Cancer Cell | Established and Emerging Biomarkers in Immune Checkpoint Inhibitor Therapy

This study systematically summarizes the current biomarker framework for predicting the efficacy of immune checkpoint inhibitors, providing critical theoretical support for designing more precise tumor immunotherapy strategies and optimizing clinical patient stratification.

 

Literature Overview

The article 'Navigating Established and Emerging Biomarkers for Immune Checkpoint Inhibitor Therapy,' published in the journal Cancer Cell, systematically explores the clinical utility and mechanistic basis of both approved and emerging biomarkers in immune checkpoint inhibitor (ICI) therapy. The review traces the development of biomarkers from PD-L1 expression and tumor mutational burden (TMB) to microsatellite instability (MSI) and neoantigens, and critically evaluates their strengths and limitations in predicting ICI response. It further analyzes the impact of multilayered factors such as the tumor microenvironment, T cell states, and gene expression profiles on treatment outcomes, proposing a multidimensional integrative model to enhance predictive accuracy.

Background Knowledge

1. The cancer challenge addressed by this study is that, although immune checkpoint inhibitors have significantly improved outcomes for patients with various cancers, only a minority achieve durable responses, highlighting an urgent need for reliable biomarkers to guide precision therapy. The current lack of universal predictive tools leads to overtreatment and resource waste.
2. The research bottlenecks for targets such as CTLA-4 and PD-1 include the high spatial and temporal heterogeneity affecting single biomarkers like PD-L1 expression, inconsistent TMB thresholds across tumor types, and the inability of these markers to reflect dynamic changes in the immune microenvironment. Additionally, primary or acquired resistance mechanisms are complex, involving defects in antigen presentation, T cell exhaustion, and immunosuppressive microenvironments.
3. The study's conceptual innovation lies in integrating genomic, transcriptomic, and microenvironmental features, proposing emerging biomarkers such as clonal TMB, tissue-resident memory (TRM) T cells, IFNγ gene signatures, and tertiary lymphoid structures (TLS) as potential complements to traditional biomarkers. By dissecting key nodes in the cancer-immunity cycle, it provides a theoretical foundation for developing more predictive composite models.

 

 

Research Methods and Experiments

The authors employed a systematic literature review approach, integrating extensive data from clinical trials, cohort studies, and mechanistic investigations. They validated findings using public cohorts such as TCGA and IMvigor, and incorporated single-cell RNA sequencing (scRNA-seq) to analyze heterogeneity within the tumor microenvironment. By comparing tumor mutational burden, PD-L1 expression levels, tumor-infiltrating lymphocyte (TIL) density, and specific gene mutation statuses between responders and non-responders, the study identified key predictive factors. Additionally, gene set enrichment analysis (GSEA) was used to uncover signaling pathways associated with ICI response, such as activation of the IFNγ pathway and suppression of the TGFβ pathway.

Key Conclusions and Perspectives

• Although PD-L1 expression is the first FDA-approved biomarker for ICI therapy, its predictive performance is limited by variability in detection methods, spatial heterogeneity, and dynamic regulation, suggesting the need to combine it with other metrics for improved accuracy
• High TMB and MSI-H status effectively predict ICI response, particularly in pan-cancer treatments, though thresholds require optimization and clonality should be considered to enhance specificity
• Clonal TMB demonstrates greater predictive value than total TMB, indicating that clonal neoantigens are more likely to elicit effective T cell responses, supporting its use as an improved biomarker
• Persistent mutation burden (pTMB) shows stronger association with response in melanoma, HNSCC, and lung cancer, suggesting mutations in genomically stable regions may be more 'visible' to the immune system
• TRM cells (tissue-resident memory T cells) are positively correlated with ICI response across multiple cancers, and their specific gene signatures may serve as potential predictive biomarkers
• The presence of TLS (tertiary lymphoid structures) along with B cell infiltration predicts favorable outcomes, underscoring the importance of adaptive immune initiation
• IFNγ-related gene expression profiles enhance predictive power, whereas EMT or TGFβ signaling correlates with resistance, supporting their inclusion in composite models
• Specific genetic alterations such as POLE/POLD1 mutations and BRCA2 loss can enhance ICI response independently of MSI, broadening the definition of hypermutated phenotypes
• Loss of B2M or HLA genes leads to defective antigen presentation, a key mechanism of primary resistance, and should be included as a negative predictive factor in screening
• Inactivating mutations in JAK1/2 disrupt IFNγ signaling and mediate acquired resistance, suggesting potential for combination targeted therapies

Research Significance and Prospects

This study emphasizes the transition from single biomarkers to multidimensional integrative models, combining genomic features, immune microenvironment status, and T cell functional phenotypes to more accurately identify patients who will benefit from ICI therapy. This shift will advance personalized cancer immunotherapy and reduce unnecessary treatment exposure.

In drug development, these emerging biomarkers can inform enrichment strategies in early clinical trials, improving success rates. For instance, in tumors with low TLS or silent IFNγ signatures, combination therapies with STING agonists or TGFβ inhibitors could be explored.

For clinical monitoring, dynamic tracking of ctDNA changes combined with TMB and immune gene profiles may enable real-time assessment of treatment response and early detection of resistance. Furthermore, the application of scRNA-seq will deepen our understanding of T cell differentiation trajectories, aiding in the development of therapies targeting 'stem-like' T cells.

 

 

Conclusion

This study comprehensively reviews the evolution of biomarkers in immune checkpoint inhibitor therapy, from conventional markers like PD-L1 and TMB to emerging indicators such as microenvironmental features and T cell states, establishing a multilayered predictive framework. Its core contribution lies in highlighting the limitations of single biomarkers and advocating for the integration of genomic, transcriptomic, and immunophenotypic information to improve predictive accuracy. This paradigm holds foundational significance for cancer care: it can optimize current ICI treatment strategies to avoid ineffective therapies, while also providing mechanistic rationale for designing novel combination therapies. In the future, clinical decision-support systems based on such integrated models may become standard, ushering tumor immunotherapy into a true era of precision medicine. The key to translating laboratory discoveries into clinical practice lies in establishing standardized testing protocols and prospectively validating the utility of composite models in clinical cohorts.

 

Stephen L Wang and Timothy A Chan. Navigating Established and Emerging Biomarkers for Immune Checkpoint Inhibitor Therapy. Cancer cell.