Abstract 2424: Integrating digital spatial profiling and H&E images to develop predictive biomarkers for immunotherapy outcomes in triple-negative breast cancer from imputed spatial gene expression

Abstract Introduction: Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognoses and limited biomarkers for predicting treatment outcomes [1]. Tumor-infiltrating lymphocytes and PD-L1 expression, while associated with immune checkpoint inhibitor (ICI) efficacy, fail to predict responses reliably [2, 3]. Spatial transcriptomics is a cutting-edge technology that enables the precise mapping of gene expression within tissue samples. However, the high cost of sequencing limits its clinical utility. This study aims to develop cost-effective predictive biomarkers by integrating spatial transcriptomic data and machine learning to impute gene expression from widely available Hematoxylin and Eosin (H&E) images. Methods: This study involves two cohorts of TNBC patients treated with pembrolizumab (N=75) and durvalumab (N=57) in the neoadjuvant setting. Digital Spatial Profiling (DSP) was used to generate Spatial transcriptomics data. Gene signatures predicting ICI outcomes were developed and validated using Least Absolute Shrinkage and Selection Operator (LASSO) regression models. Imputed gene expression was derived from H&E images, employing adaptive spatial graph neural networks (asGNN [4]), and evaluated against DSP data on a subset of patients. Signatures were trained using imputed transcriptomics directly and in combination with a hold-out set of H&E images. Prediction accuracies were evaluated against spatial transcriptomics and clinical outcomes using mean squared error and AUC. Results: Imputed gene expression from H&E images closely mirrors spatial transcriptomic profiles. Imputed spatial gene expression using asGNN achieved better prediction of DSP expression than competing methods (HE2RNA [5] and Sequoia [6]) and non-adaptive GNN methods (p=0.031). Signatures trained directly using H&E imputed spatial expression achieved accurate prediction of treatment outcomes (AUC=0.75±0.24). Integrating the DSP transcriptomics further increased the accuracy of the predictions; the signatures trained using imputed transcriptomics combined with DSP data achieved higher prediction accuracy (AUC=0.85±0.08), which significantly outperformed the non-DSP trained models (p=0.021). Conclusion: This study demonstrates the feasibility of leveraging H&E images for gene expression imputation in TNBC. Future work will optimize models across diverse cancer types and expand validation with independent datasets. This innovation advances personalized medicine by bridging the gap between cutting-edge spatial transcriptomics and routine clinical diagnostics. Citation Format: Thazin Nwe Aung, Tianci Song, Lajos Pusztai, Mark Gerstein, David L. Rimm, Jonathan H. Warrell. Integrating digital spatial profiling and H&E images to develop predictive biomarkers for immunotherapy outcomes in triple-negative breast cancer from imputed spatial gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2424.