bioRxiv ScienceSearch

Biology subjects

Wood, L. D.

Publications and source records attributed to Wood, L. D..

2 recordsLinked to original sources

GnasR201C Induces Murine Pancreatic Cystic Neoplasms through Suppression of YAP1 Signaling and Transcriptional Reprogramming

Background & AimsSomatic \"hotspot\" mutations of GNAS, which encodes for the alpha subunit of stimulatory G-protein, are present in ~60% of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. There are currently no cognate animal models that recapitulate the biology of mutant Gnas-induced IPMNs, and the underlying mechanisms that lead to the cystic pathway of neoplasia in the pancreas remain unknown.\n\nMethodsWe generated p48-Cre; LSL-KrasG12D; Rosa26R-LSL-rtTA-TetO-GnasR201C mice (Kras; Gnas mice) where pancreas-specific GnasR201C expression was induced by doxycycline administration. In this model, mutant Kras is constitutively expressed, and control mice were produced through absence of doxycycline. Separate cohorts of mice were utilized for timed necropsies and for Kaplan-Meier survival analysis. Isogenic cell lines (with doxycycline inducible mutant Gnas expression) were propagated from the resulting pancreatic ductal adenocarcinoma (PDAC).\n\nResultsCo-expression of KrasG12D and GnasR201C resulted in the development of pancreatic cystic lesions resembling human IPMNs in 100% of mice, with higher grades of epithelial dysplasia observed over time. Approximately one-third of Kras; Gnas mice developed PDAC at a median of 38 weeks post doxycycline induction. GnasR201C did not accelerate oncogenic transformation with KrasG12D, but rather, reprogrammed Ras-induced neoplasms towards a well-differentiated phenotype. GnasR201C induction led to activation of the inhibitory Hippo kinase cascade and cytoplasmic sequestration of phosphorylated YAP1 protein, a phenomenon that was also observed in human IPMN with GNAS mutations.\n\nConclusionsGNASR201C functions not as a traditional oncogene, but rather as an \"oncomodulator\" of KRAS-mediated pancreatic neoplasia, through suppression of YAP1 and transcriptional reprogramming towards a differentiated (large ductal) phenotype.

cancer biology

Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer, with an average 5-year survival rate of only 8%. Within PDAC patients, however, there is a small subset of patients who survive >10 years. Deciphering underlying reasons behind prolonged survival could potentially provide new opportunities to treat PDAC; however, no genomic, transcriptomic, proteomic, or clinical signatures have been found to robustly separate this subset of patients. Digital pathology, in combination with machine learning, provides an opportunity to computationally search for tissue morphology patterns associated with disease outcomes. Here, we developed a computational framework to analyze whole-slide images (WSI) of PDAC patient tissue and identify tissue-morphology signatures for very long term surviving patients. Our results indicate that less tissue morphology heterogeneity is significantly linked to better patient survival and that the extra-tumoral space encodes prognostic information for survival. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established a machine learning model with an AUC of 0.94. Our analysis workflow highlighted a quantitative visual-based tissue phenotype analysis that also allows direct interaction with pathology. This study demonstrates a pathway to accelerate the discovery of undetermined tissue morphology associated with pathogenesis states and prognosis and diagnosis of patients by utilizing new computational approaches.

bioinformatics