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Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors

BACKGROUND AND AIM: Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the asso...

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Autores principales: Chen, Xiuqiong, Li, Zhaona, Zhou, Jing, Wei, Qianhui, Wang, Xinyue, Jiang, Richeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760026/
https://www.ncbi.nlm.nih.gov/pubmed/36540802
http://dx.doi.org/10.7717/peerj.14566
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author Chen, Xiuqiong
Li, Zhaona
Zhou, Jing
Wei, Qianhui
Wang, Xinyue
Jiang, Richeng
author_facet Chen, Xiuqiong
Li, Zhaona
Zhou, Jing
Wei, Qianhui
Wang, Xinyue
Jiang, Richeng
author_sort Chen, Xiuqiong
collection PubMed
description BACKGROUND AND AIM: Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the association between baseline characteristics of patients with lung cancer and the efficacy of immunotherapy. METHODS: A total of 216 lung cancer patients from Tianjin Medical University Cancer Institute & Hospital who received immunotherapy between 2017 and 2021 were included in the retrospective analysis. All baseline characteristic data were collected and then univariate log-rank analysis and multivariate COX regression analysis were performed. Kaplan–Meier analysis was used to evaluate patients’ progression-free survival (PFS). A nomogram based on significant biomarkers was constructed to predict PFS rate of patients receiving immunotherapy. We evaluated the prediction accuracy of nomogram using C-indices and calibration curves. RESULTS: Univariate analysis of all collected baseline factors showed that age, clinical stage, white blood cell (WBC), lymphocyte (LYM), monocyte (MON), eosinophils (AEC), hemoglobin (HB), lactate dehydrogenase (LDH), albumin (ALB) and treatment line were significantly associated with PFS after immunotherapy. Then these 10 risk factors were included in a multivariate regression analysis, which indicated that age (HR: 1.95, 95% CI [1.01–3.78], P = 0.048), MON (HR: 1.74, 95% CI [1.07–2.81], P = 0.025), LDH (HR: 0.59, 95% CI [0.36–0.95], P = 0.030), and line (HR: 0.57, 95% CI [0.35–0.94], P = 0.026) were significantly associated with PFS in patients with lung cancer receiving immunotherapy. Patients with higher ALB showed a greater trend of benefit compared with patients with lower ALB (HR: 1.58, 95% CI [0.94–2.66], P = 0.084). Patients aged ≥51 years, with high ALB, low LDH, first-line immunotherapy, and high MON had better response rates and clinical benefits. The nomogram based on age, ALB, MON, LDH, line was established to predict the prognosis of patients treated with immune checkpoint inhibitor (ICI). The C-index of training cohort and validation cohort were close, 0.71 and 0.75, respectively. The fitting degree of calibration curve was high, which confirmed the high prediction value of our nomogram. CONCLUSION: Age, ALB, MON, LDH, line can be used as reliable predictive biomarkers for PFS, response rate and cancer control in patients with lung cancer receiving immunotherapy. The nomogram based on age, ALB, MON, LDH, line was of great significance for predicting 1-year-PFS, 2-year-PFS and 3-year-PFS in patients with advanced lung cancer treated with immunotherapy.
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spelling pubmed-97600262022-12-19 Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors Chen, Xiuqiong Li, Zhaona Zhou, Jing Wei, Qianhui Wang, Xinyue Jiang, Richeng PeerJ Epidemiology BACKGROUND AND AIM: Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the association between baseline characteristics of patients with lung cancer and the efficacy of immunotherapy. METHODS: A total of 216 lung cancer patients from Tianjin Medical University Cancer Institute & Hospital who received immunotherapy between 2017 and 2021 were included in the retrospective analysis. All baseline characteristic data were collected and then univariate log-rank analysis and multivariate COX regression analysis were performed. Kaplan–Meier analysis was used to evaluate patients’ progression-free survival (PFS). A nomogram based on significant biomarkers was constructed to predict PFS rate of patients receiving immunotherapy. We evaluated the prediction accuracy of nomogram using C-indices and calibration curves. RESULTS: Univariate analysis of all collected baseline factors showed that age, clinical stage, white blood cell (WBC), lymphocyte (LYM), monocyte (MON), eosinophils (AEC), hemoglobin (HB), lactate dehydrogenase (LDH), albumin (ALB) and treatment line were significantly associated with PFS after immunotherapy. Then these 10 risk factors were included in a multivariate regression analysis, which indicated that age (HR: 1.95, 95% CI [1.01–3.78], P = 0.048), MON (HR: 1.74, 95% CI [1.07–2.81], P = 0.025), LDH (HR: 0.59, 95% CI [0.36–0.95], P = 0.030), and line (HR: 0.57, 95% CI [0.35–0.94], P = 0.026) were significantly associated with PFS in patients with lung cancer receiving immunotherapy. Patients with higher ALB showed a greater trend of benefit compared with patients with lower ALB (HR: 1.58, 95% CI [0.94–2.66], P = 0.084). Patients aged ≥51 years, with high ALB, low LDH, first-line immunotherapy, and high MON had better response rates and clinical benefits. The nomogram based on age, ALB, MON, LDH, line was established to predict the prognosis of patients treated with immune checkpoint inhibitor (ICI). The C-index of training cohort and validation cohort were close, 0.71 and 0.75, respectively. The fitting degree of calibration curve was high, which confirmed the high prediction value of our nomogram. CONCLUSION: Age, ALB, MON, LDH, line can be used as reliable predictive biomarkers for PFS, response rate and cancer control in patients with lung cancer receiving immunotherapy. The nomogram based on age, ALB, MON, LDH, line was of great significance for predicting 1-year-PFS, 2-year-PFS and 3-year-PFS in patients with advanced lung cancer treated with immunotherapy. PeerJ Inc. 2022-12-15 /pmc/articles/PMC9760026/ /pubmed/36540802 http://dx.doi.org/10.7717/peerj.14566 Text en © 2022 Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Epidemiology
Chen, Xiuqiong
Li, Zhaona
Zhou, Jing
Wei, Qianhui
Wang, Xinyue
Jiang, Richeng
Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title_full Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title_fullStr Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title_full_unstemmed Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title_short Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
title_sort identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760026/
https://www.ncbi.nlm.nih.gov/pubmed/36540802
http://dx.doi.org/10.7717/peerj.14566
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