Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma
| Title: | Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma |
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| Authors: | Jan-Mou Lee, Yi-Ping Hung, Kai-Yuan Chou, Cheng-Yun Lee, Shian-Ren Lin, Ya-Han Tsai, Wan-Yu Lai, Yu-Yun Shao, Chiun Hsu, Chih-Hung Hsu, Yee Chao |
| Source: | Frontiers in Medicine, Vol 9 (2022) |
| Publisher Information: | Frontiers Media S.A., 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Medicine (General) |
| Subject Terms: | hepatocellular carcinoma (HCC), immunoprofiling, predictive biomarker, nivolumab (PubChem SID: 178103907), immunotherapy, artificial intelligence, Medicine (General), R5-920 |
| More Details: | Immune checkpoint inhibitors (ICI) have been applied in treating advanced hepatocellular carcinoma (aHCC) patients, but few patients exhibit stable and lasting responses. Moreover, identifying aHCC patients suitable for ICI treatment is still challenged. This study aimed to evaluate whether dissecting peripheral immune cell subsets by Mann-Whitney U test and artificial intelligence (AI) algorithms could serve as predictive biomarkers of nivolumab treatment for aHCC. Disease control group carried significantly increased percentages of PD-L1+ monocytes, PD-L1+ CD8 T cells, PD-L1+ CD8 NKT cells, and decreased percentages of PD-L1+ CD8 NKT cells via Mann-Whitney U test. By recursive feature elimination method, five featured subsets (CD4 NKTreg, PD-1+ CD8 T cells, PD-1+ CD8 NKT cells, PD-L1+ CD8 T cells and PD-L1+ monocytes) were selected for AI training. The featured subsets were highly overlapping with ones identified via Mann-Whitney U test. Trained AI algorithms committed valuable AUC from 0.8417 to 0.875 to significantly separate disease control group from disease progression group, and SHAP value ranking also revealed PD-L1+ monocytes and PD-L1+ CD8 T cells exclusively and significantly contributed to this discrimination. In summary, the current study demonstrated that integrally analyzing immune cell profiling with AI algorithms could serve as predictive biomarkers of ICI treatment. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2296-858X 39197581 |
| Relation: | https://www.frontiersin.org/articles/10.3389/fmed.2022.1008855/full; https://doaj.org/toc/2296-858X |
| DOI: | 10.3389/fmed.2022.1008855 |
| Access URL: | https://doaj.org/article/c071b1bfc5434aa39197581cde706abc |
| Accession Number: | edsdoj.071b1bfc5434aa39197581cde706abc |
| Database: | Directory of Open Access Journals |
| ISSN: | 2296858X 39197581 |
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| DOI: | 10.3389/fmed.2022.1008855 |
| Published in: | Frontiers in Medicine |
| Language: | English |