NLCIPS: Non-Small Cell Lung Cancer Immunotherapy Prognosis Score.

immunotherapy non-small cell lung cancer prognosis score smoking index

Journal

Cancer management and research
ISSN: 1179-1322
Titre abrégé: Cancer Manag Res
Pays: New Zealand
ID NLM: 101512700

Informations de publication

Date de publication:
2020
Historique:
received: 12 04 2020
accepted: 25 06 2020
entrez: 9 8 2020
pubmed: 9 8 2020
medline: 9 8 2020
Statut: epublish

Résumé

Currently in China, many immune checkpoint inhibitors (ICIs) have been approved for the treatment of non-small cell lung cancer (NSCLC). Some patients can not benefit from ICIs, and approximately 50% of patients have immunotherapy-related toxicity. Therefore, it is necessary to monitor carefully the selection of immunotherapy population using biomarkers to maximize the benefit of patients with NSCLC. A prospective analysis was performed on patients with advanced NSCLC who were treated with ICIS at our hospital from March 2018 to June 2019, up to the follow-up deadline of December 31, 2019. The primary end points were overall survival (OS) and progression-free survival (PFS), and the secondary end points were objective response rate and disease control rate. A lasso regression was used for the univariate analysis, and Cox regression analysis was used for the multivariate analysis. An efficacy prediction line chart was developed. A total of 63 patients were included in the study. The median PFS was 7.0 months (95% CI, 5.0-11.0) and did not reach the median OS. According to the lasso regression, significant univariate factors were smoking index, PD-ligand 1 expression, and neutrophil to lymphocyte ratio (NLR). According to the multivariate analysis, the Cox proportional hazards model showed that smoking index and NLR are independent predictors of PFS in immunotherapy. A model comprised of independent predictors was developed based on a multivariate logical analysis of the main cohort-non-small cell lung cancer immunotherapy prognosis score. This model is shown as a nomogram with a C-index of 0.801 (95% CI, 0.744, 0.858), which has high prediction accuracy. This predictive model, including NLR and smoking index, can achieve a 1-year PFS in immunotherapy of patients. PD-1 inhibitors have been demonstrated to be effective and safe in the clinical treatment of patients with NSCLC.

Identifiants

pubmed: 32765097
doi: 10.2147/CMAR.S257967
pii: 257967
pmc: PMC7381788
doi:

Types de publication

Journal Article

Langues

eng

Pagination

5975-5985

Informations de copyright

© 2020 Song et al.

Déclaration de conflit d'intérêts

The authors declared that they have no conflicts of interest to this work, and do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Auteurs

Peng Song (P)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Dongliang Yang (D)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.
Department of General Education Courses, Cangzhou Medical College, Beijing, People's Republic of China.

Xiaoxia Cui (X)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Hanping Wang (H)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Xiaoyan Si (X)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Xiaotong Zhang (X)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Li Zhang (L)

Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, People's Republic of China.

Classifications MeSH