Activation of PyMT in β Cells Induces Irreversible Hyperplasia, but Oncogene-Dependent Acinar Cell Carcinomas When Activated in Pancreatic Progenitors

It is unclear whether the cellular origin of various forms of pancreatic cancer involves transformation or transdifferentiation of different target cells or whether tumors arise from common precursors, with tumor types determined by the specific genetic alterations. Previous studies suggested that pancreatic ductal carcinomas might be induced by polyoma middle T antigen (PyMT) expressed in non-ductal cells. To ask whether PyMT transforms and transdifferentiates endocrine cells toward exocrine tumor phenotypes, we generated transgenic mice that carry tetracycline-inducible PyMT and a linked luciferase reporter. Induction of PyMT in β cells causes β-cell hyperplastic lesions that do not progress to malignant neoplasms. When PyMT is de-induced, β cell proliferation and growth cease; however, regression does not occur, suggesting that continued production of PyMT is not required to maintain the viable expanded β cell population. In contrast, induction of PyMT in early pancreatic progenitor cells under the control of Pdx1 produces acinar cell carcinomas and β-cell hyperplasia. The survival of acinar tumor cells is dependent on continued expression of PyMT. Our findings indicate that PyMT can induce exocrine tumors from pancreatic progenitor cells, but cells in the β cell lineage are not transdifferentiated toward exocrine cell types by PyMT; instead, they undergo oncogene-dependent hyperplastic growth, but do not require PyMT for survival

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p