Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non-small cell lung cancer.
Aged
Carcinoma, Non-Small-Cell Lung
/ diagnosis
China
/ epidemiology
Feasibility Studies
Female
Humans
Immune Checkpoint Inhibitors
/ therapeutic use
Kaplan-Meier Estimate
Lung Neoplasms
/ diagnosis
Male
Middle Aged
Prognosis
Progression-Free Survival
ROC Curve
Retrospective Studies
Risk Assessment
/ methods
immunotherapy
non-small cell lung cancer (NSCLC)
predictive
score
Journal
Cancer medicine
ISSN: 2045-7634
Titre abrégé: Cancer Med
Pays: United States
ID NLM: 101595310
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
revised:
30
06
2021
received:
18
10
2020
accepted:
01
07
2021
pubmed:
15
8
2021
medline:
24
2
2022
entrez:
14
8
2021
Statut:
ppublish
Résumé
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non-small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs). We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer-Lemeshow test (H-L test) and Spearman's correlation coefficient. Progression-free survival (PFS) and overall survival (OS) curves were generated using the Kaplan-Meier method. The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590-0.694], 0.720 (95% CI: 0.675-0.762), and 0.633 (95% CI: 0.585-0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546-0.665), 0.683 (95% CI: 0.637-0.727), and 0.666 (95% CI: 0.620-0.711), respectively (p < 0.001 for all models). The C-indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611-6.643), 0.677 (95% CI 0.652-0.682), and 0.631 (95% CI 0.617-0.645), respectively. As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision-making.
Sections du résumé
BACKGROUND
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non-small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs).
METHODS
We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer-Lemeshow test (H-L test) and Spearman's correlation coefficient. Progression-free survival (PFS) and overall survival (OS) curves were generated using the Kaplan-Meier method.
RESULTS
The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590-0.694], 0.720 (95% CI: 0.675-0.762), and 0.633 (95% CI: 0.585-0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546-0.665), 0.683 (95% CI: 0.637-0.727), and 0.666 (95% CI: 0.620-0.711), respectively (p < 0.001 for all models). The C-indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611-6.643), 0.677 (95% CI 0.652-0.682), and 0.631 (95% CI 0.617-0.645), respectively.
CONCLUSIONS
As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision-making.
Identifiants
pubmed: 34390218
doi: 10.1002/cam4.4171
pmc: PMC8446565
doi:
Substances chimiques
Immune Checkpoint Inhibitors
0
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6291-6303Subventions
Organisme : The Innovation Project of Shandong Academy of Medical Sciences
ID : 2019ZL002
Informations de copyright
© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
Références
JAMA Oncol. 2019 Oct 1;5(10):1411-1420
pubmed: 31343665
Lancet. 2016 Apr 9;387(10027):1540-1550
pubmed: 26712084
Lancet Respir Med. 2020 Sep;8(9):895-904
pubmed: 32199466
Immunotherapy. 2020 Jul;12(10):715-724
pubmed: 32522052
Lancet. 2017 Jan 21;389(10066):255-265
pubmed: 27979383
J Clin Oncol. 2019 Mar 1;37(7):537-546
pubmed: 30620668
N Engl J Med. 2015 Oct 22;373(17):1627-39
pubmed: 26412456
Clin Lung Cancer. 2020 Jan;21(1):75-85
pubmed: 31562055
Immunotherapy. 2019 Aug;11(12):993-1003
pubmed: 31319742
Curr Oncol Rep. 2010 Sep;12(5):327-34
pubmed: 20632219
Clin Lung Cancer. 2016 Sep;17(5):350-361
pubmed: 27137346
Clin Lung Cancer. 2019 May;20(3):208-214.e2
pubmed: 29803573
Nature. 2013 Aug 22;500(7463):415-21
pubmed: 23945592
Clin Lung Cancer. 2020 Jul;21(4):365-377.e5
pubmed: 32245624
Ann Oncol. 2019 Aug 1;30(8):1244-1253
pubmed: 31143921
J Clin Oncol. 2017 Dec 10;35(35):3924-3933
pubmed: 29023213
Eur J Cancer. 2017 Oct;84:212-218
pubmed: 28826074
Science. 2015 Apr 3;348(6230):124-8
pubmed: 25765070
JAMA Oncol. 2018 Mar 1;4(3):351-357
pubmed: 29327044
Lancet Oncol. 2016 Dec;17(12):e542-e551
pubmed: 27924752
Int J Cancer. 2016 Apr 15;138(8):1982-93
pubmed: 26619320
Eur J Cancer. 2019 Jan;106:144-159
pubmed: 30528799
Crit Rev Oncol Hematol. 2020 Mar;147:102893
pubmed: 32065969
Neoplasia. 2014 Oct 23;16(10):771-88
pubmed: 25379015
Cancer Med. 2021 Sep;10(18):6291-6303
pubmed: 34390218
ESMO Open. 2018 Aug 10;3(5):e000421
pubmed: 30116594
ESMO Open. 2018 Oct 2;3(6):e000406
pubmed: 30305940
Adv Ther. 2020 Mar;37(3):1145-1155
pubmed: 32002809
Lung Cancer. 2018 Jan;115:49-55
pubmed: 29290261
N Engl J Med. 2016 Nov 10;375(19):1823-1833
pubmed: 27718847
Cell Metab. 2016 Nov 8;24(5):657-671
pubmed: 27641098
N Engl J Med. 2018 May 31;378(22):2093-2104
pubmed: 29658845
J Thorac Oncol. 2020 Oct;15(10):1624-1635
pubmed: 32553694
Nat Commun. 2019 Mar 8;10(1):1125
pubmed: 30850589
Nature. 2002 Dec 19-26;420(6917):860-7
pubmed: 12490959
Int J Cancer. 2007 Dec 1;121(11):2373-80
pubmed: 17893866
Cancers (Basel). 2019 Dec 05;11(12):
pubmed: 31817541
Ann Oncol. 2016 Jan;27(1):147-53
pubmed: 26483045
J Immunother Cancer. 2018 Jan 22;6(1):5
pubmed: 29353553
Nat Rev Cancer. 2013 Nov;13(11):759-71
pubmed: 24154716
J Thorac Oncol. 2017 Sep;12(9):e140-e141
pubmed: 28838713
N Engl J Med. 2015 Jul 9;373(2):123-35
pubmed: 26028407
Br J Cancer. 2016 Feb 2;114(3):256-61
pubmed: 26794281
Int J Clin Oncol. 2018 Aug;23(4):634-640
pubmed: 29442281
Onco Targets Ther. 2019 May 29;12:4235-4244
pubmed: 31239702
Cancer Res. 2004 Aug 15;64(16):5839-49
pubmed: 15313928
Onco Targets Ther. 2018 Feb 23;11:955-965
pubmed: 29503570
Lung Cancer. 2017 Apr;106:1-7
pubmed: 28285682
Ann Oncol. 2018 Apr 1;29(4):959-965
pubmed: 29408986
Cancer Invest. 2013 Mar;31(3):183-8
pubmed: 23432821
J Thorac Oncol. 2018 Jan;13(1):97-105
pubmed: 29170120