Biomarkers in Immune Checkpoint Inhibitor Cancer Treatment

Angela Papageorgiou
University of Texas Health Science Center at Houston, Houston, TX, USA (Formerly associated with MD Anderson Cancer Center)
Correspondence to: angela.papageorgiou@gmail.com

DOI: https://doi.org/10.70389/PJS.100023

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Angela Papageorgiou – Conceptualization, Writing – original draft, review and editing
• Guarantor: Angela Papageorgiou
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: PD-L1 expression, Tumor mutational burden (TMB), Circulating tumor DNA (ctDNA), Immune checkpoint inhibitors (ICIs), Combination of biomarkers.

Peer-review
Received: 25 September 2024
Revised: 15 October 2024
Accepted: 15 October 2024
Published: 25 October 2024

Introduction to Immune Checkpoint Inhibitors and the Role of Biomarkers in Immune Checkpoint Inhibitor Therapy

Immune checkpoint inhibitors (ICIs), particularly PDL-1 and PD-1 inhibitors, have transformed cancer treatment by hijacking the innate immune system’s ability to detect and eliminate cancer cells. Under normal physiological conditions, checkpoint proteins, such as PD-1 and CTLA-4, act as regulatory “brakes” on the immune system, inhibiting it from attacking healthy tissues. Cancer cells exploit these pathways to circumvent immune detection and destruction. ICIs block checkpoint proteins, releasing these “brakes,” allowing the immune system to target and eliminate cancers. This strategy has been highly effective in treating cancers such as melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma (RCC), leading to longer survival and durable responses in a substantial number of patients.^1–5 Although many malignancies have remarkable responsiveness to ICIs and may achieve complete remission post-treatment, patient responses are heterogeneous, and some individuals may experience immune-related side effects. The latter poses substantial challenges to the routine application of these treatments.^6–10

Identifying biomarkers is essential for determining which patients will likely benefit from ICI therapy and optimizing treatment strategies. Biomarkers such as programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI) are considered key predictors for response to ICI therapy.11 For example, high PD-L1 expression and increased TMB are associated with better therapeutic outcomes in certain cancer types, enabling the selection of patients likely to benefit from this therapy.^1–4,11 However, variability in biomarker expression levels exists across different tumors. The latter underscores the need for more precise approaches. Tumor-infiltrating lymphocytes (TILs), gut microbiome diversity, and circulating tumor DNA (ctDNA) are emerging biomarkers that offer promise in improving patient selection and guiding personalized treatment strategies.^12–17

Combining multiple biomarkers can improve the accuracy of predicting responses to ICI therapy. This strategy enables clinicians to distinguish between responders and non-responders, thus creating more personalized tailored treatment plans. Consequently, this approach maximizes the benefits of ICIs while reducing unnecessary exposure to side effects and treatment costs.^7,13 Incorporating several biomarkers makes ICI therapy more effective and helps avoid overtreatment.^{3,7,9–11,13,18}

Key Biomarkers and Their Predictive Value in ICI Therapy

PD-L1 as a Predictive Biomarker in ICI Therapy: Clinical Relevance and Quantification Techniques

It is well known that PD-L1 expression is a critical biomarker for predicting the effectiveness of ICI therapy, especially in cancers such as urothelial carcinoma, melanoma, and NSCLC. Tumor cells (TCs) have PD-L1 on their surface. PD-1 receptors, located on the surface of T-cells, interact with PD-L1, leading to suppression of T-cell activity. This enables malignancies to evade immune detection.⁴ PD-L1 plays a dual role: physiologically, it is crucial for maintaining immune homeostasis by preventing excessive immune responses, while pathologically, it can be co-opted by TCs to create an immunosuppressive environment, facilitating tumor progression.^3,9,11,19 ICIs, including anti-PD-1 medications like pembrolizumab and nivolumab as well as anti-PD-L1 antibodies like atezolizumab, prevent this binding. T-cell function is restored by this inhibition, which improves their capacity to identify and eliminate TCs.20 Since better responses to ICIs are typically linked to higher PD-L1 expression levels, PD-L1 testing is vital in the clinical decision-making process for certain cancers.^3,20,21 For example, NSCLC patients with elevated PD-L1 levels often experience superior outcomes when treated with PD-1 or PD-L1 inhibitors.^22,23

However, PD-L1 is a complex predictive biomarker as its expression can vary significantly within different regions of the same tumor (ITH) and across different metastatic sites within the same patient.^3,7 This variability can lead to discrepancies in predicting treatment response. Additionally, PD-L1 expression is influenced by various factors, including prior treatments and the tumor microenvironment (TME), complicating its use as a stand-alone predictive biomarker.^3,8 Furthermore, assay variability and the cutoff values utilized for determining PD-L1 positivity contribute to clinical practice inconsistencies.²⁴ While PD-L1 serves as a predictive biomarker in the efficacy of ICI therapy in some tumor types, there is yet no single predictive marker for immune-related adverse events (irAEs). This underscores the necessity for a multifactorial biomarker strategy in predicting ICI response. Research is ongoing to increase the application of PD-L1 by integrating it with additional biomarkers, including TMB and immune cell (IC) profiling, to augment its prediction accuracy and refine patient selection for ICI therapy.^2,7,25

Given PD-L1’s role in ICI therapy, accurate quantitation of its expression is central to determining patient eligibility for ICI therapy. The most widely used method for detecting PD-L1 is immunohistochemistry (IHC), which allows for the direct evaluation of PD-L1 expression on TCs. IHC remains the gold standard but has limitations, such as the need for biopsies and reliance on subjective interpretation, which can lead to false-positive results.^3,7,26 Variability in tissue section thickness, antibody choice, and staining conditions further complicates PD-L1 detection through IHC.

Emerging techniques, including surface plasmon resonance spectroscopy, fluorescence- based methods, and electrochemical assays, are being explored to improve PD-L1 detection. These advanced methods offer the potential for more accurate and less invasive measurements, especially in cases where traditional biopsy samples are difficult to obtain.^8,14 Additionally, radionuclide-labeled nuclear imaging allows for PD-L1 expression measurement across primary tumors and metastases, offering a more comprehensive picture of PD-L1 status.^13,27 Soluble PD-L1 (sPD-L1) detection in serum through Enzyme-Linked Immunosorbent Assay (ELISA) also provides a less invasive option, especially useful for monitoring advanced cancer patients.^{13,14,28–30} Despite these advancements, PD-L1 detection remains challenging due to the technical and biological factors affecting its expression. Nevertheless, improvements in biosensing and imaging techniques hold promise for more precise and real-time monitoring of PD-L1 levels, which could significantly enhance patient selection and treatment optimization for ICI therapy.^7,8,27 Taken together, these methods aim to provide more accurate and less invasive options for quantitation of PD-L1 levels, which could ultimately enhance patient outcomes.

PD-L1 Across Cancer Types

NSCLC

PD-L1 expression plays a crucial role in the clinical management of NSCLC, guiding the use of ICIs such as pembrolizumab, nivolumab, and atezolizumab. These ICIs function by targeting the PD-1/PD-L1 pathway, boosting the immune system’s ability to attack TCs. Elevated PD-L1 expression on TCs is associated with a better response to ICIs, making PD-L1 testing an integral part of the clinical management of NSCLC. Patients with PD-L1 expression levels of 50% or higher often experience better responses to PD-1/PD-L1 inhibitors, rendering PD-L1 IHC testing a standard procedure in NSCLC treatment.^30–33 While PD-L1 IHC testing is useful, it has limitations.³⁴ For example, PD-L1 expression can vary between primary and metastatic tumor sites, potentially leading to inconsistent results.7 Additionally, repeated biopsies to assess PD-L1 expression over time are often impractical and invasive. Emerging approaches, such as measuring sPD-L1 levels in blood, offer a non-invasive alternative for serial monitoring of treatment responses and may serve as an early indicator of cancer recurrence.^8,11,13,35

Although combination therapies involving ICIs such as nivolumab and relatlimab have demonstrated efficacy in metastatic melanoma patients with low (<1%) PD-L1 expression levels, the potential of such combinations in NSCLC is still under investigation. PD-L1 expression correlates with better responses to PD-1/PD-L1 inhibitors, but its heterogeneity complicates its clinical utility.^5,11,36–38 Consequently, advances in understanding PD-L1 dynamics, including soluble forms, offer promise for improving the precision of immunotherapy in NSCLC.^4,7,20

