Abstract 3177: Prediction of pathway-omics signature of histopathology images via attention-based deep learning in lung adenocarcinoma and squamous cell carcinoma (2023)

Abstract Introduction: Unraveling the underlying signaling pathways and multi-omics expression is vital for cancer research. We aim to construct an analytic deep learning (DL) pipeline toward the discovery of pathways and multi-omics signatures with high confidence from histopathological whole slide images (WSIs) for lung cancer. Methods: A total of 458 and 442 diagnostic WSIs from TCGA-LUAD and TCGA-LUSC cohorts, respectively, were preprocessed via steps including patch generation, color normalization, blurry removal, K-means clustering, and pre-trained Convolutional Neural Network (CNN) based feature extraction. The attention-based model was trained on the CNN-extracted features that provided the importance score for each cluster which was utilized for the final prediction of (1) pathway activities, (2) gene expression, and (3) protein expression. The model was first used to predict 1387 pathway activity scores using PARADIGM as the ground truth. The thresholds for Spearman’s correlation R &amp;gt;= 0.5 and R &amp;gt;= 0.45at p-value &amp;lt;0.05 were set for LUAD and LUSC, respectively, for selecting the well-predicted pathways. Later, multiple models were trained to predict the expression value of the genes and proteins involved in these well-predicted pathways. Moreover, leveraging the attention mechanism, heat maps were generated to visualize the high and low-activated areas on the WSIs thus providing a better interpretation of the predictions. Results: The models demonstrated good performance in predicting pathway scores, genes, and proteins’ expression using the hold-out testing sets. For LUAD, the model successfully predicted important pathways such as interleukin-, uPAR-, and Sema4D-related and PIP3-activated AKT signaling pathways, which have been proven to be associated with LUAD. At the gene- and protein expression level, the model predicted IRF4, EREG, CCNA2, IL2RB, GRB2, CCNB1, STAT5A genes, and AKT protein, respectively, with high and significant correlation. Similarly, for LUSC, the model predicted highly correlated pathways such as the regulation of telomerase and inflammasomes as well as important genes including AIM2, SIN3A, EGFR, and ATM with significant correlation. Furthermore, the highly activated hot spots from the heat map could assist in decoding the model prediction for crucial tissue locations, eventually contributing to a better interpretation of the final output. Conclusion: The proposed attention-based DL pipeline not only provides high-resolution results at the pathways, and multi-omics levels, but offers a cost-effective approach that enables high throughput screening for potential targets rapidly and robustly via a single WSI. Citation Format: Han-Ru Chen, Nam Nhut Phan, Tzu-Pin Lu, Liang-Chuan Lai, Mong-Hsun Tsai, Amrita Chattopadhyay, Eric Y. Chuang. Prediction of pathway-omics signature of histopathology images via attention-based deep learning in lung adenocarcinoma and squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3177.