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Deregulation of cell signaling pathways controlling cell growth and cell survival is a common feature of all cancers. Although a core repertoire of oncogenic mechanisms is widely conserved between various malignancies, the constellation of pathway activities can vary even in patients with the same malignant disease. Modern molecularly targeted cancer drugs intervene in cell signaling compensating for pathway deregulation. Hence characterizing tumors with respect to pathway activation will become crucial for treatment decisions. Here we have used semi-supervised machine learning methodology to generate signatures of eight oncogene-inducible pathways, which are conserved across epithelial and lymphoid tissues. We combined them to patterns of pathway activity called PAPs for pathway activation patterns and searched for them in 220 morphologically, immunohistochemically and genetically well-characterized mature aggressive B-cell lymphomas including 134 cases with clinical data available. Besides Burkitt lymphoma, which was characterized by a unique pattern, the PAPs identified four distinct groups of mature aggressive B-cell lymphomas across independent gene expression studies with distinct biological characteristics, genetic aberrations and prognosis. We confirmed our findings through cross-platform analysis in an independent data set of 303 mature aggressive B-cell lymphomas.

Original publication




Journal article



Publication Date





1746 - 1754


Computational Biology, Databases, Nucleic Acid, Epithelium, Gene Expression Profiling, Humans, Immunohistochemistry, Lymph Nodes, Lymphoma, Large B-Cell, Diffuse, Signal Transduction