translational metabolic oncology

The research unit Translational Metabolic Oncology (TMO) focusses on translational oncology with a special focus on markers and mechanisms of therapy response pursuing the mission of optimizing personalized cancer treatment strategies.

Precision therapy of cancers requires a stratification of patients and an identification of therapeutic targets by integration of multi-level omics data, metabolic data, and clinical data from oncological cohorts. TMO collaborates tightly with local (LMU and TUM), national and international clinics and research partners. We have a particular focus on the investigation of radiotherapy response and the therapy of tumor recurrences of various cancer entities such as head and neck cancer and glioblastoma.

The personalized treatment of cancer requires finer grained stratification of patients for which we develop stratification markers and models predicting local and distant tumor control after radiotherapy. The grand challenges in clinical oncology are therapy failure and the occurrence of tumor relapses after radiotherapy. Main causes of therapy failures are tumor heterogeneity, tumor immune escape, tumor metabolic processes and tumor-intrinsic signaling that lead to therapeutic resistance. To this end, TMO identifies molecular and metabolic subtypes of tumors that reflect the aforementioned determinants of therapeutic failure. At the same time, we use these insights to identify novel therapeutic targets and combination therapies.

To achieve these research goals TMO combines the following research fields:

Translational bioinformatics: multi-big data bulk and single-cell omics for the in-depth characterization of oncological cohorts and model systems. Regression and deep learning-based prognostic models.
Experimental Translational Oncology: clinical cohorts and multi-omics data for prognostic patient stratification markers and therapeutic intervention targets. Clinical implications of tumor heterogeneity and tumor-microenvironment interaction.

Identification of metabolic characteristics in clinical samples and model systems, related to clinical therapy response by integration of multi-level (high-dimensional) data.

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Clinical cohorts and multi-omics data for prognostic patient stratification markers and therapeutic intervention targets. Clinical implications of tumor heterogeneity and tumor-microenvironment interaction.

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Porträt Horst Zitzelsberger_freigestellt

Prof. Horst Zitzelsberger

Head of Translational Metabolic Oncology

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Dr. Martin Selmansberger

Head of Translational Bioinformatics

Group photo of Organizers for Diabetes Conference - 31

Dr. Julia Heß

Head of Experimental Translational Oncology

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Marion Böttner

Secretary of Research Unit

Dr. Heike Anders

Daniel Samaga

Scientist

Group photo of Organizers for Diabetes Conference - 33

Elia Alonso

Sven Hermeling

Benedek Dankó

PhD Student

Claire Innerlohinger

Technician

Aaron Selmeier

Technician

Paolo Pacchioni

Gisela Dettweiler

Prof. Dr. Kristian Unger

Cooperation AI-assisted therapy decisions in oncology, LMU University Clinics

Lombardo E, Hess J, Kurza C, Riboldi M, Marschner S, Baumeister P, Lauber K, Pflugradt U, Walch A, Canis M, Klauschen F, Zitzelsberger H, Belka C, Landry G, Unger K

DeepClassPathway: Molecular pathway aware classification using explainable deep learning

Schinke H, Shi E, Lin Z, Quadt T, Kranz G, Zhou J, Wang H, Hess J, Heuer S, Belka C, Zitzelsberger H, Schumacher U, Genduso S, Riecken K, Gao Y, Wu Z, Reichel CA, Walz C, Canis M, Unger K, Baumeister P, Pan M and Gires O

A transcriptomic map of EGFR-induced epithelial-to-mesenchymal transition identifies prognostic and therapeutic targets for head and neck cancer.

Hess J, Unger K, Maihoefer C, Schüttrumpf L, Weber P, Marschner S, Wintergerst L, Pflugradt L, Baumeister P, Walch A, Woischke C, Kirchner T, Werner M, Sörensen K, Baumann M, Tinhofer I, Combs SE, Debus J, Schäfer H, Krause M, Linge A, von der Grün J, Stuschke M, Zips D, Canis M, Lauber K, Ganswindt U, Henke M, Zitzelsberger H, Belka C.

Integration of p16/HPV DNA status with a 24-miRNA-defined molecular phenotype improves clinically relevant stratification of head and neck cancer patients.

Weber P, Künstner A, Hess J, Unger K, Marschner S, Idel C, Ribbat-Idel J, Baumeister P, Gires O, Walz C, Rietzler S, Valeanu L, Herkommer T, Kreutzer L, Klymenko O, Drexler G, Kirchner T, Maihöfer C, Ganswindt U, Walch A, Sterr M, Lickert H, Canis M, Rades D, Perner S, Berriel Diaz M, Herzig S, Lauber K, Wollenberg B, Busch H, Belka C, Zitzelsberger H.

Brix N, Samaga D, Belka C, Zitzelsberger H, Lauber K

Analysis of clonogenic growth in vitro

Unger K, Fleischmann DF, Ruf V, Felsberg J, Piehlmaier D, Samaga D, Hess J, Suresh MP, Mittelbronn M, Lauber K, Budach W, Sabel M, Rödel C, Reifenberger G, Herms J, Tonn JC, Zitzelsberger H, Belka C, Niyazi M.

Improved risk stratification in younger IDH wild-type glioblastoma patients by combining a 4-miRNA signature with MGMT promoter methylation status.

Hess J, Unger K, Maihoefer C, Schüttrumpf L, Wintergerst L, Heider T, Weber P, Marschner S, Braselmann H, Samaga D, Kuger S, Pflugradt U, Baumeister P, Walch A, Woischke C, Kirchner T, Werner M, Werner K, Baumann M, Budach V, Combs SE, Debus J, Grosu AL, Krause M, Linge A, Rödel C, Stuschke M, Zips D, Zitzelsberger H, Ganswindt U, Henke M, Belka C.

A Five-MicroRNA Signature Predicts Survival and Disease Control of Patients with Head and Neck Cancer Negative for HPV Infection.

Wintergerst L, Selmansberger M, Maihoefer C, Schuttrumpf L, Walch A, Wilke C, Pitea A, Woischke C, Baumeister P, Kirchner T, Belka C, Ganswindt U, Zitzelsberger H, Unger K, Hess J

A prognostic mRNA expression signature of four 16q24.3 genes in radio(chemo)therapy-treated head and neck squamous cell carcinoma (HNSCC)

Hess J, Unger K, Orth M, Schotz U, Schuttrumpf L, Zangen V, Gimenez-Aznar I, Michna A, Schneider L, Stamp R, Selmansberger M, Braselmann H, Hieber L, Drexler GA, Kuger S, Klein D, Jendrossek V, Friedl AA, Belka C, Zitzelsberger H, Lauber K

Genomic amplification of Fanconi anemia complementation group A (FancA) in head and neck squamous cell carcinoma (HNSCC): Cellular mechanisms of radioresistance and clinical relevance.

Translational Metabolic Oncology

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