Research

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Cancer genomics & proteomics

To discover the molecular causes of cancer, we take an integrative approach combining data from large-scale cancer genomics and proteomics studies with signaling pathway information.

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Tumour-stroma interactions

We investigate how stromal cells in the tumour microenvironment are involved in cancer development and resistance to therapy.

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Signalling pathways

We develop pathway modelling approaches to understand how intra- and intercellular signalling pathways are hijacked in cancers.

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Combination therapy

We perform high-throughput combinatorial drug screens to identify synergistic drug combinations for further preclinical and clinical development.

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Highlights

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Somatic mutations in protein domains

Using cancer genomics datasets from thousands of tumor samples in 22 tumor types, we have analyzed somatic missense mutations in protein domains and discover new domain mutation hotspots. By associating mutations in infrequently altered genes with mutations in frequently altered paralogous genes that are known to contribute to cancer, this study provides many new clues to the functional role of rare mutations in cancer. (Miller et al 2015, Cell Systems, Gauthier et al, 2015, Nucleic Adics Res)

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Cell-selective proteome labeling

We have developed a new cell-selective proteome labeling method for mass spectrometry-based analysis of signalling in multicellular environments named CTAP: Cell Type specific labeling using Amino acid Precursors (Gauthier et al, 2013, Nature Methods)

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Drug synergy screen and network modelling

Using data-driven pathway modeling, we have identified CDK4 and IGF1R as promising synergistic drug targets in liposarcoma and validated a model-predicted mechanism mediating the synergistic effect (Miller et al 2013, Science Signaling)

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Phosphorylation-dependent signalling

We have analysed linear sequence motifs in phosphoproteomics datasets and created a bioinformatics resource that catalogs and predicts recognition motifs of kinases and SH2 domains in mammalian systems (Miller et al 2008, Science Signaling; Horn et al, 2014, Nature Methods)

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