Searchable abstracts of presentations at key conferences in endocrinology

ea0089b12 | Basic Science | NANETS2022

Transcriptomic Influences of Racial Disparities in Black Patients with Pancreatic Neuroendocrine Tumors

Herring Brendon , Guenter Rachael , Dhall Deepti , Chen Herbert , Yates Clayton , Bart Rose J.

Background: There are known outcome disparities between Black and White patients with pancreatic neuroendocrine tumors (pNETs). Recently, Black patients were shown to have higher rates of lymph node metastasis in smaller tumors than White patients, indicating possible differences in tumor biology. Numerous prognostic gene expression differences between racial groups have been reported in other cancers, but no such analysis has been conducted in pNETs. This study evaluated pNET...

ea0089b6 | Basic Science | NANETS2022

Detecting Cell Surface Expression of Calreticulin in Pancreatic Neuroendocrine Tumors Using a Novel [68Ga]-Radiolabeled Peptide

Guenter Rachael , Ducharme Maxwell , Herring Brendon , Montes Odalyz , McCaw Tyler , Lee Goo , Dhall Deepti , MacVicar Caroline , Chen Herbert , Lapi Suzanne E. , Larimer Benjamin , Bart Rose J.

Background: Current theragnostic techniques for pancreatic neuroendocrine tumors (pNETs) exploit the overexpression of somatostatin receptors (SSTRs) on the cell surface. However, approximately 25% of low-grade and most high-grade pNETs do not express SSTRs, requiring alternative theranostics. Calreticulin (CALR) is a protein linked to reticular calcium homeostasis and immunogenic cell death. Upon sufficient cellular insult, CALR translocates from the endoplasmic reticulum (ER...

ea0098b23 | Basic Science | NANETS2023

Calreticulin is associated with clinical characteristics in pancreatic neuroendocrine tumors

Herring Brendon , Macvicar Caroline , Guenter Rachael , Chen Weisheng , Elhussin Isra , Yates Clayton , Dhall Deepti , Chen Herbert , Lee Goo , Bart Rose J.

Background: Following IHC of CALR, H-scoring was performed by a pathologist. H-scoring was validated by MIF of the same tissue, wherein random-forest machine learning (ML) classifiers were employed to classify cells. ML classifiers were trained to distinguish between pNET cells and tumor stroma using approximately 30% of cells for the respective cell population of interest in each TMA core. Pearson’s correlations were used to evaluate the relationship between H-scoring an...