Transfer Learning-Assisted Survival Analysis of Breast Cancer Relying on the Spatial Interaction Between Tumor-Infiltrating Lymphocytes and Tumors

Whole-Slide Histopathology Image (WSI) is regarded as the gold standard for survival prediction of Breast Cancer (BC) across different subtypes. However, in cancer prognosis applications, the cost of acquiring patients' survival information is high and can be extremely difficult in practice. By considering that there exists a certain common mechanism for tumor progression among different subtypes of Breast Invasive Carcinoma(BRCA), it becomes critical to utilize data from a related subtype of BRCA to help predict the patients' survival in the target domain. To address this issue, we proposed a TILs-Tumor interactions guided unsupervised domain adaptation (T2UDA) algorithm to predict the patients' survival on the target BC subtype. Different from the existing feature-level or instance-level transfer learning strategy, our study considered the fact that the tumor-infiltrating lymphocytes (TILs) and its correlation with tumors reveal similar role in the prognosis of different BRCA subtypes. More specifically, T2UDA first employed the Graph Attention Network (GAT) to learn the node embeddings and the spatial interactions between tumor and TILs patches in WSI. Then, besides aligning the embeddings of different types of nodes across the source and target domains, we proposed a novel Tumor-TILs interaction alignment (TTIA) module to ensure that the distribution of interaction weights are similar in both domains. We evaluated the performance of our method on the BRCA cohort derived from the Cancer Genome Atlas (TCGA), and the experimental results indicated that T2UDA outperformed other domain adaption methods for predicting patients' clinical outcomes.