Islands of genomic stability in the face of genetically unstable metastatic cancer.

Introduction: Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here, we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation.

Reversible chemoresistance of pancreatic cancer grown as spheroids.

Better models are needed to identify active drugs to treat pancreatic adenocarcinoma (PAC) patients. We used 3D hanging drop cultures to produce spheroids from five PAC cell lines and tested nine FDA-approved drugs in clinical use. All PAC cell lines in 2D culture were sensitive to three drugs (gemcitabine, docetaxel and nab-paclitaxel), however most PAC (4/5) 3D spheroids acquired profound chemoresistance even at 10 µM.

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

Purpose: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening.

Materials And Methods: Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network.