Metastatic Melanoma

Similarly, PD-L1 expression plays a pivotal role in managing metastatic melanoma, especially in directing the use of ICIs. One notable treatment approach combines nivolumab and relatlimab, which has shown improvements in progression-free survival (PFS) even in patients with low (<1%) PD-L1 expression levels.^4,20,37 This suggests that dual ICI therapy may offer therapeutic benefits even in cases of low PD-L1 expression levels.11 In metastatic melanoma, like NSCLC, increased PD-L1 expression generally correlates with improved responses to ICIs such as pembrolizumab and nivolumab, though the heterogeneity of PD-L1 expression poses challenges for its application as a predictive biomarker.20,39,40 However, given the variability of PD-L1 expression across tumor sites, standardized detection methods are essential to accurately identify patients who would benefit most from ICIs.^11,13,41

Other Solid Tumors

The significance of the PD-1/PD-L1 pathway ­extends beyond melanoma and NSCLC, influencing the ­therapeutic landscape of several other cancer types:

Bladder Cancer

In the case of bladder cancer, particularly urothelial carcinoma, the PD-1/PD-L1 pathway plays a critical role. TCs express PD-L1, allowing them to evade immune detection by inhibiting T-cell activity. Blocking the PD-1/PD-L1 interaction with ICIs restores T-cell function and leads to tumor regression.^3,5,11 Clinical trials have demonstrated significant responses in advanced bladder cancer with ICIs, including improved response rates and overall survival (OS).^4,13,42,43

Colon Cancer

In colorectal cancer (CRC), PD-L1 expression is often associated with microsatellite instability-high (MSI-H) tumors, which tend to be more immunogenic and respond better to ICIs.^3,7,13 PD-L1 is more frequently observed in right-sided colon cancers and is linked to better prognoses in certain subtypes, such as those classified as Consensus Molecular Subtype ^1.5,44 The latter is characterized by high MSI and an immune- active TME, which often correlates with better responsiveness to ICI in CRC. Although PD-L1 has shown promise as a biomarker in CRC, its heterogeneity and inconsistent expression across tumors limit its ­utility.^3,7,13 Emerging non-invasive methods, such as measuring sPD-L1 in blood, may offer more consistent and accessible monitoring.^7,11,14,44

Renal Cancer

PD-1 and PD-L1 have become important therapeutic targets in renal cancer, particularly clear cell renal cell carcinoma (ccRCC). Anti-PD-1 therapies, such as nivolumab, have shown substantial efficacy in metastatic ccRCC, often in combination with anti-CTLA-4 agents.^5,11,45 These combination therapies offer an increased potential for long-term disease control compared to traditional treatments like chemotherapy, which has limited efficacy in renal cancer.^{11,13,45–48}

Gastric Cancer

PD-L1 expression also plays a significant role in gastric cancer. High PD-L1 expression on ICs within the TME has been associated with improved survival outcomes, suggesting an activated immune response that suppresses tumor growth. Studies show that patients with high PD-L1 expression, particularly on ICs, exhibit better survival rates than those with low expression, emphasizing the importance of immune activity in shaping patient prognosis.^4,5,45,49,50 Overall, understanding the variations in PD-L1 expression across cancer types is necessary to enhancing ICI therapies and advancing patient outcomes.

TMB as a Biomarker for ICI Therapy: Significance and Testing Challenges

TMB refers to the total number of non-synonymous mutations within a tumor’s DNA. High TMB is associated with an increased presence of neoantigens, which enhances the immune system’s ability to recognize the tumor and subsequently improve the efficacy of ICIs. Clinical studies have established a strong correlation between high TMB and improved responses to ICIs in cancers, such as melanoma, NSCLC, CRC, and bladder cancer. Therefore, TMB is a meaningful biomarker for stratifying patients most likely to benefit from ICI therapy. The relationship between TMB and ICI response ­arises from the increased production of immunogenic neoantigens that result from higher TMB levels, which in turn trigger a stronger immune response. For example, NSCLC and melanoma patients with high TMB levels have demonstrated higher response rates and better outcomes.⁵⁶ However, challenges remain, including variability in what is considered “high” TMB across different cancer types. Long-term data are still needed to clarify TMB’s association with OS, as many studies have not yet fully developed clinical outcomes.

Despite TMB’s potential use, several challenges hinder the standardization and clinical application of TMB as an established biomarker. Response to ICI therapy as a function of TMB varies significantly across different cancer types. Cancers such as NSCLC and melanoma often exhibit higher TMB and respond well to ICI therapy. On the contrary, RCC can still respond to ICIs despite its lower TMB.⁵¹ Technical challenges also arise from differences in sequencing methods. Whole-exome sequencing (WES) is considered the gold standard but is expensive. Conversely, although more practical, smaller gene panels may underestimate TMB.⁵¹

Additionally, tumor heterogeneity complicates TMB measurement, as it can differ across tumor regions and between primary and metastatic sites.51 Assay standardization is another issue, with inconsistent protocols and germline exclusion strategies contributing to discrepancies in TMB measurement across laboratories.51 Furthermore, TMB alone may not consistently predict ICI efficacy, as some patients with low TMB still respond to treatment, underscoring the need for more comprehensive biomarker panels.⁴⁷ A multi-omics approach, incorporating TMB with PD-L1 expression and immune profiling, may improve predictive accuracy and enable more personalized cancer treatment.⁵¹ In conclusion, while TMB holds promise as a predictive biomarker, integrating it with additional biomarkers and improving assay standardization are crucial for realizing its full clinical potential.

TILs as Biomarkers for ICI Therapy: Significance and Associated Challenges

TILs comprise natural killer cells, B cells, and T-cells (CD8+ and CD4+ subsets), which are vital ICs found in the TME. These cells play a crucial role in cancer progression and the response to ICI therapy by identifying and targeting cancer cells. Particularly, the ability of cytotoxic CD8+ T-cells to identify antigens unique to tumors is essential for inducing an immune response that kills TCs. Tumors, however, can recruit regulatory T-cells (Tregs) or upregulate immune checkpoint molecules such as PD-L1, which can result in the development of an immunosuppressive environment and immune response evasion.⁵² More thorough characterization of TIL subsets has been made possible by recent developments in mass cytometry and single-cell RNA sequencing, which have shown the heterogeneity of these ICs within the TME. These technologies have helped researchers understand the specific TIL subsets that influence ICI efficacy and tumor immune evasion.⁵²

TILs are considered predictive emerging biomarkers for assessing ICI effectiveness. High levels of CD8+ TILs have been associated with improved outcomes in several cancers, including head-and-neck squamous cell carcinoma (HNSCC), NSCLC, and melanoma.^53,54 For example, HNSCC patients with high levels of CD8+ TILs in the stroma combined with PD-L1 positivity demonstrated better PFS and OS. Similarly, increased CD8+ TIL counts were linked to improved OS in NSCLC patients.^54,55 The biological basis of TILs as predictive biomarkers stems from their direct interaction with TCs, and their presence is indicative of therapeutic efficacy. However, their ability to consistently predict outcomes can be limited by the diverse makeup of IC populations within the tumor and the constantly changing nature of immune responses.⁵²

Several obstacles limit the widespread clinical adoption of TILs as predictive biomarkers. Among these challenges is the heterogeneity of TIL populations across different tumor regions and metastatic sites. The dynamic nature of TILs during treatment also adds complexity to their predictive potential.⁵² Standardizing TIL assessment is challenging due to the various methodologies employed: for example, IHC versus multiplex immunofluorescence. Because these methods are not universally standardized, variable results may be obtained across clinical laboratories. A combined analysis of TILs with other biomarkers, such as PD-L1 expression, may provide a more comprehensive prediction of ICI efficacy.⁵³

Despite these challenges, TILs hold promise as a predictive biomarker to ICI therapy, especially when combined with other biomarkers such as PD-L1 expression and TMB. Additional research is needed to develop more refined methods of evaluating TIL subsets, including single-cell RNA sequencing, to gain deeper insights into the functional roles of TILs in the immune response.⁵² Larger patient cohorts will be necessary to validate the prognostic value of TILs. Additionally, combining TILs with molecular and immunological data will likely lead to more effective and personalized immunotherapy strategies.⁵² In brief, both TMB and TILs represent promising biomarkers for predicting responses to ICIs, though they have limitations. Their combined use in addition to other biomarkers offers the potential for more personalized and effective cancer treatments.^51–53

ctDNA in ICI Therapy: Role and Predictive Value

ctDNA consists of DNA fragments released by TCs into the bloodstream. These fragments carry tumor-specific genetic and epigenetic changes, including mutations, copy number variations, and methylation changes. ctDNA is a specific and minimally invasive biomarker. It offers real-time insights into a tumor’s mutational profile and reflects the tumor’s biology. These characteristics make it a valuable tool in cancer diagnosis, management, and treatment, especially within the ICI therapy space. These characteristics make it a valuable tool in cancer diagnosis, management, and treatment, especially within the ICI therapy space.

