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
{"title":"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。","authors":"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","doi":"10.1016/j.medj.2024.04.011","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sup>+</sup> programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Funding: </strong>This study was supported by the National Key R&D Program of China.</p>","PeriodicalId":29964,"journal":{"name":"Med","volume":null,"pages":null},"PeriodicalIF":12.8000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.\",\"authors\":\"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\",\"doi\":\"10.1016/j.medj.2024.04.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sup>+</sup> programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Funding: </strong>This study was supported by the National Key R&D Program of China.</p>\",\"PeriodicalId\":29964,\"journal\":{\"name\":\"Med\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.8000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Med\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.medj.2024.04.011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/22 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Med","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.medj.2024.04.011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.
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.
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.
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