A multi parametric Regulatory T cell Atlas in cancer to predict immunotherapy re...
A multi parametric Regulatory T cell Atlas in cancer to predict immunotherapy response adverse autoimmune events and to identify novel therapeutic targets
The advent of immune checkpoint inhibitors (ICI), that target regulatory pathways in T cells to unleash antitumor immune responses, has revolutionized cancer immunotherapy. However, despite the enormous success of ICI in the treat...
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Información proyecto RegSign
Duración del proyecto: 62 meses
Fecha Inicio: 2020-10-27
Fecha Fin: 2025-12-31
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The advent of immune checkpoint inhibitors (ICI), that target regulatory pathways in T cells to unleash antitumor immune responses, has revolutionized cancer immunotherapy. However, despite the enormous success of ICI in the treatment of cancer, a significant proportion of patients do not respond, while responses are frequently accompanied by life-threatening autoimmune related adverse events (irAEs). Predictive biomarkers of clinical responses and new therapeutic strategies to harness anti-tumor immunity, while keeping autoimmunity in check represent a currently unmet need. Tregs, are an ideal candidate, since they are physiologically engaged in maintenance of self-tolerance, are the dominant suppressive population in the tumor microenvironment (TME), promote tumor growth, correlate with poor prognosis and importantly, represent a fundamental impediment of cancer immunotherapy success. Building upon my extensive expertise on Tregs in autoimmunity and cancer, the abundance of circulating Tregs in cancer patients, and preliminary data demonstrating a robust transcriptomic and metabolic reprogramming of circulating Tregs in advanced melanoma patients experiencing irAEs, RegSign proposes: 1) to integrate single cell mass-cytometric, transcriptomic, metabolic and stability analysis of circulating Tregs to create a liquid biopsy for prediction of ICI efficacy and irAEs development, 2) to identify novel Treg-based therapeutic targets via innovative humanized patient derived xenograft models and 3) to modulate Treg metabolic checkpoints for the management of irAEs. Decoding Tregs signatures in cancer will empower the discovery of personalized predictive biomarkers and novel targets for cancer immunotherapy.