Protein analysis of formalin-fixed breast cancer tissues for diagnosis, prognosis, and therapy guidance

Coordinator:    Prof. Dr. Karl-Friedrich Becker
Institution: Institut für Pathologie der Technischen Universität München
The challenge of translating proteomic profiling to the bedside is to apply technologies for the analysis of tumor tissues routinely obtained at biopsy or surgery without substantially modifying the clinical workflow. Clinical tissues are typically formalin-fixed and paraffin-embedded (FFPE) for histopathological diagnosis, preventing their routine use for multiplex or high-resolution proteomic technologies, e.g. protein lysate microarrays or 2D-gel electrophoresis. In order to shift diagnosis to prediction, novel tools are needed for precise protein measurements of clinical tissues. We have developed technologies (patent pending) for tissue proteomics to elucidate protein expression quantitatively in FFPE samples.

The main goals of this consortium are:
(a) precise measurements of disease markers in routine FFPE samples to assist in therapy decisions for breast cancer patients;
(b) extension of our technology for the identification and characterization of novel protein markers for response prediction and prognosis.

Specifically, we use our technology
(1) to determine urokinase-type plasminogen activator (uPA) and its inhibitor, PAI-1, as predictive markers for node-negative breast cancers in FFPE samples;
(2) to measure tyrosine kinase-dependent signaling pathways for antibody-based therapy;
(3) to establish new markers for response prediction using differential 2D-gel electrophoresis and mass spectrometry. Our novel proteomics tools have the potential to become standard techniques in molecular pathology for analyzing FFPE tissues to improve patient outcome.

Latest results can be found in detail in the descriptions of the subprojects

Tissue sample

Tissue samples in all histological laboratories are typically formalin-fixed and paraffin embedded. A novel technology developed by the consortium will be used for improved diagnostic protein biomarker analysis.

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