Rationale: T-PLL is the most frequent mature T-cell leukemia, yet it occurs at an incidence of 0.6/Mio in the EU. Its chemotherapy resistance translates into an average patient survival of <20 months.
There are no approved drugs for T-PLL and numerous exploratory or comparative trials to test novel options are not feasible. First inhibitor screens, piloted by our teams, uncovered promising, but also differential sensitivities. However, we are still far from informed implementation of new molecular knowledge into clinical application. This is mostly due to lack of integration of available multi-level profiling data and a rudimentary understanding of their functional impact.
Moreover, gene-regulatory, particularly epigenetic changes and metabolic cellular states, both also known to influence treatment responses in cancer, have not been addressed in T-PLL. Overall, we miss a concept of how drug activity patterns relate to T-PLL’s molecular landscape. Hypothesis: Integration of genomic, epigenetic, and phenotypic data will allow predictions of differential compound sensitivities, which in conjunction with clinical information will aid in individual treatment decisions and future trial designs. Aims / Methods: The 5 teams of ERANET-PLL will capitalize on unique prerequisites, e.g. a large repository of well-annotated material, an open registry, or T-PLL animal models.
We will analyze to which degree genomic and epigenetic alterations as well as basal and inhibitor-induced metabolic signatures dictate differential substance activities. Bio-computational modelling will integrate these genotypic and phenotypic dimensions towards prediction tools of in-vitro drug sensitivities and synergies. Drug candidates will be validated in various preclinical systems. The extracted set of molecular strata will finally be interrogated in a prospective interventional study. Expected Results: Biomarkers that discern T-PLL patient subsets based on vulnerabilities towards targeted compounds.