Single-Cell Multiplex Immune Biomarker Prediction from Histopathology Across Cancer Types and Clinical Endpoints

Abstract

Abstract Background The spatial organization of cellular lineages within the tumor microenvironment governs disease progression and therapeutic response, but multiplexed spatial profiling remains costly, technically complex, and tissue-destructive. Routine hematoxylin and eosin (H&E) histopathology is inexpensive and widely available but lacks explicit molecular information. This study aims to determine whether single-cell, biomarker-informed cellular states can be inferred directly from H&E images to enable scalable, non-destructive spatial biology across cancer types and clinical endpoints. Methods We present CellPlexer, a deep learning framework trained on more than 36 million co-registered cells from paired H&E and multiplex immunofluorescence images to jointly segment nuclei and classify each cell into one of 15 hierarchically defined biomarker states. A spatially aware transformer integrates single-cell embeddings with patch-level histomorphology through attention-based multimodal fusion, generating a multi-scale whole-slide representation for clinical prediction. We validated CellPlexer across four independent cohorts spanning lung, breast, and colorectal cancer. Results Here we show that CellPlexer consistently outperformed morphology-only baselines across recurrence, treatment response, survival, and mutation prediction tasks, achieving an AUC of 72.19% for pathological complete response prediction in triple-negative breast cancer (75.06% with clinical variable integration) and 83.09% for BRAF mutation prediction in colorectal cancer. Conclusions These results demonstrate that morphologically inferred, single-cell biomarker states capture clinically and biologically meaningful tumor microenvironment organization, providing a scalable, non-destructive framework for extracting proteomic-level insights from widely available clinical histopathology archives.