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Pathway activation patterns in diffuse large B-cell lymphomas
S Bentink1, S Wessendorf, C Schwaenen
1Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
Abstract:
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.
Insights
Machine learning identified pathway activation patterns (PAPs) in aggressive B-cell lymphomas. These patterns revealed four distinct patient groups with unique characteristics and prognoses, aiding personalized cancer treatment.
Area of Science:
- Oncology
- Bioinformatics
- Cancer Biology
Background:
- Cancer involves deregulation of cell growth and survival pathways.
- Pathway activity varies even within the same cancer type.
- Targeted therapies require understanding tumor-specific pathway activation.
Purpose of the Study:
- To develop pathway activation signatures for cancer.
- To classify mature aggressive B-cell lymphomas based on pathway activity.
- To identify distinct patient subgroups for improved treatment strategies.
Main Methods:
- Utilized semi-supervised machine learning to create pathway signatures.
- Combined signatures into pathway activation patterns (PAPs).
- Analyzed PAPs in well-characterized aggressive B-cell lymphoma cohorts.
Main Results:
- Identified PAPs conserved across tissue types.
- Discovered four distinct molecular subgroups within aggressive B-cell lymphomas, separate from Burkitt lymphoma.
- These subgroups exhibited unique biological features, genetic aberrations, and prognoses.
- Findings were validated in an independent dataset.
Conclusions:
- Pathway activation patterns (PAPs) effectively stratify mature aggressive B-cell lymphomas.
- This molecular subtyping offers insights into distinct disease biology.
- PAPs have potential for guiding personalized cancer therapy decisions.
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