Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.Peer Reviewe

DNA methylation reveals distinct cells of origin for pancreatic neuroendocrine carcinomas and pancreatic neuroendocrine tumors.

BACKGROUND Pancreatic neuroendocrine neoplasms (PanNENs) fall into two subclasses: the well-differentiated, low- to high-grade pancreatic neuroendocrine tumors (PanNETs), and the poorly-differentiated, high-grade pancreatic neuroendocrine carcinomas (PanNECs). While recent studies suggest an endocrine descent of PanNETs, the origin of PanNECs remains unknown. METHODS We performed DNA methylation analysis for 57 PanNEN samples and found that distinct methylation profiles separated PanNENs into two major groups, clearly distinguishing high-grade PanNECs from other PanNETs including high-grade NETG3. DNA alterations and immunohistochemistry of cell-type markers PDX1, ARX, and SOX9 were utilized to further characterize PanNECs and their cell of origin in the pancreas. RESULTS Phylo-epigenetic and cell-type signature features derived from alpha, beta, acinar, and ductal adult cells suggest an exocrine cell of origin for PanNECs, thus separating them in cell lineage from other PanNENs of endocrine origin. CONCLUSIONS Our study provides a robust and clinically applicable method to clearly distinguish PanNECs from G3 PanNETs, improving patient stratification

Assessing endocrine disruption in freshwater fish species from a "hotspot" for estrogenic activity in sediment.

Little is known about sediment-bound exposure of fish to endocrine disrupting chemicals (EDC) under field conditions. This study aimed to investigate potential routes of EDC exposure to fish and whether sediment-bound contaminants contribute towards exposure in fish. Tench (Tinca tinca) and roach (Rutilus rutilus) as a benthic and pelagic living fish species, respectively, were sampled at the Luppe River, previously described as a "hotspot" for accumulation of EDC in sediment. A field reference site, the Laucha River, additionally to fish from a commercial fish farm as reference were studied. Blackworms, Lumbriculus variegatus, which are a source of prey for fish, were exposed to sediment of the Luppe River and estrogenic activity of worm tissue was investigated using in vitro bioassays. A 153-fold greater estrogenic activity was measured using in vitro bioassays in sediment of the Luppe River compared the Laucha River. Nonylphenol (NP; 22 mg/kg) was previously identified as one of the main drivers of estrogenic activity in Luppe sediment. Estrogenic activity of Luppe exposed worm tissue (14 ng 17β-estradiol equivalents/mg) indicated that food might act as secondary source to EDCs. While there were no differences in concentrations of NP in plasma of tench from the Luppe and Laucha, vitellogenin, a biomarker for exposure to EDCs, was induced in male tench and roach from the Luppe River compared to both the Laucha and cultured fish by a factor of 264 and 90, respectively. However, no histological alterations in testis of these fish were observed. Our findings suggest that sediments substantially contribute to the overall EDC exposure of both benthic and pelagic fish but that the exposure did not impact gonad status of the fish

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes

Bioavailability and impacts of estrogenic compounds from suspended sediment on rainbow trout (Oncorhynchus mykiss).

Numerous environmental pollutants have the potential to accumulate in sediments, and among them are endocrine-disrupting chemicals (EDCs). It is well documented that water-borne exposure concentrations of some potent EDCs, more specifically estrogenic- active compounds (ECs), can impair the reproduction of fish. In contrast, little is known about the bioavailability and effects of sediment-associated ECs on fish. Particularly, when sediments are disturbed, e.g., during flood events, chemicals may be released from the sediment and become bioavailable. The main objectives of this study were to evaluate a) whether ECs from the sediment become bioavailable to fish when the sediment is suspended, and b) whether such exposure leads to endocrine responses in fish. Juvenile rainbow trout (Oncorhynchus mykiss) were exposed over 21 days to constantly suspended sediments in the following treatments: i) a contaminated sediment from the Luppe River, representing a "hotspot" for EC accumulation, ii) a reference sediment (exhibiting only background contamination), iii) three dilutions, 2-, 4- and 8-fold of Luppe sediment diluted with the reference sediment, and iv) a water-only control. Measured estrogenic activity using in vitro bioassays as well as target analysis of nonylphenol and estrone via LC-MS/MS in sediment, water, fish plasma, as well as bile samples, confirmed that ECs became bioavailable from the sediment during suspension. ECs were dissolved in the water phase, as indicated by passive samplers, and were readily taken up by the exposed trout. An estrogenic response of fish to Luppe sediment was indicated by increased abundance of transcripts of typical estrogen responsive genes, i.e. vitelline envelope protein α in the liver and vitellogenin induction in the skin mucus. Altered gene expression profiles of trout in response to suspended sediment from the Luppe River suggest that in addition to ECs a number of other contaminants such as dioxins, polychlorinated biphenyls (PCBs) and heavy metals were remobilized during suspension. The results of the present study demonstrated that sediments not only function as a sink for ECs but can turn into a significant source of pollution when sediments are resuspended as during flood-events. This highlights the need for sediment quality criteria considering bioavailability sediment-bound contaminants in context of flood events

P-selectin-dependent leukocyte adhesion is governed by endolysosomal two-pore channel 2

Summary: Upon proinflammatory challenges, endothelial cell surface presentation of the leukocyte receptor P-selectin, together with the stabilizing co-factor CD63, is needed for leukocyte capture and is mediated via demand-driven exocytosis from the Weibel-Palade bodies that fuse with the plasma membrane. We report that neutrophil recruitment to activated endothelium is significantly reduced in mice deficient for the endolysosomal cation channel TPC2 and in human primary endothelial cells with pharmacological TPC2 block. We observe less CD63 signal in whole-mount stainings of proinflammatory-activated cremaster muscles from TPC2 knockout mice. We find that TPC2 is activated and needed to ensure the transfer of CD63 from endolysosomes via Weibel-Palade bodies to the plasma membrane to retain P-selectin on the cell surface of human primary endothelial cells. Our findings establish TPC2 as a key element to leukocyte interaction with the endothelium and a potential pharmacological target in the control of inflammatory leukocyte recruitment