PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：作为最致命的妇科癌症，卵巢癌（OV）具有免疫疗法反应的潜力。然而,免疫检查点阻断等免疫疗法仅取得适度的治疗效果.这项研究旨在提出一种广义的基质免疫预后特征（SIPS），以识别可能从免疫疗法中受益的OV患者。
方法：纳入本研究的2097例OV患者在III/IV期患有高级别浆液性卵巢癌。通过Cox回归和Lasso算法收集并分析470个免疫相关特征,以概括可信的SIPS。进一步分析了SIPS特征与肿瘤微环境之间的相关性。通过靶向主要抑制性基质成分（CAFs，癌症相关成纤维细胞）体外和体内。利用四种机器学习方法预测肿瘤免疫亚型，基质免疫签名升级为23个基因签名.
结果：SIPS有效区分了培训和验证队列中的高危个体，其中高SIPS成功预测了几个免疫治疗队列中较差的生存率。SIPS信号与基质成分呈正相关，特别是肿瘤微环境中的CAFs和免疫抑制细胞，表明关键的抑制性基质免疫网络。CAFs标记PDGFRB抑制剂和一线PARP抑制剂的组合基本上抑制了肿瘤生长并促进了OV小鼠的存活。基质免疫特征被升级为23-基因特征以改善临床效用。几种抑制基质免疫特征的药物类型，如EGFR抑制剂，可能是卵巢癌潜在免疫治疗组合的候选药物。
结论：基质免疫特征可以有效预测OV患者的免疫治疗敏感性。针对基质的免疫治疗和辅助药物可以增强卵巢癌的免疫治疗效果。
方法：纳入本研究的2097例OV患者在III/IV期患有高级别浆液性卵巢癌。通过Cox回归和Lasso算法收集并分析470个免疫相关特征,以概括可信的SIPS。进一步分析了SIPS特征与肿瘤微环境之间的相关性。通过靶向主要抑制性基质成分（CAFs，癌症相关成纤维细胞）体外和体内。利用四种机器学习方法预测肿瘤免疫亚型，基质免疫签名升级为23个基因签名.
结果：SIPS有效区分了培训和验证队列中的高危个体，其中高SIPS成功预测了几个免疫治疗队列中较差的生存率。SIPS信号与基质成分呈正相关，特别是肿瘤微环境中的CAFs和免疫抑制细胞，表明关键的抑制性基质免疫网络。CAFs标记PDGFRB抑制剂和一线PARP抑制剂的组合基本上抑制了肿瘤生长并促进了OV小鼠的存活。基质免疫特征被升级为23-基因特征以改善临床效用。几种抑制基质免疫特征的药物类型，如EGFR抑制剂，可能是卵巢癌潜在免疫治疗组合的候选药物。
结论：基质免疫特征可以有效预测OV患者的免疫治疗敏感性。针对基质的免疫治疗和辅助药物可以增强卵巢癌的免疫治疗效果。
