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Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways., Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA)., Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs., Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients., Competing Interests: Conflict of interest: J.C. has provided scientific advice and/or received honoraria or funding for continuous medical education from AAA, Advanz Pharma, Amgen, Bayer, Esteve, Hutchmed, Ipsen, Merck, Novartis, Roche, Sanofi, Lilly, Eisai, Bayer, and ITM, and has received research support from Pfizer, Novartis, Astrazeneca, Eisai, AAA, Amgen, Bayer, Roche, Gilead, and ITM. M.B. reports Advisory from Novartis, Ipsen, and Pfizer. P.J.-F. has received speaker honoraria or consultant honoraria from Adacap, Astellas, BMS, Lilly, and MSD. D.C. has provided scientific advice and/or received honoraria or funding for continuous medical education from Janssen Oncology, Roche/Genetech, Astellas Pharma, AstraZeneca, Pfizer, Novartis, Ipsen, BMS, MSD, Bayer, Lilly, Sanofi, Pierre Fabre, and Boerhinger. F.S. has received honoraria for medical education and research activity from Ipsen, Novartis, Pfizer, Advanced Accelerator Applications, MSD/Merck, and Hutchmed. A.T. has provided scientific advice and/or received honoraria from ADACAP, Ipsen, Novartis, and Pfizer. R.G.-C. has provided scientific advice and/or received honoraria or funding for continuous medical education from ADACAP, Advanz Pharma, Amgen, Bayer, BMS, Boerhinger, Esteve, Hutchmed, Ipsen, Merck, Midatech Pharma, MSD, Novartis, PharmaMar, Pierre Fabre, Roche, Servier, and Sanofi, and has received research support from Pfizer, BMS, and MSD. The remaining authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Society of Endocrinology.)

Poorly differentiated gastroenteropancreatic neuroendocrine carcinomas are aggressive neoplasms of challenging clinical management. A small proportion of patients with early-stage disease may achieve long-term survival, but the majority of patients present with rapidly lethal metastatic disease. Current standard of care still follows the treatment paradigm of small cell lung cancer, a far more common G3 neuroendocrine neoplasm, although emerging molecular and clinical data increasingly question this approach. In this article, we will briefly summarize epidemiology and prognosis of gastroenteropancreatic neuroendocrine carcinomas to emphasize the very low incidence, aggressive nature, and orphan status of this tumor entity. We will also discuss the current pathological classification and its limitations, as well as recent data on their differential biological background compared with small cell lung cancer, and its potential implications for patients care. Then, we will review the standard of care of systemic therapy, basically focused on platinum-based cytotoxic chemotherapy, including some recent randomized trials providing evidence regarding efficacy of irinotecan vs etoposide platinum doublets. Finally, we will present a comprehensive overview of novel therapeutic strategies in current clinical development, including recently reported data on immunotherapy, tumor-agnostic therapies (microsatellite instability, high tumor mutational burden, NTRK and RET gene fusions, BRAF or KRAS inhibitors), and additional treatment strategies targeting other tumor vulnerabilities (ie, Notch pathway, novel targets for radioligand therapy), and provide some insights regarding unmet needs and future perspectives to improve patient's care and prognosis., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.)

Neuroendocrine neoplasms (NENs) are mutationally quiet (low number of mutations/Mb), and epigenetic mechanisms drive their development and progression. We aimed at comprehensively characterising the microRNA (miRNA) profile of NENs, and exploring downstream targets and their epigenetic modulation. In total, 84 cancer-related miRNAs were analysed in 85 NEN samples from lung and gastroenteropancreatic (GEP) origin, and their prognostic value was evaluated by univariate and multivariate models. Transcriptomics (N = 63) and methylomics (N = 30) were performed to predict miRNA target genes, signalling pathways and regulatory CpG sites. Findings were validated in The Cancer Genome Atlas cohorts and in NEN cell lines. We identified a signature of eight miRNAs that stratified patients in three prognostic groups (5-year survival of 80%, 66% and 36%). Expression of the eight-miRNA gene signature correlated with 71 target genes involved in PI3K-Akt and TNFα-NF-kB signalling. Of these, 28 were associated with survival and validated in silico and in vitro. Finally, we identified five CpG sites involved in the epigenetic regulation of these eight miRNAs. In brief, we identified an 8-miRNA signature able to predict survival of patients with GEP and lung NENs, and identified genes and regulatory mechanisms driving prognosis in NEN patients., (© 2023 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu&#95;cnio/beyondcell ., (© 2021. The Author(s).)

Purpose: High-throughput "-omic" technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential., Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC-MS, CE-MS and LC-MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential., Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential., Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

Motivation: Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases., Results: DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables., Availabilityand Implementation: http://www.dreimt.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited., Results: vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies., Availability and Implementation: http://www.vulcanspot.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)