Cargando…
Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors
BACKGROUND: Immune checkpoint inhibitors (ICIs) have appeared as a promising therapy regimen for non-small cell lung cancer (NSCLC), but with an unsatisfying therapeutic response and inefficiency of a single predictive biomarker in patients’ selection. METHODS: Central data of clinicopathologic feat...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575979/ https://www.ncbi.nlm.nih.gov/pubmed/33145297 http://dx.doi.org/10.21037/atm-20-4297 |
_version_ | 1783597918927716352 |
---|---|
author | Chai, Rong Fan, Yinxing Zhao, Jiayi He, Fan Li, Jianong Han, Yiping |
author_facet | Chai, Rong Fan, Yinxing Zhao, Jiayi He, Fan Li, Jianong Han, Yiping |
author_sort | Chai, Rong |
collection | PubMed |
description | BACKGROUND: Immune checkpoint inhibitors (ICIs) have appeared as a promising therapy regimen for non-small cell lung cancer (NSCLC), but with an unsatisfying therapeutic response and inefficiency of a single predictive biomarker in patients’ selection. METHODS: Central data of clinicopathologic features, peripheral blood indicators, and treatment records were collected in advanced NSCLC patients accepting PD-1 inhibitors in Changhai Hospital from July 2016 to September 2019. The OS probability nomogram was developed according to Akaike Information Criterion (stepAIC) selected factors. The predictive accuracy of the nomogram was assessed by discrimination and calibration. C-index and decision curve analysis were used to compare with the previously reported model (Botticelli Model). Computers resampling 500 times (Bootstrap 500 times) were performed to validate the model internally. According to the nomogram-based total point scores (TPS), we divided patients into different risk groups. RESULTS: A total of 110 patients were enrolled in this study. Six predictors, including liver metastasis, Eastern Cooperative Oncology Group Performance Status (ECOG PS), second- or third-line immunotherapy, baseline levels of CRP, cytokeratin 19 fragment (CYFRA21-1), were selected to set up the nomogram. The C-index of the current nomogram was 0.81 (95% CI: 0.72–0.80), keeping the same accuracy as the earlier one. Calibration plots showed slight underestimation in patients with predictive mortality <44% at 12 months and overestimation in patients with predictive mortality >44%. Decision curve analysis showed that the current nomogram was with a higher net benefit rate than the earlier model. According to the cut-off points of TPS, patients were divided into three subgroups: low risk (TPS ≤118), intermediate-risk (118< TPS ≤189), and high risk (TPS >189). A significant OS difference was observed among subgroups. Median OS was 6.6, 4.5, 1.3 months, respectively. CONCLUSIONS: We proposed a novel nomogram model on easily available and inexpensive clinicopathologic features, peripheral blood indicators which is beneficial in individual risk assessment for advanced NSCLC patients before receiving PD-1 inhibitors, and assisting clinicians in accurately determining therapeutic decisions. |
format | Online Article Text |
id | pubmed-7575979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-75759792020-11-02 Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors Chai, Rong Fan, Yinxing Zhao, Jiayi He, Fan Li, Jianong Han, Yiping Ann Transl Med Original Article BACKGROUND: Immune checkpoint inhibitors (ICIs) have appeared as a promising therapy regimen for non-small cell lung cancer (NSCLC), but with an unsatisfying therapeutic response and inefficiency of a single predictive biomarker in patients’ selection. METHODS: Central data of clinicopathologic features, peripheral blood indicators, and treatment records were collected in advanced NSCLC patients accepting PD-1 inhibitors in Changhai Hospital from July 2016 to September 2019. The OS probability nomogram was developed according to Akaike Information Criterion (stepAIC) selected factors. The predictive accuracy of the nomogram was assessed by discrimination and calibration. C-index and decision curve analysis were used to compare with the previously reported model (Botticelli Model). Computers resampling 500 times (Bootstrap 500 times) were performed to validate the model internally. According to the nomogram-based total point scores (TPS), we divided patients into different risk groups. RESULTS: A total of 110 patients were enrolled in this study. Six predictors, including liver metastasis, Eastern Cooperative Oncology Group Performance Status (ECOG PS), second- or third-line immunotherapy, baseline levels of CRP, cytokeratin 19 fragment (CYFRA21-1), were selected to set up the nomogram. The C-index of the current nomogram was 0.81 (95% CI: 0.72–0.80), keeping the same accuracy as the earlier one. Calibration plots showed slight underestimation in patients with predictive mortality <44% at 12 months and overestimation in patients with predictive mortality >44%. Decision curve analysis showed that the current nomogram was with a higher net benefit rate than the earlier model. According to the cut-off points of TPS, patients were divided into three subgroups: low risk (TPS ≤118), intermediate-risk (118< TPS ≤189), and high risk (TPS >189). A significant OS difference was observed among subgroups. Median OS was 6.6, 4.5, 1.3 months, respectively. CONCLUSIONS: We proposed a novel nomogram model on easily available and inexpensive clinicopathologic features, peripheral blood indicators which is beneficial in individual risk assessment for advanced NSCLC patients before receiving PD-1 inhibitors, and assisting clinicians in accurately determining therapeutic decisions. AME Publishing Company 2020-09 /pmc/articles/PMC7575979/ /pubmed/33145297 http://dx.doi.org/10.21037/atm-20-4297 Text en 2020 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Chai, Rong Fan, Yinxing Zhao, Jiayi He, Fan Li, Jianong Han, Yiping Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title | Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title_full | Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title_fullStr | Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title_full_unstemmed | Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title_short | Prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-PD-1 inhibitors |
title_sort | prognostic nomogram on clinicopathologic features and serum indicators for advanced non-small cell lung cancer patients treated with anti-pd-1 inhibitors |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575979/ https://www.ncbi.nlm.nih.gov/pubmed/33145297 http://dx.doi.org/10.21037/atm-20-4297 |
work_keys_str_mv | AT chairong prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors AT fanyinxing prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors AT zhaojiayi prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors AT hefan prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors AT lijianong prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors AT hanyiping prognosticnomogramonclinicopathologicfeaturesandserumindicatorsforadvancednonsmallcelllungcancerpatientstreatedwithantipd1inhibitors |