Molecular Diagnostics and Biomarkers

    Aims

    Tumor cells are changing at tremendous speed. On the one hand, this property promotes their rampant growth and the development of resistance to therapy, on the other hand, it results in no tumor being the same as the other, making reliable prognoses for individual patients often difficult.

    The goal of Research Area 1 is to find individual parameters that can improve the diagnosis of tumors or better predict the response to therapy. For example, the composition of all proteins in a cell can be qualitatively and quantitatively analyzed by means of mass spectrometry. The measured profile (“proteome”) contains unique information – similar to a fingerprint – and by comparing tumor cells with healthy cells of the same patient, new prognostic and diagnostic criteria can be identified.

     

    At the FCI, such new parameters are subjected to a comprehensive characterization, they are functionally, preclinically and clinically evaluated and validated. In particular, the question arises as to whether the altered protein signatures can be routinely measured as part of the individual profiling of cancer patients. However, this requires a better understanding of how altered protein profiles can be interpreted, their prognostic and predictive relevance, and how informative they are for the diagnosis of disease mechanisms.
    At the same time, this technology, which has made significant progress in recent years, can uncover previously unknown oncogenic mechanisms in model organisms. This will lay the basis for the development of molecularly targeted therapies.

    Resources

    Under the umbrella of the FCI, the existing platforms for oncological proteomics at the Frankfurt site will be merged. The available devices allow the quantitative mass spectrometric analysis of protein expression patterns, interaction networks, post-translational modifications (phosphorylation, acetylation, ubiquitination), protein-protein and protein-RNA complexes in preclinical models, patient primary tumor cells and formalin-fixed tumor tissues. The optimization of mass spectrometric methods is continuously being pursued, such as the tandem mass-tag (TMT) labeling method, which allows the multiplexing of clinical samples and thus highly accurate quantitative comparability with low sample size requirements. This proteome platform will work closely with all clinical translation programs and many discovery & development projects.
    The interaction with the proteomic unit at the Max Planck Institute in Bad Nauheim, moreover, allows the expansion of capacities as well as a close exchange with regard to further technical developments. Embedded in the German Cancer Consortium (DKTK) also provides access to a state-of-the-art high-throughput sequencing platform for tumor genome and transcriptome analysis.
    The FCI will receive the biomaterials and clinical data needed for the planned research in Research Area 1 through its seamless link with the University Cancer Center (UCT) in Frankfurt (see Research Area 5).

    People

    The leader of Research Area 1 is Thomas Oellerich, DKTK-Professor for Molecular Diagnostics. The position is supported by a junior group for clinical proteomics led by Kuan-Ting Pan.

    In order to ensure an adequate bioinformatic analysis of the expected large, multidimensional data sets, a DKTK professorship for medical bioinformatics, which is located at the FCI, is currently also recruited. In addition, Prof. Dr. med. Peter Wild, this year’s newly appointed director of the Institute of Pathology of the University Hospital Frankfurt, is closely involved in this program.

    The FCI project work is supported by a Staff Scientist.

    The following scientists participate in Research Area 1:

    Thomas Oellerich

    DKTK Prof. Transl. Proteomics

    Kuan-Ting Pan

    JG Clinical Proteomics

    • Christian Brandts
    • Florian Büttner
    • Christian Münch
    • Thomas Oellerich
    • Kuan-Ting Pan
    • Karlheinz Plate
    • Michael Rieger
    • Sebastian Wagner
    • Peter Wild
    • Staff Scientist: Andrea di Fonzo