  Many efforts have been made to discover tumor-specific microenvironment
elements (TMEs) from immunostained tissue sections. However, the identification
of yet unknown but relevant TMEs from multiplex immunostained tissues remains a
challenge, due to the number of markers involved (tens) and the complexity of
their spatial interactions. We present NaroNet, which uses machine learning to
identify and annotate known as well as novel TMEs from self-supervised
embeddings of cells, organized at different levels (local cell phenotypes and
cellular neighborhoods). Then it uses the abundance of TMEs to classify
patients based on biological or clinical features. We validate NaroNet using
synthetic patient cohorts with adjustable incidence of different TMEs and two
cancer patient datasets. In both synthetic and real datasets, NaroNet
unsupervisedly identifies novel TMEs, relevant for the user-defined
classification task. As NaroNet requires only patient-level information, it
renders state-of-the-art computational methods accessible to a broad audience,
accelerating the discovery of biomarker signatures.
