Alterations of fatty acid metabolism and pharmacological counteraction in ZBTB7A deficient leukemia

ZBTB7A is a transcription factor frequently mutated in acute myeloid leukemia (AML) patients with t(8;21) translocation (which results in the RUNX1-RUNX1T1 fusion protein). We use CRISPR-Cas9 to knockout ZBTB7A and mimic these loss-of-function mutations in order to study ZBTB7A effect in metabolism. Our ultimate goal is to find metabolic drug targets for therapy. For that, we perform metabolic tracing by mass spec, metabolic flux assays, and treatment with several metabolic inhibitors. Besides, since ZBTB7A prevents RUNX1-RUNX1T1-mediated clonal expansion in vitro, we are interested in investigating the cooperation of ZBTB7A knockout and RUNX1-RUNX1T1 during leukemogenesis in a mouse model. 
 

Experimentelle Leukämie- und Lymphom-Forschung (ELLF), Medizinische Klinik und Poliklinik III, Klinikum der Universität München, Munich

Pathogenesis of acute leukemia - AG Prof. Dr. med. Philipp Greif 
Medizinische Klinik und Poliklinik III

Munich, Germany

E-Mail: vanessa.arfelli@med.uni-muenchen.de
 

Tumour evolution and immune microenvironment dynamics in response to neoadjuvant treatment in oesophagael adenocarcinoma

Patients with locally advanced oesophageal cancer (OAC) are treated with surgical resection and neoadjuvant chemotherapy or radiochemotherapy. However, 50% to 60% of tumours are resistant to neoadjuvant therapy, leading to an overall poor outcome with a 5-year survival of 12.6%. The ecological and evolutionary dynamics responsible for treatment failure are incompletely understood. In my research I perform a multi-omics study (whole exome sequencing, RNA-sequencing, T-cell receptor sequencing and image mass cytometry)  with a multi-timepoint strategy to examine treatment response at clonal resolution and to investigate genetic and transcriptomic changes induced by neoadjuvant therapy in OAC patients. This project will provide fundamental insights into clonal dynamics, transcriptomic changes and dynamics of the microenvironment in response to neoadjuvant treatment in OAC patients, in order to provide fundamental insights into treatment response on the molecular level.
 

Department of Gastroenterology, Technical University Hospital, Munich

Munich, Germany

E-Mail: melissa.barroux@mri.tum.de



 

Metabolic features of high-risk multiple myeloma

The risk of progression and the response to therapy in multiple myeloma are determined by genomic risk factors including deletion of the tumor suppressor p53. Importantly, p53 is known to profoundly impact cellular metabolism. In our project, we will characterize the metabolic landscape of high-risk myeloma in cell line models and primary patient samples through metabolomic analyzes. We furthermore aim to identify underlying mechanisms and metabolite mediated effects on the tumor environment by employing transcriptomic technologies and functional assays. Ultimately our goal is to identify resistance mechanisms to established drugs and to discover novel therapeutic targets in multiple myeloma.
 

Department of Internal Medicine III
LMU Klinikum

Cancer- and Immunometabolism Research Group (CIM)
Genzentrum LMU

E-Mail: maximilian.funk@med.uni-muenchen.de

Identification of tumor-antigen specific T cell receptors for the treatment of pediatric cancers

Despite significant survival improvements, cancer ranges among the leading causes of deaths in children and young adults. Immunotherapy including adoptive T cell transfer emerges as promising therapeutic strategy in pediatric hematological and solid malignancies. 
The short- and mid-term goals of the project are to identify and characterize T cell receptors (TCRs) in vitro and in preclinical models targeting overexpressed and shared antigens in pediatric cancers. The aim is to provide data for efficacy and safety of selected TCRs for further evaluation in phase I clinical trials.
 

Kinderklinik München Schwabing, Klinik und Poliklinik für Kinder- und Jugendmedizin, Klinikum Schwabing und Klinikum Rechts der Isar (AöR) der Technischen Universität München 

Munich, Germany

E-Mail: hendrik.gassmann@tum.de

Spatial transcriptomic-based dissection of tumor-infiltrating lymphocytes (TIL) for the improvement of immunotherapies and cellular therapies in pancreatic cancer

The field of artificial intelligence is rapidly expanding, providing novel insights into tumor biology. Single-cell RNA-sequencing (scRNA-Seq) for example can provide information of cell-to-cell interactions at an unforeseen resolution. However, one fundamental limitation of scRNA-seq is the loss of spatial information, which is known to influence prognosis and treatment response of certain cancers. Building on our previous successful investigations, we hypothesize that we can leverage spatially-resolved gene expression profiling to identify pivotal signaling pathways in tumor-infiltrating lymphocytes (TIL) and exploit these pathways for engineering of next-generation cellular therapies.
 

