Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction
This work addresses a critical problem for clinical tumor diagnosis and precision medicine by improving TME subtype classification, though it appears incremental as it builds on existing representation learning techniques.
The paper tackles the challenge of predicting Tumor MicroEnvironment (TME) subtypes across 23 cancer types by proposing PathoTME, a genomics-guided Siamese representation learning framework using Whole Slide Images, which achieves better performance than state-of-the-art methods on the TCGA dataset.
The characterization of Tumor MicroEnvironment (TME) is challenging due to its complexity and heterogeneity. Relatively consistent TME characteristics embedded within highly specific tissue features, render them difficult to predict. The capability to accurately classify TME subtypes is of critical significance for clinical tumor diagnosis and precision medicine. Based on the observation that tumors with different origins share similar microenvironment patterns, we propose PathoTME, a genomics-guided Siamese representation learning framework employing Whole Slide Image (WSI) for pan-cancer TME subtypes prediction. Specifically, we utilize Siamese network to leverage genomic information as a regularization factor to assist WSI embeddings learning during the training phase. Additionally, we employ Domain Adversarial Neural Network (DANN) to mitigate the impact of tissue type variations. To eliminate domain bias, a dynamic WSI prompt is designed to further unleash the model's capabilities. Our model achieves better performance than other state-of-the-art methods across 23 cancer types on TCGA dataset. Our code is available at https://github.com/Mengflz/PathoTME.