In ICI therapy, ctDNA is used for dynamic monitoring of tumor progression and treatment response. While traditional tissue biopsies can be invasive and static, capturing tumor characteristics at only one point in time, ctDNA can reflect real-time tumor dynamics through a simple blood test, making it particularly valuable in advanced cancers where timely intervention is crucial. ctDNA is released into the bloodstream through apoptosis, necrosis, and active secretion by TCs. The presence of ctDNA corresponds to both primary and metastatic tumor activity and therefore provides a comprehensive overview of the overall tumor burden. Due to its short half-life, ranging from minutes to hours, ctDNA is a valuable tool for monitoring response to ICI therapy in real-time. Specifically, upon successful reactivation of the immune system in response to ICI therapy and subsequent successful elimination of TCs, ctDNA levels decrease signaling a positive treatment response. The clinical utility of ctDNA lies in its ability to track mutations and genetic alterations associated with the tumor. Thus, it enables the detection of early resistance mechanisms in ICI therapy and allows clinicians to adjust treatment strategies accordingly to prevent treatment failure.

One of the most crucial advantages of ctDNA is its capability to provide information on ICI treatment efficacy in real time. ctDNA changes can often precede those radiographically visualized or available by biopsy, allowing for intervention earlier. For example, a decrease in ctDNA levels within the first 4–6 weeks of ICI therapy has been associated with improved PFS and OS. Early reductions in ctDNA, due to its short half-life, serve as predictive markers for positive treatment response, allowing clinicians to adapt treatment strategies based on ctDNA dynamics.^56,57 ctDNA alone holds prognostic value, as elevated ctDNA levels are associated with worse outcomes, including reduced PFS and OS. In the postoperative setting, ctDNA serves as a marker for minimal residual disease (MRD). The presence of ctDNA after treatment indicates a higher risk of recurrence, identifying high-risk patients who may benefit from adjuvant ICI therapies. Persistent ctDNA levels during or after ICI treatment are associated with an increased likelihood of relapse, underscoring ctDNA’s role in risk stratification and personalized follow-up strategies.⁵⁶

Another benefit of using ctDNA is its ability to facilitate early detection of resistance mechanisms as resistance to ICI therapy remains a major clinical challenge. ctDNA analysis can detect emerging resistance mechanisms early by identifying new mutations that confer resistance to ICIs. For example, in lung cancer patients treated with EGFR inhibitors, resistance mutations like T790M were detected in ctDNA, allowing for early ­therapeutic adjustments, such as switching to alternative therapies or implementing combination regimens to overcome resistance.⁵⁶ Despite its effectiveness in monitoring advanced cancers, ctDNA has limitations in early-stage cancers, particularly those with smaller tumor burdens, where its detection sensitivity is reduced. Tumors <1 cm may not release sufficient ctDNA for detection. Whereas this limitation reduces ctDNA’s sensitivity for early cancer detection, it underlines its greater utility in metastatic disease, where the tumor burden is higher and ctDNA is more reliably detectable.⁵⁶

ctDNA’s dynamic recapitulation of a tumor’s mutational profile makes it a promising biomarker for predicting responses to ICI therapy. Studies have shown that patients who experience a significant reduction in ctDNA during treatment often have longer PFS and OS.⁵⁸ Furthermore, ctDNA analysis helps identify non- responders early during a therapeutic regimen, ­enabling timely adjustments to avoid unnecessary ­toxicities and costs. Even in cases with low PD-L1 expression levels, ctDNA reductions can still indicate a positive response to ICIs, highlighting its complementary role to other biomarkers.⁵⁷

ctDNA Technological Advancements and Associated Challenges

Recent technological advancements have significantly improved the utility of ctDNA as a predictive biomarker in cancer therapy, particularly within the ICI space. Next-generation sequencing (NGS) enhances ctDNA detection, even at low variant allele frequencies. By analyzing a broad spectrum of genetic alterations, NGS provides comprehensive tumor profiling, offering insights into tumor heterogeneity and mutational burden, which are critical for predicting responses to ICIs.⁵⁷ Dynamic monitoring through sequential ctDNA analysis allows clinicians to track the evolution of tumors in real time during the course of ICI therapy. This method helps distinguish true disease progression from pseudoprogression, a phenomenon where IC infiltration initially enlarges tumors on imaging.⁵⁷ Additionally, ctDNA can be combined with other biomarkers, such as circulating tumor cells (CTCs) and IC populations, to provide a more comprehensive view of the TME and immune response. This multiparametric approach enhances the predictive accuracy of ICI efficacy.⁵⁷

Additionally, digital droplet PCR (ddPCR) enables the quantification of ctDNA mutations. Thus, ddPCR is especially useful for monitoring MRD and detecting early recurrence. When combined with NGS, ddPCR further increases the sensitivity of detecting specific mutations.⁵⁶ Moreover, blood-based tumor mutation burden (bTMB) assays provide a non-invasive alternative to TMB testing. Given that high bTMB levels correlate with better responses to ICIs, bTMB assays offer a less invasive way to assess tumor mutation burden. Therefore, bTMB assays allow clinicians to monitor mutation burden over time and are particularly useful for patients who cannot undergo traditional tissue biopsies.⁵⁷ In short, ctDNA is a valuable tool for real-time monitoring and predicting ICI responses.

There are several limitations to the clinical use of ctDNA. One major challenge is its sensitivity and detection thresholds, particularly in tumors with a low mutation burden or minimal ctDNA release. For example, studies have shown that ctDNA is undetectable in the plasma of about 20% of metastatic cancers, which hampers effective monitoring of ICI efficacy in these cases.⁵⁹ Other challenges include tumor heterogeneity and the development of dynamic changes during tumor evolution. The spatial and temporal heterogeneity of tumors can lead to variations in ctDNA levels. This variability in ctDNA release makes it difficult to establish consistent biomarkers across different cancer types. Another significant challenge is the lack of standardization in ctDNA testing. Without standardized ctDNA threshold levels, integrating ctDNA testing into routine clinical practice is difficult. Variability in assays, sampling time points, and analysis methods further impede the widespread clinical application of ctDNA.⁵⁹

In summary, ctDNA offers significant promise as a real-time monitoring tool in ICI therapy, providing valuable insights into tumor dynamics, treatment response, and resistance mechanisms. However, challenges regarding its sensitivity, tumor heterogeneity, and standardization must be addressed to enable its broader clinical application. Overcoming these limitations is necessary for ctDNA to maximize its capabilities in guiding tailored ICI treatment strategies and improving patient outcomes.

Gene Expression Profiles in ICI Therapy: Insights and Future Directions

Gene expression profiles (GEPs) are valuable predictors of ICI response across various cancers. While examining the expression of genes related to immune activity, the TME, and other cellular processes, specific gene signatures have been identified that may predict which patients are more likely to benefit from ICI therapy. These insights offer new approaches for improving patient selection and enhancing the personalization of cancer treatments.

Gene expression signatures have demonstrated their potential to guide ICI therapy in several cancers. Specifically, in triple-negative breast cancer (TNBC), a 37-gene signature was developed to predict the pathological complete response to ICIs combined with chemotherapy. This GEP accurately identified responders and other molecular signatures like PD-1 and PD-L1 expression. It was also validated in independent TNBC patient populations, underscoring the importance of GEPs in predicting ICI response in TNBC.⁶⁰ Similarly, in melanoma, gene expression profiling identified a four-gene tumor immune-relevant signature—SEL1L3, HAPLN3, BST2, and IFITM1—that was associated with OS and performed better than other biomarkers in predicting response to CTLA4 antibody therapy.⁶⁰ In metastatic renal cell carcinoma (mRCC), 14 genes from whole-blood profiling accurately classified responders to ICIs. Lastly, in NSCLC, a two-gene signature comprising CAMP and IL17A was developed to predict prognosis in patients treated with ICIs. This signature demonstrated strong predictive ability. Immune infiltration analysis revealed significant differences in TME between high- and low-risk patients, further supporting its clinical utility.⁶¹ The above findings highlight the emerging role of gene expression signatures in optimizing ICI therapy across various cancer types.

