Leveraging single-cell foundation models for accurate survival outcome prediction

TL;DR

This paper explores using single-cell foundation models to improve cancer survival predictions from bulk RNA-seq data.

Contribution

The novel EGSP model combines foundation model embeddings with gene and clinical data to enhance survival prediction accuracy.

Findings

The EGSP model achieved a mean concordance index of 0.724 across 25 cancer types.

Embeddings from scFoundation showed lower redundancy with gene expression while retaining complementary signals.

Prognostic embeddings captured interpretable biological programs like tumor differentiation and immune activity.

Abstract

Foundation models trained on large-scale single-cell transcriptomes can capture rich molecular representations of cellular states, yet their potential for cancer survival prediction from bulk RNA-seq data remains largely unexplored.

We applied the single-cell foundation model scFoundation to derive patient-level embeddings across 25 cancer types from TCGA and systematically evaluated their prognostic value under both cancer-specific and pan-cancer settings. To leverage complementary information, we developed an Embedding–Gene–Survival Prediction (EGSP) model that integrates foundation model embeddings with gene expression and clinical variables. EGSP achieved a mean concordance index (C-index) of 0.724 across cancers and exceeded 0.8 in seven cancer types, consistently outperforming single-modality models and existing multi-omics survival approaches. Comparative analyses showed that embeddings derived from pretrained scFoundation weights exhibited lower redundancy with gene expression while retaining complementary prognostic signals relative to pan-cancer fine-tuned embeddings. Explainable AI analyses further revealed that prognostic embeddings capture interpretable biological programs related to tumor differentiation, immune activity, and tumor-intrinsic growth, enabling transparent survival prediction at both cohort and patient levels. Overall, single-cell foundation model embeddings provide biologically meaningful and partially non-redundant survival signals that substantially improve bulk RNA-seq–based prognostic modeling.

https://github.com/weiliu123/EGSP.

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