Computational Pediatric Oncology
Deeper understanding and targeted treatment of pediatric tumors
AG Bockmayr
Prof. Dr. med. Michael Bockmayr and his team are leveraging bioinformatics methods and artificial intelligence (AI) to improve the diagnosis and treatment of childhood cancer. Experts need to evaluate large and complex datasets in an integrated manner to enable the analysis of each disease, thereby paving the way for personalized treatment options for every child. These datasets are derived from histopathological, molecular, and bioinformatical analyses. AI is particularly effective in evaluating these data as it can efficiently process complex clinical, histological, and molecular data. The result: exact information on the disease, biomarkers, and prognosis, leading to personalized treatments for children with cancer.
After studying medicine and mathematics in Berlin and Cambridge, Michael Bockmayr received his doctorate in medicine in 2017 from the Charité – Universitätsmedizin Berlin, focusing on a bioinformatical topic. He began his medical practice at the Department of Pediatric Hematology and Oncology at the University Medical Center Hamburg-Eppendorf, completing his specialist training in 2023. At the same time, he conducted research on topics in bioinformatics and artificial intelligence with a focus on applications in pediatric hematology and oncology. He was supported, among others, by a clinician-scientist fellowship from the Mildred Scheel Cancer Career Center Hamburg. In 2023, he was appointed an assistant professor in medical bioinformatics and AI in pediatric hematology and oncology. He established his own research group, which has also been based at the Research Institute Children's Cancer Center Hamburg since the beginning of 2025. Alongside his scientific work, Michael Bockmayr continues to work clinically in pediatric oncology.
German Cancer Aid
Federal Ministry of Education and Research
Erich and Gertrud Roggenbuck Foundation
[1] M. Körner, M. Spohn, U. Schüller, M. Bockmayr Transcriptomics-based characterization of the immuno-stromal microenvironment in pediatric low-grade glioma OncoImmunology 2024 Aug 9;13(1):2386789. https://doi.org/10.1080/2162402X.2024.2386789
[2] L. Pohl, M. Leitheiser, D. Obrecht, L. Schweizer, A. Wefers, A. Eckhardt, M. Raffeld, D. Sturm, K. Pajtler, S. Rutkowski, K. Fukuoka, K. Ichimura, M. Bockmayr*, U. Schüller* Molecular characteristics and improved survival prediction in a cohort of 2023 ependymomas Acta Neuropathol. 2024 Jan 24;147(1):24. https://doi.org/10.1007/s00401-023-02674-x
[3] M. Bockmayr, K. Harnisch, L. Pohl, L. Schweizer, T. Mohme, M. Körner, M. Alawi, A. Suwala, M. Dorostkar, C. Monoranu, M. Hasselblatt, A. Wefers, D. Capper, J. Hench, S. Frank, T. Richardson, I. Tran, E. Liu, M. Snuderl, L. Engertsberger, M. Benesch, A. von Deimling, D. Obrecht, M. Mynarek, S. Rutkowski, M. Glatzel, J. Neumann, U. Schüller Comprehensive profiling of myxopapillary ependymomas identifies a distinct molecular subtype with relapsing disease Neuro Oncol. 2022 Oct 3;24(10):1689-1699. https://doi.org/10.1093/neuonc/noac088
[4] S. Safaei, M. Mohme, J. Niesen, U. Schüller*, M. Bockmayr* DIMEimmune: Robust estimation of infiltrating lymphocytes in CNS tumors from DNA methylation profiles
OncoImmunology. 2021 Jun;10(1):1932365 https://doi.org/10.1080/2162402X.2021.1932365
[5] A. Binder*, M. Bockmayr*, M. Hägele, S. Wienert, D. Heim, K. Hellweg, M. Ishii, A. Stenzinger, A. Hocke, C. Denkert, K.-R. Müller*, F. Klauschen* Morphological and molecular breast cancer profiling through explainable machine learning Nat Mach Intell. 2021 Mar;3(4):355-366. https://doi.org/10.1038/s42256-021-00303-4
[6] P. Jurmeister*, M. Bockmayr*, P. Seegerer, T. Bockmayr, D. Treue, G. Montavon, C. Vollbrecht, A. Arnold, D. Teichmann, K. Bressem, U. Schüller, M. von Laffert, K.R. Müller, D. Capper*, F. Klauschen*: Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases Sci Transl Med. 2019 Sep 11;11(509). https://dx.doi.org/10.1126/scitranslmed.aaw8513
(* = joint first/last authors)
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