Technology
Scientists at Goethe University Frankfurt and DKFZ Heidelberg have developed a proteogenomic diagnostic platform for diffuse large B-cell lymphoma (DLBCL) that classifies tumors into seven proteogenotypes reflecting distinct pathophysiological features.
By combining genomic, proteomic and tumor microenvironment features, the platform enables early risk stratification and supports more personalized treatment decisions.
A particularly important finding is the identification of PG4-like DLBCL, a clinically aggressive subgroup associated with poor outcome under standard R-CHOP therapy.
The approach uses patient tissue, such as FFPE lymph node or other lymphoma tissue, and applies sequencing, proteomics and immunoassays together with an XGBoost-based machinelearning classifier to distinguish PG4-like cases from other DLBCL subtypes with high accuracy.