Abteilung für Klinische Pharmakologie
Medizinische Klinik und Poliklinik III, LMU Klinikum

Arbeitsgruppe Immunpharmakologie

Munich, Germany

E-Mail: adrian.Gottschlich@med.uni-muenchen.de

 OXPHOS-Dependent Radiosensitization of Patient-Derived Pancreatic Cancer Organoids

Pancreatic ductal adenocarcinoma is a highly lethal entity with poor prognosis. Neoadjuvant chemoradiotherapy aims to achieve resectability, but intrinsic radioresistance and hypoxia hinder therapeutical success. This project investigates whether inhibiting oxidative phosphorylation (OXPHOS) can enhance radiosensitivity by increasing intracellular oxygen levels. For a translational approach we are using patient-derived organoids, to assess the efficacy of OXPHOS inhibitors combined with radiation. We employ a range of techniques, including immunohistochemistry, cell viability assays, 3D fluorescence microscopy and RNA sequencing to characterize radiation response.
 

Working Group Personalized Radiobiology

Department of Radiation Oncology at the University Hospital rechts der Isar, Technical University of Munich

Munich, Germany

E-Mail: christopher.kessler@tum.de



 

DNA-targeted Click Chemistry for Precision Cancer Therapy 

Our project develops a DNA-targeting click chemistry platform to selectively induce double-stranded DNA breaks in cancer cells. Using this modular nanomedicine approach, we aim to maximize tumor cell killing while minimizing systemic toxicity. We evaluate antitumor efficacy, immune activation, and biodistribution in preclinical models, with the goal of translating this targeted strategy into innovative therapies for hematologic and solid malignancies.
 

Department of Internal Medicine II – Gastroenterology, TUM Hospital

Munich, Germany

E-Mail: yizhu.li@tum.de

Understanding Inflammation-Driven Mechanisms of Resistance to T-Cell-Based Immunotherapies

CAR T cells and bispecific T-cell engagers have revolutionized the treatment landscape of B-cell malignancies. However, a substantial portion of patients still relapses, leading to dismal outcomes. In this setting, immune dysregulation has emerged as a critical driver of therapeutic resistance. Aim of this project is to further characterize the influence of inflammation-driven immune dysregulation on T-cell fitness. To test this, we will apply high-dimensional immune monitoring to define T-cell phenotypes, cytokine landscapes, and myeloid cell compositions in our cohort of r/r LBCL patients treated with bispecific T-cell engagers.
 

Department of Medicine III, LMU University Hospital

Munich, Germany

E-Mail: giulia.magno@med.uni-muenchen.de

 

Leveraging omics data for AML prognostication and prediction of therapeutic outcomes

Acute Myeloid Leukemias (AML) are malignancies affecting myeloid hematopoietic stem cells. The prognosis depends on underlying genomic, transcriptomic and epigenomic aberrations. Historically, genetic data have been leveraged to predict survival. Today, a much larger wealth of available data combined with the introduction of multiple new drug classes makes both the prediction of therapeutic outcomes and a data-based selection of the best possible therapy more complex. We aim to integrate multi-omic data from large cohorts of AML patients to better predict outcomes and aid therapeutic decision-making.
 

Department of Medicine III, LMU University Hospital, Munich

Munich, Germany

E-Mail: Christian.Rausch@med.uni-muenchen.de



 

DKTK Organoid Platform; Droplet Microfluidics for PDO Culture

In Prof. Reichert’s group I am co-coordinating the establishment of the DKTK Organoid Platform with its first two use cases, NeoMatch and MetPredict. Both are prospective multicenter pilot trials: NeoMatch aims to  confirm  the  ability  of  pancreatic  cancer  patient-derived  organoids  (PDOs)  in  forecasting response to neoadjuvant chemotherapy, and MetPredict the ability of colorectal cancer liver metastasis patient-derived organoids (CRC LM PDOs) in forecasting liver metastasis recurrence, respectively. Additionally, I am building a microfluidic workflow and aim to demonstrate its effectiveness in improving our current pipeline for generating PDOs of pancreatic ductal adenocarcinoma. Ultimately, this work will result in a better understanding of tumor heterogeneity and plasticity, as well as the interplay with the tumor microenvironment – major obstacles in successful treatment of PDAC, which to this day has a low 5-year-survival rate, despite our best efforts and recent advances. Moreover, breakthroughs relating to PDO model systems would most certainly be translatable to precision medicine applications with an even more direct impact on patients.
 

