SPARC dependent collagen deposition and gemcitabine delivery in a genetically engineered mouse model of pancreas cancer.

BACKGROUND:Pancreatic ductal adenocarcinoma (PDAC) is characterised by extensive matrix deposition that has been implicated in impaired drug delivery and therapeutic resistance. Secreted protein acidic and rich in cysteine is a matricellular protein that regulates collagen deposition and is highly upregulated in the activated stroma subtype with poor prognosis in PDAC and were generated and analysed at different stages of carcinogenesis by histological grading, immunohistochemistry for epithelial and stromal markers, survival and preclinical analysis. Pharmacokinetic and pharmacodynamic studies were conducted by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and immunohistochemistry following gemcitabine treatment (100 mg/kg) in vivo. FINDINGS:Global genetic ablation of in a Kras^G12D driven mouse model resulted in significantly reduced overall and mature collagen deposition around early and advanced pancreatic intraepithelial neoplasia (PanIN) lesions and in invasive PDAC (p < .001). However, detailed pathological scoring and molecular analysis showed no effects on PanIN to PDAC progression, vessel density (CD31), tumour incidence, grading or metastatic frequency. Despite comparable tumour kinetics, ablation of duanyu1842RC resulted in a significantly shortened survival in mice (280 days versus 485 days, p < .03, log-rank-test). Using LC-MS/MS, we show that duanyu1842RC dependent collagen deposition does not affect intratumoural gemcitabine accumulation or immediate therapeutic response in tumour bearing and duanyu1842RC ablation reduces the collagen-rich microenvironment in murine PDAC. Moreover, global duanyu1842RC depletion did not affect tumour growth kinetics, grading or metastatic frequency. Notably, the dense-collagen matrix did not restrict access of gemcitabine to the tumour. These findings may have direct translational implications in clinical trial design.

KEYWORDS: {{ getKeywords(articleDetailText.words) }}