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  • 1
    Online Resource
    Online Resource
    Establishment of Novel Neuroendocrine Carcinoma Patient-Derived Xenograft Models for Receptor Peptide-Targeted Therapy
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 8 ( 2022-04-10), p. 1910-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 8 ( 2022-04-10), p. 1910-
    Abstract: Gastroenteropancreatic neuroendocrine neoplasms (GEP NENs) are rare cancers consisting of neuroendocrine carcinomas (NECs) and neuroendocrine tumors (NETs), which have been increasing in incidence in recent years. Few cell lines and pre-clinical models exist for studying GEP NECs and NETs, limiting the ability to discover novel imaging and treatment modalities. To address this gap, we isolated tumor cells from cryopreserved patient GEP NECs and NETs and injected them into the flanks of immunocompromised mice to establish patient-derived xenograft (PDX) models. Two of six mice developed tumors (NEC913 and NEC1452). Over 80% of NEC913 and NEC1452 tumor cells stained positive for Ki67. NEC913 PDX tumors expressed neuroendocrine markers such as chromogranin A (CgA), synaptophysin (SYP), and somatostatin receptor-2 (SSTR2), whereas NEC1452 PDX tumors did not express SSTR2. Exome sequencing revealed loss of TP53 and RB1 in both NEC tumors. To demonstrate an application of these novel NEC PDX models for SSTR2-targeted peptide imaging, the NEC913 and NEC1452 cells were bilaterally injected into mice. Near infrared-labelled octreotide was administered and the fluorescent signal was specifically observed for the NEC913 SSTR2 positive tumors. These 2 GEP NEC PDX models serve as a valuable resource for GEP NEN therapy testing.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14081910
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 2
    Online Resource
    Online Resource
    Altered MANF Expression in Pancreatic Acinar and Ductal Cells in Chronic Alcoholic Pancreatitis: A Cross-Sectional Study
    MDPI AG ; 2023
    In:  Biomedicines Vol. 11, No. 2 ( 2023-02-02), p. 434-
    Details
    In: Biomedicines, MDPI AG, Vol. 11, No. 2 ( 2023-02-02), p. 434-
    Abstract: Background: Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an endoplasmic reticulum (ER) stress response protein that plays an important role in pancreatic functions. As both alcohol and ER stress response proteins are involved in the pathogenesis of pancreatitis, we sought to investigate the expression of MANF in chronic alcoholic pancreatitis (CAP) and chronic non-alcoholic pancreatitis (CNP). Methods: A cohort of chronic pancreatitis tissues was gathered from routine surgical pathology (n = 77) and autopsy (n = 10) cases and tissue microarrays were created. Sampled tissues were reviewed and designated as representing CAP (n = 15), CNP (n = 58), or normal pancreatic tissue (NPT) (n = 27). MANF immunohistochemistry (IHC) and digital image analysis were performed to obtain an estimation of tissue fibrosis and an optical density (OD) of MANF IHC in ducts and acini for each case. The averaged values for these variables among histologic designations were compared. Results: The amount of fibrous tissue of the combined CAP and CNP group (chronic alcoholic and non-alcoholic pancreatitis, CANP) exceeded that of the NPT group (70% vs. 34%, p 〈 0.0001). The MANF OD in ducts of CANP was significantly higher than that of NPT (0.19 vs. 0.10, p 〈 0.05). The MANF OD in ducts of CAP was significantly higher than that of CNP (0.27 vs. 0.17, p 〈 0.05). The MANF OD in acini of CAP was significantly lower than that in CNP (0.81 vs. 1.05, p 〈 0.05). Finally, there was a statistically significant positive relationship between the amount of fibrosis and MANF OD in ducts (p 〈 0.001). Conclusions: MANF expression was higher in ducts of CAP than CNP. In contrast, MANF expression in acini was lower in CAP than CNP and NPT. There was a positive correlation between fibrosis and MANF levels in the ducts.
    Type of Medium: Online Resource
    ISSN: 2227-9059
    URL: Article
    DOI: 10.3390/biomedicines11020434
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720867-9
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  • 3
    Online Resource
    Online Resource
    Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning
    MDPI AG ; 2023
    In:  International Journal of Molecular Sciences Vol. 24, No. 24 ( 2023-12-12), p. 17401-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 24, No. 24 ( 2023-12-12), p. 17401-
    Abstract: Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms242417401
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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