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Julia Hess

Institute for Diabetes and Cancer Translational Metabolic Oncology (TMO)

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

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

About our Research

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:

  1. 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.
  2. Experimental Translational Oncology: clinical cohorts and multi-omics data forprognostic patient stratification markers and therapeutic intervention targets. Clinical implications of tumor heterogeneity and tumor-microenvironment interaction.
  3. Metabolic therapy response: tumor metabolism and therapy response. Metabolic characterization and stratification. Multi-level data from clinical samples and pre-clinical models for metabolic intervention strategies.

Our Research Groups

ZYTO_Metabolic_therapy_response
Helmholtz Munich | ©Martin Selmansberger
Selmansberger Lab

Translational Bioinformatics

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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therapeutic targets and interventions
Helmholtz Munich | Julia Hess
Hess Lab

Experimental Translational Oncology Group

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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Our Staff at Translational Metabolic Oncology

Porträt Horst Zitzelsberger

Prof. Horst Zitzelsberger

Head of Translational Metabolic Oncology View profile
Martin Selmansberger

Dr. Martin Selmansberger

Head of Translational Bioinformatics
Porträt Julia Hess

Dr. Julia Heß

Head of Experimental Translational Oncology View profile
Porträt Marion Böttner

Marion Böttner

Secretary of Research Unit
Porträt Daniel Samaga

Daniel Samaga

Scientist
Porträt Benedek Danko

Benedek Dankó

PhD Student
Porträt Claire Innerlohinger

Claire Innerlohinger

Technician

Aaron Selmeier

Technician
Porträt Gisela Dettweiler

Gisela Dettweiler

Kristian Unger

Prof. Dr. Kristian Unger

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

Laura Holler

Technician

Latest Publications of Our Research Unit

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2022 Cancers

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.

2019 Clin Cancer Res.

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.

Contact Research Unit Office

Porträt Marion Böttner

Marion Böttner

Secretary of Research Unit

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