Project Details
Projekt Print View

Functional proteogenomic characterization of mantle cell lymphoma

Applicant Dr. Julius Enßle
Subject Area Hematology, Oncology
Term from 2021 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 463138470
 
Final Report Year 2024

Final Report Abstract

Mantle-cell lymphoma (MCL) is a B-cell Non-Hodgkin-Lymphoma (B-NHL) characterized by molecular and clinical heterogeneity. Despite recent advances in the use of targeted agents, immunochemotherapy, and cell therapeutics, drug resistance and relapses occur frequently. Hence, it is of high relevance to comprehensively characterize MCL, including different layers of biological and disease-related information, to identify high-risk patients and tailor novel therapies based on deep molecular profiles. To this end, we elucidated the proteogenomic landscape of 261 patients with de novo MCL. We used transcriptomic expression data from RNA sequencing and generated global proteome expression data by mass spectrometry analysis from formalin-fixed and paraffin-embedded tissue material. For multi-omics data integration of the different data layers, we applied a multiview latent variable model with domain-informed structured sparsity in the form of B-NHL-related feature sets. By inference of multi-omic latent factors within and across all data modalities, we identified distinct MCL proteogenotypes and multi-omic factors that associate with survival phenotypes. These proteogenotypes were mostly not linked to previously established risk factors, disease characteristics, or morphologic phenotypes. Moreover, characteristic biological motifs were enriched in these proteogenotypes, which were associated with differences in tumor proliferation, immune cell signals, and stromal infiltration. Together, this expands the present biological classification of MCL by including proteogenotypes with prognostic relevance. The integrated proteogenomic dataset provides a comprehensive resource for further molecular and translational research in MCL.

Publications

 
 

Additional Information

Textvergrößerung und Kontrastanpassung