Project description:We report the application of bulk RNAseq of live cells from pancreas of KPC or KPC-OG genetic mice at 6 weeks of age. These sponteneous tumors were unperturbed otherwise until timepoint.
Project description:This study used Illumina strand-specific, paired-end RNA-sequencing to examine gene expression differences between matched murine tumor- and metastasis-derived mouse pancreatic ductal adenocarcinoma (PDAC) cells grown as three-dimensional, organoid cultures. The study analyzed 16 organoid lines derived from matched primary PDAC tumors and PDAC metastases from 6 KPC (KrasLSL-G12D; Trp53LSL-R172H; Pdx1-Cre) mice.
Project description:This study used 10X Genomics, single-cell RNA-sequencing to examine the differentiation states of cancer cells present in tumors derived from the KrasLSL-G12D; Trp53LSL-R172H; Pdx1-Cre (KPC) mouse model of pancreatic ductal adenocarcinoma. The study analyzed tumors from 8 different mice.
Project description:This study used 10X Genomics, single-cell RNA-sequencing to examine the cell types present in the KrasLSL-G12D; Trp53LSL-R172H; Pdx1-Cre (KPC) mouse model for pancreatic ductal adenocarcinoma. The study analyzed tumors from 4 different mice. For each tumor, we performed flow sorting to isolate all viable cells, and to isolate a fibroblast-enriched population of cells for single-cell RNA-seq to determine the transcriptomes of individual cells in KPC pancreatic ductal adenocarcinoma tumors.
Project description:Pancreatic ductal adenocarcinoma (PDA) is characterized by abundant desmoplasia and poor tissue perfusion. These features are proposed to limit access of therapies to neoplastic cells and blunt treatment efficacy. Indeed, several agents that target the PDA microenvironment promote chemotherapy delivery and improve anti-neoplastic responses in murine models of PDA. Here, we employed the FG-3019 monoclonal antibody directed against the pleiotropic matricellular signaling molecule connective tissue growth factor (CTGF/CCN2). FG-3019 treatment increased PDA cell killing and led to a dramatic tumor response without altering gemcitabine delivery. Microarray expression profiling revealed the down-regulation by FG-3019 of several anti-apoptotic transcripts, including the master regulator Xiap, down-regulation of which has been shown to sensitize PDA to gemcitabine. Decreases in XIAP protein by FG-3019 in the presence and absence of gemcitabine were confirmed by immunoblot, while increases in XIAP protein were seen in PDA cell lines treated with recombinant CTGF. Therefore, alterations in survival cues following targeting of tumor microenvironmental factors may play an important role in treatment responses in animal models and, by extension, PDA patients. Total RNA was isolated from KPC mouse PDA tumors 9 days after initiation of treatment with IgG (n=7 biological replicates), FG-3019 (n=5), IgG + gemcitabine (n=6), or FG-3019 + gemcitabine (n=6) and hybridized to Affymetrix 430A 2.0 microarrays. CEL files were processed by GC-RMA and rescaled using median per-gene normalization in GeneSpring GX 7.3.1.
Project description:We investigated the genetic profiles of IL33 and PD1-treated group 2 innate lymphoid cells (ILC2) harvested from KPC tumors and draining lymph nodes in a pancreatic ductal adenocarcinoma (PDAC) mouse model.
Project description:Recently, Bailey et al (2016, Nature) defined four subtypes of pancreatic cancer that are associated with distinct histopathological characteristics and differential survival, namely, Squamous, Pancreatic Progenitor, Immunogenic, and ADEX (Aberrantly Differentiated Endocrine eXocrine). We set out to assess by RNASeq whether loss of CXCR2 was significantly associated with a specific PDAC subtype. Pancreatic tumors were harvested from KPC or KPC Cxcr2-/- mice at endpoint (n=5 v 5), RNA prepared, and RNASeq analysis carried out. Reads were analysed using the bcbio-nextgen framework (https://bcbio-nextgen.readthedocs.org/en/latest/). After quality control and adaptor trimming, reads were aligned to the mouse genome build (UCSC mouse mm10) using STAR. Counts for known genes were generated using the function featureCounts in the R/Bioconductor package \Rsubread\. The R/Bioconductor package edgeR was used to identify differentially expressed genes.