Notable GEPs in Different Cancers

It has been demonstrated that several gene signatures can predict ICI responses:

Four-Gene GEP Signature in Melanoma: This specific signature, comprising SEL1L3, HAPLN3, BST2, and IFITM1, was significantly associated with IC infiltration and OS in melanoma patients. It outperformed known biomarkers in predicting response to anti-CTLA4 therapy, such as ipilimumab.⁶⁰ TNBC-ICI Signature:  A 37-gene expression profile (GEP) developed for triple-negative breast cancer (TNBC), known as TNBC-ICI, demonstrated strong performance in predicting response to immune checkpoint inhibitor (ICI) therapy. It outperformed other gene expression markers like PD-1 and PD-L1 expression, indicating its potential as a reliable predictive biomarker in TNBC patients.⁶⁰

NSCLC Immune-Related Gene Signature: In NSCLC, a gene signature consisting of CAMP and IL17A demonstrated superior performance in predicting patient prognosis undergoing ICI treatment. It also enabled the stratification of patients based on their immune microenvironment, which points to its potential as a biomarker for NSCLC patients receiving ICI therapy.⁶¹ Interferon Gamma Signature (IFN-γ): The IFN-γ signature showed a higher response rate and improved PFS in NSCLC patients treated with ICI therapy, regardless of PD-L1 expression. Tumor Inflammation Signature (TIS): TIS includes 18 genes related to inflammatory cells and pro-inflammatory cytokines. It allows discrimination of ICI response in various solid tumors and is therefore considered a robust predictive biomarker for immuno_therapy outcomes.

Antigen Presentation Machinery (APM) and Immunoproteasome Signatures: These signatures are associated with tumor immunogenicity and the APM. High expression of these genes correlates with better response rates and OS in NSCLC and melanoma patients undergoing ICI therapy. Transforming growth factor-β (TGF-β) Gene Signature: In urothelial carcinoma, increased levels of TGF-β expression in the TME have been linked to a worse outcome in response to PD-L1 inhibition. This implies that tumors with high TGF-β activity, induced by cancer- associated fibroblasts, could be less susceptible to PD-1 inhibition alone. For individuals who do not ­respond well to PD-1 inhibitors, combining TGF-β ­inhibitors with PD-1 blockade may overcome this ­resistance and improve the efficacy of ICI treatment.

Current Evidence, Predictive Value, and Future Directions for GEPs

GEPs are gaining traction as valuable tools in predicting ICI efficacy: PD-L1 Expression: PD-L1 expression assessed via IHC has been the first approved marker for use in clinical practice. High PD-L1 expression correlates with better responses to ICI treatments. However, variability across tumors and inconsistent predictive value across cancer types present challenges. Circulating sPD-L1 also shows potential as a biomarker, though results are conflicting.¹⁰ TMB: A higher TMB correlates with increased neoantigen formation and improved responses to ICIs. Studies have shown that patients with high TMB experience longer OS and PFS. Despite its promise, integrating TMB into clinical practice faces challenges, such as genome sequencing complexity and the need for uniform cutoff points.⁶²

Mismatch Repair Deficiency (MMRD) and MSI: MMRD and MSI-H tumors have a high mutational load, making them more immunogenic and responsive to ICIs. This correlation has led to the FDA’s approval of ICIs for MSI-H and MMRD solid tumors, although their occurrence is rare in some cancers like NSCLC.⁶² Predictive Gene Expression Signatures: A study involving metastatic melanoma patients treated with ICIs evaluated 28 predictive gene expression signatures. Signatures related to IFN-γ-responsive genes, T-cell markers, and chemokines in the tumor immune microenvironment were generally predictive of response to ICIs. Notably, these signatures had higher predictive values in on-treatment samples than pre-treatment samples.⁶³

Neutrophil-to-Lymphocyte Ratio (NLR): A retrospective analysis of 132 mRCC patients treated with ICI-based therapy showed that a dynamic change in NLR was a significant predictive and prognostic biomarker. Monitoring changes in NLR during therapy provided essential predictive and prognostic information beyond baseline risk assessments.¹⁰ TME: Gene expression-based signatures developed from IFN-γ-responsive genes and T-cell markers in the TME were predictive of ICI responders. IC infiltration, particularly with cytotoxic T-cells, was associated with positive responses to ICIs, while tumors of non-responders were enriched with stromal-related cell types, indicating the importance of TME in predicting ICI efficacy.⁶⁴

GEPs offer significant potential for advancing personalized immunotherapy. The higher predictive values of gene signatures in on-treatment samples suggest that ICIs induce immune cellular activation and recruitment to the TME. This dynamic change in immune signatures could be more predictive of ICI benefits than baseline gene expression profiling. On-treatment biomarkers, such as CTCs and gene expression  signatures, may offer insights into early responses to ICIs and aid in identifying patients with primary resistance.⁶⁵ Future approaches are likely to integrate multiple biomarkers, including GEPs, TMB, PD-L1 expression, and other immune-related factors, to enhance predictive accuracy for ICI response.

This multifactorial strategy, combined with omics techniques and artificial intelligence (AI), could lead to more precise tailored treatment approaches.¹⁰ Use of GEPs in combination with other biomarkers may help tailor immunotherapy to individual patients, thereby improving outcomes and reducing unnecessary treatment.⁶³ However, intratumor heterogeneity (ITH) remains a challenge, and further studies with larger sample sizes are needed.

NLR as a Prognostic Biomarker in ICI Treatment: Mechanistic Insights and Clinical Evidence

The NLR has gained attention as a prognostic biomarker for ICI treatment. Studies have shown its potential in predicting treatment outcomes in advanced cancers, including NSCLC. Research from the Ohio State University Comprehensive Cancer Center demonstrated that NLR values at baseline and during treatment are significant predictors of OS in patients treated with ICIs. Patients with NLR values below 5 at baseline and upon treatment had notably longer OS. Interestingly, a moderate decrease in NLR during treatment correlated with the longest survival, while significant increases or decreases in NLR were linked to poorer survival outcomes. This non-linear relationship between changes in NLR and patient outcomes underscores its prognostic value.⁶³

Moreover, in NSCLC patients treated with ICIs, a lower pre-treatment NLR was associated with durable clinical benefit (DCB). Among patients with genetic alterations in genes such as EGFR, ALK, or ROS1, a high NLR (≥5.9) corresponded with significantly lower median survival from the start of ICI treatment. This positions NLR as a readily available clinical marker for predicting ICI response, particularly in NSCLC patients with specific molecular alterations.⁶⁶ NLR reflects the balance between pro-tumor inflammation (neutrophils) and the anti-tumor immune ­response (lymphocytes). A high NLR generally indicates a pro-tumor inflammatory state and a weakened immune system. Neutrophils promote tumor progression by secreting factors that enhance tumor growth and suppress T-cell activity, whereas lymphocytes play a central role in anti-tumor immunity. Thus, an elevated NLR may signal an impaired immune response, leading to poorer treatment outcomes.¹⁰

In advanced cancer patients, a moderate decrease in NLR during ICI treatment was associated with better survival outcomes, while extreme changes (either a significant decrease or an increase in NLR) were linked to worse survival.¹⁰ Additionally, in NSCLC, patients with lower pre-treatment NLRs and without specific molecular alterations (e.g., EGFR, ALK, and ROS1) had better clinical outcomes, reinforcing NLR’s potential as a predictive biomarker for ICI response.⁶⁶

Practical Applications and Limitations of NLR in Clinical Settings

Because NLR can be derived from routine blood tests, it can be conveniently used in clinical practice. Studies show that monitoring early changes in NLR during ICI treatment can offer important prognostic insights, providing clinicians with a direct method to assess treatment response.⁶⁷ While NLR is practical, it has limited specificity and sensitivity. In NSCLC patients, NLR was identified as one of the pre-treatment markers associated with ICI response, in combination with hemoglobin levels and performance status. Although NLR was associated with patient outcomes in NSCLC, a multivariable analysis indicated that hemoglobin levels and performance status were more significant predictors of response to ICI therapy, disease control rate, PFS, and OS.⁶⁸ Additionally, NLR may be influenced by the underlying inflammatory conditions or other factors that affect neutrophil and lymphocyte counts, thus reducing its predictive accuracy. Therefore, NLR in combination with additional biomarkers provides a more comprehensive evaluation of ICI response.⁶⁷

In short, the NLR is a practical, cost-effective, and accessible biomarker that provides valuable prognostic insights for ICI treatment, especially in advanced cancers and NSCLC. However, due to its limitations in specificity and sensitivity, NLR is most effective when combined with other biomarkers. NLR reflects the balance between inflammation and immune response, and its dynamic changes during treatment can offer important insights into patient outcomes. Therefore, integrating NLR with other immune-related markers and clinical parameters can further enhance its predictive value.

Conclusion on the Role of Emerging Biomarkers in ICI Therapy

Incorporating emerging biomarkers such as TILs, ctDNA, GEPs, and NLR into ICI therapy is a significant advancement in precision immune-oncology. While each biomarker presents its strengths and limitations, their combined use offers improved predictive accuracy, personalized therapeutic strategies, and better monitoring of resistance mechanisms. Further validation will be essential to maximize their clinical utility and ensure their effectiveness in routine practice.

