Abstract Introduction: Tumor purity, defined as the proportion of malignant cells within a tumor region, is a critical factor in cancer research and clinical practice. Accurate tumor purity estimates (TPEs) are crucial in triple-negative breast cancer (TNBC), where tumor heterogeneity complicates diagnosis, biomarker interpretation, and therapeutic decisions. Traditional pathological assessment of tumor purity is limited by observer variability and scalability. Spatial transcriptomics (ST) integrates whole-transcriptome data with spatial context, enabling high-resolution and scalable estimation of tumor purity directly from H DHMC and Cedars-Sinai Medical Center), where slide-level purity was derived from aggregated patch-level predictions by the ST-informed model and HoVerNet. Results: Across tumor regions, the ST-supervised deep learning model achieved a spot-level purity correlation of 0.88 (p 0.001) with spot-level Hovernet-derived TPE. When aggregating across the internal and external cohorts (n=29), slide-level ST-informed TPE showed a Spearman correlation of 0.83 (p 0.001) with Hovernet-derived TPE. Conclusion: This proof-of-concept study shows that ST can guide computational models to derive TPE directly from H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1442.
Le et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: