Molecular Tumor Board

Executive Summary

Today, treatment alternatives for every oncology patient are individually discussed by medical

experts in so-called tumor boards. However, the molecular analysis of each tumors is very complex and the assessment of individual tumor variants time-consuming. Therefore, specialized molecular tumor boards focus also on the analysis and therapy assessment incorporating fine-grained molecular data.

We worked together with subject-matter experts to defined and evaluate a standardized clinical process, designed adequate tool support, and integrated relevant data source for an improved tumor board experience in molecular tumor boards. For example, we applied the scrum board approach, which is well-known from software engineering, to the clinical challenge.

Background

Molecular analysis of tumors is the key driver of precision medicine in oncology. It enables the matching of mutations to targeted drugs and hence more personalized treatments. With its steady advances and ever decreasing costs, it is likely to become a standard procedure for diagnosis and treatment. In molecular tumor boards, regular meetings among medical experts, treatment options are discussed and suggested for each patient based on the tumor’s genetic markup. A molecular tumor board (MTB) consists of 4 major phases: preparation, meeting, treatment suggestion documentation and follow-up. Our MTB Support Engine aims to streamline the entire process in order to tackle the key challenges of molecular tumor boards. The tool lends the Scrum methodology that has already proven itself in software development.

The most critical part of a MTB is the preparation. Genetic variants need to be researched upon available medication. For this purpose, oncologists query various databases and search engines. This is time-consuming and therefore constitutes a key bottleneck for a comprehensive rollout. The MTB support engine accelerates the preparatory research work by

  1. Showing available information resources for a given variant
  2. Ranking and visualizing variants by various criteria and displaying most important ones first.
  3. Simplifying the saving of reviewed information in a click-and-collect fashion

The MTB Support Engine furthermore lets doctors generate a presentation view of relevant information with a single click. In addition, an emphasis it put on collaborative work. Every participant has access to the system allowing preparing themselves. During the meeting, decided upon treatment suggestions can be documented for inspection by the treating physician and allows a structured follow-up.

Most importantly, all information including researched annotations are saved. This allows the rapid retrieval of historic cases with genetically similar patients. Leveraging data integrated from multiple hospitals, even the discovery of novel correlations between variants and diseases is made possible in the long run.

Related Content

Project Partners

Publications

Research Publications

  • 1.Borchert, F., Lohr, C., Modersohn, L., Witt, J., Langer, T., Follmann, M., Gietzelt, M., Arnrich, B., Hahn, U., Schapranow, M.-P.: GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers. Proceedings of the Language Resources and Evaluation Conference. bll. 3650–3660. European Language Resources Association, Marseille, France (2022). 
  • 2.Henkenjohann, R., Bergner, B., Borchert, F., Bougatf, N., Hund, H., Eils, R., Schapranow, M.-P.: An Engineering Approach towards Multi-Site Virtual Molecular Tumor Board Software Support. In: Pissaloux, E., Papadopoulos, G., Achilleos, A., en Velázquez, R. (reds.) ICT for Health, Accessibility and Wellbeing. IHAW 2021. bll. 156–170. Springer, Cham (2022). 
  • 3.Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021). 
  • 4.Rasheed, A., Borchert, F., Kohlmeyer, L., Henkenjohann, R., Schapranow, M.-P.: A Comparison of Concept Embeddings for German Clinical Corpora. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). bll. 2314–2321 (2021). 
  • 5.Borchert, F., Meister, L., Langer, T., Follmann, M., Arnrich, B., Schapranow, M.-P.: Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA Annual Symposium Proceedings. bll. 237–246. American Medical Informatics Association (2021). 
  • 6.Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. 22, (2021). 
  • 7.Borchert, F., Lohr, C., Modersohn, L., Hahn, U., Langer, T., Wenzel, G., Follmann, M., Schapranow, M.-P.: "Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 13, 19–22 (2020). 
  • 8.Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. bll. 38–48. Association for Computational Linguistics, Online (2020). 

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