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Emerging Predictive Biomarkers of Immunotherapy Sensitivity in Patients with Non-Small Cell Lung Cancer
Eleonora Gariazzo1,2, Francesca Colamartini1, Martina Ubaldi1
1Medical Oncology, Santa Maria Della Misericordia Hospital, University of Perugia, Perugia, Italy.
Abstract:
In recent years, the therapeutic landscape of non-small cell lung cancer (NSCLC) has been transformed by immune checkpoint inhibitors (ICIs), which have led - in some patients - to unprecedented survival expectancy. Nevertheless, identifying patients most likely to benefit from ICI remains a major challenge. While PD-L1 expression and tumor mutation burden (TMB) represent established predictive biomarkers, their predictive ability still needs to be improved, which underscores the need for identifying additional (bio)markers for treatment selection. Recent research has highlighted multiple emerging biomarkers, including genomic alterations (eg, KEAP1, STK11, SMARCA4), markers of metabolic pathway dysregulation (IDO, adenosine axis), tumor-infiltrating lymphocytes, and blood-based biomarkers (eg soluble markers of inflammation, germline HLA diversity, and circulating tumor DNA). Host-related determinants, such as the history of tobacco exposure and the body mass index, further contribute to immunotherapy outcomes. In addition, artificial intelligence (AI) and machine learning (ML) approaches are enabling integration of multidimensional data, leading to predictive scoring systems which have outperformed conventional biomarkers in certain settings. This review synthesizes current evidence on established and emerging predictive biomarkers in NSCLC, highlighting the potential of combining biological, host, and computational features to inform precision immunotherapy strategies.
Insights
Identifying patients who will benefit from immune checkpoint inhibitors (ICIs) for non-small cell lung cancer (NSCLC) is challenging. This review explores established and emerging biomarkers, including genomic, metabolic, and host factors, alongside AI approaches, to improve treatment selection.
Area of Science:
- Oncology
- Immunotherapy
- Biomarker Discovery
Background:
- Immune checkpoint inhibitors (ICIs) have transformed non-small cell lung cancer (NSCLC) treatment, improving survival for some patients.
- Predicting response to ICIs remains difficult, as established biomarkers like PD-L1 expression and tumor mutation burden (TMB) have limitations.
- There is a critical need for novel biomarkers to optimize patient selection for immunotherapy.
Purpose of the Study:
- To review established and emerging predictive biomarkers for ICI therapy in NSCLC.
- To highlight the potential of integrating diverse data types for improved treatment strategies.
- To discuss the role of host factors and computational approaches in precision immunotherapy.
Main Methods:
- Literature review synthesizing current evidence on NSCLC biomarkers.
- Analysis of established biomarkers (PD-L1, TMB) and emerging markers (genomic alterations, metabolic pathways, immune cells, blood-based markers).
- Inclusion of host-related factors (tobacco history, BMI) and artificial intelligence (AI)/machine learning (ML) approaches.
Main Results:
- Emerging biomarkers include genomic alterations (KEAP1, STK11, SMARCA4), metabolic dysregulation markers (IDO, adenosine), tumor-infiltrating lymphocytes, and blood-based markers (ctDNA, inflammation).
- Host factors like tobacco exposure and BMI influence immunotherapy outcomes.
- AI/ML models integrate multidimensional data, showing potential to outperform conventional biomarkers.
Conclusions:
- Combining biological, host, and computational features offers a promising strategy for precision immunotherapy in NSCLC.
- Further research into novel biomarkers and integrated predictive models is essential for maximizing ICI efficacy.
- Personalized treatment selection based on comprehensive biomarker profiles will enhance patient outcomes in NSCLC.
