Screening biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression.

Biomarkers Immunotherapy NSCLC PD-L1

Journal

Journal of cancer research and clinical oncology
ISSN: 1432-1335
Titre abrégé: J Cancer Res Clin Oncol
Pays: Germany
ID NLM: 7902060

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 31 05 2023
accepted: 09 07 2023
pubmed: 20 7 2023
medline: 20 7 2023
entrez: 19 7 2023
Statut: ppublish

Résumé

Immunotherapy plays an important role in non-small cell lung cancer (NSCLC); in particular, immune checkpoint inhibitors (ICIs) therapy has good therapeutic effects in PD-L1-positive patients. This study aims to screen NSCLC patients with PD-L1-positive expression and select effective biomarkers for ICI immunotherapy. Collected tumor samples from the Affiliated Cancer Hospital of Xinjiang Medical University and 117 patients with stage III-IV NSCLC were included in the study. All patients were on first- or second-line therapy and not on targeted therapy. Based on the molecular profiles and clinical features, we screened biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression. 117 NSCLC patients receiving ICIs immunotherapy were enrolled. First, we found that immunotherapy was more effective in patients with positive PD-L1 expression. Second, we found that ROS1 gene mutations, KRAS gene mutations, tumor stage, and the endocrine system diseases history are independent prognostic factors for PD-L1 positive patients. Then we combined independent risk factors and constructed a new Nomogram to predict the therapeutic efficacy of ICIs immunotherapy in PD-L1 positive patients. The Nomogram integrates these factors into a prediction model, and the predicted C-statistic of 3 months, 6 months and 12 months are 0.85, 0.84 and 0.85, which represents the high predictive accuracy of the model. We have established a model that can predict the efficacy of ICIs immunotherapy in PD-L1 positive patients. The model consists of ROS1 gene mutations, KRAS gene mutations, tumor staging, and endocrine system disease history, and has good predictive ability.

Identifiants

pubmed: 37468609
doi: 10.1007/s00432-023-05160-9
pii: 10.1007/s00432-023-05160-9
pmc: PMC10587271
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

12965-12976

Subventions

Organisme : Department of Science and Technology of Xinjiang Uygur Autonomous Region
ID : 2022D14010
Organisme : Life Oasis Public Welfare Service Center in Qujiang District, Quzhou City
ID : BJHA-CRP-003

Informations de copyright

© 2023. The Author(s).

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Auteurs

Xiaodan Zhu (X)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China.

Bo Yu (B)

Department of Medicine, Beijing USCI Medical Laboratory, No. 65, Xingshikou Road, Haidian District, Beijing, 100195, China.

Yanli Shen (Y)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China.

Yan Zhao (Y)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China.

Xiyujing Fu (X)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China.

Yunji Zhu (Y)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China.

Guomin Gu (G)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China. 2001xiaogu@163.com.

Chunling Liu (C)

Department of Pulmonary Medicine, Affiliated Cancer Hospital of Xinjiang Medical University, No. 789 Suzhou East Street, Xinshi District, Urumqi, 830000, Xinjiang, China. Liudeyouxiang66@sina.com.

Classifications MeSH