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Present and Emerging Biomarkers in Immunotherapy for Metastatic Non-Small Cell Lung Cancer: A Review
Raida M Khwaja1, Quincy S-C Chu1
1Division of Medical Oncology, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB T6G 1Z2, Canada.
Abstract:
Targeting the immune system, especially the PDL-1/PD-1 axis, has significantly improved the outcomes of metastatic lung cancer patients. However, only a portion of patients will benefit significantly from PD(L)1 therapeutics alone or in combination with either chemotherapy or anti-CTLA4 antibody. It is therefore important to study predictive biomarkers to help select the patients who will experience the most benefit from immunotherapy. In this paper, the current status of PDL-1 expression on tumour cells, the smoking status of patients, tumour mutational burden, gut microbiome and STK11 and KEAP1 mutations in the tumour as predictive biomarkers for PD(L)-1-based immunotherapy are summarized.
Insights
Predictive biomarkers like PD-L1 expression, smoking history, tumor mutational burden, and specific gene mutations (STK11, KEAP1) can identify metastatic lung cancer patients likely to benefit from PD-(L)1 immunotherapy.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Immunotherapy targeting the PD-L1/PD-1 axis has improved outcomes for metastatic lung cancer.
- However, not all patients respond to current PD-(L)1 therapies, necessitating biomarker identification.
Purpose of the Study:
- To summarize current predictive biomarkers for PD-(L)1-based immunotherapy in metastatic lung cancer.
- To guide patient selection for optimal treatment response.
Main Methods:
- Review of current literature on predictive biomarkers.
- Analysis of PD-L1 expression, smoking status, tumor mutational burden, gut microbiome, and STK11/KEAP1 mutations.
Main Results:
- PD-L1 expression levels on tumor cells are a key biomarker.
- Tumor mutational burden and specific genetic mutations (STK11, KEAP1) show predictive potential.
- Smoking status and gut microbiome composition may also influence treatment response.
Conclusions:
- Identifying predictive biomarkers is crucial for selecting patients who will benefit most from PD-(L)1 immunotherapy.
- A multi-faceted biomarker approach may enhance treatment efficacy and patient outcomes.
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