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 this https URL

基于肿瘤来源相似的微环境模式，我们提出了 PathoTME，一个利用基因组引导的孪生表示学习框架，使用全切片图像进行泛癌肿瘤微环境亚型预测。