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Jiang YQ, Wang ZX, Zhong M, Shen LJ, Han X, Zou X, Liu XY, Deng YN, Yang Y, Chen GH, Deng W, Huang JH. Investigating Mechanisms of Response or Resistance to Immune Checkpoint Inhibitors by Analyzing Cell-Cell Communications in Tumors Before and After Programmed Cell Death-1 (PD-1) Targeted Therapy: An Integrative Analysis Using Single-cell RNA and Bulk-RNA Sequencing Data. Oncoimmunology 2021; 10:1908010. [PMID: 33868792 PMCID: PMC8023241 DOI: 10.1080/2162402x.2021.1908010] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Currently, a significant proportion of cancer patients do not benefit from programmed cell death-1 (PD-1)-targeted therapy. Overcoming drug resistance remains a challenge. In this study, single-cell RNA sequencing and bulk RNA sequencing data from samples collected before and after anti-PD-1 therapy were analyzed. Cell-cell interaction analyses were performed to determine the differences between pretreatment responders and nonresponders and the relative differences in changes from pretreatment to posttreatment status between responders and nonresponders to ultimately investigate the specific mechanisms underlying response and resistance to anti-PD-1 therapy. Bulk-RNA sequencing data were used to validate our results. Furthermore, we analyzed the evolutionary trajectory of ligands/receptors in specific cell types in responders and nonresponders. Based on pretreatment data from responders and nonresponders, we identified several different cell-cell interactions, like WNT5A-PTPRK, EGFR-AREG, AXL-GAS6 and ACKR3-CXCL12. Furthermore, relative differences in the changes from pretreatment to posttreatment status between responders and nonresponders existed in SELE-PSGL-1, CXCR3-CCL19, CCL4-SLC7A1, CXCL12-CXCR3, EGFR-AREG, THBS1-a3b1 complex, TNF-TNFRSF1A, TNF-FAS and TNFSF10-TNFRSF10D interactions. In trajectory analyses of tumor-specific exhausted CD8 T cells using ligand/receptor genes, we identified a cluster of T cells that presented a distinct pattern of ligand/receptor expression. They highly expressed suppressive genes like HAVCR2 and KLRC1, cytotoxic genes like GZMB and FASLG and the tissue-residence-related gene CCL5. These cells had increased expression of survival-related and tissue-residence-related genes, like heat shock protein genes and the interleukin-7 receptor (IL-7R), CACYBP and IFITM3 genes, after anti-PD-1 therapy. These results reveal the mechanisms underlying anti-PD-1 therapy response and offer abundant clues for potential strategies to improve immunotherapy.
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Affiliation(s)
- Yi-Quan Jiang
- Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou China
| | - Zi-Xian Wang
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Artificial Intelligence Laboratory of Sun Yat-Sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ming Zhong
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Artificial Intelligence Laboratory of Sun Yat-Sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lu-Jun Shen
- Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou China
| | - Xue Han
- Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou China
| | - Xuxiazi Zou
- Department of Breast Surgery, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Artificial Intelligence Laboratory of Sun Yat-Sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xin-Yi Liu
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yi-Nan Deng
- Department of Hepatic Surgery and Liver Transplantation Center of the Third Affiliated Hospital, Organ Transplantation Institute, Sun Yat-sen University, Guangzhou, China
| | - Yang Yang
- Department of Hepatic Surgery and Liver Transplantation Center of the Third Affiliated Hospital, Organ Transplantation Institute, Sun Yat-sen University, Guangzhou, China
| | - Gui-Hua Chen
- Department of Hepatic Surgery and Liver Transplantation Center of the Third Affiliated Hospital, Organ Transplantation Institute, Sun Yat-sen University, Guangzhou, China
| | - Wuguo Deng
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Artificial Intelligence Laboratory of Sun Yat-Sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jin-Hua Huang
- Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou China
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