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bioRxiv · 10.64898/2026.10.06.757083

Pan-cancer estimation of prognostic immune biomarkers from histopathology using artificial intelligence

Li, Y.·Diab, A. R.·Lamane, A.·Lindsay, J. R.·Rodig, S. J.·Irizarry, R. A.·Lotter, W.
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Abstract

Multiplex immunofluorescence (mIF) assays such as ImmunoProfile provide standardized quantification of prognostic immune markers but are costly and not routinely used in clinical settings. We developed a weakly-supervised AI model, HistoProfile, to estimate intratumoral cell densities of key immune biomarkers (CD8, FOXP3, PD-1, and PD-L1) and the PD-L1 tumor proportion score from routine H&E-stained whole-slide images. Trained on a large pan-cancer cohort with paired H&E and mIF data, HistoProfile discriminated biomarker-high from biomarker-low tumors in held-out samples and tumor-specific subgroups (AUROCs of 0.78-0.89 across biomarkers). Critically, the estimated biomarkers preserved prognostic stratification in downstream survival analyses, and showed cancer-specific survival associations across TCGA cohorts where mIF is unavailable. These findings support scalable H&E-based estimation of key immune biomarker measurements for large-scale immune profiling and risk stratification.

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Li, Y., Diab, A. R., Lamane, A., Lindsay, J. R., Rodig, S. J., Irizarry, R. A., Lotter, W.. 2026-10-13. Pan-cancer estimation of prognostic immune biomarkers from histopathology using artificial intelligence. https://doi.org/10.64898/2026.10.06.757083

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