Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

IF 12.8 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Med Pub Date : 2024-08-09 Epub Date: 2024-05-22 DOI:10.1016/j.medj.2024.04.011

Jie Zhao, Lu Wang, Anda Zhou, Shidi Wen, Wenfeng Fang, Li Zhang, Jianchun Duan, Hua Bai, Jia Zhong, Rui Wan, Boyang Sun, Wei Zhuang, Yiwen Lin, Danming He, Lina Cui, Zhijie Wang, Jie Wang

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引用次数: 0

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.

Findings: 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.

Funding: This study was supported by the National Key R&D Program of China.

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非小细胞肺癌前线或晚线免疫疗法单独或与化疗一起使用可产生持久临床疗效的决策模型。

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不确定，机器学习模型的不透明性也阻碍了其实用性。我们的目标是为生物标志物提供令人信服的证据，并开发一种透明的决策树模型：我们整合了来自真实世界多中心公共队列和 Choice-01 试验（ClinicalTrials.gov：NCT03856411）的 3288 名接受 ICI 治疗的 NSCLC 患者的数据。研究人员对 50 多个特征进行了检查，以预测 ICIs 带来的持久临床获益（DCBs）。确定了值得注意的生物标志物，以建立决策树模型。此外，我们还研究了肿瘤微环境和外周CD8+程序性死亡-1（PD-1）+ T细胞受体（TCR）特征：多变量逻辑回归分析发现，肿瘤组织学、PD配体1 (PD-L1)表达、肿瘤突变负荷、肿瘤线和ICI治疗方案是重要因素。EGFR、KRAS、KEAP1、STK11和破坏性TP53突变亚型与DCB相关。决策树（DT10）模型使用了十个临床病理学和基因组标记物，在训练集上显示出预测DCB的卓越性能（曲线下面积 [AUC] = 0.82），在测试集上的表现一直优于其他模型。DT10预测的DCB患者生存期更长、肿瘤炎症免疫表型更丰富（67%）、外周TCR多样性更高，而DT10预测的NDB（非持久获益）组则表现为沙漠免疫表型更丰富（86%）、外周TCR克隆性更高：该模型可有效预测鳞状和非鳞状肺癌前线/后线 ICI 治疗（无论是否化疗）后的 DCB，为临床医生利用成本效益变量进行疗效预测提供了有价值的见解：本研究得到了国家重点研发计划的支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Med MEDICINE, RESEARCH & EXPERIMENTAL-

CiteScore

17.70

自引率

0.60%

发文量

102

期刊介绍： Med is a flagship medical journal published monthly by Cell Press, the global publisher of trusted and authoritative science journals including Cell, Cancer Cell, and Cell Reports Medicine. Our mission is to advance clinical research and practice by providing a communication forum for the publication of clinical trial results, innovative observations from longitudinal cohorts, and pioneering discoveries about disease mechanisms. The journal also encourages thought-leadership discussions among biomedical researchers, physicians, and other health scientists and stakeholders. Our goal is to improve health worldwide sustainably and ethically. Med publishes rigorously vetted original research and cutting-edge review and perspective articles on critical health issues globally and regionally. Our research section covers clinical case reports, first-in-human studies, large-scale clinical trials, population-based studies, as well as translational research work with the potential to change the course of medical research and improve clinical practice.