Cargando…
A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer
The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to s...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797141/ https://www.ncbi.nlm.nih.gov/pubmed/35095850 http://dx.doi.org/10.3389/fimmu.2021.778276 |
_version_ | 1784641480224145408 |
---|---|
author | Peng, Jie Zou, Dan Han, Lijie Yin, Zuomin Hu, Xiao |
author_facet | Peng, Jie Zou, Dan Han, Lijie Yin, Zuomin Hu, Xiao |
author_sort | Peng, Jie |
collection | PubMed |
description | The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy. |
format | Online Article Text |
id | pubmed-8797141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87971412022-01-29 A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer Peng, Jie Zou, Dan Han, Lijie Yin, Zuomin Hu, Xiao Front Immunol Immunology The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy. Frontiers Media S.A. 2021-12-15 /pmc/articles/PMC8797141/ /pubmed/35095850 http://dx.doi.org/10.3389/fimmu.2021.778276 Text en Copyright © 2021 Peng, Zou, Han, Yin and Hu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Peng, Jie Zou, Dan Han, Lijie Yin, Zuomin Hu, Xiao A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title | A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title_full | A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title_fullStr | A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title_full_unstemmed | A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title_short | A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer |
title_sort | support vector machine based on liquid immune profiling predicts major pathological response to chemotherapy plus anti-pd-1/pd-l1 as a neoadjuvant treatment for patients with resectable non-small cell lung cancer |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797141/ https://www.ncbi.nlm.nih.gov/pubmed/35095850 http://dx.doi.org/10.3389/fimmu.2021.778276 |
work_keys_str_mv | AT pengjie asupportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT zoudan asupportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT hanlijie asupportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT yinzuomin asupportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT huxiao asupportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT pengjie supportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT zoudan supportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT hanlijie supportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT yinzuomin supportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer AT huxiao supportvectormachinebasedonliquidimmuneprofilingpredictsmajorpathologicalresponsetochemotherapyplusantipd1pdl1asaneoadjuvanttreatmentforpatientswithresectablenonsmallcelllungcancer |