The Miller laboratory at the University of Cambridge's CRUK Cambridge Institute aims to systematically interrogate how tumour-intrinsic and –extrinsic factors cooperate in cancer development using combined experimental and computational analysis. To decipher intercellular communication in the tumour microenvironment, we develop and apply technologies that can decompose the transcriptome or label the proteome of specific cell populations in multicellular settings. Using cell-selective proteome labelling we can also track the cell-of-origin of secreted proteins, and we are interested in applying these technologies for cancer biomarker discovery. Our long-term research goal is to better understand how immune cells and stromal cells are involved in oncogenesis and how this can be exploited in cancer therapy and early disease detection and monitoring.
This work shows an independent and comprehensive benchmarking of recently developed and widely used tumour microenvironment cell estimation methods based on bulk expression data and integrates the tools into a consensus approach (Jimenez-Sanchez, Cast & Miller, 2019, Cancer Research)
In this study we show how distinct tumour-immune microenvironments co-exist within a single individual and may help to explain the heterogeneous fates of metastatic lesions often observed post-therapy (Jimenez-Sanchez et al, 2017, Cell)
Using cancer genomics datasets from thousands of tumour samples in 22 tumour types, we have analysed 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, 2016, Nucleic Acids Res)
We have developed a new cell-selective proteome labelling 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)
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)