MoESurv: A Zero-Sample and Transferable Survival Prediction Framework for Rare Cancers Using Mixture of Experts.

We propose MoESurv, a zero-sample survival prediction framework that leverages a mix-ture-of-experts architecture to extract generalizable prognostic patterns from pan-cancer data.

The model integrates shared experts, cancer-specific experts, and routing experts within an autoencoder to disentangle common and type-specific survival features.

Further-more, external validation across diverse populations and independent cohorts-including a Chinese glioma cohort (CGGA mRNAseq_693, C-index=0.7433), a rare GBM IDH-mutant subtype (C-index=0.8064), and the pan-cancer PCAWG cohort (C-index=0.7090)-demonstrated that MoESurv possesses the most robust predictive per-formance, highlighting its generalizability.