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

Haoyue Guo; Binglei Li; Li Diao; Hao Wang; Peixin Chen; Minlin Jiang; Lishu Zhao; Yayi He; Caicun Zhou

doi:10.1080/2162402X.2021.1947665

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

Oncoimmunology. 2021 Jul 13;10(1):1947665. doi: 10.1080/2162402X.2021.1947665. eCollection 2021.

Authors

Haoyue Guo^{1

2}, Binglei Li³, Li Diao⁴, Hao Wang^{1

2}, Peixin Chen^{1

2}, Minlin Jiang^{1

2}, Lishu Zhao^{1

2}, Yayi He^{1

2}, Caicun Zhou^{1

2}

Affiliations

¹ Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China.
² School of Medicine, Tongji University, Shanghai, China.
³ Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai, China.
⁴ Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Abstract

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.

Keywords: Pulmonary sarcomatoid carcinoma; immune checkpoint; immunohistochemistry; machine learning; prognosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

B7-H1 Antigen / genetics
CD8-Positive T-Lymphocytes
Carcinoma*
Galectins
Humans
Lung Neoplasms* / diagnosis
Prognosis
Tumor Microenvironment

Substances

B7-H1 Antigen
Galectins
LGALS9 protein, human

Grants and funding

This study was supported in part by a grant of National Natural Science Foundation of China (81802255), Clinical research project of Shanghai Pulmonary Hospital (FKLY20010), Young Talents in Shanghai (2019 QNBJ), ‘Dream Tutor’ Outstanding Young Talents Program (fkyq1901), Clinical research project of Shanghai Pulmonary Hospital (FKLY20001), Respiratory medicine, a key clinical specialty construction project in Shanghai, Promotion and application of multidisciplinary collaboration system for pulmonary noninfectious diseases, Clinical Research Project of Shanghai Pulmonary Hospital (fk18005), Key Discipline in 2019 (oncology), Project of Shanghai Municipal Science and Technology Commission (Project of Municipal Science and Technology Commission), Scientific research project of Shanghai Pulmonary Hospital (fkcx1903), Shanghai Municipal Commission of Health and Family Planning (2017YQ050), Innovation Training Project of SITP of Tongji University, and key projects of leading talent (19411950300), and Youth project of hospital management research fund of Shanghai Hospital Association (Q1902037);Young Talents in Shanghai [2019 QNBJ];‘Dream Tutor’ Outstanding Young Talents Program [fkyq1901];Clinical Research Project of Shanghai Pulmonary Hospital [fk18005];Key Discipline in 2019 (oncology);Youth project of hospital management research fund of Shanghai Hospital Association [Q1902037];Scientific research project of Shanghai Pulmonary Hospital [fkcx1903];Shanghai Municipal Commission of Health and Family Planning[2017YQ050];Innovation Training Project of SITP of Tongji University;key projects of leading talent [19411950300];Project of Shanghai Municipal Science and Technology Commission (Project of Municipal Science and Technology Commission);