Our research group focuses on the collection, analysis and integration of various omics data from malignant tumors. We are specialized on the analysis of histological images as well as molecular data such as genomics, epigenetics, transcriptomics and proteomics. By using different bioinformatics methods as well as artificial intelligence, we hope to better understand the complexity of tumors and thus contribute to a better diagnosis and therapy of malignant diseases.
Digital Pathology & AI
In surgical pathology, the microscopic examination of ultra-thin tissue sections continues to be the most important method for everyday diagnostics. However, these images can contain far more information than the human eye can see. Machine learning methods can be used, for example, to draw conclusions about certain molecular properties of a tumor or to identify prognostic features. By using explainable artificial intelligence, specific areas of an image which are relevant for classification can, for example, be highlighted with pixel precision as a so-called heat map. For this, we are cooperating closely with the research group of Prof. Klaus-Robert Müller at the Technical University (TU) Berlin.
Proteins are the basic molecular tools of our cells. They fulfill a variety of tasks, such as transmitting information, transportation of other molecules or fighting off bacteria. The totality of all proteins in the body is called the ‘proteome’. In malignant diseases, DNA mutations also lead to changes in the composition and structure of proteins, compromising their normal function (e.g. regulation of cell proliferation). By determining the proteome using mass spectrometry, we can gain detailed insight into the activity of different signaling pathways. For this, we cooperate closely with the group of Dr. Philipp Mertins at the Max Delbrück Center for Molecular Medicine (MDC) Berlin.
DNA methylation is a dynamic, so-called epigenetic modification of the genome, which plays a central role in the regulation of gene activity. From a diagnostic point of view, it is particularly interesting that different tumors are often characterized by distinct DNA methylation profiles. This could support conventional diagnostics in the near future. For this purpose, first the DNA methylation profile of different reference tumors is determined and then a computer algorithm is trained to recognize these specific signatures. In our group, both array-based techniques and sequencing methods (bisulfite sequencing) are used for DNA methylation analysis.
Single cell analysis
Cells of the immune system and the adjacent soft tissue play an important role in virtually all cancer types. In molecular "bulk" analyses, which are commonly used to date, malignant and non-malignant cells of a tumor are analyzed together. Single cell analyses, on the other hand, collect the profile of all cells separately, allowing a deeper insight into the respective properties of different cell populations and better resolving tumor heterogeneity. Our research group is currently establishing methods for the analysis of genomic, transcriptomic and epigenetic information from single cells.
Labeling of specific cell surface marker using specific antibodies allows for a more precise subtyping of all cells of a tumor. Multiplex immunofluorescence can label numerous different markers on a single tissue section and thereby not only provides information about the cellular composition of a tumor, but also allow insights into the spatial interaction between the cells.
Single-cell gene regulatory network prediction by explainable AI Keyl P, Bischoff P, Dernbach G, Bockmayr M, Fritz R, Horst D, Blüthgen N, Montavon G, Müller KR, Klauschen F. Nucleic Acids Res. 2023 Jan 11:gkac1212. doi: 10.1093/nar/gkac1212.
DNA methylation-based classification of sinonasal tumors Jurmeister P, Glöß S, Roller R, Leitheiser M, Schmid S, Mochmann LH, Payá Capilla E, Fritz R, Dittmayer C, Friedrich C, Thieme A, Keyl P, Jarosch A, Schallenberg S, Bläker H, Hoffmann I, Vollbrecht C, Lehmann A, Hummel M, Heim D, Haji M, Harter P, Englert B, Frank S, Hench J, Paulus W, Hasselblatt M, Hartmann W, Dohmen H, Keber U, Jank P, Denkert C, Stadelmann C, Bremmer F, Richter A, Wefers A, Ribbat-Idel J, Perner S, Idel C, Chiariotti L, Della Monica R, Marinelli A, Schüller U, Bockmayr M, Liu J, Lund VJ, Forster M, Lechner M, Lorenzo-Guerra SL, Hermsen M, Johann PD, Agaimy A, Seegerer P, Koch A, Heppner F, Pfister SM, Jones DTW, Sill M, von Deimling A, Snuderl M, Müller KR, Forgó E, Howitt BE, Mertins P, Klauschen F, Capper D. Nat Commun. 2022 Nov 28;13(1):7148. doi: 10.1038/s41467-022-34815-3.
Patient-level proteomic network prediction by explainable artificial intelligence Keyl P, Bockmayr M, Heim D, Dernbach G, Montavon G, Müller KR, Klauschen F. NPJ Precis Oncol. 2022 Jun 7;6(1):35. doi: 10.1038/s41698-022-00278-4.
Morphological and molecular breast cancer profiling through explainable machine learning Binder A, Bockmayr M, Hägele M, Wienert S, Heim D, Hellweg K, Ishii M, Stenzinger A, Hocke A, Denkert C, Müller KR, Klauschen F. Nat Machine Intelligence. 2021 (3)355-366 doi:10.1038/s42256-021-00303-4
Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma Bischoff P, Trinks A, Obermayer B, Pett JP, Wiederspahn J, Uhlitz F, Liang X, Lehmann A, Jurmeister P, Elsner A, Dziodzio T, Rückert JC, Neudecker J, Falk C, Beule D, Sers C, Morkel M, Horst D, Blüthgen N, Klauschen F. Oncogene. 2021 Dec;40(50):6748-6758. doi: 10.1038/s41388-021-02054-3.
Comprehensive micro-scaled proteome and phosphoproteome characterization of archived retrospective cancer repositories Friedrich C, Schallenberg S, Kirchner M, Ziehm M, Niquet S, Haji M, Beier C, Neudecker J, Klauschen F, Mertins P. Nat Commun. 2021 Jun 11;12(1):3576. doi: 10.1038/s41467-021-23855-w.