2020
DOI: 10.1101/2020.06.12.146811
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

DNA methylation reveals distinct cells of origin for pancreatic neuroendocrine carcinomas (PanNECs) and pancreatic neuroendocrine tumors (PanNETs)

Abstract: Pancreatic Neuroendocrine Neoplasms (PanNENs) comprise a rare and heterogeneous group of tumors derived from neuroendocrine cells of the pancreas. Despite recent genetic and epigenetic characterization, biomarkers for improved patient stratification and personalized therapy are sparse and targeted therapies, including the mTOR inhibitor Everolimus, have shown limited success. To better define PanNENs tumors we performed multi-omic analyses on 59 tumors with varying grades (NET G1, NET G2, NET G3 and NEC), comb… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 100 publications
(159 reference statements)
0
2
0
Order By: Relevance
“…Technically, the latter two were aggregated into a single artificial cell type called 'exocrine-like' by summation over the acinar and ductal proportions. The reasons for designing a mixed endocrine/exocrine model were: (i) the trans-differentiation of endocrine to exocrine cell types and vice versa occurs in mouse models of pancreatic injury, regeneration, and carcinogenesis [49][50][51], (ii) panNEC share mutational profiles with pancreatic adenocarcinoma [52][53][54] and may exhibit areas of pancreatic adenocarcinoma [12], (iii) the DNA methylation analyses in panNEC suggested acinar cells as the cell of origin [55], and (iv) adult pancreatic stem or progenitor-like cells are proposed to reside in the exocrine compartment [56][57][58][59]. Three panNEN and five GEP-NEN datasets were deconvolved with twelve combinations of the deconvolution algorithm and scRNA training dataset to uncover whether a transcriptomic deconvolution of panNENs and non-pancreatic GEP-NENs was possible and to identify which combination was most effective.…”
Section: Deconvolution Algorithms Cell Type Models and Evaluation Dat...mentioning
confidence: 99%
“…Technically, the latter two were aggregated into a single artificial cell type called 'exocrine-like' by summation over the acinar and ductal proportions. The reasons for designing a mixed endocrine/exocrine model were: (i) the trans-differentiation of endocrine to exocrine cell types and vice versa occurs in mouse models of pancreatic injury, regeneration, and carcinogenesis [49][50][51], (ii) panNEC share mutational profiles with pancreatic adenocarcinoma [52][53][54] and may exhibit areas of pancreatic adenocarcinoma [12], (iii) the DNA methylation analyses in panNEC suggested acinar cells as the cell of origin [55], and (iv) adult pancreatic stem or progenitor-like cells are proposed to reside in the exocrine compartment [56][57][58][59]. Three panNEN and five GEP-NEN datasets were deconvolved with twelve combinations of the deconvolution algorithm and scRNA training dataset to uncover whether a transcriptomic deconvolution of panNENs and non-pancreatic GEP-NENs was possible and to identify which combination was most effective.…”
Section: Deconvolution Algorithms Cell Type Models and Evaluation Dat...mentioning
confidence: 99%
“…Young et al performed a comprehensive analysis of the immune response and showed that immunotherapy may be clinically beneficial for patients with the metastasislike primary (MLP)-1 subtype [ 106 ]. In addition, Simon et al performed multi-omics on PNENs of various grades and revealed the mechanisms involved in PNENs [ 107 ]. If PNENs can be further genetically analyzed and subdivided into Ki-67 grading before surgery, it will become an attractive option for the management and preoperative risk stratification of patients with PNENs.…”
Section: Role Of Eus-fna In Pnensmentioning
confidence: 99%