Abstract：
BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model.
METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.
RESULTS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality.
CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables.
BACKGROUND: This study was supported by the National Key R&D Program of China.
METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.
RESULTS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality.
CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables.
BACKGROUND: This study was supported by the National Key R&D Program of China.
摘要：
背景：免疫检查点抑制剂（ICIs）的预测性生物标志物和模型已在非小细胞肺癌（NSCLC）中得到广泛研究。然而,许多生物标志物的证据仍然没有定论，机器学习模型的不透明性阻碍了实用性。我们旨在为生物标志物提供令人信服的证据，并开发透明的决策树模型。
方法：我们整合了实际多中心的3,288例ICI治疗的NSCLC患者的数据，公共队列和Choice-01试验(ClinicalTrials.gov:NCT03856411)。检查了50多个功能，以预测ICI的持久临床益处（DCB）。鉴定了值得注意的生物标志物以建立决策树模型。此外,我们探索了肿瘤微环境和外周CD8+程序性死亡-1(PD-1)+T细胞受体(TCR)谱.
结果：多变量逻辑回归分析确定了肿瘤组织学，PD-配体1（PD-L1）表达，肿瘤突变负担，线,和ICI治疗方案是重要因素。EGFR突变亚型,KRAS,KEAP1、STK11和破坏性TP53突变与DCB相关。决策树(DT10)模型，使用十个临床病理和基因组标记，在预测训练集中的DCB方面表现出优异的性能（曲线下面积[AUC]=0.82），并且在测试集中始终优于其他模型。DT10预测的DCB患者表现出更长的生存期，丰富的炎症肿瘤免疫表型（67％），和更高的外围TCR多样性，而DT10预测的NDB（非持久性益处）组显示出丰富的沙漠免疫表型（86％）和更高的外周TCR克隆性。
结论：该模型可有效预测前/后行ICI治疗后的DCB，有或没有化疗，鳞状和非鳞状肺癌，为临床医生提供有价值的见解，使用成本效益变量进行疗效预测。
背景：本研究得到了国家重点研发计划的支持。
方法：我们整合了实际多中心的3,288例ICI治疗的NSCLC患者的数据，公共队列和Choice-01试验(ClinicalTrials.gov:NCT03856411)。检查了50多个功能，以预测ICI的持久临床益处（DCB）。鉴定了值得注意的生物标志物以建立决策树模型。此外,我们探索了肿瘤微环境和外周CD8+程序性死亡-1(PD-1)+T细胞受体(TCR)谱.
结果：多变量逻辑回归分析确定了肿瘤组织学，PD-配体1（PD-L1）表达，肿瘤突变负担，线,和ICI治疗方案是重要因素。EGFR突变亚型,KRAS,KEAP1、STK11和破坏性TP53突变与DCB相关。决策树(DT10)模型，使用十个临床病理和基因组标记，在预测训练集中的DCB方面表现出优异的性能（曲线下面积[AUC]=0.82），并且在测试集中始终优于其他模型。DT10预测的DCB患者表现出更长的生存期，丰富的炎症肿瘤免疫表型（67％），和更高的外围TCR多样性，而DT10预测的NDB（非持久性益处）组显示出丰富的沙漠免疫表型（86％）和更高的外周TCR克隆性。
结论：该模型可有效预测前/后行ICI治疗后的DCB，有或没有化疗，鳞状和非鳞状肺癌，为临床医生提供有价值的见解，使用成本效益变量进行疗效预测。
背景：本研究得到了国家重点研发计划的支持。
