Spatial omics for deep characterisation of the cancer ecosystem and its association with disease prognostication and treatment response prediction Group Zlobec, Williams The cancer ecosystem comprises tumour, stroma (cellular component) and extracellular matrix (ECM), together the stroma and ECM make the tumour microenvironment (TME). The Williams group utilises spatially resolved technologies including Nanostring GeoMx Digital Spatial Profiler (DSP), CosMx Spatial Molecular Imager (SMI) and MACSima platforms to examine how the composition and architecture of the cancer ecosystem defines disease phenotypes. Current projects include: Examination of TME heterogeneity and its association with epithelial identity and plasticity in colorectal cancer. Deep characterisation of the biochemical and structural properties of the ECM for predictive and prognostic biomarker identification. Spatially resolved transcriptomic profiling of the cancer ecosystem in colorectal cancer. Primary antibodies for tissue visualisation - Green: PanCK (epithelium), Blue: DNA (nuclei). Region of interest selection – Red: tumour, Yellow: tumour microenvironment
Digital pathology & AI to gain novel insights into colorectal cancer Group Zlobec, Williams Our Sinergia project uses AI to gain new insights into the biology of colorectal cancers. We investigate morphomolecular relationships, including the molecular subtypes and intratumoral heterogeneity in order to learn new interpretable & clinically important features from histopathology images. We use various computational methods, including graphs and deep learning) to evaluate the structural and spatial patterns at the tumor invasion front in neoadjuvantly treated patients. We’ve extended our scope to understanding transcriptional subtypes using spatial transcriptomic and spatial protein expression analysis. The tumor microenvironment, with its complex stromal patterns and immune contexture are important focus points. Collaborators on this project include M. Rodriguez (IBM Research), M. Anisimova (ZHAW), B. Snijder (ETH Zürich), A. Fischer (HES-SO & UniFribourg) and V. Koelzer (UniZürich). Epithelial cell and lymphocyte graphs in colorectal cancer
Building tools for computer-assisted diagnostics Group Zlobec, Williams In addition to exploratory tissue analysis, our team builds, tests and validates in-house, open-source and commercially available algorithms for potential diagnostic use and workflow integration. We are generating a pan-lymph node metastasis algorithm using state-of-the-art deep learning methods. We then streamline processes from the lab to data analysis, and on to visualisation of results and interaction of our algorithms with pathologists scores and feedback. Together with our expert pathologist colleagues, we collaborate on a variety of algorithms including PD-L1 (Tereza Losmanova), H. pylori (Bastian Dislich), IBD scoring (Aart Mookhoek), tumor budding- CD8 scores (Heather Dawson), breast biomarkers (Wiebke Solass) and pancreas pathology (Martin Wartenberg). Computational Analysis of Colorectal Cancer Metastases in Lymph Nodes
