- Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non–small cell lung cancer
-
Uiju Cho, Soyoung Im, Hyung Soon Park
-
J Pathol Transl Med. 2024;58(2):49-58. Published online February 26, 2024
-
DOI: https://doi.org/10.4132/jptm.2024.01.31
-
-
3,818
View
-
306
Download
-
1
Web of Science
-
1
Crossref
-
 Abstract
 PDF
- Treatment challenges persist in advanced lung cancer despite the development of therapies beyond the traditional platinum-based chemotherapy. The early 2000s marked a shift to tyrosine kinase inhibitors targeting epidermal growth factor receptor, ushering in personalized genetic-based treatment. A further significant advance was the development of immune checkpoint inhibitors (ICIs), especially for non–small cell lung cancer. These target programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4, which enhanced the immune response against tumor cells. However, not all patients respond, and immune-related toxicities arise. This review emphasizes identifying biomarkers for ICI response prediction. While PD-L1 is a widely used, validated biomarker, its predictive accuracy is imperfect. Investigating tumor-infiltrating lymphocytes, tertiary lymphoid structure, and emerging biomarkers such as high endothelial venule, Human leukocyte antigen class I, T-cell immunoreceptors with Ig and ITIM domains, and lymphocyte activation gene-3 counts is promising. Understanding and exploring additional predictive biomarkers for ICI response are crucial for enhancing patient stratification and overall care in lung cancer treatment.
-
Citations
Citations to this article as recorded by  - Machine learning methods for histopathological image analysis: Updates in 2024
Daisuke Komura, Mieko Ochi, Shumpei Ishikawa
Computational and Structural Biotechnology Journal.2025; 27: 383. CrossRef
|
E-submission
