Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks.

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naive colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine. Spatial information in the tumour microenvironment may be exploited to optimise diagnosis, prognosis and therapy. Here, the authors develop a spatial analytics computational and systems pathology platform (SpAn) based on highly multiplexed antibody imaging on colorectal cancer samples to infer emergent network biology and predict 5-year risk of recurrence.