SENESCENCE-BASED SYSTEMS-MEDICINE STRATIFICATION FOR INDIVIDUALIZED LYMPHOMA THERAPY (SeneSys for iLymTx)
Personalized precision oncology aims for individually targeted therapies utilizing cancer mutations as actionable lesions. Despite the convincing underlying idea behind and remarkable successes seen in some tumor entities, the clinical benefit of such therapies is, unfortunately, often not long-lasting, because molecular heterogeneity, intramolecular mutations or collateral pathway activation account for resistance. Therefore, we pursue the conceptually novel strategy to target not a singular molecular lesion but rather a cancer-specific cellular “state switch”, typically presenting with a variety of pharmacological vulnerabilities. We are particularly interested in the treatment-induced state switch into cellular senescence, an apoptosis-comparable, terminal cell-cycle exit program that preferably malignant cells enter in response to anti-cancer therapy.
Our clinical focus is diffuse large B-cell lymphoma (DLBCL), a highly aggressive malignancy to which about one third of the patients ultimately succumbs. Neither additional substances nor genome analyzes have so far been able to replace and support the decades-old "R-CHOP" standard of care, thereby underscoring the unmet clinical need in this challenging cancer entity. DLBCL reflects the prime and long-standing clinical, preclinical and modeling expertise of the consortium partners, who contribute large high-quality clinical/molecular co-annotated datasets, actively run clinical trials for DLBCL patients, and cooperated in other consortia previously. Specifically, we build here on the computational description of novel DLBCL subtypes as well as senescence models and their mechanistic underpinnings, and link the static “at diagnosis” molecular profiles and the dynamic “under treatment” anticipation of a senescent state to clinical outcome on an individual basis. In this systems-medicine-driven demonstrator project, we propose a dynamic lymphoma characterization of distinct “state/fate” cluster models, which not only will serve as an outcome predictor, but also explain treatment resistance and, above all, will create the conceptual framework for innovative status-based sequential therapies such as the selective elimination of senescent cells (so-called “senolysis”).