T-FITNESS · FINE-TUNING T CELL NETWORKS OF EXHAUSTION BY SYNTHETIC SENSORS

Cell and gene therapies offer a massive paradigm shift from current treatment options and hold the potential to cure previously untreatable diseases. Naturally-occurring and genetically modified T cells with chimeric antigen (CAR) or T cell receptors (TCR) have demonstrated remarkable curative capacities against advanced hematologic malignancies but have shown limited efficacy in treating solid tumors. Major barriers hindering the full antitumor potential of T cells are the immunosuppressive signals and persisting antigenic stimuli within the tumor microenvironment that inexorably push T cells into a highly dysfunctional state called “exhaustion”. Herein, we propose a groundbreaking technology, T-FITNESS, which will enable antitumor T cells to become refractory to exhaustion. At the core of the platform are microRNA (miRNA)-based synthetic logic circuits capable of rewiring the transcriptional networks orchestrating T cell exhaustion. By harnessing the power of CRISPR/Cas genome editing, we will integrate sensors of miRNAs upregulated in exhausted cells into untranslated regions of one or more transcription factors driving T cell exhaustion, to enable their fine-tuned downregulation. We will validate the reprogramming efficacy of T-FITNESS by performing extensive functional analyses in vitro and in vivo and advance the best circuits towards the clinic by developing an automated cGMP-compliant manufacturing process for point-of-care production of T-FITNESS-edited CAR-T cells. To develop this innovative platform, we will bring together a multidisciplinary consortium of academic and industry partners that combine their unique expertise in T cell therapy and immunology, synthetic biology, genome editing, cGMP manufacturing, bioinformatics, and communication. Easily integrable within CAR-T, TCR-T, and tumor-infiltrating lymphocyte (TIL) platforms, T-FITNESS will unleash the curative potential of T cell therapy for the benefit of an ever-growing number of cancer patients.