Novel therapies in JAK/STAT driven T-cell malignancies (JAKSTAT-TARGET)

Mature T-cell leukemias/lymphomas (MaTCL) are hematologic malignancies of mostly incurable prospects in light of limited efficient therapies. As a heterogeneous group of tumors, single MaTCL types are rare, which impedes large-volume biomaterial and data collections and clinical studies. Hyperactivating somatic mutations in JAK/STAT genes stand out as high-incidence aberrations across MaTCL entities. Particularly the model diseases investigated here, T-cell prolymphocytic leukemia (T- PLL) and T-cell large granular lymphocyte leukemia (T-LGLL), carry those in 50-70% of cases. This interdisciplinary JAKSTAT-TARGET consortium of immunologists, hematologists, structural chemists, and systems biologists capitalizes on unique resources such as clinical registry-linked sample repositories, new high-fidelity mouse models, pipelines of structure-based target design, and in-silico machine learning tools. We propose that targeting the JAK/STAT signalling network in synergistic combinations with drugs inhibiting other inter-connected key driver pathways will improve individualized anti-leukemic efficacy. We will achieve this by 3 objectives (O): O1 will address the causal T-cell-receptor and cytokine mediated impact on genome integrity and on the occurrence of JAK/STAT mutations. It interrogates the biochemical and functional consequences of the mutated and unmutated clones, and derives actionable differential vulnerabilities. O2 will study the performance of identified synergistic drug combinations using in-house developed optimized STAT- inhibitors in primary patient samples and novel animal models. O3 will implement drug-screen data from patient material into an ongoing clinical trial. Ultimately, with machine-learning algorithms we will integrate the harmonized data of genomic profiles, drug-sensitivity patterns, and clinical outcomes from all objectives toward multi-omics guided predictions of optimal choices for trial designs and individual-patient based therapy selection.