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.
We investigate how stromal cells in the tumour microenvironment are involved in cancer development and resistance to therapy.
We develop pathway modelling approaches to understand how intra- and intercellular signalling pathways are hijacked in cancers.
We perform high-throughput combinatorial drug screens to identify synergistic drug combinations for further preclinical and clinical development.
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 Acids Res)
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)