Klinik und Poliklinik für Innere Medizin II, Technical University of Munich

Munich, Germany

E-Mail: martin.trossbach@tum.de



 

Validate and improve the treatment effects of CAR T cells against brain metastases from lung cancer

Lung carcinoma represents the primary contributor to global cancer-related fatalities, 50% of patients develop brain metastases throughout disease progression. The median survival time of patients with brain metastases accepting current treatment strategies is < 12 months. CAR T cells emerged as a powerful treatment for hematological malignancies, however, the treatment effect of CAR T cells against solid tumor is limited, especially in brain tumors. To uncover the underlying mechanisms leading to its failure, we established a syngeneic orthotopic immunocompetent brain metastasis model in mice by combining a chronic cranial window with repetitive intracerebral two-photon laser scanning-microscopy. This approach enabled the in vivo-characterization of anti-tumor effects of fluorescent CAR T-cells and cerebral lung cancer metastasis on a single-cell level over weeks, including the migration, infiltration and persistence of CAR T cells inside tumor area, and interaction between CAR T cells and tumor cells or tumor associated macrophages/microglia. With the expertise in neurosurgery, coupled with the experience of mouse model in the field of cellular immunotherapy, continues our research in the field of CAR T treatment for brain malignancies, furthermore, we are establishing a mouse model to dissect neurotoxic side effects during CAR T cell treatment.
 

Department of Neurosurgery, Klinikum Großhadern, LMU University Hospital

Munich, Germany

E-Mail: tao.xu@med.uni-muenchen.de





 

Deep learning-based retrieval of cancer histopathology

In pathology, pattern recognition relies on a well-developed frame of reference, yet it may fall short when encountering rare malignant subtypes. We are developing a scalable deep learning–based retrieval engine for pathology slides that enables targeted access to morphologically similar cases. Whole-slide images are processed into multi-scale patches and encoded using transformer-based models. Users can retrieve similar cases along with diagnostic codes and contextual data, facilitating rapid reference lookup and supporting differential diagnosis.

 

Institute of Pathology, Ludwig Maximilian University

Munich, Germany

E-Mail: maximilian.roesel@med.uni-muenchen.de
 

Microbiome–Host Interactions in Colorectal Disease and Cancer

My research focuses on the functional interplay between the microbiome, tumor tissue, and the immune system in gastrointestinal cancers. Using spatial transcriptomics and 16S-FISH, I aim to identify microbiome-associated signatures directly within tissue and link them to histopathological features. Complementary organoid models are used to mechanistically investigate how dysbiosis influences tumor development and progression. The goal is to define predictive and prognostic microbiome-based markers with translational relevance.
 

Institut für Allgemeine Pathologie und Pathologische Anatomie der Technischen Universität München
 
Munich, Germany

E-Mail: anna.sax@tum.de

Development of a Fully Automated CTV Segmentation Model for Resection Cavities of Brain Metastases in a Multi-Center Patient Cohort

Automated clinical target volume (CTV) delineation for post-op brain metastasis radiotherapy remains challenging due to post-op changes and the complex structure of the dura mater. In this project, we are developing a deep learning–based workflow for automated segmentation of resection cavities and dura mater on postoperative T1c-MRI, followed by interactive guideline-based CTV generation. Using multicenter imaging data, we investigate anatomically constrained intra-parenchymal and adaptive dural margin expansion. The project combines artificial intelligence, image segmentation, and python-based geometric modeling to improve consistency and efficiency in radiotherapy treatment planning.
 

Working Group "Artifical Intelligence in Radiation Oncology"

Department of Radiation Oncology at the University Hospital rechts der Isar
Technical University of Munich

Munich, Germany

E-Mail: qm.nguyen@tum.de