Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors.
Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors.
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DOI：
10.1007/s00262-022-03221-5
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发表时间：
2023-01
影响因子：
5.8
通讯作者：
Li, Ning
中科院分区：
文献类型：
--
作者：
Wei, Chen;Wang, Mengyu;Gao, Quanli;Yuan, Shasha;Deng, Wenying;Bie, Liangyu;Ma, Yijie;Zhang, Chi;Li, Shuyi;Luo, Suxia;Li, Ning
免疫检查点抑制剂（ICI）在各种恶性肿瘤中显示出持久的反应。然而，ICI治疗的反应是不可预测的，预测生物标志物的研究需要改进。共入组了120例接受ICI治疗的患者和40例接受非ICI治疗的患者。在ICI治疗前和首次评估前，通过流式细胞术分析作为液体活检生物标志物的外周血免疫细胞标志物（PBIM）。在ICI队列中，患者被随机分为训练（n = 91）和验证（n = 29）队列。应用机器学习算法构建预后和预测免疫相关模型。使用训练群组，开发了基于四个中心PBIM的基于外周血免疫细胞的签名（BICS）。在训练和验证队列以及整个队列中，BICS预测总生存期（OS）获益的准确性很高。高BICS组的无进展生存期和OS显著短于低BICS组。BICS证明了患者实现持久临床结局的预测能力。通过整合这些PBIM，我们进一步构建并验证了支持向量机递归和特征消除分类器模型，该模型可以稳健地预测将获得最佳临床获益的患者。基于PBIM的动态监测作为一种非侵入性、成本效益高、高度特异性和敏感性的生物标志物，在接受ICI治疗的患者中具有广泛的预后和预测效用。在线版本包含补充材料，可通过10.1007/s 00262 -022-03221-5获取。
Immune checkpoint inhibitors (ICIs) have shown durable responses in various malignancies. However, the response to ICI therapy is unpredictable, and investigation of predictive biomarkers needs to be improved. In total, 120 patients receiving ICI therapy and 40 patients receiving non-ICI therapy were enrolled. Peripheral blood immune cell markers (PBIMs), as liquid biopsy biomarkers, were analyzed by flow cytometry before ICI therapy, and before the first evaluation. In the ICI cohort, patients were randomly divided into training (n = 91) and validation (n = 29) cohorts. Machine learning algorithms were applied to construct the prognostic and predictive immune-related models. Using the training cohort, a peripheral blood immune cell-based signature (BICS) based on four hub PBIMs was developed. In both the training and the validation cohorts, and the whole cohort, the BICS achieved a high accuracy for predicting overall survival (OS) benefit. The high-BICS group had significantly shorter progression-free survival and OS than the low-BICS group. The BICS demonstrated the predictive ability of patients to achieve durable clinical outcomes. By integrating these PBIMs, we further constructed and validated the support vector machine-recursive and feature elimination classifier model, which robustly predicts patients who will achieve optimal clinical benefit. Dynamic PBIM-based monitoring as a noninvasive, cost-effective, highly specific and sensitive biomarker has broad potential for prognostic and predictive utility in patients receiving ICI therapy. The online version contains supplementary material available at 10.1007/s00262-022-03221-5.
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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
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Nabet BY;Esfahani MS;Moding EJ;Hamilton EG;Chabon JJ;Rizvi H;Steen CB;Chaudhuri AA;Liu CL;Hui AB;Almanza D;Stehr H;Gojenola L;Bonilla RF;Jin MC;Jeon YJ;Tseng D;Liu C;Merghoub T;Neal JW;Wakelee HA;Padda SK;Ramchandran KJ;Das M;Plodkowski AJ;Yoo C;Chen EL;Ko RB;Newman AM;Hellmann MD;Alizadeh AA;Diehn M
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Nadler SG
影响因子：
11.5
作者：
Kim, Kyung Hwan;Cho, Jinhyun;Shin, Eui-Cheol
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Shin, Eui-Cheol
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20.3
作者：
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通讯作者：
Fong, Lawrence