Neuroendocrine tumors: From anatomopathology to clinical presentation

Anatomopathological classification of Neuroendocrine tumors (NETs), today, covers a pivotal role in correctly identifying the disease and establish the right diagnostic and therapeutic approach it is needed in order to manage the patient. Depending on its grading and staging, NENs can have very different prognostic perspectives. Basing on WHO 2017 classification, in this paper will be explored their main characteristics, diving into main histotypes, dividing them into functional and non-functional tumors, keeping in mind their main locations: gastroenteropancreatic tract and lungs. Their typical clinical presentation and diagnostic strategies will be explained, mainly focusing on nuclear medicine and the importance of receptor overexpression (especially represented by somatostatin receptors, or SSTRs). This is the knowledge on which is based the diagnostic and therapeutic approach with peptide radiopharmaceuticals, especially 68Ga-DOTA-peptides (today, the gold standard in well-differentiated neuroendocrine neoplasms, only with the exception of insulinoma, that shows a low density of these molecules on its cellular surface)

Comprehensive genomic profiling of high grade neuroendocrine gastrointestinal neoplasms

Background. Gastro-entero-pancreatic (GEP) high-grade neuroendocrine neoplasms (H-NENs) are a heterogenous group of aggressive neoplasms which includes neuroendocrine tumours (NETs) G3 and neuroendocrine carcinomas (NECs). Due to the rarity of these neoplasms, a comprehensive molecular characterization is still lacking. Aim of the study. The aim of this study is to define the genomic profile of H-NENs (NET G3, NEC <55% ki-67 and NEC ≥55% ki-67). Material and methods. Genomic characterization of 40 cases of GEP-H-NENs (20 cases of NET G3, 8 of NEC <55% ki-67 and 12 of NEC ≥55% ki-67) was subject to DNA and RNA assay targeting 523 genes by Next Generation sequencing, assessing of all variant types including microsatellite instability (MSI) and Tumour Mutational Burden (TMB) (TrueSight Oncology 500, Illumina). Results. Based on genomic data, our samples were classified as MSI, chromosomally instable (CIN) and genomically stable (GS). MSI was found in 1/20 (5%) NET G3, 0/8 NEC <55% ki-67 and 2/12 (16%) NEC ≥55% ki-67. CIN was found in 6/20 (30%) NET G3, 5/8 (63%) NEC<55% ki-67 and 6/12 (50%) NEC ≥55% ki-67. 13/20 (65%) NET G3, 3/8 (38%) NEC <55% ki-67 and 4/12 (33%) NEC ≥55% ki-67 were GS. A high TMB was found in 0/20 NET G3, 1/8 (13%) NEC <55% ki-67 and 5/12 (42%) NEC ≥55% ki-67. The most commonly found amplifications comprise: CDK4/6, EGFR, FGF10, RICTOR, MYC family genes, MET. Fusions genes were found in 6/40 (15%) cases and included: HFM1-ETV1, SEL1L-EGFR, CNTN5-KMT2A, KMT2A-EED, BCL2-KCTD, FLT1-HUWE1, SLC37A1-ERG. Conclusions. This study sheds light on the biology of H-NENs. Genomic profiling of H-NENs has shown that NET G3, NEC <55% ki-67 and NEC ≥55% ki-67 have are a heterogenous in their molecular profiles, while sharing share some frequently altered genes. Further genomic analysis are required to identify potential druggable alterations and predictive biomarkers.  Background. Gastro-entero-pancreatic (GEP) high-grade neuroendocrine neoplasms (H-NENs) are a heterogenous group of aggressive neoplasms which includes neuroendocrine tumours (NETs) G3 and neuroendocrine carcinomas (NECs). Due to the rarity of these neoplasms, a comprehensive molecular characterization is still lacking. Aim of the study. The aim of this study is to define the genomic profile of H-NENs (NET G3, NEC <55% ki-67 and NEC ≥55% ki-67). Material and methods. Genomic characterization of 40 cases of GEP-H-NENs (20 cases of NET G3, 8 of NEC <55% ki-67 and 12 of NEC ≥55% ki-67) was subject to DNA and RNA assay targeting 523 genes by Next Generation sequencing, assessing of all variant types including microsatellite instability (MSI) and Tumour Mutational Burden (TMB) (TrueSight Oncology 500, Illumina). Results. Based on genomic data, our samples were classified as MSI, chromosomally instable (CIN) and genomically stable (GS). MSI was found in 1/20 (5%) NET G3, 0/8 NEC <55% ki-67 and 2/12 (16%) NEC ≥55% ki-67. CIN was found in 6/20 (30%) NET G3, 5/8 (63%) NEC<55% ki-67 and 6/12 (50%) NEC ≥55% ki-67. 13/20 (65%) NET G3, 3/8 (38%) NEC <55% ki-67 and 4/12 (33%) NEC ≥55% ki-67 were GS. A high TMB was found in 0/20 NET G3, 1/8 (13%) NEC <55% ki-67 and 5/12 (42%) NEC ≥55% ki-67. The most commonly found amplifications comprise: CDK4/6, EGFR, FGF10, RICTOR, MYC family genes, MET. Fusions genes were found in 6/40 (15%) cases and included: HFM1-ETV1, SEL1L-EGFR, CNTN5-KMT2A, KMT2A-EED, BCL2-KCTD, FLT1-HUWE1, SLC37A1-ERG. Conclusions. This study sheds light on the biology of H-NENs. Genomic profiling of H-NENs has shown that NET G3, NEC <55% ki-67 and NEC ≥55% ki-67 have are a heterogenous in their molecular profiles, while sharing share some frequently altered genes. Further genomic analysis are required to identify potential druggable alterations and predictive biomarkers.

