Journal of Cancer Diagnosis
Open Access

Biomarkers; Immune Checkpoint Blockade; PD-L1 Expression; Tumor Mutational Burden (TMB); Microsatellite Instability (MSI); Liquid Biopsy; Gut Microbiome; Proteomics; Tumor Microenvironment; Neoantigens; Cancer Immunotherapy

Introduction

The field of predictive and prognostic biomarkers in solid tumors undergoing immune checkpoint blockade is vital for guiding personalized treatment and ultimately improving patient outcomes [1].

Understanding the intricate mechanisms driving both effective responses and persistent resistance to immune checkpoint blockade therapies is crucial. It outlines how a range of factors, from the tumor microenvironment to systemic immune changes, contribute to patient outcomes, highlighting established biomarkers and exploring novel candidates that could better predict who benefits and who might need alternative strategies [2].

Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) are recognized as critical biomarkers, often correlating with enhanced responses to immune checkpoint inhibitors across various cancer types. This provides insights into their mechanistic basis and practical utility in patient selection for targeted treatments [3].

Liquid biopsies offer transformative potential for biomarker discovery, providing non-invasive, real-time insights into tumor evolution and treatment response through circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and other blood-based components. This paves the way for more adaptable and precise therapeutic strategies [4].

Beyond tumor-centric markers, the gut microbiota profoundly influences the efficacy and toxicity of cancer immunotherapy. Specific microbial signatures are associated with favorable responses to immune checkpoint inhibitors, and understanding the mechanisms through which these commensal bacteria modulate anti-tumor immunity paves the way for microbiota-targeted interventions to optimize patient outcomes [5].

PD-L1 expression remains a foundational biomarker for predicting response to immune checkpoint inhibitors, despite complexities and limitations associated with its testing, including variability in assays, scoring methods, and tumor heterogeneity [6].

Tumor neoantigen burden is a crucial predictive biomarker for the effectiveness of immune checkpoint blockade therapies. A higher number of neoantigens, arising from somatic mutations, can lead to increased immunogenicity and stronger anti-tumor immune responses, offering valuable insights for patient stratification and the development of personalized cancer vaccines [7].

Immune cell infiltration patterns and specific gene expression signatures within the tumor microenvironment provide valuable insights into a patient's likelihood of responding to immune checkpoint inhibitors, especially in non-small cell lung cancer [8].

The utility of circulating biomarkers like PD-L1 and ctDNA in predicting and monitoring immunotherapy responses is expanding, offering dynamic, non-invasive insights beyond traditional tissue biopsies into tumor burden, immune activation, and resistance mechanisms [9].

Finally, proteomic biomarkers, through comprehensive analysis of protein expression profiles and post-translational modifications, enhance the prediction and monitoring of patient responses by revealing intricate biological pathways beyond genomic markers, paving the way for novel diagnostic and prognostic tools [10].

Description

The success of immune checkpoint blockade (ICB) in solid tumors relies heavily on identifying robust predictive and prognostic biomarkers. These markers are essential for personalizing treatment strategies and ultimately improving patient outcomes. Established biomarkers such as PD-L1 expression, Tumor Mutational Burden (TMB), and Microsatellite Instability (MSI) form the bedrock of current clinical decision-making [1]. PD-L1 expression, while a foundational marker, necessitates careful consideration due to assay variability, diverse scoring methods, and the inherent heterogeneity of tumors [6]. High TMB and MSI status consistently correlate with enhanced responses to ICB across various cancer types, offering significant insights into their underlying mechanisms and practical utility for selecting patients [3].

A deeper investigation into the complex interplay of factors driving effective responses versus persistent resistance to ICB is paramount. This includes a spectrum of influences, from localized tumor microenvironment characteristics to broader systemic immune changes, all contributing to diverse patient outcomes [2]. A key player in this dynamic is tumor neoantigen burden. A higher presence of neoantigens, which stem from somatic mutations, significantly increases tumor immunogenicity, thereby fostering stronger and more effective anti-tumor immune responses [7]. This offers invaluable information for patient stratification and can even guide the development of bespoke cancer vaccines.

The tumor microenvironment's intricate architecture and cellular composition are increasingly recognized as critical determinants of immunotherapy efficacy. Specific immune cell infiltration patterns and unique gene expression signatures within this microenvironment act as predictive biomarkers for immunotherapy success. This is particularly evident in non-small cell lung cancer, where the spatial distribution and types of immune cells, alongside particular genetic profiles, offer valuable clues about a patient's likelihood of responding to immune checkpoint inhibitors [8]. These insights emphasize that the immune context of the tumor is as crucial as its genetic makeup.

