Immune index: A gene and cell prognostic signature for immunotherapy response prediction in hepatocellular carcinoma
The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found tha...
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| Published in | Pharmacological research Vol. 187; p. 106583 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.01.2023
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| Abstract | The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found that IMI low HCCs were enriched in stem cell and proliferating signatures, and yielded more TP53 mutation and 17p loss compared with IMI high HCCs. More importantly, patients with high IMI exhibited better immune-checkpoint blockade (ICB) response. To facilitate clinical application, we employed machine learning algorithms to develop a gene model of the IMI (IMIG), which contained 10 genes. According to our HCC cohort examination and single-cell level analysis, we found that IMIG high HCCs exhibited favorable survival outcomes and high levels of NK and CD8+ T cells infiltration. Finally, after coculture with autologous tumor infiltrating lymphocytes, IMIG high tumor cells exhibited a better response to nivolumab treatment. Collectively, the IMI and IMIG may serve as powerful tools for the prognosis, classification and ICB treatment response prediction of HCC.
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| AbstractList | The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found that IMI low HCCs were enriched in stem cell and proliferating signatures, and yielded more TP53 mutation and 17p loss compared with IMI high HCCs. More importantly, patients with high IMI exhibited better immune-checkpoint blockade (ICB) response. To facilitate clinical application, we employed machine learning algorithms to develop a gene model of the IMI (IMIG), which contained 10 genes. According to our HCC cohort examination and single-cell level analysis, we found that IMIG high HCCs exhibited favorable survival outcomes and high levels of NK and CD8+ T cells infiltration. Finally, after coculture with autologous tumor infiltrating lymphocytes, IMIG high tumor cells exhibited a better response to nivolumab treatment. Collectively, the IMI and IMIG may serve as powerful tools for the prognosis, classification and ICB treatment response prediction of HCC.The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found that IMI low HCCs were enriched in stem cell and proliferating signatures, and yielded more TP53 mutation and 17p loss compared with IMI high HCCs. More importantly, patients with high IMI exhibited better immune-checkpoint blockade (ICB) response. To facilitate clinical application, we employed machine learning algorithms to develop a gene model of the IMI (IMIG), which contained 10 genes. According to our HCC cohort examination and single-cell level analysis, we found that IMIG high HCCs exhibited favorable survival outcomes and high levels of NK and CD8+ T cells infiltration. Finally, after coculture with autologous tumor infiltrating lymphocytes, IMIG high tumor cells exhibited a better response to nivolumab treatment. Collectively, the IMI and IMIG may serve as powerful tools for the prognosis, classification and ICB treatment response prediction of HCC. The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found that IMI low HCCs were enriched in stem cell and proliferating signatures, and yielded more TP53 mutation and 17p loss compared with IMI high HCCs. More importantly, patients with high IMI exhibited better immune-checkpoint blockade (ICB) response. To facilitate clinical application, we employed machine learning algorithms to develop a gene model of the IMI (IMIG), which contained 10 genes. According to our HCC cohort examination and single-cell level analysis, we found that IMIG high HCCs exhibited favorable survival outcomes and high levels of NK and CD8+ T cells infiltration. Finally, after coculture with autologous tumor infiltrating lymphocytes, IMIG high tumor cells exhibited a better response to nivolumab treatment. Collectively, the IMI and IMIG may serve as powerful tools for the prognosis, classification and ICB treatment response prediction of HCC. [Display omitted] The heterogeneity of tumor immune microenvironment (TIME) plays important roles in the development and immunotherapy response of hepatocellular carcinoma (HCC). Using machine learning algorithms, we introduced the immune index (IMI), a prognostic model based on the HCC immune landscape. We found that IMI low HCCs were enriched in stem cell and proliferating signatures, and yielded more TP53 mutation and 17p loss compared with IMI high HCCs. More importantly, patients with high IMI exhibited better immune-checkpoint blockade (ICB) response. To facilitate clinical application, we employed machine learning algorithms to develop a gene model of the IMI (IMI ), which contained 10 genes. According to our HCC cohort examination and single-cell level analysis, we found that IMI high HCCs exhibited favorable survival outcomes and high levels of NK and CD8 T cells infiltration. Finally, after coculture with autologous tumor infiltrating lymphocytes, IMI high tumor cells exhibited a better response to nivolumab treatment. Collectively, the IMI and IMI may serve as powerful tools for the prognosis, classification and ICB treatment response prediction of HCC. |
| ArticleNumber | 106583 |
| Author | Song, Bin Cui, Xiuliang Cui, Longjiu Wang, Duowei Wang, Hongyang Liu, Erdong Zhou, Wenxia Han, Lu Zhang, Yongxiang Fu, Jing Fu, Gongbo |
| Author_xml | – sequence: 1 givenname: Xiuliang surname: Cui fullname: Cui, Xiuliang organization: National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China – sequence: 2 givenname: Lu surname: Han fullname: Han, Lu organization: Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China – sequence: 3 givenname: Longjiu surname: Cui fullname: Cui, Longjiu organization: Biliary Tract Department II, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China – sequence: 4 givenname: Gongbo surname: Fu fullname: Fu, Gongbo organization: National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China – sequence: 5 givenname: Erdong surname: Liu fullname: Liu, Erdong organization: National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China – sequence: 6 givenname: Duowei surname: Wang fullname: Wang, Duowei organization: Center for New Drug Safety Evaluation and Research, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210017, China – sequence: 7 givenname: Bin surname: Song fullname: Song, Bin organization: Department of Pancreatic Surgery, Changhai Hospital, Naval Medical University, Shanghai 200438, China – sequence: 8 givenname: Yongxiang surname: Zhang fullname: Zhang, Yongxiang organization: Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China – sequence: 9 givenname: Wenxia surname: Zhou fullname: Zhou, Wenxia email: zhouwx@bmi.ac.cn organization: Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China – sequence: 10 givenname: Hongyang surname: Wang fullname: Wang, Hongyang email: hywangk@vip.sina.com organization: National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China – sequence: 11 givenname: Jing surname: Fu fullname: Fu, Jing email: fujing-724@163.com organization: National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China |
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| Keywords | Immune microenvironment Prognosis Immune therapy Single cell Hepatocellular carcinoma HCC ICB TIME |
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| SubjectTerms | Carcinoma, Hepatocellular - drug therapy Carcinoma, Hepatocellular - genetics CD8-Positive T-Lymphocytes Hepatocellular carcinoma Humans Immune microenvironment Immune therapy Immunotherapy Liver Neoplasms - drug therapy Liver Neoplasms - genetics Prognosis Single cell Tumor Microenvironment |
| Title | Immune index: A gene and cell prognostic signature for immunotherapy response prediction in hepatocellular carcinoma |
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