Multiparametric tissue profiling to identify prognostic biomarkers and resistance mechanisms in B-cell Lymphoma
Resistance to immunotherapies can be mediated by intrinsic factors of tumor cells or the tumor microenvironment (TME). Recent data suggest that not only the composition of cell types and cell characteristics, but also the spatial organization of the TME may play a central role in understanding resistance mechanisms and identifying biomarkers to predict therapy response.
Multiplexed imaging technologies have greatly improved the ability to study healthy and diseased tissue at the single cell level while maintaining the spatial context. Co-detection by indexing (CODEX) is a multiplexed imaging technique based on the use of antibodies conjugated to unique oligonucleotides that can be imaged using fluorescently labeled complementary oligonucleotides. Repeated imaging cycles allow the examination of up to 57 biomarkers in one tissue section, resulting in a high-dimensional dataset that enables detailed characterization of cell composition, phenotype, and spatial features within the TME.
We apply multiparametric immunofluorescence to identify biomarkers of therapy response, resistance mechanisms and to understand disease biology. In our research, we focus on hematological malignancies such as lymphoma and leukemia, especially in the context of immunotherapies. For example, in the international research collaboration BIALYMP we investigate the modulators of response to bispecific antibodies in Follicular and Diffuse Large B cell Lymphoma.
In the international research consortium SYMMETRY we investigate tumor subpopulations in Diffuse Large B cell Lymphoma and their influence on chemotherapy resistance.
Beyond hematologic malignancies, we apply multiparametric immunofluorescence together with collaboration partners to understand disease biology and therapy response in many disease situations. One such collaborations is funded by the Düsseldorf School of Oncology and aims to investigate tumor-immune-microenvironment interactions within multiple distinct malignant entities and in an integrative cross-entity analysis.
