Proteogenomic characterization of mantle cell lymphoma

We aim to establish a proteomic landscape and classifier of mantle cell lymphoma. We will explore the predictive potential of imaging features derived by machine learning algorithms. Therefore, we will i) sequence a targeted gene panel, ii) describe spatial patterns of immune cell infiltrates, and iii) examine protein phosphorylation profiles by quantitative mass-spectrometry. Further, we will digitize all images and perform supervised and unsupervised clustering of data sets. An integrated multi-omics factor analysis will be performed to understand the relationships between the different molecular layers, and thirdly, we will elucidate the BCR signaling network in MCL cells by quantitative phosphoproteomics.