Identifying prognosis-associated spatial patterns by integrating bulk RNA-seq and spatial transcriptomic data.

The latest spatial transcriptomics (ST) technology can characterize spatial-resolved intra-tumor heterogeneities, and while large-scale traditional bulk transcriptomic datasets possess valuable clinical phenotype information. To link spatial features with survival information, we present stSurvTrans, a deep transfer learning framework for prognosis-associated spatial patterns identification. stSurvTrans harmonizes bulk RNA sequencing (RNA-seq) and ST data based on conditional variational autoencoder (CVAE), and utilizes a Weibull module to transfer clinical survival information from bulk samples to ST data. We benchmarked stSurvTrans on three simulated datasets, demonstrating its accuracy and superiority. Furthermore, in ST data from hepatocellular carcinoma (HCC), stSurvTrans identified the bile duct tumor thrombus, a spatial structure associated with worse prognosis.