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Mei T, Wang T, Zhou Q. Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients. Clin Exp Med 2024; 24:60. [PMID: 38554212 PMCID: PMC10981593 DOI: 10.1007/s10238-024-01324-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/05/2024] [Indexed: 04/01/2024]
Abstract
In recent years, various types of immunotherapy, particularly the use of immune checkpoint inhibitors targeting programmed cell death 1 or programmed death ligand 1 (PD-L1), have revolutionized the management and prognosis of non-small cell lung cancer. PD-L1 is frequently used as a biomarker for predicting the likely benefit of immunotherapy for patients. However, some patients receiving immunotherapy have high response rates despite having low levels of PD-L1. Therefore, the identification of this group of patients is extremely important to improve prognosis. The tumor microenvironment contains tumor, stromal, and infiltrating immune cells with its composition differing significantly within tumors, between tumors, and between individuals. The omics approach aims to provide a comprehensive assessment of each patient through high-throughput extracted features, promising a more comprehensive characterization of this complex ecosystem. However, features identified by high-throughput methods are complex and present analytical challenges to clinicians and data scientists. It is thus feasible that artificial intelligence could assist in the identification of features that are beyond human discernment as well as in the performance of repetitive tasks. In this paper, we review the prediction of immunotherapy efficacy by different biomarkers (genomic, transcriptomic, proteomic, microbiomic, and radiomic), together with the use of artificial intelligence and the challenges and future directions of these fields.
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Affiliation(s)
- Ting Mei
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Ting Wang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Qinghua Zhou
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China.
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Cheng G, Zhang F, Xing Y, Hu X, Zhang H, Chen S, Li M, Peng C, Ding G, Zhang D, Chen P, Xia Q, Wu M. Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. Front Immunol 2022; 13:893198. [PMID: 35844508 PMCID: PMC9286729 DOI: 10.3389/fimmu.2022.893198] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/27/2022] [Indexed: 12/12/2022] Open
Abstract
Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.
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Affiliation(s)
- Guoping Cheng
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | | | | | - Xingyi Hu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - He Zhang
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | | | | | | | - Guangtai Ding
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Dadong Zhang
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Peilin Chen
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Qingxin Xia
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Meijuan Wu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
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