PO.PS01.09 · 人群科学
机器学习利用真实世界临床数据实现对免疫检查点阻断治疗患者结局的准确预测
Machine learning enables accurate prediction of patient outcomes for immune checkpoint blockade using real-world clinical data
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摘要 Abstract
中文摘要
背景：尽管免疫检查点阻断（ICB）治疗能够产生持久的临床缓解并显著改善患者结局，但准确的结局预测因子至关重要，因为多达半数的晚期疾病患者获益有限或无获益。准确的ICB结局预测将改善治疗分层、减少不必要的毒性，并改善癌症患者的结局。
方法：我们采用最先进的机器学习生存模型，在我们大型的多癌种机构队列中准确预测ICB治疗后患者的生存。这项回顾性研究纳入了2,090名患者，包括晚期黑色素瘤（n=908）、晚期非小细胞肺癌（NSCLC，n=878）或转移性肾细胞癌（RCC，n=304），这些患者于2011-2025年在Moffitt癌症中心接受了抗PD-1/PD-L1和/或抗CTLA-4的ICB治疗。从电子健康记录中提取了50余项治疗前临床和实验室特征，并针对总生存和无进展生存进行分析。我们以75/25随机划分训练和测试生存支持向量机模型以预测患者结局。
结果：Cox比例风险分析为每个数据集识别出11-44个具有统计学意义的特征以纳入各模型，包括血清白蛋白、中性粒细胞与淋巴细胞比值、血压、心率和ECOG评分。我们的模型在黑色素瘤中AUC值高达0.83，在NSCLC中为0.80，在RCC中为0.85，在基于多癌种数据训练的进展模型中表现更佳。在每个有可用数据的时间点，我们的模型均优于PD-L1和TMB。
结论：我们的工作展示了机器学习结合易获取的临床和实验室特征在预测ICB患者结局方面的前景，其表现优于当前的生物标志物。我们模型与PD-L1的时间依赖性受试者工作特征曲线下面积（AUC）：真实世界数据集 样本量 模型6个月OS的AUC PD-L1 6个月OS的AUC 模型24个月OS的AUC PD-L1 24个月OS的AUC 模型6个月PFS的AUC PD-L1 6个月PFS的AUC 模型24个月PFS的AUC PD-L1 24个月PFS的AUC 黑色素瘤 n = 908 黑色素瘤：0.80，多癌种：0.79 0.60 黑色素瘤：0.72，多癌种：0.70 0.51 黑色素瘤：0.72，多癌种：0.79 0.64 黑色素瘤：0.73，多癌种：0.83 N/A 非小细胞肺癌 n = 878 NSCLC：0.77，多癌种：0.70 0.56 NSCLC：0.71，多癌种：0.62 0.59 NSCLC：0.66，多癌种：0.69 0.57 NSCLC：0.67，多癌种：0.80 0.61 肾细胞癌 n = 304 RCC：0.79，多癌种：0.85 0.62 RCC：0.80，多癌种：0.74 0.56 RCC：0.78，多癌种：0.77 N/A RCC：0.82，多癌种：0.68 N/A
查看英文原文 English abstract
Background: While immune checkpoint blockade (ICB) therapy can produce durable clinical responses and substantially improve patient outcomes, accurate predictors of outcomes are critical as up to half of patients with advanced disease derive limited or no benefit. Accurate ICB outcome prediction will improve treatment stratification, reduce unnecessary toxicity, and enhance outcomes for cancer patients.
Methods: We used state-of-the-art machine learning survival models to accurately predict patient survival after ICB therapy in our large multi-cancer institutional cohort. This retrospective study included 2,090 patients with advanced melanoma (n=908), advanced non-small cell lung cancer (NSCLC, n=878), or metastatic renal cell carcinoma (RCC, n=304) who underwent anti-PD-1/PD-L1 and/or anti-CTLA-4 ICB therapy at Moffitt Cancer Center from 2011-2025. Over 50 pre-treatment clinical and laboratory features were abstracted from electronic health records and analyzed against overall and progression-free survival. We trained and tested survival support vector machine models to predict patient outcomes with a 75/25 random split.
Results: Cox PH analysis identified 11-44 statistically significant features per data set for inclusion into each model, including serum albumin, neutrophil-to-lymphocyte ratio, blood pressure, heart rate, and ECOG scores. Our models achieved AUC values of up to 0.83 in melanoma, 0.80 in NSCLC, and 0.85 in RCC, with enhanced performance in progression models trained on multi-cancer data. Our models outperformed PD-L1 and TMB at each time point where data is available.
Conclusions: Our work demonstrates the promise of machine learning with accessible clinical and laboratory features to predict ICB patient outcomes with improved performance versus current biomarkers. Time-dependent receiver operating characteristic area under the curve (AUC) for our model vs PD-L1 Real-world data set Sample size Model AUC at 6mo, OS PD-L1 AUC at 6mo, OS Model AUC at 24mo, OS PD-L1 AUC at 24mo, OS Model AUC at 6mo, PFS PD-L1 AUC at 6mo, PFS Model AUC at 24mo, PFS PD-L1 AUC at 24mo, PFS Melanoma n = 908 Melanoma: 0.80 , Multi: 0.79 0.60 Melanoma: 0.72 , Multi: 0.70 0.51 Melanoma: 0.72, Multi: 0.79 0.64 Melanoma: 0.73, Multi: 0.83 N/A Non-small cell lung cancer n = 878 NSCLC: 0.77 , Multi: 0.70 0.56 NSCLC: 0.71 , Multi: 0.62 0.59 NSCLC: 0.66, Multi: 0.69 0.57 NSCLC: 0.67, Multi: 0.80 0.61 Renal cell carcinoma n = 304 RCC: 0.79, Multi: 0.85 0.62 RCC: 0.80 , Multi: 0.74 0.56 RCC: 0.78 , Multi: 0.77 N/A RCC: 0.82 , Multi: 0.68 N/A
利益披露 Disclosure
A. Pybus, None..
T. Jolaogun, None.