Aggressive Blood Cancer: Contribution of Enzyme MALT1 Uncovered

MALT1 Is A Therapeutic Target in Lymphoma

Lymphoma is a type of cancer that is very heterogeneous in nature and originates in the lymphatic system. Many fast-growing aggressive lymphomas are resistant to current therapies. MALT1 protease is important for the survival of many aggressive lymphomas and thus a promising target to treat such cancers. Several academic and industrial drug discovery programs, one headed by Prof. Daniel Krappmann at Helmholtz Munich’s Molecular Targets and Therapeutic Center, have promoted clinical trials that evaluate MALT1 inhibitors for the treatment of cancer. However, the exact mechanisms of how MALT1 drives lymphoma cell survival have largely remained elusive. The present study led by the biomedical scientist Daniel Krappmann in cooperation with computational scientist Michael Menden demonstrates that MALT1 protease activity drives lymphoma cell survival by controlling mRNA metabolism. The work highlights that oncogenic mutations inside the cancer cells are directly affecting the stability of distinct mRNAs, opening an avenue for new therapeutic targets and molecular biomarkers to classify patients and predict patient responses to therapy.

Development of New Precision Therapies for Lymphomas

Based on these findings, the researchers aim to develop new biomarkers for the prediction of lymphoma patients that will most likely profit from MALT1-directed therapy, which in turn will help to improve the design of the clinical trials. Further, the data will facilitate monitoring of the patients' clinical responses, when they are treated with MALT1 inhibitors. Finally, regulation of mRNA metabolism may open avenues for new therapeutic approaches to improve anti-lymphoma therapies.

About the scientists

Prof. Dr. Daniel Krappmann, Director of Signaling and Translation at the Molecular Targets and Therapeutic Center at Helmholtz Munich

Prof. Dr. Michael P. Menden, Principal Investigator at the Institute of Computational Biology at the Computational Health Center at Helmholtz Munich and Associate Professor at the University of Melbourne, Department of Biochemistry and Pharmacology

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_{This work was supported by the Deutsche Krebshilfe award grant 70112622 and the Deutsche Forschungsgemeinschaft (DFG) – Project ID 210592381 – SFB 1054 A04 and ID 360372040 – SFB 1335 P07 to D.K. and the European Union's Horizon 2020 Research and Innovation Programme (Grant agreement No. 950293 - COMBAT-RES) to M.P.M and by the Federal Ministry of Education and Research (BMBF)-funded German Network for Bioinformatics Infrastructure (de.NBI).}