Five-Factor Prognostic Model for Survival of Post-Platinum Patients with Metastatic Urothelial Carcinoma Receiving PD-L1 Inhibitors.
Five-Factor Prognostic Model for Survival of Post-Platinum Patients with Metastatic Urothelial Carcinoma Receiving PD-L1 Inhibitors.
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五因素预后模型用于接受PD-L1抑制剂的转移性尿路上皮癌的生存。
DOI：
10.1097/ju.0000000000001199
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Pond GR
中科院分区：
文献类型：
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作者：
Sonpavde G;Manitz J;Gao C;Tayama D;Kaiser C;Hennessy D;Makari D;Gupta A;Abdullah SE;Niegisch G;Rosenberg JE;Bajorin DF;Grivas P;Apolo AB;Dreicer R;Hahn NM;Galsky MD;Necchi A;Srinivas S;Powles T;Choueiri TK;Pond GR
由于现有模型是在化疗背景下构建的，因此有必要建立接受PD-1/PD-L1抑制剂治疗的铂类药物治疗后转移性尿路上皮癌（mUC）患者总生存期（OS）的预后模型。使用了来自I/II期试验的患者水平数据，这些试验评价了mUC患者接受含铂化疗后的PD-L1抑制剂。推导数据集包括2项评价atezolizumab的I/II期试验（n=405）。评价avelumab（n=242）和durvalumab（n=198）的两项I/II期试验构成验证数据集。考克斯回归分析评估候选预后因素与OS的关联。使用衍生数据集采用逐步选择来选择最佳模型。在avelumab和durvalumab数据集中评估了鉴别和校准。在采用atezolizumab推导数据集的最佳模型中确定的5个预后因素为ECOG-PS（1 vs. 0; HR 1.80; 95% CI [1.36-2.36]），肝转移（HR 1.55; 95% CI [1.20-2.00]），血小板计数（HR 2.22; 95% CI [1.54-3.18]）、嗜中性粒细胞-淋巴细胞比率（HR 1.94; 95% CI [1.57-2.40]）和乳酸脱氢酶（HR 1.60; 95% CI [1.28-1.99]）。在低、中、高风险群体之间，生存率存在明显差别。推导中的c-统计量为0.692，avelumab和durvalumab验证数据集中的c-统计量分别为0.671和0.773。开发了一个基于网络的交互式工具，用于根据风险因素计算预期生存概率。经验证的5因素模型在3种PD-L1抑制剂治疗铂类药物治疗后mUC的生存期方面具有令人满意的预后性能，可能有助于分层、解释和设计在铂类药物治疗后背景下纳入PD-1/PD-L1抑制剂的试验。
A prognostic model for overall survival (OS) of post-platinum patients with metastatic urothelial carcinoma (mUC) receiving PD-1/PD-L1 inhibitors is necessary since existing models were constructed in the chemotherapy setting. Patient level data were used from phase I/II trials evaluating PD-L1 inhibitors following platinum-based chemotherapy for mUC. The derivation dataset consisted of 2 phase I/II trials evaluating atezolizumab (n=405). Two phase I/II trials that evaluated avelumab (n=242) and durvalumab (n=198) comprised the validation datasets. Cox regression analyses evaluated the association of candidate prognostic factors with OS. Stepwise selection was employed to select an optimal model using the derivation dataset. Discrimination and calibration were assessed in the avelumab and durvalumab datasets. The 5 prognostic factors identified in the optimal model employing the atezolizumab derivation dataset were ECOG-PS (1 vs. 0; HR 1.80; 95% CI [1.36–2.36]), liver metastasis (HR 1.55; 95% CI [1.20–2.00]), platelet count (HR 2.22; 95% CI [1.54–3.18]), neutrophil-lymphocyte ratio (HR 1.94; 95% CI [1.57–2.40]) and lactate dehydrogenase (HR 1.60; 95% CI [1.28–1.99]). There was robust discrimination of survival between low, intermediate and high-risk groups. The c-statistic was 0.692 in the derivation and 0.671 and 0.773 in the avelumab and durvalumab validation datasets, respectively. A web-based interactive tool was developed to calculate the expected survival probabilities based on risk factors. A validated 5-factor model has satisfactory prognostic performance for survival across 3 PD-L1 inhibitors to treat mUC post-platinum and may assist in stratification, interpreting and designing trials incorporating PD-1/PD-L1 inhibitors in the post-platinum setting.