Characterization of stromal and innate lymphoid cell populations involved in immunotherapy resistance in High-Grade Serous Ovarian
High-grade serous ovarian cancers (HGSOC) are one of the most aggressive gynecological diseases in women and require new therapeutic strategies to improve patient outcomes. In recent years, cancer treatment has taken a step forward with the advent of immunotherapy, but it failed to be efficient in HGSOC. Recent data demonstrated thatmultiple cell types of the tumor microenvironment (TME) are involved in the anti-tumor response. Cancer associated fibroblasts (CAF) and innate lymphoid cells (ILC), components of the TME, sustain tumor progression through multiple mechanisms. Partners 1 and 2 identified specific CAF (CAF-S1) and ILC (ILCreg) populations, which display immunosuppressive functions on T cells. These studies highlight the importance of gaining a better knowledge of cell interactions within the TME, which influence response to therapy. Coupled with the bioinformatic expertise of Partner 3 in single cell analysis and deep learning approaches, our consortium is at the forefront of research to further investigate CAF-S1 and ILCreg and their complementary functions in mediating immunotherapy resistance in HGSOC patients. Partners of this proposal will focus their research program on 3 main axes: - Define the origin and plasticity dynamics of CAF-S1 and ILCreg in HGSOC - Determine their interactions with other immune components leading to immunotherapy resistance in HGSOC - Development of a new artificial intelligence (AI) tool to predict immunotherapy response Using cutting-edge technologies, the Partners will define how the heterogeneity of CAF and ILC is generated and will provide a comprehensive map of the TME evolution during HGSOC progression. By uncovering CAF-S1 and ILCreg reciprocal interactions with immune cells, we will better understand how they regulate tumor immunity. This deep characterization of the tumor ecosystem will help us to develop a new tool, based on deep learning and AI, to predict patient response to immunotherapy.
Funded by the European Union under the Horizon Europe Framework Programme - Grant Agreement Nº: 101095654. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them.