Variability in Biomarker Expression: ITH and Inter-tumor Heterogeneity and Impact on Biomarker Reliability and Consistency

ITH: ITH is defined as multiple genetically distinct cell populations within a single tumor and arises due to genetic mutations, epigenetic changes, and varying microenvironments. ITH contributes significantly to the development of resistance to ICIs. During the evolution of a tumor, different regions may acquire unique genetic alterations, creating diverse subclones within the same tumor. As a result, tumors with high heterogeneity may exhibit varying responses to treatment across their different subclones. This, in turn, can lead to an initial positive response followed by resistance, as resistant subclones proliferate, leading to inconsistent treatment outcomes. In NSCLC patients, higher levels of ITH have been associated with poorer prognoses and reduced responses to ICIs, including decreased DCB, objective response rate, and PFS. This implies that tumors with higher heterogeneity contain subclonal populations capable of evading immune surveillance and resisting therapy.69 High ITH can lead to inconsistent treatment responses, as different subclones may have varying sensitivities to ICIs. However, ITH can function as an independent predictor of ICI response. Even tumors with high TMB may respond poorly if they contain high ITH. This suggests that while TMB is a useful biomarker as it measures the total num ber of mutations, it may not fully recapitulate a tumor’s evolution and its subclonal diversity. Evaluation of ITH in combination with TMB provides a more comprehensive understanding of the probability of the ICI response.

Inter-tumor Heterogeneity: Inter-tumor heterogeneity is the genetic and phenotypic diversity observed among tumors from different patients or among multiple tumors within the same patient. It is influenced by factors such as tumor origin, genetic background, environmental exposure, and immune system interactions and requires a tailored approach to treatment. The variability in biomarker expression across different tumors affects the predictive power of biomarkers. One such example is TMB. Although TMB is often used to predict ICI response, its effectiveness varies across cancers, which poses limitations in the establishment of universal biomarker thresholds. A case in point is cancers with low TMB that may still respond to ICIs due to other factors like immune infiltration. This means that biomarker expression must be evaluated within the context of the specific tumor type and the individual characteristics of each patient.

Impact on Biomarker Reliability and Consistency, Challenges, and Solutions

Both ITH and inter-tumor heterogeneity complicate the clinical application of biomarkers, such as TMB. Blood-based biomarkers, such as ctDNA and blood-based intra-tumor heterogeneity (bITH), are being considered as solutions to the challenges posed by traditional tissue biopsies. These biomarkers offer a more dynamic and comprehensive overview of the tumor’s genetic profile at a specific point in time. bITH, for instance, determines the genetic diversity of ctDNA and is a more reliable and consistent predictive biomarker for treatment efficacy.⁷⁰ Due to the complexity introduced by heterogeneity, a single biomarker may not accurately predict ICI response. A combination of biomarkers—such as TMB, ITH, and molecular or immunological markers—should be evaluated to provide a holistic view of the tumor’s characteristics. This multi-biomarker approach can enhance precision in patient stratification and guide personalized treatment decisions.

Variability in biomarker expression, such as PD-L1 and TMB, poses challenges for their standardized use for predicting ICI efficacy. Different methodologies, such as WES and panel-based assays, yield variable results, which further complicates the use of these biomarkers. However, emerging technologies, like single-cell sequencing, offer the potential to evaluate ITH more deeply. Meanwhile, ctDNA analysis offers a snapshot of a tumor’s overall genomic profile, though its application in capturing dynamic ITH remains limited. ITH and inter-tumor heterogeneity significantly impact the reliability and consistency of biomarkers used to predict ICI efficacy. Because of the existence of diverse subclonal populations as a result of ITH, the utilization of biomarkers like TMB is not straightforward. Similarly, inter-tumor heterogeneity adds complexity across different cancer types. Improving biomarker predictive accuracy requires a comprehensive approach that considers the tumor’s genetic and immunological context. This includes exploring blood-based metrics like bITH and combining multiple biomarkers to better capture the complexity of tumor biology.

Differences in Testing Methods and Dynamic Changes in Biomarkers for ICI Efficacy

The methods used to assess biomarkers for ICI efficacy affect the probability of predicting patient responses to these therapies. However, variability in these testing methods exists. When coupled with evolving biomarker profiles during treatment, their predictive power and clinical utility are compromised. PD-L1 expression, TMB, and ctDNA are central to guiding immunotherapy treatment strategies.

PD-L1 Expression Assessment

Various IHC assays, such as 28-8, SP263, and 22C3, utilized to measure PD-L1 expression, have demonstrated highly consistent results. However, the SP142 assay, a companion diagnostic for atezolizumab, can often detect lower PD-L1 expression levels, which may lead to false negatives. Therefore, whether PD-L1 can be considered a reliable biomarker is dependent on the assay utilized. The variability in PD-L1 expression patterns in TCs and ICs is another parameter that affects PD-L1’s reliability as a predictive marker. Moreover, the lack of standardized criteria for the determination of PD-L1 expression levels may lead to inconsistent results across different studies. Additionally, PD-L1 expression can vary across different tumor regions and may change over time. While some studies report high concordance between primary and metastatic tumors, others have shown discrepancies between the two. This spatial and temporal heterogeneity in PD-L1 expression levels makes it cumbersome to use PD-L1 consistently as a predictor of ICI response. Taken together, the above factors further complicate the utilization of PD-L1 expression as a biomarker for ICI efficacy.

TMB Challenges

WES or panel-based assays are often used to quantify TMB. Differences between these methodologies in terms of sensitivity and specificity lead to ­inconsistent TMB results. The lack of uniformity across assessment methods complicates TMB’s use as a reliable ­biomarker. Furthermore, the absence of a standardized cutoff value to define what constitutes “high” TMB creates additional challenges. Without an established standardized threshold, it becomes difficult to utilize TMB consistently as a predictive biomarker across various clinical settings and cancer types.

ctDNA Pitfalls

ctDNA assays—as blood-based methods—are non-invasive and can capture a tumor’s genomic profile. These tests are valuable for real-time monitoring but may not fully capture ITH. This limits ctDNA’s ability to predict treatment responses.

Response Criteria Challenges

Traditional response criteria, such as RECIST 1.1, often do not capture the atypical response patterns associated with ICIs, including phenomena like pseudo-progression. To address these limitations, modified response criteria have been developed, such as irRECIST and iRECIST. These new refined criteria account for the delayed or unusual response patterns frequently observed during ICI treatment, providing a more accurate assessment of therapeutic efficacy.

Implications for Predictive Accuracy and Patient Stratification

The variability in biomarker testing methods, especially for PD-L1 and TMB, affects the accuracy of predicting ICI responses. Standardization of these assays is essential to improve their predictive reliability. Moreover, variability in biomarker expression and testing methods complicates patient stratification, making it difficult to accurately identify which patients will benefit most from ICIs. One solution to the above limitations is a multi-biomarker approach which can enhance patient stratification and lead to more tailored treatments. Given the dynamic nature of biomarkers and variability among testing methods, there is an obvious need for standardized protocols in sample collection, processing, and analysis. Establishing clear guidelines for sample collection, processing, and analysis is critical for consistency and reliability. Additionally, utilization of quality control measures can help minimize variability and enhance the predictive accuracy of ICI efficacy.

Dynamic Nature of Biomarkers and the Need for Standardized Protocols in ICI Treatment

Biomarkers such as PD-L1, TMB, and ctDNA are essential in helping predict responses to ICI therapy. However, these biomarkers not only show variability at baseline levels but also undergo dynamic changes during treatment. Naturally, monitoring these real-time changes can optimize treatment decisions. During ICI treatment, several dynamic biomarkers have emerged as predictive indicators of efficacy. These biomarkers evolve throughout the treatment process, providing critical insights into patient responses.

Alpha-fetoprotein (AFP) and Related Indicators: In hepatocellular carcinoma (HCC), a reduction in AFP levels during ICI treatment has been associated with increased treatment efficacy. Specifically, a reduction >50% in AFP levels has been shown to correlate with improved OS and PFS. A 75% reduction in AFP within 6 weeks can effectively distinguish between responders and non-responders.^61,71

bITH: bITH is a novel marker used to predict ICI response. In NSCLC patients treated with ICIs plus chemotherapy, bITH scores increase post treatment and are associated with shorter PFS and poorer outcomes. The latter implicates bITH’s potential as a prognostic marker for disease progression.⁷⁰

NLR: Increases in NLR during treatment have been associated with poor patient outcomes. Patients with baseline and on-treatment NLR values below five tend to show better survival, while a 10% increase in NLR by the 4th week indicates worse OS and PFS, making NLR a dynamic marker of the host immune response.⁶⁷

Monocyte Index: The monocyte index is calculated by dividing classical monocytes by PD-L1+ monocytes and is an independent risk factor for PFS and OS. It is reflective of the adaptation during treatment.16 In a study of HCC patients with DCB, a higher ratio of classical monocytes in peripheral blood was observed on day 7 as compared to day 0.

