作为最致命的妇科癌症，卵巢癌（OV）具有潜力对免疫疗法产生反应。然而，免疫检查点阻断等免疫疗法只能取得适度的治疗效果。本研究旨在提出一种广义的基质-免疫预后标记（SIPS），以识别可能从免疫治疗中获益的OV患者。研究中包括的2097名OV患者均为高级别浆液性卵巢癌，处于III/IV期。通过Cox回归和Lasso算法对收集到的470个免疫相关标记进行分析，以得出可靠的SIPS。进一步分析了SIPS标记与肿瘤微环境之间的相关性。通过体外和体内靶向主要抑制性基质成分（癌关联成纤维细胞，CAFs）对SIPS指示的基质免疫抑制作用进行了验证。利用四种机器学习方法预测肿瘤免疫亚型，将基质免疫标记升级为23基因标记。SIPS能有效区分训练和验证队列中的高风险个体，其中高SIPS能成功预测多个免疫治疗队列中较差的生存情况。SIPS标志与基质成分，特别是CAFs和肿瘤微环境中的免疫抑制性细胞呈正相关，表明了至关重要的基质免疫抑制网络。CAFs标记物PDGFRB抑制剂与一线PARP抑制剂的联合显著抑制了肿瘤生长并促进了携带卵巢癌小鼠的生存。将基质免疫标记升级为23基因标记以提高临床效用。抑制基质免疫标记的几种药物类型，如EGFR抑制剂，可能成为卵巢癌潜在免疫治疗联合的候选药物。基质免疫标记能有效预测OV患者的免疫治疗敏感性。免疫治疗和针对基质的辅助药物可以增强卵巢癌免疫治疗的效果。©2023年BioMed Central Ltd.，Springer Nature的一部分。

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.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.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.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.© 2023. BioMed Central Ltd., part of Springer Nature.