Reverting immune suppression to elicit brain metastasis control
Treatment strategies against brain metastases (BrM) do not alter disease course leading to 2-year overall survival below 10%. Emerging protocols that are effective against disseminated cancer are leading to a paradoxical increase in brain relapse. Thus, BrM. represents a growing societal challenge as it often becomes the most relevant clinical entity in patients with an otherwise controlled systemic disease. In the brain, immune checkpoint blockade (ICB) may not be sufficient to overcome the hurdles associated with an established immune suppressive tumor microenvironment (TME), as reflected by the limited benefits on symptomatic metastases, likely due to reduced abundance of T cells and penetration of therapeutic antibodies compared to other organs. Thus, we hypothesize that lifting immune suppression locally is a pre-requisite to achieve full benefit of immunotherapies in BrM. Aim 1: we will apply RNAseq (single cell and bulk) and spatially resolved multispectral imaging to clinical cohorts of human BrM, including patients treated with ICB, to define, in a holistic approach, TME candidates that govern local immunosuppression and/or resistance to ICB in BrM. Aim 2: we will take advantage of a Phase II trial using a STAT3 inhibitor in patients with BrM as well as preclinical models and patient-derived organotypic cultures (METPlatform), to explore the functional and molecular consequences of targeting STAT3 and/or other recently identified candidates on (1) BrM progression and (2) resistance to ICB. This will be complemented by longitudinal biomarkers tracking in liquid biopsies using next generation biosensors, with the objective to integrate them in a portable point-of-care tool kit. Aim 3: we will leverage the data sets generated in Aim1 and 2 to delineate the broader impact of immunotherapies in human BrM, predict new determinants of local immunosuppression and anticipate resistance mechanisms, that will be explored in rationally designed pre-clinical studies.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.