Serum Protein Markers: Elevated levels of serum protein markers such as SLFN11 have been associated with better ICI responses, particularly in HCC. These markers offer insights into patient stratification and may enable the optimization of treatment plans.

CTCs and ctDNA: Early changes in ctDNA levels have been correlated with improved OS across various cancer types, independent of PD-L1 and TMB status. ctDNA can detect MRD and inform decisions, while CTCs provide complementary non-invasive biomarker information.

Liquid biopsy is a non-invasive and real-time technique for monitoring biomarkers, such as ctDNA and NLR. Thus, it allows us to gain valuable insights into tumor dynamics and immune responses without the need for repeated tissue biopsies.⁵² This feature is particularly useful for tracking disease progression and evaluating treatment response.⁷² Technological advancements, including NGS73 and ddPCR74, enhance the detection of low- frequency mutations in ctDNA, allowing for an in-depth understanding of tumor evolution during therapy.⁷⁵ Despite the clinical advantages, the cost of repeated testing and the requirement for advanced health care pose significant challenges.^76,77 Balancing the cost and benefits these methods offer is essential for integrating dynamic biomarker monitoring into routine clinical practice.⁷⁸ These biomarkers, while individually distinct, ­collectively underscore the importance of dynamic monitoring during ICI treatment. Their integration offers a more comprehensive view of patient responses, enabling real-time adjustments to optimize therapeutic outcomes.⁷⁹

The Importance of Standardized Protocols and Quality Assurance in Biomarker Testing for ICI Therapy

The absence of standardized protocols for biomarkers such as PD-L1, TMB, and ctDNA hinders their clinical use. Inconsistencies in results arise from variability in sample collection, processing, and assay techniques.9,80 For example, PD-L1 testing includes IHC assays, such as 28-8, SP263, and 22C3, which often demonstrate high concordance; however, SP142, employed for atezolizumab, exhibits reduced sensitivity, potentially leading to false negatives. Likewise, TMB quantification differs based on the utilization of WES or panel-based assays, with no universally recognized threshold for categorizing “high” mutational burden.¹⁶ The evaluation of PD-L1 expression on both TCs and ICs complicates interpretation due to different staining cutoffs as well as the spatial and temporal variability of PD-L1 expression, particularly between primary and metastatic sites.^9,16 To mitigate these issues, standardization of protocols for sample collection, processing, and analysis is essential. Establishing standardized protocols and rigorous quality control methods may ensure more dependable and comparable outcomes across various clinical environments.²⁵

Dynamic biomarker changes during ICI treatment offer valuable insights into disease progression and patient response.^16,25 However, their capability as predictive biomarkers is obscured by the variability in testing methodologies. Standardizing biomarker assays, refining response criteria, and ensuring rigorous quality control are essential to improving the clinical utility of biomarkers such as PD-L1, TMB, and ctDNA.¹⁶ Overcoming these challenges will enable better patient stratification and personalized immunotherapy strategies, ultimately enhancing treatment outcomes.^17,25

Cost and Accessibility: Economic Considerations in Biomarker Testing

The development, implementation, and administration of ICI medicines incur significant expenses. Consequently, we must evaluate the cost implications of biomarker testing for ICI therapy. The overall financial burden encompasses the treatment expenses, the costs associated with managing side effects, and those for biomarker testing to ensure patient stratification. Immunotherapy is notoriously expensive, particularly for prolonged treatments aimed at attaining durable responses. These medicines frequently include the additional financial burden of managing side effects. Patients encountering complications from immunotherapies may pay costs up to fourfold greater than those without such complications, with expenses varying from $17,570 to $30,534 for issues including respiratory, hematologic, and gastrointestinal disorders. Furthermore, addressing these adverse effects may increase the total treatment expenditure by an additional $21,041−$31,179.⁸⁰ These numbers demonstrate the significant cost burden that immunotherapy imposes on both patients and health-care systems.

The cost-effectiveness of biomarker testing for immunotherapy significantly differs depending on the type of cancer and the treatment administered. Pembrolizumab, when utilized as a first- line treatment with PD-L1 biomarker evaluation, has been shown to satisfy the $100,000 per quality-adjusted life year threshold in NSCLC, rendering it a cost-effective choice. Conversely, in malignancies such as bladder, cervical, and TNBCs, these treatments frequently fail to achieve comparable criteria.81 This highlights the necessity for meticulous financial analysis when evaluating biomarker testing and treatment strategies for various cancer types. To alleviate these economic challenges, it is essential to create cost-efficient biomarker approaches. The use of reliable biomarkers that can precisely forecast patient responses to immunotherapy facilitates the optimization of treatment selection, improves treatment outcomes, and mitigates the risk of expenditures on ineffective therapies. In conclusion, cost-effective biomarker strategies may substantially alleviate the financial load on health-care systems and patients.

Personalized Treatment Approaches: Using Biomarkers to Tailor ICI Therapy to Individual Patients

Personalized immunotherapy tailors ICI therapy to individual patients based on specific biomarkers, in combination with genomic and molecular profiling. This methodology is especially significant in NSCLC, as diagnostic approaches enable the identification of predictive biomarkers and inform treatment decisions.⁸² In clinical practice, personalized treatments guided by these biomarkers have shown efficacy, particularly in NSCLC. For example, anti-PD-1 antibodies such as nivolumab and pembrolizumab have demonstrated efficacy in patients with PD-L1-positive tumors. In a case study, a patient with NSCLC had considerable tumor reduction and a prolonged response after receiving anti-PD-1 antibody therapy.⁸² Overall, personalized ICI therapy, guided by biomarker profiling, can improve treatment outcomes by providing targeted therapeutic options, increasing patient survival rates, and optimizing the efficacy of immunotherapy.

Completed and Active Clinical Trials: Key Points and Biomarker Exploration

Numerous clinical trials have been completed,⁸³ ­focusing on enhancing the efficacy of ICIs. Table 1 ­outlines completed clinical trials from ClinicalTrials.gov that investigated the role of biomarkers in enhancing ICI efficacy. These trials encompass various approaches to improve patient outcomes through biomarkers. Some trials focused on predictive biomarkers for immunotherapy responses and adverse effects, such as evaluating TMB in NSCLC and plasma markers in HCC. Others aimed to discover novel biomarkers, including sphingolipids in melanoma and radiomic analysis in solid tumors. Additionally, certain trials assessed non-invasive methods, like ctDNA kinetics, to optimize monitoring.

Table 1: Completed clinical trials investigating biomarkers for immune checkpoint inhibitor (ICI) therapy.
NCT Number	Classification	Brief Summary
NCT01621490	Predictive Biomarkers	The study evaluates biomarkers in melanoma patients treated with nivolumab or nivolumab plus ipilimumab.
NCT03486119	Predictive Biomarkers	The trial tests if myeloid-derived suppressor cell ratio can predict nivolumab response in NSCLC patients.
NCT03627026	Predictive Biomarkers	This trial identifies biomarkers of response to immune checkpoint inhibitors in melanoma patients.
NCT05326906	Predictive Biomarkers	The trial aims to identify biomarkers predictive of immune-related hepatitis in lung cancer patients.
NCT03664024	Predictive Biomarkers	The study evaluates tumor mutation burden in predicting response to pembrolizumab and chemotherapy in NSCLC patients.
NCT06408753	Predictive Biomarkers	The trial investigates biomarkers predictive of outcome and toxicity in hepatocellular carcinoma treated with immunotherapy and SBRT.
NCT04189679	Predictive Biomarkers	This trial examines the potential of metabolomics profiles as biomarkers for NSCLC response to immune checkpoint inhibitors.
NCT03658460	Predictive Biomarkers/ Biomarker Discovery	The study focuses on finding new biomarkers to predict lung cancer patients’ response to immunotherapy.
NCT04079283	Biomarker Discovery	The study assesses CT radiomic analysis to predict immunotherapy outcomes in solid tumors.
NCT03089606	Non-Invasive Biomarker Strategies	This trial assesses 11C-methyl-L-tryptophan PET imaging for predicting pembrolizumab response in melanoma.
NCT04606940	Non-Invasive Biomarker Strategies	This study evaluates ctDNA kinetics to optimize liquid biopsy timing for head and neck cancer patients on immunotherapy.
NCT05662527	Neoadjuvant Biomarker Study	The study evaluates pembrolizumab as neoadjuvant treatment in early-stage colon cancer with deficient mismatch repair.
NCT06169904	Combination Therapy with Biomarker Focus	This study explores integrating B7 family molecules with tumor mutation burden to improve immunotherapy response prediction in UC.

