastases (notably hepatic). The molecular pathogenesis of these tumours is increasingly yet incompletely understood. Those arising from the small bowel (SB) or pancreas typically occur sporadically; the latter may occur within the context of hereditary tumour predisposition syndromes. NENs can also be associated with endocrinopathy of hormonal hypersecretion. Tangible advances in the development of novel biomarkers, functional imaging modalities and therapy are especially applicable to this sub-set of tumours. The management of SB and pancreatic neuroendocrine tumours (NET) may be challenging, and often comprises a multidisciplinary approach wherein surgical, medical, interventional radiological and radiotherapeutic modalities are implemented. This review provides a comprehensive overview of the epidemiology, pathophysiology, diagnosis and treatment of SB and pancreatic NETs. Moreover, we provide an outlook of the future in these tumour types which will include the development of precision oncology frameworks for individualised therapy, multi-analyte predictive biomarkers, artificial intelligence-derived clinical decision support tools and elucidation of the role of the microbiome in NEN development and clinical behaviour.

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.

Multigene-based PCR tests are time-consuming and limiting aspects of the protocol include increased risk of operator-based variation. In addition, such protocols are complex to transfer and reproduce between laboratories. Evaluate the clinical utility of a pre-spotted PCR plate (PSP) for a novel multigene (n = 51) blood-based gene expression diagnostic assay for neuroendocrine tumors (NETs). A pilot study (n = 44; 8 controls and 36 NETs) was undertaken to compare C_Q, normalized gene expression and algorithm-based output (NETest score). Gene expression was then evaluated between matched blood:tumor tissue samples (n = 7). Thereafter, two prospective sets (diagnostic: n = 167; clinical validation: n = 48, respectively) were evaluated for diagnostic and clinical utility value. Two independent molecular diagnostics facilities were used to assess assay reproducibility and inter-laboratory metrics. Samples were collected (per CLIA protocol) processed to mRNA and cDNA and then either run per standard assay (liquid primers) or on PSPs. Separately, matching plasma samples were analyzed for chromogranin A (CgA). Statistics included non-parametric testing, Pearson-concordance, Predictive Modeling and AUROC analyses. In the pilot study (n = 44), C_Q values were highly concordant (r: 0.82, p<0.0001) and normalized gene expression data significantly related (p<0.0001) (Pearson-pairwise correlation). NETest values were not different (49.7±33 standard vs. 48.5±31.5 PSP) and the overall concordance in output 96%. Predictive modelling confirmed this concordance (F1 score = 0.95). Gene expression levels were highly correlated between blood and tumor tissue (R: 0.71–0.83). In the diagnostic cohort (n = 30 controls, n = 87 non-NET controls, n = 50 NET), NETest was significantly lower (p<0.0001) in controls (11±6.5) and non-NET controls (13±18) than NETs (61±31). The AUROCs were 0.93–0.97 and the diagnostic accuracy was 90–97.5%. As a diagnostic, the PSP-NETest was significantly better than CgA (accuracy: 56%, p96%) scores and was significantly better (p96%) NETest results. Moreover, it functions significantly more accurately than CgA.

Objectives: Germ-line genetic variation may affect clinical outcomes of cancer patients. We applied a candidate-gene approach to evaluate the effect of putative markers on survival of patients with pancreatic cancer. We also examined gene-radiotherapy and gene-chemotherapy interactions, aiming to explain interindividual differences in treatment outcomes. Methods: In total, 211 patients with pancreatic cancer were recruited in a population-based study. Sixty-four candidate genes associated with cancer survival or treatment response were selected from existing publications. Genotype information was obtained from a previous genome-wide association study data set. The main effects of genetic variation and gene-specific treatment interactions on overall survival were examined by proportional hazards regression models. Results: Fourteen genes showed evidence of association with pancreatic cancer survival. Among these, rs1760217, located at the DPYD gene; rs17091162 at SERPINA3; and rs2231164 at ABCG2 had the lowest P of 10^−4.60, 0.0013, and 0.0023, respectively. We also observed that 2 genes, RRM1 and IQGAP2, had significant interactions with radiotherapy in association with survival, and 2 others, TYMS and MET, showed evidence of interaction with 5-fluorouracil and erlotinib, respectively. Conclusions: Our study suggested significant associations between germ-line genetic polymorphisms and overall survival in pancreatic cancer, as well as survival interactions between various genes and radiotherapy and chemotherapy. Abbreviations: CTR - Connecticut Tumor Registry, GWAS - genome-wide association study, PubMed - US National Library of Medicine publications citation database, SEER - Surveillance, Epidemiology and End Results (US Cancer Registry Network), SNP - single-nucleotide polymorphism

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.

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.