Zhou Jian-Guo, Donaubauer Anna-Jasmina, Frey Benjamin, Becker Ina, Rutzner Sandra, Eckstein Markus, Sun Roger, Ma Hu, Schubert Philipp, Schweizer Claudia, Fietkau Rainer, Deutsch Eric, Gaipl Udo, Hecht Markus
Department of Radiation Oncology, Universitätsklinikum Erlangen, Erlangen, Germany.
Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
J Immunother Cancer. 2021 Feb;9(2). doi: 10.1136/jitc-2020-001845.
The predictive power of novel biological markers for treatment response to immune checkpoint inhibitors (ICI) is still not satisfactory for the majority of patients with cancer. One should identify valid predictive markers in the peripheral blood, as this is easily available before and during treatment. The current interim analysis of patients of the ST-ICI cohort therefore focuses on the development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with metastatic cancer to ICI targeting the programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) axis.
A total of 104 patients were prospectively enrolled. 54 immune cell subsets were prospectively analyzed in patients' peripheral blood by multicolor flow cytometry before treatment with ICI (pre-ICI; n=89), and after the first application of ICI (n=65). Pre-ICI, patients were randomly allocated to a training (n=56) and a validation cohort (n=33). Univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator Cox model were used to create a predictive immune signature, which was also checked after the first ICI, to consider the dynamics of changes in the immune status.
Whole blood samples were provided by 89 patients pre-ICI and by 65 patients after the first ICI. We identified a LIPS which is based on five immune cell subtypes: CD14 monocytes, CD8+/PD-1 T cells, plasmacytoid dendritic cells, neutrophils, and CD3/CD56/CD16 natural killer (NK)T cells. The signature achieved a high accuracy (C-index 0.74 vs 0.71) for predicting overall survival (OS) benefit in both the training and the validation cohort. In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0.26, 95% CI 0.12 to 0.56, p=0.00025; HR 0.30, 95% CI 0.10 to 0.91, p=0.024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI.
Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood.
NCT03453892.
对于大多数癌症患者而言，新型生物标志物对免疫检查点抑制剂（ICI）治疗反应的预测能力仍不尽人意。应在外周血中识别有效的预测标志物，因为外周血在治疗前和治疗期间都很容易获取。因此，ST-ICI队列患者的当前中期分析着重于基于液体免疫谱的特征（LIPS）的开发和验证，以预测转移性癌症患者对靶向程序性细胞死亡蛋白1（PD-1）/程序性细胞死亡配体1（PD-L1）轴的ICI的反应。
前瞻性纳入了104例患者。在ICI治疗前（ICI前；n = 89）和首次应用ICI后（n = 65），通过多色流式细胞术对患者外周血中的54种免疫细胞亚群进行前瞻性分析。在ICI前，患者被随机分配到训练队列（n = 56）和验证队列（n = 33）。使用单变量Cox比例风险回归分析和最小绝对收缩和选择算子Cox模型创建预测性免疫特征，并在首次ICI后进行检查，以考虑免疫状态变化的动态情况。
89例患者在ICI前和65例患者在首次ICI后提供了全血样本。我们确定了一种基于五种免疫细胞亚型的LIPS：CD14单核细胞、CD8 + / PD-1 T细胞、浆细胞样树突状细胞、中性粒细胞和CD3 / CD56 / CD16自然杀伤（NK）T细胞。该特征在训练队列和验证队列中预测总生存（OS）获益时均达到了较高的准确性（C指数分别为0.74和0.71）。在两个队列中，低风险组的OS均显著长于高风险组（HR分别为0.26，95％CI为0.12至0.56，p = 0.00025；HR为0.30，95％CI为0.10至0.91，p = 0.024）。对于整个队列，LIPS还可预测无进展生存（PFS）。所确定的LIPS不受除脑转移以外的临床病理特征影响。LIPS中的NKT细胞和中性粒细胞可作为首次给予ICI后OS和PFS的动态预测生物标志物。
我们的研究确定了一种基于外周全血中免疫细胞亚群的、用于接受PD-1 / PD-L1 ICI治疗的癌症患者生存的预测性LIPS。
NCT03453892。
