An Unsupervised Workflow for Explainable Biomarker Identification Based on Multiplex Data

Discovery of explainable biomarkers is a complex process which is typically driven by a-priori hypothesis and expert annotations. This contribution introduces an almost entirely annotation and hypothesis free workflow to discover predictive biomarkers derived from cell phenotypes. It relies on self-supervised learning, clustering and survival analysis of cell centric image patches. The workflow is successfully evaluated on mIF images of 2L+ mNSCLC samples from a clinical study (NCT01693562). Two potential biomarkers are identified that closely align with the known relevant biology.