Our group is exploring how different immune cell signals contribute to the development of different B cell malignancies and how metabolic cues support the escape of B cell intrinsic checkpoint that usually prevent transformation. The current focuses of our laboratory are on the transformation of chronic lymphocytic leukemia to an aggressive lymphoma as well as the plasma cell disorder multiple myeloma. Mouse models recapitulating major mechanisms of the human diseases play an important role in our work. Using transgenic mouse lines with the Cre-loxP technology, we investigate the influence of several (signalling) factors on the development and the progression of diseases in these animal models.
CLL and Richter’s Transformation:
We have established a model to mimic the transformation of CLL to an aggressive lymphoma by additional oncogene activation. We have observed that acute oncogene activation in CLL cells induces cell death in the vast majority of CLL cells (Ecker et al, Nat. Comm. 2021). However, some cells survive and then outgrow the initial tumor and show several features of Richter cells. We now aim to use this model to understand which mechanism support the survival of the cells giving rise to the aggressive lymphoma and verify these in human samples. Understanding those mechanisms may help to prevent or treat the aggressive Richter lymphoma, which has a very poor clinical outcome.
CLL and drug resistance:
Another focus of our group is to understand how different signals from the microenvironment support the expansion of CLL cells despite the highly effective treatment with the BTK inhibitor ibrutinib or the BCL2 inhibitor venetoclax. We investigate strategies to prevent these interactions and to breach the strong immunosuppressive character of CLL cells, which then are tested in combination with immunotherapy (CAR T cells and checkpoint inhibition) in CLL.
We have generated a novel mouse model that reflects the human plasma cell disorder multiple myeloma in several ways. To gain insight into the pathogenesis of this deadly disease, we aim to analyse which factors contribute to the disease evolution in our model and compare it with data of human myeloma samples.
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