Foreword to the special issue on advances in neuroendocrine neoplasms

No abstract availabl

A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal

The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Understanding the role played by genetic variations in diseases, exploring genomic variants, and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, the authors propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis

Perfil metabolómico de los tumores neuroendocrinos de origen gastrointestinal y pulmonar : papel pronóstico y relevancia biológica

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, leída el 11-02-2022Reprogrammed metabolism encompasses the capacity of cells to respond or adapt their metabolic signalling to support and enable cell survival in unfavourable or hostile conditions. This ability is enhanced in cancer cells to improve their adaptive phenotype and maintain both viability and uncontrolled proliferation. Metabolic flexibility is therefore one of the key hallmarks of cancer, although pathways involved in the metabolic plasticity of each cancer type remain to be elucidated. Metabolites are the final products of this adaptation, reflecting the aberrant changes in the genomic, transcriptomic and proteomic variability of tumors, and therefore provide useful biological and clinical information on cancer biology. This, together with the fact that metabolomics can be easily performed in readily accessible biological samples (i.e. plasma, urine), makes metabolic profiling of cancer patients a promising tool to characterize the tumor phenotype and identify novel biomarkers of potential clinical use...La reprogramación del metabolismo permite a las células para responder o adaptar su regulación metabólica para permitir la supervivencia celular en condiciones desfavorables u hostiles. Esta capacidad aumenta en las células cancerosas para mejorar su fenotipo adaptativo y mantener tanto la viabilidad como la proliferación incontrolada. Así, la flexibilidad metabólica es una de las características distintivas del cáncer, aunque todavía quedan por dilucidar las vías implicadas en la plasticidad metabólica de cada tipo de tumor. Los metabolitos son los productos finales de esta adaptación, que en último término reflejan los cambios aberrantes que sufren los tumores reflejando la variabilidad genómica, transcriptómica y proteómica de los mismos y, por lo tanto, proporcionando información relevante sobre la biología del cáncer. Además, el estudio de los perfiles de metabolitos (metabolómica) puede realizarse fácilmente en muestras biológicas de fácil acceso (plasma, orina), constituyendo así una herramienta prometedora para caracterizar el fenotipo tumoral e identificar nuevos biomarcadores de potencial utilidad clínica...Fac. de MedicinaTRUEunpu

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests

Improving the evaluation and treatment of neuroendocrine disorders

The aim of this thesis was to improve the diagnosis and treatment of neuroendocrine disorders. In addition, it shows further possibilities to achieve this goal in the long term

Improving the evaluation and treatment of neuroendocrine disorders

The aim of this thesis was to improve the diagnosis and treatment of neuroendocrine disorders. In addition, it shows further possibilities to achieve this goal in the long term

Endocrine and Neuroendocrine Cancers

Endocrine and neuroendocrine tumors originate from endocrine cells but are heterogeneous in terms of clinical presentation, disease outcome, and treatments available. This Special Issue addresses specific aspects of pre-clinical, clinical, and translational research and clinical management of these diseases with the aim of providing novel insights, addressing current unmet needs, and discussing future treatment perspectives