Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer
Immune checkpoint inhibitors (ICIs) have dramatically improved the outcomes of non-small cell lung cancer patients and have increased the possibility of long-term survival. However, few patients benefit from ICIs, and no predictive biomarkers other than tumor programmed cell death ligand 1 (PD-L1) e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/7/1855 |
| _version_ | 1797438814336057344 |
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| author | Ryota Ushio Shuji Murakami Haruhiro Saito |
| author_facet | Ryota Ushio Shuji Murakami Haruhiro Saito |
| author_sort | Ryota Ushio |
| collection | DOAJ |
| description | Immune checkpoint inhibitors (ICIs) have dramatically improved the outcomes of non-small cell lung cancer patients and have increased the possibility of long-term survival. However, few patients benefit from ICIs, and no predictive biomarkers other than tumor programmed cell death ligand 1 (PD-L1) expression have been established. Hence, the identification of biomarkers is an urgent issue. This review outlines the current understanding of predictive markers for the efficacy of ICIs, including PD-L1, tumor mutation burden, DNA mismatch repair deficiency, microsatellite instability, CD8<sup>+</sup> tumor-infiltrating lymphocytes, human leukocyte antigen class I, tumor/specific genotype, and blood biomarkers such as peripheral T-cell phenotype, neutrophil-to-lymphocyte ratio, interferon-gamma, and interleukin-8. A tremendous number of biomarkers are in development, but individual biomarkers are insufficient. Tissue biomarkers have issues in reproducibility and accuracy because of intratumoral heterogeneity and biopsy invasiveness. Furthermore, blood biomarkers have difficulty in reflecting the tumor microenvironment and therefore tend to be less predictive for the efficacy of ICIs than tissue samples. In addition to individual biomarkers, the development of composite markers, including novel technologies such as machine learning and high-throughput analysis, may make it easier to comprehensively analyze multiple biomarkers. |
| first_indexed | 2024-03-09T11:43:44Z |
| format | Article |
| id | doaj.art-a335cf33485440abaf6ef6deb34dd279 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T11:43:44Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-a335cf33485440abaf6ef6deb34dd2792023-11-30T23:27:48ZengMDPI AGJournal of Clinical Medicine2077-03832022-03-01117185510.3390/jcm11071855Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung CancerRyota Ushio0Shuji Murakami1Haruhiro Saito2Kanagawa Cancer Center, Department of Thoracic Oncology, 2-3-2 Nakao, Asahi, Yokohama 241-8515, JapanKanagawa Cancer Center, Department of Thoracic Oncology, 2-3-2 Nakao, Asahi, Yokohama 241-8515, JapanKanagawa Cancer Center, Department of Thoracic Oncology, 2-3-2 Nakao, Asahi, Yokohama 241-8515, JapanImmune checkpoint inhibitors (ICIs) have dramatically improved the outcomes of non-small cell lung cancer patients and have increased the possibility of long-term survival. However, few patients benefit from ICIs, and no predictive biomarkers other than tumor programmed cell death ligand 1 (PD-L1) expression have been established. Hence, the identification of biomarkers is an urgent issue. This review outlines the current understanding of predictive markers for the efficacy of ICIs, including PD-L1, tumor mutation burden, DNA mismatch repair deficiency, microsatellite instability, CD8<sup>+</sup> tumor-infiltrating lymphocytes, human leukocyte antigen class I, tumor/specific genotype, and blood biomarkers such as peripheral T-cell phenotype, neutrophil-to-lymphocyte ratio, interferon-gamma, and interleukin-8. A tremendous number of biomarkers are in development, but individual biomarkers are insufficient. Tissue biomarkers have issues in reproducibility and accuracy because of intratumoral heterogeneity and biopsy invasiveness. Furthermore, blood biomarkers have difficulty in reflecting the tumor microenvironment and therefore tend to be less predictive for the efficacy of ICIs than tissue samples. In addition to individual biomarkers, the development of composite markers, including novel technologies such as machine learning and high-throughput analysis, may make it easier to comprehensively analyze multiple biomarkers.https://www.mdpi.com/2077-0383/11/7/1855non-small cell lung cancerbiomarkeranti-programmed cell death ligand 1tumor-infiltrating lymphocytestumor mutation burdenhuman leukocyte antigen class I |
| spellingShingle | Ryota Ushio Shuji Murakami Haruhiro Saito Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer Journal of Clinical Medicine non-small cell lung cancer biomarker anti-programmed cell death ligand 1 tumor-infiltrating lymphocytes tumor mutation burden human leukocyte antigen class I |
| title | Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer |
| title_full | Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer |
| title_fullStr | Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer |
| title_full_unstemmed | Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer |
| title_short | Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer |
| title_sort | predictive markers for immune checkpoint inhibitors in non small cell lung cancer |
| topic | non-small cell lung cancer biomarker anti-programmed cell death ligand 1 tumor-infiltrating lymphocytes tumor mutation burden human leukocyte antigen class I |
| url | https://www.mdpi.com/2077-0383/11/7/1855 |
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