Many of these studies investigated predictive biomarkers that can inform response to treatment and identify potential adverse effects. For example, NCT03664024 and NCT03486119 trials evaluated TMB and myeloid-derived suppressor cell ratios, respectively, to predict pembrolizumab and nivolumab responses in NSCLC. Similarly, metabolomic profiles and sphingolipid biomarkers were explored in NCT04189679 and NCT03627026 trials to refine response prediction in lung cancer and melanoma. Beyond predictive biomarkers, several studies aim to discover new biomarkers that could facilitate patient stratification. This includes NCT03658460, which focuses on biomarkers for lung cancer immunotherapy, and NCT04079283, which uses computed tomography radiomic analysis to predict immunotherapy outcomes in solid tumors. Non-invasive strategies are also represented, as seen in NCT03089606 and NCT04606940, which explore 11C-methyl-L-tryptophan positron emission tomography (PET) imaging and ctDNA kinetics to optimize monitoring in melanoma and head-and-neck cancers.

In addition, other trials investigated biomarkers to predict and manage irAEs. Specifically, NCT05662527 examined the safety and efficacy of pembrolizumab in mismatch repair deficient early-stage colon cancer, with a focus on understanding and managing irAEs. Similarly, NCT05326906 aimed to identify biomarkers predictive of immune- related hepatitis in lung cancer patients receiving ICIs. Other studies, such as NCT06169904, investigated combination therapies that integrate B7 family molecules with TMB to improve response prediction in urothelial carcinoma. Collectively, these trials underscore the role of biomarkers in enhancing ICI-based cancer therapies. By focusing on predictive markers for treatment response and adverse effects, as well as non-invasive techniques such as ctDNA kinetics and PET imaging, these studies intend to refine patient stratification and optimize therapeutic monitoring. Additionally, the trials highlight ongoing efforts to discover new biomarkers and implement biomarker-driven strategies for neoadjuvant and combination therapies in specific cancer types, such as NSCLC, melanoma, and urothelial carcinoma. Together, these studies contribute to more precise and personalized ICI therapies.

Ongoing trials focus on improving patient stratification and selection, optimizing therapeutic responses and combination therapies, and managing irAEs to ultimately personalize treatment approaches. By exploring various strategies to enhance the efficacy of ICIs, these trials aim to tailor ICI therapy more precisely to individual patients. Table 2 summarizes active and recruiting clinical trials investigating predictive biomarkers and strategies in ICI therapy.

Table 2: Summary of active and recruiting Clinical Trials focused on investigating predictive biomarkers and strategies in ICI therapy.
NCT Number	Classification	Brief Summary
NCT04138628	Biomarker Discovery	Identifying indications for initiating immunotherapy in metastatic bladder cancer using molecular techniques.
NCT06364917	Biomarker Discovery	Comparing two immune therapy treatments for NSCLC without PD-L1 expression.
NCT05862259	Biomarker Discovery	Data collection to establish and verify immunotherapy predictive models in malignant tumors.
NCT06321640	Immunotherapy Resistance and Toxicity	The study addresses challenges in targeted therapies and ICIs, aiming for biomarker-driven treatment in oncology.
NCT03409016	Immunotherapy Resistance and Toxicity	Pilot study evaluating biomarkers predictive of immune-related adverse events in immunotherapy.
NCT06536257	Immunotherapy Resistance and Toxicity	Study testing biomarker models of immunotherapy resistance in melanoma and other solid tumors.
NCT04913311	Immunotherapy Resistance and Toxicity	Study on lung inflammation and immunotherapy effects in non-small cell lung cancer.
NCT05139706	Immunotherapy Resistance and Toxicity	Studying predictive biomarkers of immune-related adverse events in cancer patients treated with ICIs.
NCT05173298	Multiomics and Combination Therapies	Exploring multiomics data in patients with advanced hepatocellular carcinoma treated with atezolizumab and bevacizumab.
NCT05197504	Multiomics and Combination Therapies	Investigating transcriptome analysis of advanced HCC for precise classification and clinical significance.
NCT04892849	Multiomics and Combination Therapies	Evaluating the combination of radiotherapy and PD-1/PD-L1 inhibitors in relapsed or metastatic cancers.
NCT06152523	Multiomics and Combination Therapies	This study combines multiple immunotherapies for MSI/dMMR cancers to improve treatment outcomes.
NCT05725915	Non-Invasive Biomarker Techniques	Aiming to establish a multi-parameter model to predict the efficacy of ICI combined with chemotherapy in advanced NSCLC.
NCT05742269	Non-Invasive Biomarker Techniques	Refining therapy selection for breast cancer patients using PET/CT imaging with 89Zr-atezolizumab.
NCT02644369	Non-Invasive Biomarker Techniques	Evaluating gene changes and immune biomarkers in patients with solid tumors treated with pembrolizumab.
NCT04253080	Predictive Biomarkers	Quantifying immune cells expressing IL4I1 enzyme in melanoma patients to identify predictive biomarkers.
NCT04146064	Predictive Biomarkers	Study evaluating breathprint analysis to predict benefit from immunotherapy in various cancers.
NCT06287320	Predictive Biomarkers	Prospective cohort study evaluating lymphocyte subsets as biomarkers in advanced NSCLC.
NCT05810402	Predictive Biomarkers	Identifying a predictive biomarker in advanced HCC using a liquid biopsy approach.
NCT04009967	Predictive Biomarkers	Identifying biomarkers predictive of response to pembrolizumab in high-risk prostate cancer.
NCT05878977	Predictive Biomarkers	Study on microbiome and exosomal mRNA in melanoma patients treated with ICIs.
NCT05285579	Predictive Biomarkers	Prospective study identifying biomarkers for progression-free survival in renal cell carcinoma.
NCT05916755	Predictive Biomarkers	Study analyzing predictive biomarkers in TNBC treated with neoadjuvant chemotherapy and immune checkpoint inhibitors.

Some trials concentrate on predictive biomarkers to identify patients who are most likely to benefit from ICIs. For example, NCT05916755 explores PFS in TNBC using genomics and proteomics to establish predictive biomarkers for neoadjuvant chemotherapy with or without ICIs. In RCC, NCT05285579 assesses angiogenesis and immune response biomarkers to predict ICI efficacy in metastatic cases. Additionally, NCT05878977 investigates how gastrointestinal microbiome composition and exosomal mRNA expression may serve as predictive biomarkers for ICI response in advanced melanoma. Techniques like breathprint analysis (NCT04146064) and liquid biopsy (NCT05810402) also aim to predict treatment responses across different cancer types, such as melanoma and HCC.

Several trials in Table 2 emphasize immunotherapy resistance and toxicity, with NCT05139706 and NCT06536257 assessing biomarkers linked to irAEs to improve patient safety. Similarly, NCT03409016 focuses on identifying biomarkers predictive of irAEs, helping mitigate the risk of adverse side effects associated with ICI therapies. These trials address the risks associated with ICI treatments, aiming to enhance patient safety and minimize side effects. Importantly, there is a strong emphasis on multi-omics and combination therapies. Trials such as NCT05173298 and NCT06152523 utilize advanced molecular profiling, integrating treatment approaches for MSI/ DNA mismatch repair deficiency (dMMR) cancers and HCC to improve precision and personalization in cancer therapy. Biomarker discovery and non-invasive techniques are also central to these trials. Non-invasive methods, including PET/CT imaging (NCT05742269) and molecular techniques (NCT04138628), are being developed to support therapy selection in cancers like breast and bladder. Together, the trials outlined in Table 2 contribute to refining and optimizing ICI therapy strategies, promoting a more personalized approach to immunotherapy across a diverse range of cancers.

Future Directions in Biomarker Research for ICI Therapy

Soluble Immune Checkpoint Proteins

Soluble forms of immune checkpoint proteins, including PD-L1, PD-1, and CTLA-4, are emerging as predictive biomarkers. High plasma levels of these soluble molecules were associated with hyperactivation or terminal exhaustion of T-lymphocytes, as represented by Hayashi et al.^84,85 These soluble molecules may be used in combination with conventional PD-L1 IHC to serve as prognostic biomarkers. However, further investigations are needed to elucidate the heterogeneity observed across various patient subpopulations.

