An automated data integration platform for interpreting genomic data and reporting treatment options in molecular tumor boards

Aims of this collaborative project

With growing knowledge of biomarkers and resent developments in sequencing techniques, nowaday genomic data is considered as extremely valuable and indispensable for the diagnosis and therapy recommendation particularly of tumor patients. The complexity of interpreting genomic data is hindering its application in the routine clinical context.
In the MTB-Report project the consortium aims to develop a tool to support the decision finding in a molecular tumor board (MTB). Biomarkers and other omics data from the patient will be compared to a multitude of available data bases in order to find case studies with a  parameter set equivalent to the patient’s one. Based on tumor type and certainty of the study an evidence level is assigned and used to propose the most relevant to the MTB in form a short report.

For access to the clinical data and to ensure data privacy the tool will be embedded in the IT-infrastructure. Clinical experts will define use cases for an optimal usage of the reporting tool and will validate the results.

Project partners

Department of Medical Bioinformatics (UMG)

Prof. Tim Beißbart
Dr. Jürgen Dönitz

Department of Medical Informatics (UMG)

Prof. Ulrich Sax

Department of Hematology and Medical Oncology (UMG)

Prof. Annalen Bleckmann (now Münster University Hospital, UKM)
Dr. Raphael Koch 

MTB-Report Seminar

The MTB-Report Seminar Series is a joint event of all partners and members of the MTB-Report project. Monthly, members of the different groups as well as invited speakers present their projects.

Unless announced otherwise, the seminar takes place monthly on the fourth Wednesday at 10.30am . Due to the SARS-CoV-2 related measures, the seminar takes place via video conference .

Coordination/Contact: Dr. Jürgen Dönitz 

Date: 5.10.2022, 10.30 am

Speaker: Nicole Schmidt

Topic: Targeting DNA damage response in peripheral T-cell lymphomas



Peripheral T-cell lymphomas (PTCLs) present significant diagnostic and therapeutic challenges. Following current treatment regimens, the majority of patients either do not achieve a remission or experience an early relapse with fatal outcome. Hence, the development of innovative therapeutic strategies targeting the underlying biology of PTCLs is highly demanded.


Utilizing bioinformatic methodology for molecular tumorboards, genetic data from >1800 patients with T-cell lymphomas (TCL) were analyzed and queried for potential therapeutics addressing genetic alterations in TCL. Based on the results of this approach, a broad in vitro drug screening was performed to assess the activity of potential therapeutics in genetically and transcriptionally characterized cell lines of TCL and address potential biomarkers of response and resistance. Furthermore, functional apoptosis profiling, combination drug screening and mechanistic work up was performed to identify and mechanistically explore novel drug combinations.


Integration of genetic alterations and drug sensitivity screens revealed novel therapeutic strategies for PTCL. Notably, multiple components of DNA damage response pathways were suggested as therapeutic targets, most prominently the cell cycle regulator WEE1. Indeed, the clinical-grade WEE1 kinase inhibitor adavosertib potently induced premature mitotic entry, accumulation of DNA damage and induction of apoptosis in PTCL cell lines. To further enhance the therapeutic effects of WEE1 inhibition, we explored potential combination strategies through mechanistic studies and functional apoptosis profiling. Here, we identified synergistic effects in DNA damage response pathways, cell cycle regulation and modulation of anti-apoptotic proteins through combined WEE1 and JAK/STAT inhibition. Indeed, the combination of adavosertib and ruxolitinib showed marked cytotoxicity in genetically characterized subsets of T-cell lymphoma. 


Our results classified WEE1 as a promising therapeutic target for genetically defined PTCL subgroups. Mechanistic studies identified concepts for rational combination strategies of WEE1 and JAK/STAT inhibition in PTCL subtypes.

Former and upcoming Talks

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Kurz NS, Perera-Bel J, Höltermann C, Tucholski T, Yang J, Beissbarth T, Dönitz J: 
Identifying Actionable Variants in Cancer – The Dual Web and Batch Processing Tool MTB-Report
Volume 296: German Medical Data Sciences 2022 – Future Medicine: More Precise, More Integrative, More Sustainable

Schlotzig V, Kornrumpf K, König A, Tucholski T, Hügel J, Overbeck TR, Beissbarth T, Koch R, Dönitz J.
Predicting the Effect of Variants of Unknown Significance in Molecular Tumor Boards with the VUS-Predict Pipeline.
Stud Health Technol Inform. 2021 Sep 21;283:209-216.
doi: 10.3233/SHTI210562

Borchert, F., Mock A., ...., Hügel J., et al.
Knowledge bases and software support for variant interpretation in precision oncology
Briefings in Bioinformatics, bbab134, 2021, 1–17

Publications from preliminary work

Perera-Bel J., Leha A., Beißbarth T.
Bioinformatic Methods and  Resources for Biomarker Discovery, Validation, Development, and  Integration.
In: Badve S., Kumar G. (eds) Predictive Biomarkers in  Oncology. Springer, Cham. (2019)

Perera-Bel J, Hutter B, Heining C, Bleckmann A, Fröhlich M, Fröhling S, Glimm H, Brors B, Beißbarth T.
From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards.
Genome  Med. 2018 Mar 15;10(1):18. 

Contact data of the coordinator


Prof. Dr. Tim Beißbarth

Prof. Dr. Tim Beißbarth

contact information


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