Annals of Pancreatic Cancer最新文献

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with one of the lowest survival rates. Early detection, an improved understanding of tumor biology, and novel therapeutic discoveries are needed in order to improve overall patient survival. Scientific progress towards meeting these goals relies upon accurate modeling of the human disease. From two-dimensional (2D) cell lines to the advanced modeling available today, we aim to characterize the critical tools in efforts to further understand PDAC biology. The National Center for Biotechnology Information's PubMed and the Elsevier's SCOPUS were used to perform a comprehensive literature review evaluating preclinical human-derived PDAC models. Keywords included pancreatic cancer, PDAC, preclinical models, KRAS mutations, xenograft, co-culturing fibroblasts, co-culturing lymphocytes and PDAC immunotherapy Initial search was limited to articles about PDAC and was then expanded to include other gastrointestinal malignancies where information may complement our effort. A supervised review of the key literature's references was utilized to augment the capture of relevant data. The discovery and refinement of techniques enabling immortalized 2D cell culture provided the cornerstone for modern cancer biology research. Cell lines have been widely used to represent PDAC in vitro but are limited in capacity to model three-dimensional (3D) tumor attributes and interactions within the tumor microenvironment. Xenografts are an alternative method to model PDAC with improved capacity to understand certain aspects of 3D tumor biology in vivo while limited by the use of immunodeficient mice. Advances of in vitro modeling techniques have led to 3D organoid models for PDAC biology. Co-culturing models in the 3D environment have been proposed as an efficient modeling system for improving upon the limitations encountered in the standard 2D and xenograft tumor models. The integrated network of cells and stroma that comprise PDAC in vivo need to be accurately depicted ex vivo to continue to make progress in this disease. Recapitulating the complex tumor microenvironment in a preclinical model of human disease is an outstanding and urgent need in PDAC. Definitive characterization of available human models for PDAC serves to further the core mission of pancreatic cancer translational research.

Background: We theoretically derived a new quantitative metric reflecting the product of T1 signal intensity and contrast media concentration (T1C) using first principles for the signal provided by the gradient echo sequence. This metric can be used with conventional gadolinium contrast-enhanced magnetic resonance imaging (CE-MRI) exams. We used this metric to test our hypothesis that gadolinium enhancement changes with pancreatic ductal adenocarcinoma (PDA) treatment response, and that this metric may differentiate responders from non-responders.

Methods: Out of 264 initially identified patients, a final total of 35 patients with PDA were included in a retrospective study of responders (n=24) and non-responders (n=11), which used changes in cancer antigen 19-9 (CA 19-9) and tumor size as reference standards. T1C was computed for the pancreatic mass in the arterial, portal venous, and delayed phases in pre-treatment and post-treatment MRIs. Changes in measurements and correlations with treatment response were assessed by repeated measures analysis of variance and paired t-tests.

Results: In the treatment responder group, T1C significantly increased in the arterial, portal venous, and delayed phases (P=7.57e-5, P=3.25e-4, P=1.75e-4). In the non-responder group, T1C did not significantly change in any phase (P>0.58). Post-treatment T1C significantly differed between responders and non-responders (P=0.044) by repeated measures analysis of variance.

Conclusions: T1C significantly increases in all phases of CE-MRI in responders to treatment, but does not change in non-responders. T1C correlates with treatment response, can be computed from clinical MRI exams, and may be useful as an additional metric to stratify patients undergoing treatment.