DOI: 10.5061/dryad.1vhhmgqzc
title: Functional antagonism between STAT3 and SMAD4 regulates EMT

description: Oncogenic mutations in KRAS are among the most common in cancer. Classical
 models suggest that loss of epithelial characteristics and the acquisition
 of mesenchymal traits are associated with cancer aggressiveness and
 therapy resistance. We identify STAT3 as a genetic modifier of
 TGF-beta-induced epithelial to mesenchymal transition in mutant KRAS
 tumors. RNA sequencing was performed with murine cells expressing mutant
 KRAS either overexpressing hyperactive STAT3Y640, or CRISPR-mediated
 knockout of STAT3, SMAD4, or KRAS. Excel files of differential expression
 compared to control mutant RAS cells or fpkm files are provided.
description: Total RNA was isolated using the PureLink RNA kit (ThermoFisher)
 and phenol-extracted. Whole exome RNA sequencing with bioinformatics was
 performed by Novogene Corp.. Excel data files include: 1)
 KRAS<sup>G12D</sup>:p53null mouse embryo fibroblasts (MEF
 KP)(Ischenko et al. 2013 PNAS) and MEF KP expressing hyperactive STAT3
 Y640F, 2) Murine
 KRAS<sup>G12D</sup>:p53<sup>R172H</sup> PDAC
 (KPC)(Hingorani et al. 2005 Cancer Cell) and KPC KRAS knockout cells, 3)
 KPC and KPC STAT3 knockout cells, 4) KPC SMAD4 knockout cells.
description: # Functional antagonism between STAT3 and SMAD4 regulates EMT ###
 Contributors: Stephen DAmico, Varvara Kirillov, Oleski Petrenko, and Nancy
 Reich **Description**: We evaluated STAT3 as a cancer dependency in
 KRAS-mediated tumorigenesis with established models using mouse embryonic
 fibroblasts and pancreatic ductal adenocarcinoma cells. The data highlight
 antagonistic epistasis between SMAD4 and STAT3, where SMAD4 expressing
 tumors are poorly differentiated in the absence of STAT3, while SMAD4
 deficient tumors are well-differentiated in the presence of STAT3.
 **Techniques**: Data include RNA Sequencing analysis of genetically
 modified tumorigenic cells lines. Total RNA was isolated using the
 PureLink RNA kit (ThermoFisher) and phenol-extracted. Whole exome RNA
 sequencing with bioinformatics was performed by Novogene Corp.. Excel data
 files are included: 1\) Gene ID, log fold change, and fpkm for RNA
 expression in KRASG12D:p53null mouse embryo fibroblasts (MEF KP)(Ischenko
 et al. 2013 PNAS) and MEF KP expressing hyperactive STAT3 Y640F 2\) Gene
 ID, log fold change, and fpkm for RNA expression in Murine
 KRASG12D:p53R172H PDAC (KPC)(Hingorani et al. 2005 Cancer Cell) and KPC
 KRAS knockout cells, 3\) Gene ID, log fold change, and fpkm for RNA
 expression in KPC and KPC STAT3 knockout cells, 4\) Gene ID and fpkm for
 RNA expression in KPC SMAD4 knockout KPC cells **Abbreviation  
             Description** Gene ID                       Ensembl gene
 identification number Gene name                 Abbreviation of gene name
 Log2 fold change         log2 difference between samples
 Pvalue                         probability difference due to chance
 Padj                             Pvalue incorporating multiple comparison
 testing Chrom                         chromosome number for location of
 gene \+ or -                           plus or minus gene encoding DNA
 strand Start                            aligned numerical locus on
 chromosome start of cDNA sequencing End                             
 aligned numerical locus on chromosome end of cDNA sequencing Gene
 length                nucleotide length of gene Gene
 type                   protein coding, RNA, pseudogene, processed
 transcript Gene description        known function of protein or RNA
 Readcount                   number of cDNA fragments sequences (reads)
 mapped to each gene fpkm                            fragments per kilobase
 of million reads mapped