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Updated: Jul 2, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non-small cell lung
Uiju Cho1, Soyoung Im1, Hyung Soon Park2
1Department of Pathology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea.
Abstract:
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.
Insights
Identifying new biomarkers is crucial for predicting responses to immune checkpoint inhibitors (ICIs) in advanced lung cancer, improving patient selection and treatment outcomes.
Area of Science:
- Oncology
- Immunology
Background:
- Advanced lung cancer treatment has evolved beyond chemotherapy, incorporating targeted therapies and immune checkpoint inhibitors (ICIs).
- Immune checkpoint inhibitors (ICIs) targeting PD-L1 and CTLA-4 have shown efficacy in non-small cell lung cancer but lack universal response and can cause toxicities.
Purpose of the Study:
- To review and emphasize the importance of identifying predictive biomarkers for immune checkpoint inhibitor (ICI) response in advanced lung cancer.
- To explore current and emerging biomarkers beyond PD-L1 for enhanced patient stratification.
Main Methods:
- Literature review focusing on biomarkers for immune checkpoint inhibitor (ICI) response in lung cancer.
- Analysis of established and novel biomarkers including PD-L1, tumor-infiltrating lymphocytes, and others.
Main Results:
- Programmed death-ligand 1 (PD-L1) is a validated biomarker but has imperfect predictive accuracy for ICI response.
- Emerging biomarkers such as tumor-infiltrating lymphocytes, tertiary lymphoid structures, high endothelial venules, Human Leukocyte Antigen class I, and lymphocyte-activation gene-3 show promise.
Conclusions:
- Accurate prediction of ICI response in lung cancer requires exploring biomarkers beyond PD-L1.
- Further research into novel biomarkers is essential for improving patient stratification and optimizing treatment strategies for lung cancer.
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