The Prediction Potential of the Pretreatment Lung Immune Prognostic Index for the Therapeutic Outcomes of Immune Checkpoint Inhibitors in Patients With Solid Cancer: A Systematic Review and Meta-Analysis.

Abstract

BACKGROUND:

The lung immune prognostic index (LIPI) is recently developed to predict immune checkpoint inhibitors (ICIs) treatment outcomes for non-small cell lung cancer. However, its predictive value for other types of cancer remained unclear. This meta-analysis aimed to evaluate the association between pretreatment LIPI score and therapeutic outcomes in cancer patients treated with ICIs.

METHODS:

We searched PubMed, Cochrane Library literature databases and EMBASE for abstracts and full-text articles published from the inception of the database until 16th, Nov 2020. Meta-analyses were performed separately for progression-free survival (PFS) and overall survival (OS) by using the random-effects model.

RESULTS:

A total of 12 studies involving 4883 patients receiving ICIs treatment were identified for the primary analysis. The pooled results implied that compared with good LIPI score groups, patients with poor or intermediate LIPI score were significantly associated with worse OS (HR=3.33, 95%CI 2.64-4.21, P < 0.001, I2 = 64.2%; HR=1.71, 95%CI 1.43-2.04, P < 0.001, I2 = 43.6%, respectively) and PFS (HR=2.73,95%CI 2.00-3.73, P < 0.001, I2 = 78.2%; HR=1.43, 95%CI 1.28-1.61, P < 0.001, I2 = 16.3%, respectively). Also, for 1873 patients receiving chemotherapy, a poor LIPI score was significantly associated with worse OS (HR=2.30, 95%CI 1.73-3.07, P < 0.001; I2 = 56.2%) and PFS (HR=1.92,95%CI 1.69-2.17; P < 0.001; I2 = 0.0%) compared with good LIPI score groups.

CONCLUSIONS:

A good LIPI score was significantly correlated with improved OS and PFS in cancer patients receiving ICIs or chemotherapy, regardless of the types of cancer.

• Authors: Hui Liu, Xiao-Li Yang, Xiao-Yun Yang, Zhao-Ru Dong, Zhi-Qiang Chen, Jian-Guo Hong, Tao Li
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 691002 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 34631525 (Publication Type: Systematic Review)
• Copyright: Copyright © 2021 Liu, Yang, Yang, Dong, Chen, Hong and Li.