Related Experiment Video
Updated: Jun 5, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Translating lineage-resolved single-cell programs to bulk clinical prognosis: Adversarial generative learning reveals
Bao Le1, Doan Phuong Quy Nguyen2, Doan Huu Nhat Binh2
1Research Group in Pharmaceutical and Biomedical Sciences, Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Background:
Breast cancer transcriptional programs span malignant epithelial cells, and the tumor microenvironment (TME), yet tumor-normal contrasts are often confounded by lineage identity and cell-type composition, limiting clinically actionable signatures.
Methods:
Our objective was to construct a lineage-aware model that prioritizes compact cell-type-specific tumor-normal gene signatures for downstream pathway interpretation and TCGA-BRCA projection, rather than to use classification performance as the final endpoint. Using GSE268662, we built an integrated single-cell atlas with eight major cell types grouped into four lineages (epithelial, immune, stromal, vascular). For each cell type, we trained a lineage-aware adversarial autoencoder-classifier (WGAN-GP) on within-cell-type tumor and normal DEG pools, ranked genes by input-to-logit importance, selected signature size by k-sensitivity (AUROC/AUPRC), and projected signatures into TCGA-BRCA for PAM50-stratified activity and survival analyses.
Results:
Optimal signatures were compact (Endothelial 50; Fibroblast 20; Pericytes/SMC 10; Epithelial 100; Myeloid 20; T cells 20; Mast 30; B cells 30). At best_k, AUROC ranged from 0.766 to 0.904 and AUPRC from 0.463 to 0.953 (Mast AUROC 0.904; Myeloid AUROC 0.894; B-cell AUPRC 0.953). Top genes were lineage-consistent (e.g., HSPG2/COL4A2/THY1/SPARC; COL1A1/COL1A2/COL3A1; STAT1; LEF1; CXCR4/IGHG3). Enrichment analyses highlighted immune interferon and TNFα/NFκB signaling and antigen presentation, contrasted with stromal/vascular ECM-receptor interaction and focal adhesion pathways; epithelial hallmarks linked EMT and apoptosis. In TCGA-BRCA, epithelial and stromal scores increased broadly in tumors, whereas immune and vascular signals were more subtype-dependent. Within subtype, immune risk scores provided the strongest overall survival separation (C-index up to ∼0.92).
Conclusion:
A lineage-aware GAN framework yields compact, interpretable signatures that capture ecosystem-level biology (immune activity, ECM remodeling, epithelial plasticity) and can be projected into TCGA-BRCA for subtype-aware prognostic stratification in breast cancer.
