Dataset Information

Prognostic factors for the outcome of nonfunctioning pancreatic neuroendocrine tumors in MEN1: a systematic review of literature.

ABSTRACT: Metastatic duodenopancreatic neuro-endocrine tumors (dpNETs) are the most important disease-related cause of death in patients with multiple endocrine neoplasia type 1 (MEN1). Nonfunctioning pNETs (NF-pNETs) are highly prevalent in MEN1 and clinically heterogeneous. Therefore, management is controversial. Data on prognostic factors for risk stratification are limited. This systematic review aims to establish the current state of evidence regarding prognostic factors in MEN1-related NF-pNETs. We systematically searched four databases for studies assessing prognostic value of any factor on NF-pNET progression, development of distant metastases, and/or overall survival. In- and exclusion, critical appraisal and data-extraction were performed independently by two authors according to pre-defined criteria. Thirteen studies (370 unique patients) were included. Prognostic factors investigated were tumor size, timing of surgical resection, WHO grade, methylation, p27/p18 expression by immunohistochemistry (IHC), ARX/PDX1 IHC and alternative lengthening of telomeres. Results were complemented with evidence from studies in MEN1-related pNET for which data could not be separately extracted for NF-pNET and data from sporadic NF-pNET. We found that the most important prognostic factors used in clinical decision making in MEN1-related NF-pNETs are tumor size and grade. NF-pNETs <2 cm may be managed with watchful waiting, while surgical resection is advised for NF-pNETs ≥2 cm. Grade 2 NF-pNETs should be considered high risk. The most promising and MEN1-relevant avenues of prognostic research are multi-analyte circulating biomarkers, tissue-based molecular factors and imaging-based prognostication. Multi-institutional collaboration between clinical, translation and basic scientists with uniform data and biospecimen collection in prospective cohorts should advance the field.

SUBMITTER: Sadowski SM

PROVIDER: S-EPMC7304521 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Nonfunctioning pancreatic neuroendocrine tumors (NFPanNETs) may be sporadic or inherited because of germline mutations associated with von Hippel-Lindau disease (VHL) or multiple endocrine neoplasia type 1 (MEN1). The clinical behavior of NFPanNETs is difficult to predict, even in tumors of the same stage and grade. The authors analyzed genotype-specific patterns of transcriptional messenger RNA (mRNA) levels of NFPanNETs to understand the molecular features that determine PanNET phenotype. METHODS:Thirty-two samples were included for genome-wide mRNA gene expression analysis (9 VHL-associated, 10 MEN1-associated, and 9 sporadic NFPanNETs and 4 purified normal islet cell [NIC] samples). Validation of genes was performed by real-time polymerase chain reaction analysis and immunohistochemistry. Gene expression profiles were analyzed by tumor genotype, and pathway analysis was curated. RESULTS:Consensus clustering of mRNA expression revealed separate clustering of NICs, VHL-associated NFPanNETs, and MEN1-associated NFPanNETs; whereas some sporadic tumors clustered with MEN1. Four of 5 MEN1-like sporadic PanNET subtypes had loss of heterozygosity at the MEN1 gene locus. Pathway analysis demonstrated subtype-specific pathway activation, comprising angiogenesis and immune response in VHL; neuronal development in MEN1; protein ubiquitination in the new MEN1/sporadic subtype; and cytokinesis and cilium/microtubule development in sporadic NFPanNETs. Among many genes, platelet-derived growth factor receptor ? (PDGFRB), lymphoid enhancer-binding factor-1 (Lef-1), cyclin-dependent kinase 4 (CDK4), and CDK6 were upregulated in VHL or MEN1 NFPanNETs, providing potential subtype-specific treatment targets. CONCLUSIONS:Distinct mRNA expression patterns were identified in sporadic-associated, VHL-associated, and MEN1-associated NFPanNETs. The current results uncover new pathways involved in NFPanNETs that are subtype-specific and provide potential new diagnostic or therapeutic targets based on tumor subtype. Cancer 2018;124:636-47. © 2017 American Cancer Society.

Project description:ObjectivesTo explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI.MethodsTwo hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models.ResultsTumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively.ConclusionThe developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs.Clinical relevance statementOur study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs.Key pointsThe diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Dataset Information

Prognostic factors for the outcome of nonfunctioning pancreatic neuroendocrine tumors in MEN1: a systematic review of literature.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets