MTB-Report
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-Seminar
The MT-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 10.30 am .
Coordination/Contact: Dr. Jürgen Dönitz
Date:Wednesday: 24.04.2024 at 12.30pm
Location: online
Speaker: Prof. Dr. Jan Baumbach
Topic: My health, my data - Privacy-preserving prognostic model learning on distributed data for MTB decision support
Abstract: tba
If you would like to subscribe on our mailing list, please click here.
Press releases
Press release from the UMG:
Molekulares Tumorboard: Zielgerichtet gegen fortgeschrittene Krebserkrankungen vorgehen
Press release from the Volkswagenstiftung:
Big Data: 18 Millionen Euro für 16 innovative Projekte in den Lebenswissenschaften
Publications
Yang J, Beißbarth T, Dönitz J.
Onkopipe: A Snakemake Based DNA-Sequencing Pipeline for Clinical Variant Analysis in Precision Medicine.
Stud Health Technol Inform. 2023 Sep 12;307:60-68. doi: 10.3233/SHTI230694. PMID: 37697838.
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
doi:10.3233/SHTI220806
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
doi: https://doi.org/10.1093/bib/bbab134
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.
doi:10.1186/s13073-018-0529-2.
Contact data of the coordinator
contact information
- telephone: +49 551 3961781
- fax: +49 551 3961783
- e-mail address: tim.beissbarth(at)bioinf.med.uni-goettingen.de
- location: 2.107
That might also interest you