Emerging evidence highlights the profound influence of systemic factors, such as the gut microbiota, on the outcomes of cancer immunotherapy. The gut microbiota not only impacts the efficacy but also the toxicity of immune checkpoint inhibitors. Specific microbial signatures have been identified that correlate with favorable responses, unraveling the mechanisms through which commensal bacteria modulate anti-tumor immunity [5]. These findings open doors for microbiota-targeted interventions, aiming to optimize patient responses and minimize adverse effects.

The advent of liquid biopsies has revolutionized biomarker discovery by providing non-invasive, real-time insights into tumor evolution. Circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and other blood-based components offer dynamic information about treatment response and resistance mechanisms, enabling more adaptive and precise therapeutic strategies [4]. This expansion of circulating biomarkers also includes circulating PD-L1 and other systemic markers, which provide continuous, non-invasive monitoring of tumor burden and immune activation [9]. Complementing these genomic and cellular approaches, proteomic biomarkers offer a deeper understanding. Comprehensive analysis of protein expression profiles and post-translational modifications can reveal intricate biological pathways involved in anti-tumor immunity, going beyond genomic data to pave the way for novel diagnostic and prognostic tools in immunotherapy [10].

Conclusion

The landscape of predictive and prognostic biomarkers in solid tumors treated with immune checkpoint blockade is rapidly evolving. Established markers like PD-L1 expression, Tumor Mutational Burden (TMB), and Microsatellite Instability (MSI) are crucial for guiding personalized treatment strategies and improving patient outcomes. PD-L1, for instance, remains a foundational marker, despite complexities in testing, assay variability, and tumor heterogeneity. High TMB and MSI status often correlate with enhanced response to immune checkpoint inhibitors across various cancer types, providing insights into their mechanistic basis and practical utility in patient selection. Beyond these, emerging biomarkers are broadening our understanding. The tumor microenvironment characteristics, gut microbiome, and specific gene signatures play significant roles. For example, the gut microbiota profoundly influences the efficacy and toxicity of cancer immunotherapy, with specific microbial signatures linked to favorable responses. Non-invasive approaches like liquid biopsies are transforming biomarker discovery, using circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and other blood-based components to provide real-time information on tumor evolution and treatment response. Similarly, circulating PD-L1 and other systemic markers offer dynamic insights into immune activation and resistance. Furthermore, comprehensive analysis of protein expression profiles and post-translational modifications through proteomic biomarkers offers a deeper understanding beyond genomic markers, paving the way for novel diagnostic and prognostic tools. Understanding these diverse biomarkers is essential for predicting responses, overcoming resistance, and refining patient selection for immune checkpoint blockade therapies.

References

1. Lei S, Lingyan L, Qiuyue Z (2022) Predictive and prognostic biomarkers in immune checkpoint blockade for solid tumors: an update.Cancer Immunol Immunother 71:1537-1555.

   Indexed at, Google Scholar, Crossref

2. Jonathan DS, Yana K, David SS (2019) Mechanisms and Biomarkers of Response and Resistance to Immune Checkpoint Blockade.Semin Cancer Biol 60:1-12.

   Indexed at, Google Scholar, Crossref

3. Aurélien M, Jacques P, Olivier M (2020) Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) as Biomarkers for Immunotherapy: An Update.Cancers (Basel) 12:623.

   Indexed at, Google Scholar, Crossref

4. Mona N, David H, Shreya P (2019) Liquid Biopsy for Biomarker Discovery in Immunotherapy.Cancers (Basel) 11:645.

   Indexed at, Google Scholar, Crossref

5. Vancheswaran G, Amira A, David SS (2023) The Gut Microbiota in Cancer Immunotherapy: Current Status and Perspectives.Curr Oncol Rep 25:647-661.

   Indexed at, Google Scholar, Crossref

6. Claire D, Christopher LR, Matthew RL (2021) PD-L1 Expression as a Biomarker for Immunotherapy: A Review.Arch Toxicol 95:139-152.

   Indexed at, Google Scholar, Crossref

7. Mark Y, Dung TL, Robert AA (2019) Tumor Neoantigen Burden: A Predictive Biomarker for Checkpoint Blockade Immunotherapy.Oncology 96:1-15.

   Indexed at, Google Scholar, Crossref

8. Hong L, Xiaojuan S, Xiaoping W (2022) Immune cell infiltration and gene expression signatures as predictive biomarkers for immunotherapy in non-small cell lung cancer.Cell Immunol 377:104618.

   Indexed at, Google Scholar, Crossref

9. Etienne L, Anne S, Aurélie P (2020) Circulating Biomarkers in Immunotherapy: A Focus on PD-L1 and Beyond.Cancers (Basel) 12:2218.

   Indexed at, Google Scholar, Crossref

10. Xuan Z, Wei L, Bin G (2023) Proteomic Biomarkers for Predicting and Monitoring Immunotherapy Response.J Pers Med 13:584.

    Indexed at, Google Scholar, Crossref