PDF(Pubmed)
Abstract：
Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8-12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.
摘要：
针对程序性死亡-1（PD-1）途径的免疫检查点阻断疗法在患有各种癌症类型的患者中显示出显著的功效和持久的反应。治疗效果的早期预测对于优化治疗计划和避免潜在的副作用很重要。在这项工作中,我们开发了一种有效的机器学习预测方法,该方法使用常规血液学和生化参数来预测泛癌患者PD-1联合治疗的疗效.共431例鼻咽癌患者，接受PD-1检查点抑制剂联合治疗的食管癌和肺癌纳入本研究.患者分为两组：进行性疾病（PD）和疾病控制（DC）组。在PD-1治疗之前和第三周收集血液学和生化参数。开发并训练了六个机器学习模型，以预测8-12周时PD-1联合治疗的疗效。通过统计学分析PD-1联合治疗3周前后57项血液生物标志物,热图，主成分分析不能准确预测治疗结果.然而,有了机器学习模型，AdaBoost分类器和GBDT都表现出高水平的预测效率，临床上可接受的AUC值超过0.7。AdaBoost分类器在6种机器学习模型中表现出最高的性能，灵敏度为0.85，特异性为0.79。我们的研究证明了机器学习根据血液学和生化参数的变化预测PD-1联合治疗疗效的潜力。
