Cargando…

Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score

BACKGROUND: Treatment by immune checkpoint blockade (ICB) provides a remarkable survival benefit for multiple cancer types. However, disease aggravation occurs in a proportion of patients after the first couple of treatment cycles. METHODS: RNA sequencing data was retrospectively collected. 6 tumour...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Si-Yang Maggie, Sun, Hao, Zhou, Jia-Ying, Zhang, Jia-Tao, Yin, Kai, Chen, Zhi-Hong, Su, Jian, Zhang, Xu-Chao, Yang, Jin-Ji, Zhou, Qing, Tu, Hai-Yan, Wu, Yi-Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571398/
https://www.ncbi.nlm.nih.gov/pubmed/34715621
http://dx.doi.org/10.1016/j.tranon.2021.101254
_version_ 1784595012244209664
author Liu, Si-Yang Maggie
Sun, Hao
Zhou, Jia-Ying
Zhang, Jia-Tao
Yin, Kai
Chen, Zhi-Hong
Su, Jian
Zhang, Xu-Chao
Yang, Jin-Ji
Zhou, Qing
Tu, Hai-Yan
Wu, Yi-Long
author_facet Liu, Si-Yang Maggie
Sun, Hao
Zhou, Jia-Ying
Zhang, Jia-Tao
Yin, Kai
Chen, Zhi-Hong
Su, Jian
Zhang, Xu-Chao
Yang, Jin-Ji
Zhou, Qing
Tu, Hai-Yan
Wu, Yi-Long
author_sort Liu, Si-Yang Maggie
collection PubMed
description BACKGROUND: Treatment by immune checkpoint blockade (ICB) provides a remarkable survival benefit for multiple cancer types. However, disease aggravation occurs in a proportion of patients after the first couple of treatment cycles. METHODS: RNA sequencing data was retrospectively collected. 6 tumour-immune related features were extracted and combined to build a lung cancer-specific predictive model to distinguish responses as progression disease (PD) or non-PD. This model was trained by 3 public pan-cancer datasets and a lung cancer cohort from our institute, and generated a lung cancer-specific integrated gene expression score, which we call LITES. It was finally tested in another lung cancer dataset. RESULTS: LITES is a promising predictor for checkpoint blockade (area under the curve [AUC]=0.86), superior to traditional biomarkers. It is independent of PD-L1 expression and tumour mutation burden. The sensitivity and specificity of LITES was 85.7% and 70.6%, respectively. Progression free survival (PFS) was longer in high-score group than in low-score group (median PFS: 6.0 vs. 2.4 months, hazard ratio=0.45, P=0.01). The mean AUC of 6 features was 0.70 (range=0.61-0.75), lower than in LITES, indicating that the combination of features had synergistic effects. Among the genes identified in the features, patients with high expression of NRAS and PDPK1 tended to have a PD response (P=0.001 and 0.01, respectively). Our model also functioned well for patients with advanced melanoma and was specific for ICB therapy. CONCLUSIONS: LITES is a promising biomarker for predicting an impaired response in lung cancer patients and for clarifying the biological mechanism underlying ICB therapy.
format Online
Article
Text
id pubmed-8571398
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Neoplasia Press
record_format MEDLINE/PubMed
spelling pubmed-85713982021-11-10 Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score Liu, Si-Yang Maggie Sun, Hao Zhou, Jia-Ying Zhang, Jia-Tao Yin, Kai Chen, Zhi-Hong Su, Jian Zhang, Xu-Chao Yang, Jin-Ji Zhou, Qing Tu, Hai-Yan Wu, Yi-Long Transl Oncol Original Research BACKGROUND: Treatment by immune checkpoint blockade (ICB) provides a remarkable survival benefit for multiple cancer types. However, disease aggravation occurs in a proportion of patients after the first couple of treatment cycles. METHODS: RNA sequencing data was retrospectively collected. 6 tumour-immune related features were extracted and combined to build a lung cancer-specific predictive model to distinguish responses as progression disease (PD) or non-PD. This model was trained by 3 public pan-cancer datasets and a lung cancer cohort from our institute, and generated a lung cancer-specific integrated gene expression score, which we call LITES. It was finally tested in another lung cancer dataset. RESULTS: LITES is a promising predictor for checkpoint blockade (area under the curve [AUC]=0.86), superior to traditional biomarkers. It is independent of PD-L1 expression and tumour mutation burden. The sensitivity and specificity of LITES was 85.7% and 70.6%, respectively. Progression free survival (PFS) was longer in high-score group than in low-score group (median PFS: 6.0 vs. 2.4 months, hazard ratio=0.45, P=0.01). The mean AUC of 6 features was 0.70 (range=0.61-0.75), lower than in LITES, indicating that the combination of features had synergistic effects. Among the genes identified in the features, patients with high expression of NRAS and PDPK1 tended to have a PD response (P=0.001 and 0.01, respectively). Our model also functioned well for patients with advanced melanoma and was specific for ICB therapy. CONCLUSIONS: LITES is a promising biomarker for predicting an impaired response in lung cancer patients and for clarifying the biological mechanism underlying ICB therapy. Neoplasia Press 2021-10-26 /pmc/articles/PMC8571398/ /pubmed/34715621 http://dx.doi.org/10.1016/j.tranon.2021.101254 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research
Liu, Si-Yang Maggie
Sun, Hao
Zhou, Jia-Ying
Zhang, Jia-Tao
Yin, Kai
Chen, Zhi-Hong
Su, Jian
Zhang, Xu-Chao
Yang, Jin-Ji
Zhou, Qing
Tu, Hai-Yan
Wu, Yi-Long
Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title_full Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title_fullStr Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title_full_unstemmed Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title_short Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
title_sort prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571398/
https://www.ncbi.nlm.nih.gov/pubmed/34715621
http://dx.doi.org/10.1016/j.tranon.2021.101254
work_keys_str_mv AT liusiyangmaggie predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT sunhao predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT zhoujiaying predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT zhangjiatao predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT yinkai predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT chenzhihong predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT sujian predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT zhangxuchao predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT yangjinji predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT zhouqing predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT tuhaiyan predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore
AT wuyilong predictionofunfavourableresponsetocheckpointblockadeinlungcancerpatientsthroughanintegratedtumourimmuneexpressionscore