Xiaoying Wang, Maoteng Duan, Anthony J. Snyder, Po‐Lan Su, Jianying Li, Jordan E. Krull, Jiacheng Jin, Yuhan Sun, Yuhan Sun, Kai He, Weidong Wu, Weiqing Chen, Kai He, Chi Zhang, Sha Cao, Jing Zhao, Dong Xu, Guangyu Wang, Zihai Li, Gang Xin, David P. Carbone, Zihai Li, Richard L. Carpenter, Qin Ma
Abstract Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperformed other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captured occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have a metastatic disease. Notably, EmitGCL identified HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which was validated across five independent cohorts of patients (n=420). Furthermore, we demonstrated YY1 transcription factor as a key driver of breast cancer metastasis which was validated through in-silico and CRISPR-based migration assays, suggesting that YY1 is a potential therapeutic target for deterring metastasis.