An immune-based risk-stratification system for predicting prognosis in pulmonary sarcomatoid carcinoma (PSC)

Pulmonary sarcomatoid carcinoma (PSC) is an uncommon subtype of lung cancer, and immune checkpoint blockade promises in clinical benefit. However, virtually nothing is known about the expression of common immune checkpoints in PSC. Here, we performed immunohistochemistry (IHC) to detect nine immune-related proteins in 97 PSC patients. Based on the univariable Cox regression, random forests were used to establish risk models for OS and DFS. Moreover, we used the GSEA, CIBERSORT, and ImmuCellAI to analyze the enriched pathways and microenvironment. Univariable analysis revealed that CD4 (P = 0.008), programmed cell death protein 1 (PD-1; P = 0.003), galectin-9 (Gal-9) on tumor cells (TCs; P = 0.021) were independent for DFS, while CD4 (P = 0.020), PD-1 (P = 0.004), Gal-9 (P = 0.033), and HLA on TILs (P = 0.031) were significant for OS. Meanwhile, the expression level of CD8 played a marginable role in DFS (P = 0.061), limited by the number of patients. The combination of Gal-9 on TC with CD4 and PD-1 on TILs demonstrated the most accurate prediction for DFS (AUC: 0.636-0.791, F1-score: 0.635–0.799), and a dramatic improvement to TNM-stage (P < 0.001 for F1-score of 1-y, 3-y, and 5-yDFS). A similar finding was also observed in the predictive ability of CD4 for OS (AUC: 0.602-0.678, F1-score: 0.635–0.679). CD4 was negatively associated with the infiltration of neutrophils (P = 0.015). PDCD1 (coding gene of PD-1) was positively correlated to the number of exhausted T cells (Texs; P = 0.020) and induced regulatory T cells (iTregs; P = 0.021), and LGALS9 (coding gene of Gal-9) was positively related to the level of dendritic cells (DCs; P = 0.021). Further, a higher combinational level of CD4, PDCD1 on TILs, and LAGLS9 on TCs were proved to be infiltrated with more M1-type macrophages (P < 0.05). We confirmed the expression status of nine immune-related proteins and established a TNM-Immune system for OS and DFS in PSC to assist clinical risk-stratification.