Imaging Biomarkers

Imaging biomarkers are a non-invasive, real-time means of assessing the immunological attributes of malignancies. Imaging techniques, including PET and single-photon emission computed tomography, are being tested for quantitation of IC populations, particularly CD8+ T-cells, within the TME.86 Current active trials in imaging tracers are assessing the immune state of both the tumor and the host before and after ICI therapy.

TMB

TMB is positively correlated to improved responses to ICIs. Nevertheless, there are a variety of challenges that arise with TMB, including inter-tumor type variability and undefined cutoff levels. The most recent studies focus on combining TMB with additional biomarkers, such as PD-L1 expression, to further optimize patient stratification.

Expression of Human Leukocyte Antigen (HLA) Class I Molecules

HLA class I molecules activate cytotoxic T-lymphocytes. Consequently, CTLs detect and eliminate TCs. Tumors with high expression levels of HLA-I are associated with significant CD8+ T-cell infiltration and superior clinical outcomes to ICIs, whereas loss of HLA-I expression may lead to tumor immune escape. HLA-I status assessment may also enable the selection of patients likely to benefit from ICI therapy.

Genetic Markers and Alterations

Specific gene signatures, among which the IFN-γ signature is of note, have emerged as predictors of better responses to ICIs in different types of malignancies, including NSCLC. Elevation of IFN-γ reflects an active immune response and generally corresponds to better outcomes following ICI therapy. Moreover, genes such as KRAS and STK11/LKB1, are reported to confer resistance to ICIs. The latter highlights the predictive value of genetic profiling in the immunotherapeutic response.

Biomarkers for Combined Therapeutic Modalities

Combinations of ICIs and antiangiogenic therapy have shown potential benefits in HCC. Biomarkers predicting responses from various combinations are imperative for effective therapeutic strategies. Translational research using tumor tissue and blood specimens is essential for predictive immunological biomarkers associated with clinical outcomes. Future Directions and the Role of AI and Machine Learning in ICI Biomarker Discovery AI and Multi-Omics Approaches in Biomarker Discovery for ICI Therapy

Integration of multi-omics approaches—including genomics, proteomics, and transcriptomics— is expected to improve biomarker identification. Advanced technologies, such as single-cell sequencing and spatial transcriptomics, provide an enhanced understanding of the tumor immune microenvironment and enable more precise patient stratification. Furthermore, these technologies enable the identification of novel predictive biomarkers for ICI therapy with greater accuracy.⁸⁷ Secondly, AI and machine learning (ML) approaches are employed to analyze large datasets generated from these multi-omics studies. AI and ML can identify complex patterns, help uncover previously unrecognized biomarkers, and in turn promote more tailored approaches to cancer immunotherapy.⁶⁰ These methods must be validated in clinical trials before their routine utilization in clinical settings.

The involvement of AI and ML in biomarker identification for cancer immunotherapy has significantly grown in recent years, driven by the escalating complexity of clinical data and the necessity for accuracy in forecasting treatment responses. The principal AI and ML components examined in biomarker discovery encompass the application of AI for predicting immunotherapy responses, AI-facilitated immunoprofiling in ICI therapy, ML for biomarker identification, and the augmentation of radiomics via AI. AI methodologies, including ML and deep learning (DL), are employed to get significant insights from radiological and histological medical images to forecast therapeutic responses in malignancies, such as melanoma, NSCLC, and HCC. Two primary AI methodologies for predicting ICI responses are surrogate biomarker prediction and end-to-end prediction. The former AI model is designed to predict established biomarkers, such as MSI, TMB, TILs, or PD-L1 expression, whereas the latter directly predicts treatment outcomes, including OS or PFS, based on image data analysis.⁸⁸

AI has considerably improved the identification of predictive biomarkers for ICIs by providing insights into immune system interactions and drug response mechanisms. A study on HCC employed AI-driven immunoprofiling to identify specific IC markers, such as PD-1+ CD8 T-cells and PD-L1+ monocytes, which were associated with improved outcomes in patients receiving nivolumab, an anti-PD-1 antibody. This AI-driven methodology surpasses conventional molecular techniques, offering enhanced accuracy as well as prevision in immunoprofiling.⁸⁹ ML models, such as random forest classifiers and support vector machines, have been effectively utilized on complex datasets to identify key IC subsets indicative of ICI response. In a study of HCC patients, ML identified five IC populations, including PD-L1+ monocytes and CD8 T-cells, that correlated with favorable treatment responses. The incorporation of these immunological profiles into AI models yielded superior prediction performance, with area under the curve values between 0.8417 and 0.875.⁸⁹

AI in ICI Response Prediction and Emerging Challenges

AI has transformed radiomics by processing imaging data to uncover metrics associated with tumor biology and treatment efficacy. In cancer immunotherapy, AI has facilitated the creation of image-based biomarkers that predict treatment outcomes by examining attributes such as tumor morphology, texture, and cellular density through convolutional neural networks.^88,89 These non-invasive biomarkers can be tracked during treatment, offering a more thorough understanding of tumor dynamics compared to conventional biopsy methods. Although AI presents considerable potential in biomarker discovery, certain challenges require resolution. A main limitation is overfitting, wherein AI models excel on training data yet fail when analyzing novel, independent datasets. This issue is especially prevalent in clinical research with limited sample numbers. An additional issue is the interpretability of AI models, especially with complex DL networks, which might hinder the decision-making process.^88,89

While AI offers significant promise in biomarker discovery, several challenges must be addressed. One key limitation is overfitting, where AI models perform well on training data but fail to generalize to new, independent datasets. This issue is particularly prevalent in clinical research involving small sample sizes. Another challenge is the interpretability of AI models, particularly with complex DL networks, which can impede the decision-making process.^88,89 The role of AI in biomarker development is anticipated to grow with the emergence of more sophisticated computational techniques. The integration of AI with large-scale datasets, including genomics, proteomics, and metabolomics, may facilitate the creation of more comprehensive biomarker panels. Furthermore, integrating AI with real-time patient data during immunotherapy may enable dynamic biomarker evaluation, resulting in tailored and adaptable treatment approaches. In summary, AI and ML enable researchers to uncover previously unidentified complicated trends in tumor biology and immune responses, thus enhancing the precision of predictions regarding ICI therapy efficacy. As these AI-driven methods develop, they can transform tailored cancer therapies and enhance patient outcomes across numerous malignancies.

Final Thoughts and Future Perspectives on Predictive Biomarkers in ICI Therapy

This review analyzes both established and emerging biomarkers used to predict the effectiveness of ICI therapy. Key biomarkers, including PD-L1 expression, TMB, and TILs, have been extensively validated across multiple malignancies, including melanoma, NSCLC, and RCC. These biomarkers can identify patients who are most likely to benefit from ICIs, facilitating more tailored and effective cancer therapies. Emerging biomarkers such as ctDNA and GEPs may enhance the identification of responders to ICIs, provide real-time insights into tumor dynamics, and allow for immediate modifications to a therapeutic regimen. Novel strategies, such as metabolic signatures and blood-derived biomarkers like the NLR, are gaining acceptance as predictive biomarkers and for their capacity to measure immune function. These advancements illustrate that biomarkers have become essential for the use of ICI therapy.

Nonetheless, the validation and clinical application of predictive biomarkers for ICI therapy face substantial challenges. ITH and inter-tumor heterogeneity negatively impact the reliability of biomarkers such as PD-L1 and TMB, resulting in discrepancies in predicting treatment responses. The standardization of biomarker testing is problematic, given that different methodologies can produce inconsistent results, thereby restricting clinical utility. Moreover, alterations in biomarker levels during therapy due to tumor progression, coupled with the absence of comprehensive multi-biomarker panels, result in inaccurate patient classification. Future research opportunities involve tackling the problems by developing multiparametric biomarker strategies and integrating multiple markers, including PD-L1, TMB, TILs, and ctDNA, to improve predictive accuracy.

AI and ML can enhance the efficacy of predictive biomarkers and the personalization of ICI therapy. Moreover, liquid biopsies for non-invasive biomarker assessment provide a method for real-time monitoring and the execution of adaptive strategies throughout therapy. The discipline of ICI therapy has significantly revolutionized the immunotherapy space, yet further advancements in biomarker identification remain key. Creating biomarkers that can predict both treatment responses and resistance mechanisms will be essential for enhancing the application of ICIs in more types of malignancies. Personalized ICI therapy, driven by validated biomarkers, will result in more precise, effective, and individualized treatments that enhance efficacy while reducing unnecessary interventions and related side effects.

References

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Cite this article as:
Papageorgiou A. Biomarkers in Immune Checkpoint Inhibitor Cancer Treatment. Premier Journal of Science 2024:3;100023

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