Synthetic vulnerabilities of mesenchymal subpopulations in pancreatic cancer

Malignant neoplasms evolve in response to changes in oncogenic signalling. Cancer cell plasticity in response to evolutionary pressures is fundamental to tumour progression and the development of therapeutic resistance. Here we determine the molecular and cellular mechanisms of cancer cell plasticity in a conditional oncogenic Kras mouse model of pancreatic ductal adenocarcinoma (PDAC), a malignancy that displays considerable phenotypic diversity and morphological heterogeneity. In this model, stochastic extinction of oncogenic Kras signalling and emergence of Kras-independent escaper populations (cells that acquire oncogenic properties) are associated with de-differentiation and aggressive biological behaviour. Transcriptomic and functional analyses of Kras-independent escapers reveal the presence of Smarcb1–Myc-network-driven mesenchymal reprogramming and independence from MAPK signalling. A somatic mosaic model of PDAC, which allows time-restricted perturbation of cell fate, shows that depletion of Smarcb1 activates the Myc network, driving an anabolic switch that increases protein metabolism and adaptive activation of endoplasmic-reticulum-stress-induced survival pathways. Increased protein turnover renders mesenchymal sub-populations highly susceptible to pharmacological and genetic perturbation of the cellular proteostatic machinery and the IRE1-α–MKK4 arm of the endoplasmic-reticulum-stress-response pathway. Specifically, combination regimens that impair the unfolded protein responses block the emergence of aggressive mesenchymal subpopulations in mouse and patient-derived PDAC models. These molecular and biological insights inform a potential therapeutic strategy for targeting aggressive mesenchymal features of PDAC.

Nature doi: 10.1038/nature21064

see more...

Source: Nature Biological Sciences Research
Powered by WordPress | Designed by: video game | Thanks to search engine optimization, seo agency and Privater Sicherheitsdienst