RESUMEN
Adoptive transfer of tumor antigen-specific CD8+ T cells can limit tumor progression but is hampered by the T cells' rapid functional impairment within the tumor microenvironment (TME). This is in part caused by metabolic stress due to lack of oxygen and glucose. Here, we report that fenofibrate treatment of human ex vivo expanded tumor-infiltrating lymphocytes (TILs) improves their ability to limit melanoma progression in a patient-derived xenograft (PDX) mouse model. TILs treated with fenofibrate, a peroxisome proliferator receptor alpha (PPARα) agonist, switch from glycolysis to fatty acid oxidation (FAO) and increase the ability to slow the progression of autologous melanomas in mice with freshly transplanted human tumor fragments or injected with tumor cell lines established from the patients' melanomas and ex vivo expanded TILs.
RESUMEN
Accurate, robust, and fast delineation of the clinical target volume (CTV) for the use in radiotherapy of rectal cancer (RC) is highly sought-after. Convolutional neural networks (CNNs) have proven themselves very effective in various segmentation tasks on medical images. Despite this, their application in CTV delineation is not yet fully explored. This study uses the three-dimensional fully convolutional neural network architecture called V-net for CTV delineation. The West China Hospital (Chengdu, China) provided this study with 120 annotated CT scans. For improved performance and to battle data scarcity, the available scans were augmented. Trained on 100 CT-scans for 20 hours and tested on 20 previously unseen CT-scans the network achieved a mean dice similarity coefficient (DSC) of 0.90 and a mean delineation time per CTV of 0.60 seconds. The proposed method is compared with two other state-of-the-art CNNs and is shown to be superior.