Multiomics Prediction of Response Rates to Therapies to Inhibit Programmed Cell Death 1 and Programmed Cell Death 1 Ligand 1
Multiomics Prediction of Response Rates to Therapies to Inhibit Programmed Cell Death 1 and Programmed Cell Death 1 Ligand 1
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DOI：
10.1001/jamaoncol.2019.2311
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发表时间：
2019-11-01
期刊：
影响因子：
28.4
通讯作者：
Ruppin, Eytan
中科院分区：
文献类型：
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作者：
Lee, Joo Sang;Ruppin, Eytan
抑制程序性细胞死亡的重要疗法及其配体(抗PD-1/PD-L1)在许多癌症中提供了显著的生存益处，但这些治疗方法的疗效在不同的癌症类型中差别很大。识别与癌症类型特异性反应相关的潜在变量仍然是一个重要的开放研究挑战。目的系统地评估大量新抗原、检查点和免疫反应相关变量，以确定准确预测不同癌症类型中抗PD-1/PD-Li治疗反应的关键变量。设计、设置和参与者这项分析使用了公开可用的癌症基因组图谱中7187名患者的全部外显子组和RNA测序，以及从临床试验收集的21种癌症类型的客观应答率(ORR)数据。被考虑的3个不同类别的36个变量与(1)肿瘤新抗原相关。(2)肿瘤微环境与炎症；(3)检查点靶点。评估每类变量及其组合在预测抗PD-1/PD-LI治疗ORR方面的性能。预测的准确性用Spearman相关性进行量化，使用标准的留一法交叉验证来测量，这是一种通过将数据分成两段来评估统计模型的统计方法：一段用于训练模型，另一段用于验证模型。数据收集于2018年10月19日至31日，并分析了2018年11月1日至12月14日的数据。结果在36个变量中，估计的CD8*T细胞丰度对各种癌症类型的抗PD-1/PD-L1治疗的反应最具预测性(Spearman R=0.72；P<；2.3 x 10(-4))。其次是肿瘤突变负荷(Spearman R=0.68；P<；6.2×10(-4))，以及PD1基因高表达样本的比例(Spearman R=0.68；P<；6.9×10(-4))。值得注意的是，前3个变量涵盖了所考虑的3个类别，它们的组合与疗效高度相关(Spearman R=0.90；P<；4.1 x 10(-8))，解释了在不同肿瘤类型中观察到的ORR方差的80%以上。结论和我们所知的相关性，这是第一次对不同肿瘤类型中与抗PD-I/PD-Li治疗反应相关的不同变量进行系统评估。研究结果表明，这三个关键变量可以解释大多数观察到的癌症交叉反应变异性，但它们的相对解释作用在特定的癌症类型中可能有所不同。
IMPORTANCE Therapies to inhibit programmed cell death land its ligand (anti-PD-1/PD-L1) provide significant survival benefits in many cancers, but the efficacy of these treatments varies considerably across different cancer types. Identifying the underlying variables associated with this cancer type-specific response remains an important open research challenge.OBJECTIVE To evaluate systematically a multitude of neoantigen-, checkpoint-, and immune response-related variables to determine the key variables that accurately predict the response to anti-PD-1/PD-LI therapy across different cancer types.DESIGN, SETTING, AND PARTICIPANTS This analysis of a broad range of data used whole-exome and RNA sequencing of 7187 patients from the publicly available Cancer Genome Atlas and the objective response rate (ORR) data of 21 cancer types obtained from a collection of clinical trials. Thirty-six variables of 3 distinct classes considered were associated with (1) tumor neoantigens. (2) tumor microenvironment and inflammation, and (3) the checkpoint targets. The performance of each class of variables and their combinations in predicting the ORR to anti-PD-1/PD-LItherapy was evaluated. Accuracy of predictions was quantified with Spearman correlation measured using a standard leave-one-out cross-validation, a statistical method of evaluating a statistical model by dividing data into 2 segments: one to train the model and the other to validate the model. Data were collected from October 19 through 31, 2018, and were analyzed from November 1 through December 14, 2018.MAIN OUTCOMES AND MEASURES Response to anti-PD-1/PD-1 therapy.RESULTS Among the 36 variables, estimated CD8* T-cell abundance was the most predictive of the response to anti-PD-1/PD-L1 therapy across cancer types (Spearman R = 0.72; P < 2.3 x 10(-4)). followed by the tumor mutational burden (Spearman R = 0.68; P < 6.2 x 10(-4)), and the fraction of samples with high PD1 gene expression (Spearman R = 0.68; P < 6.9 x 10(-4)). Notably these top 3 variables cover the 3 classes considered, and their combination is highly correlated with response (Spearman R = 0.90; P < 4.1 x 10(-8)), explaining more than 80% of the ORR variance observed across different tumor types.CONCLUSIONS AND RELEVANCE That we know of, this is the first systematic evaluation of the different variables associated with anti-PD-I/PD-LI therapy response across different tumor types. The findings suggest that the 3 key variables can explain most of the observed cross-cancer response variability, but their relative explanatory roles may vary in specific cancer types.