| Autor: |
Dong Yang, Mei-Han Duan, Qiu-Er Yuan, Zhi-Ling Li, Chuang-Hua Luo, Lan-Yue Cui, Li-Chao Li, Ying Xiao, Xian-Ying Zhu, Hai-Liang Zhang, Gong-Kan Feng, Guo-Chen Liu, Rong Deng, Jun-Dong Li, Xiao-Feng Zhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Translational Medicine, Vol 21, Iss 1, Pp 1-16 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1479-5876 |
| DOI: |
10.1186/s12967-023-04422-x |
| Popis: |
Abstract Background As the most lethal gynecologic cancer, ovarian cancer (OV) holds the potential of being immunotherapy-responsive. However, only modest therapeutic effects have been achieved by immunotherapies such as immune checkpoint blockade. This study aims to propose a generalized stroma-immune prognostic signature (SIPS) to identify OV patients who may benefit from immunotherapy. Methods The 2097 OV patients included in the study were significant with high-grade serous ovarian cancer in the III/IV stage. The 470 immune-related signatures were collected and analyzed by the Cox regression and Lasso algorithm to generalize a credible SIPS. Correlations between the SIPS signature and tumor microenvironment were further analyzed. The critical immunosuppressive role of stroma indicated by the SIPS was further validated by targeting the major suppressive stroma component (CAFs, Cancer-associated fibroblasts) in vitro and in vivo. With four machine-learning methods predicting tumor immune subtypes, the stroma-immune signature was upgraded to a 23-gene signature. Results The SIPS effectively discriminated the high-risk individuals in the training and validating cohorts, where the high SIPS succeeded in predicting worse survival in several immunotherapy cohorts. The SIPS signature was positively correlated with stroma components, especially CAFs and immunosuppressive cells in the tumor microenvironment, indicating the critical suppressive stroma-immune network. The combination of CAFs’ marker PDGFRB inhibitors and frontline PARP inhibitors substantially inhibited tumor growth and promoted the survival of OV-bearing mice. The stroma-immune signature was upgraded to a 23-gene signature to improve clinical utility. Several drug types that suppress stroma-immune signatures, such as EGFR inhibitors, could be candidates for potential immunotherapeutic combinations in ovarian cancer. Conclusions The stroma-immune signature could efficiently predict the immunotherapeutic sensitivity of OV patients. Immunotherapy and auxiliary drugs targeting stroma could enhance immunotherapeutic efficacy in ovarian cancer. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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