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Abstract：
BACKGROUND: Avelumab first-line (1 L) maintenance is a standard of care for advanced urothelial carcinoma (aUC) based on the JAVELIN Bladder 100 phase 3 trial, which showed that avelumab 1 L maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) vs BSC alone in patients who were progression free after receiving 1 L platinum-containing chemotherapy. Here, we comprehensively screened JAVELIN Bladder 100 trial datasets to identify prognostic factors that define subpopulations of patients with longer or shorter OS irrespective of treatment, and predictive factors that select patients who could obtain a greater OS benefit from avelumab 1 L maintenance treatment.
METHODS: We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient-reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan-Meier curves. Results were summarized in a multivariable Cox model.
RESULTS: Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C-reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T-cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T-cell infiltration. An analysis in patients with PD-L1+ tumors had similar findings to those in the overall population.
CONCLUSIONS: Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets.
METHODS: We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient-reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan-Meier curves. Results were summarized in a multivariable Cox model.
RESULTS: Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C-reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T-cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T-cell infiltration. An analysis in patients with PD-L1+ tumors had similar findings to those in the overall population.
CONCLUSIONS: Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets.
摘要：
背景：基于JAVELIN膀胱1003期试验，Avelumab一线（1L）维持治疗是晚期尿路上皮癌（aUC）的标准治疗，这表明,在接受1L含铂化疗后无进展的患者中,与单独使用BSC相比,avelumab1L维持+最佳支持治疗(BSC)显著延长了总生存期(OS)和无进展生存期(PFS).这里,我们全面筛选了JAVELIN膀胱100试验数据集，以确定定义OS较长或较短患者亚群的预后因素，而与治疗无关。以及选择可以从阿维鲁单抗1L维持治疗中获得更大OS益处的患者的预测因素。
方法：我们进行了机器学习分析来筛选大量的基线协变量，包括病人的人口统计,疾病特征，实验室值，分子生物标志物，和患者报告的结果。从先前报道的分析和建立的预后和预测标志物中鉴定协变量。在具有治疗相互作用的单变量Cox模型中进一步处理从随机生存森林模型中选择的变量,并使用相关性分析和Kaplan-Meier曲线进行视觉检查。结果总结在多变量Cox模型中。
结果：最终模型中包含的与OS相关的预后基线协变量被分配到avelumab1L维持治疗，东部肿瘤协作组的表现状况，转移部位，最长靶病变直径之和，血液中C反应蛋白和碱性磷酸酶的水平，肿瘤内基质中的淋巴细胞比例，肿瘤突变负担，和肿瘤CD8+T细胞浸润。潜在的预测因素包括转移部位，肿瘤突变负荷，和肿瘤CD8+T细胞浸润。对PD-L1+肿瘤患者的分析结果与总体人群相似。
结论：对JAVELIN膀胱100试验数据的机器学习分析确定了aUC患者avelumab1L维持治疗的潜在预后和预测因素，这需要在其他临床数据集中进行进一步评估。
方法：我们进行了机器学习分析来筛选大量的基线协变量，包括病人的人口统计,疾病特征，实验室值，分子生物标志物，和患者报告的结果。从先前报道的分析和建立的预后和预测标志物中鉴定协变量。在具有治疗相互作用的单变量Cox模型中进一步处理从随机生存森林模型中选择的变量,并使用相关性分析和Kaplan-Meier曲线进行视觉检查。结果总结在多变量Cox模型中。
结果：最终模型中包含的与OS相关的预后基线协变量被分配到avelumab1L维持治疗，东部肿瘤协作组的表现状况，转移部位，最长靶病变直径之和，血液中C反应蛋白和碱性磷酸酶的水平，肿瘤内基质中的淋巴细胞比例，肿瘤突变负担，和肿瘤CD8+T细胞浸润。潜在的预测因素包括转移部位，肿瘤突变负荷，和肿瘤CD8+T细胞浸润。对PD-L1+肿瘤患者的分析结果与总体人群相似。
结论：对JAVELIN膀胱100试验数据的机器学习分析确定了aUC患者avelumab1L维持治疗的潜在预后和预测因素，这需要在其他临床数据集中进行进一步评估。
