Chuan Wang
A5014469246- Peptidase Inhibition and Analysis
- Cancer Immunotherapy and Biomarkers
- Pancreatic and Hepatic Oncology Research
- Immune Cell Function and Interaction
- Immune cells in cancer
- IgG4-Related and Inflammatory Diseases
- Cancer, Hypoxia, and Metabolism
- Lung Cancer Research Studies
- Neuroendocrine Tumor Research Advances
- Congenital Diaphragmatic Hernia Studies
- Cancer Research and Treatments
University of Liverpool
2022-2023
Abstract Myofibroblastic cancer-associated fibroblast (myoCAF)–rich tumors generally contain few T cells and respond poorly to immune-checkpoint blockade. Although myoCAFs are associated with poor outcome in most solid tumors, the molecular mechanisms regulating myoCAF accumulation remain unclear, limiting potential for therapeutic intervention. Here, we identify ataxia-telangiectasia mutated (ATM) as a central regulator of phenotype. Differentiating myofibroblasts vitro cultured ex vivo...
<div>Abstract<p>Myofibroblastic cancer-associated fibroblast (myoCAF)–rich tumors generally contain few T cells and respond poorly to immune-checkpoint blockade. Although myoCAFs are associated with poor outcome in most solid tumors, the molecular mechanisms regulating myoCAF accumulation remain unclear, limiting potential for therapeutic intervention. Here, we identify ataxia-telangiectasia mutated (ATM) as a central regulator of phenotype. Differentiating myofibroblasts...
<div>Abstract<p>Myofibroblastic cancer-associated fibroblast (myoCAF)–rich tumors generally contain few T cells and respond poorly to immune-checkpoint blockade. Although myoCAFs are associated with poor outcome in most solid tumors, the molecular mechanisms regulating myoCAF accumulation remain unclear, limiting potential for therapeutic intervention. Here, we identify ataxia-telangiectasia mutated (ATM) as a central regulator of phenotype. Differentiating myofibroblasts...
<p>Significant DDR genesets (from Leading-Edge analysis in GSEA) used to make the DDR_TGFB_signature geneset</p>
<p>Significant DDR genesets (from Leading-Edge analysis in GSEA) used to make the DDR_TGFB_signature geneset</p>
<p>macros for image quantifications</p>
<p>list of primer sequences, antibody details and primary fibroblasts used</p>
<p>macros for image quantifications</p>
<p>supplementary figures and legends</p>
<p>GSEA between different tumour stromal datasets from GEO and DDR & myoCAF genesets</p>
<p>GSEA between different tumour stromal datasets from GEO and DDR & myoCAF genesets</p>
<p>Non-redundant list of the leading-edge genes and ToppFun pathway analysis from genesets in Suppl. File 4 used to make DDR-TGFB_signature geneset</p>
<p>list of primer sequences, antibody details and primary fibroblasts used</p>
<p>supplementary figures and legends</p>
<p>Non-redundant list of the leading-edge genes and ToppFun pathway analysis from genesets in Suppl. File 4 used to make DDR-TGFB_signature geneset</p>