Hypoxia contributes to tumor resistance to conventional anti-cancer therapies, such as radiotherapy and chemotherapy. We have previously reported that hypoxia is also involved in tumor resistance to immunotherapy by activating several mechanisms allowing tumor cells escape from Cytotoxic T Lymphocytes and Natural Killer cells. Because hypoxia is primarily mediated by the transcription factor HIF-1alpha, we postulated that impairing the transcriptional activity of HIF-1alpha, by preventing its dimerization with HIF-1beta, would enhance the anti-tumor immune response and improve the clinical benefit of immunotherapy based on Immune Checkpoint Blockades (ICBs). By using CRISPR/Cas9 technology, we have generated B16-F10 melanoma cells expressing HIF-1alpha mutant deleted in the domain responsible for its interaction with HIF-1beta. In mice bearing HIF-1alpha mutant, we showed that the tumor growth and weight were significantly inhibited compared to control. Such inhibition in tumor growth was associated with a significant increase in the infiltration of major cytotoxic immune cells into the tumor microenvironment and translated to a substantial improvement of mice survival. The aim of this project is to investigate the molecular mechanisms underlying the infiltration of immune cells into the tumor microenvironment, and evaluate the impact of modulating hypoxiaon the tumor vasculature in terms of blood vessel quality and integrity. Another aspect of the project is to assess the therapeutic benefit from ICBs-based immunotherapy in combination with hypoxia targeting strategy in appropriate preclinical mouse models. The net outcome of this project is to i) understand how manipulating hypoxia can improve the benefit of cancer immunotherapy and ii) provide the preclinical proof of concept to setup innovative cutting-edge cancer immunotherapy approaches.
|Effective start/end date||1/11/21 → 1/12/25|
- FNRS - Fonds National de la Recherche Scientifique: €96,824.00
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.