The PI focuses on cancer research at the interface between patients and in vivo and in vitro model systems in prostate carcinoma (PCa) and anaplastic large cell lymphoma (ALCL). PCa is the most frequently diagnosed cancer in men. We investigate PCa aggressiveness through hormonal (Aksoy et al. 2020), JAK/STAT (Pencik et al. 2015) and epigenetically (Limberger et al. 2022) regulated pathways. The resulting data complexity is analyzed in human patient samples and transgenic mouse models by Bioinformatics and AI, which leads to the identification of potential biomarkers for aggressive PCa entites exemplified by Oberhuber et al. 2020. Another focus is anaplastic large cell lymphoma (ALCL), a highly malignant T-cell lymphoma regulated by ALK fusions in 50% of the cases. We could show the importance of the PDGFRß for the survival of tumorcells (Laimer et al 2012) and demonstrated recently STAT5 as druggable downstream target of PDGFRß (Garces et al 2022). In addition, we are interested in the role of microplatics in tumorigenesis of coloncancer (Gruber et al 2022). This is topic of a recently funded 4 Mill € project headed by LK.
Pathological and molecular examination of transgenic mouse models is undertaken to identify the biological features supporting initiation, invasion and dissemination of PCa and ALCL. We aim to elucidate the roles of Jak/Stat signaling, but also those of hormone action (androgen and thyroid hormones in PCa) and altered metabolic processes (citrate cycle, oxidative phosphorylation and lipid degradation) in depth to identify novel drivers of tumor progression and mechanisms controlling metastasis in PCa and ALCL.
The laboratory uses transgenic mouse models and human tumor tissue specimens for analyses. We apply genomics, transcriptomics and proteomics-based screens on formalin fixed paraffin embedded (FFPE) material in order to identify key players and pathways in the tumorigenic processes. Suspected candidates are subjected to detailed functional in vitro and murine xenograft in vivo experiments with the attempt to carry out a therapeutic intervention. Omics data are correlated retrospectively with tumor progression, treatment and patient survival data to discover new biomarkers which are important for diagnosis, prognosis and therapy.