Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in patients with advanced cancer. We investigated a novel approach using machine learning to identify a baseline composite cytokine signature via clearance, which, in turn, could be associated with OS in advanced melanoma. Peripheral nivolumab clearance and cytokine data from patients treated with nivolumab in two phase III studies (n = 468 (pooled)) and another phase III study (n = 158) were used for machine-learning model development and validation, respectively. Random forest (Boruta) algorithm was used for feature selection and classification of nivolumab clearance. The 16 top-ranking baseline inflammatory cytokines reflecting immune-cell modulation were selected as a composite signature to predict nivolumab clearance (area under the curve (AUC) = 0.75; accuracy = 0.7). Predicted clearance (high vs. low) via the cytokine signature was significantly associated with OS across all three studies (P < 0.01), regardless of treatment (nivolumab vs. chemotherapy).

中文翻译：

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一种机器学习方法，用于识别与晚期黑色素瘤患者的Nivolumab清除率相关的预后性细胞因子信号。

免疫检查点抑制剂清除率降低是晚期癌症患者总生存期（OS）改善的预测指标。我们研究了一种利用机器学习通过清除来识别基线复合细胞因子特征的新方法，而该特征又可能与晚期黑色素瘤中的OS有关。在两项III期研究（n = 468（合并））和另一次III期研究（n = 158）中，使用nivolumab治疗的患者的外周nivolumab清除率和细胞因子数据分别用于机器学习模型的开发和验证。随机森林（Boruta）算法用于nivolumab清除率的特征选择和分类。选择反映免疫细胞调节的16种排名最高的基线炎症细胞因子作为复合标记，以预测nivolumab清除率（曲线下面积（AUC）= 0.75；准确度= 0.7）。在所有三项研究中，通过细胞因子签名预测的清除率（高或低）与OS均显着相关（P <0.01），而与治疗无关（诺华单抗vs.化疗）。