Background

Immunotherapy has made encouraging progress in the treatment of urothelial carcinoma, but only a small percentage of patients respond effectively to the immune checkpoint blockade (ICB). Our study aims to develop a classifier could effectively predict the response to ICB.

Methods

Support vector machines recursive feature elimination (SVM-RFE) algorithm was used to feature selection, then compared nine common binary classification algorithms through machine learning, we selected the classifier with the highest prediction performance (LASSO logistics classifier). Ten-fold cross-validation was used to avoid the overfitting effect.

Results

We developed a classifier on a urothelial carcinoma cohort treated with PD-L1 inhibitor Atzolizumab (IMvigor210 cohort, n = 272) and calculated a tumor mutational burden-related LASSO score (TLS) using the LASSO algorithm, which was significantly correlated with Tumor mutational burden (TMB) and neoantigen burden. We validated the efficacy of TLS in predicting prognosis and immunotherapy benefit in internal (IMvigor210) and external validation set (TCGA-BLCA, n = 406), respectively.

Conclusions

After in-depth analysis, we provide a new idea for stratified treatment of such patients, that is, patients with high TLS should use ICB and also may benefit from hormone therapy, while patients with low TLS respond poorly to ICB and maybe benefit from targeting TGFβ.

中文翻译：

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肿瘤突变负荷相关分类器可预测局部晚期和转移性尿路上皮癌对PD-L1阻滞的反应。

背景

免疫疗法在尿路上皮癌的治疗方面取得了令人鼓舞的进展，但是只有一小部分患者对免疫检查点封锁（ICB）有效。我们的研究旨在开发一个可以有效预测对ICB响应的分类器。

方法

使用支持向量机递归特征消除（SVM-RFE）算法进行特征选择，然后通过机器学习比较九种常见的二元分类算法，选择了预测性能最高的分类器（LASSO物流分类器）。十倍交叉验证可避免过度拟合的影响。

结果

我们针对用PD-L1抑制剂Atzolizumab（IMvigor210队列，n = 272）治疗的尿路上皮癌队列开发了分类器，并使用LASSO算法计算了与肿瘤突变负荷相关的LASSO评分（TLS），这与肿瘤突变负荷显着相关（TMB）和新抗原负担。我们分别在内部（IMvigor210）和外部验证集（TCGA-BLCA，n = 406）中验证了TLS预测预后和免疫疗法获益的功效。

结论

经过深入分析，我们为分层治疗此类患者提供了新思路，即高TLS的患者应使用ICB并可能从激素治疗中受益，而低TLS的患者对ICB的反应较差并且可能受益于靶向治疗TGFβ。