SPARC: A mechanism-aware spatial representation from routine histology predicts cancer survival and therapy response

Abstract

Abstract Understanding the molecular mechanisms that drive treatment response is central to personalized cancer care, but assays such as spatial transcriptomics are not yet scalable in routine clinical practice. A critical question, then, is whether this deeper molecular insight can be extracted directly from routine histology. Here, we introduce SPARC, a framework that infers spatially resolved activity maps for 40 gene expression programs directly from H&E slides. Integrating predicted program maps with morphological features improves survival prediction in 17 of 18 cancer types across 8,383 patients and matches a multi-omic method requiring paired RNA sequencing. SPARC also stratifies bevacizumab response in ovarian cancer (odds ratio = 8.08) and trastuzumab response in breast cancer (odds ratio = 3.44), while H&E image-only baselines yield non-significant separation between responders and non-responders. Unsupervised anal-ysis of predicted maps reveals canonical tumor microenvironment compartments and spatial interaction patterns directly from tissue morphology, linking predictive perfor-mance of clinical outcomes to underlying biological mechanisms.