Biomarkers in the era of cancer immunotherapy: zooming in from periphery to tumor microenvironment (2025)
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| Credit: Statista |
Since the early days of CIT, it was recognized that the benefit is driven by complicated biological mechanisms, which involves a cross-talk between the tumor, its microenvironment, and the overall immune system of the patient (2, 3). Studies aiming to understand and explore CIT mechanisms of action and pharmacodynamic and response prediction biomarkers have initially evaluated each component independently, but ultimately need to look at these questions in concert. Technological advancements in the field combined with the availability of better translational models and ever-expanding clinical datasets enabled basic, translational, and clinical researchers to have a deeper understanding of this complex interaction (4–8).
This combined Research Topic of Frontiers in Immunology and Frontiers in Oncology provides further insight into developments in the field.
Bladder cancer is one of the earliest indications that were shown to be sensitive to CIT. Analyzing genetic and genomic data combined with clinical information from multiple data sets, Xu et al. stratify bladder cancer patients into low- and high-risk groups using a fibroblast growth factor receptor 3 (FGFR3)-related gene signature score. The signature includes components of metabolism and immune signaling genes. Consequently, the low-risk patients with the lower score present with an abundance of immune cell enrichment and are more responsive to CIT, but are less sensitive to certain chemotherapy agents.
The immune landscape, and its prognostic significance, in head and neck squamous cell carcinoma (HNSCC) remains to be better characterized. Baysal et al. present a study combining CIBERSORT-based analyses of prognostic significance of immune cell types in a TCGA cohort, with IHC analyses of an additional cohort. Together, the study identifies novel prognostic associations of CIBER-sort-defined cell types, as well as distinct prognostic IHC-determined immune-regulatory proteins with targeting potential.
Lung cancer is recognized among the most sensitive indications to CIT. Zhao et al. identify a gene expression signature composed of lactic acid metabolism-related genes, which can differentiate lung cancer cell lines from normal bronchial epithelium cells. This signature shows an inverse correlation to survival from three studies, including in response to CIT, correlating positively with tumor mutation burden and mutation rate, but identifies patients with low signature scores and higher TMB as best responders. These data add to the rationale of evaluating lactic acid metabolism targeted CIT (9). Ji et al. explore the association of genomic alterations of development signaling pathway-related genes and response to CIT and identify SMO mutation as correlating with a response but also immune signatures not only in NSCLC cohorts but also in other cancer types.
PD-L1 expression continues to be analyzed regarding its usefulness as a prognostic and/or response-predictive biomarker for CIT. Khan et al. here contribute to this research area with a meta-analysis of publications on metastatic triple-negative breast cancer. The study identifies significant associations between PD-L1 expression and better objective response rate as well as overall survival. The findings emphasize the importance of PD-L1 as a response biomarker and suggest further analyses by including larger randomized studies with non-CIT-treated patients towards better discrimination between the prognostic and predictive nature of PD-L1.
In parallel to continued efforts to use “single/few-marker” approaches for biomarker discovery, there is an emerging field of multi-omics studies. These have the possibility to capture more complex, but also more informative, tumor biology. This approach is represented in the current volume by the study of Wong et al., in multiple myeloma in which patient samples from a phase 1 atezolizumab trial are analyzed. Although preliminary, this study illustrates some of the potential of this type of approach but also emphasizes the need for larger cohorts to obtain more conclusive results.
Combining data from peripheral blood immune cells and patient-matched tumor genetic markers, Dutta et al. explore the potential of predicting response to CIT in non-small cell lung cancer patients. The team identifies baseline memory CD4+ and CD8+ T-cell subsets in the peripheral blood and pathogenic or likely pathogenic mutation in the tumor to be differentially prevalent between responders and non-responders. If validated in a larger dataset, this will be a very interesting approach to identify patients who are more likely to benefit from CIT.
The abundance, maturity, and spatial distribution of tertiary lymphoid structures (TLS) from hematoxylin and eosin (H&E) staining and gene expression data are evaluated by Liang et al. as a prognostic biomarker for CIT response in laryngeal squamous cell carcinoma. The team finds that early non-follicular and follicle-like TLS are associated with immunosuppression and increased immune infiltration, respectively. The maturation of the TLS is associated with higher expression of XCL2, suggesting a cross-talk between immune cell activation and TLS maturation in laryngeal squamous cell carcinoma.
Dejardin et al. provide a comprehensive overview of statistical methods for analyzing early clinical trials in CIT, underscoring the vital role of biomarkers. They detail how biomarkers are used to understand the mechanism of action, optimize dosage, predict and manage adverse events, and identify patient subgroups for enrichment. This work serves as a single source for statistical models and principles for analyzing biomarker data, addressing challenges like high-dimensional data and correlating treatment-related changes with clinical outcomes to ensure robust and reproducible results. It also highlights the importance of validating biomarkers to establish their clinical utility.
Similarly, Qin et al. review biomarkers from diverse sources (tumor cells, microenvironment, liquid biopsy, gut microbiome, metabolites) and computational models (mechanistic and machine learning) for predicting the effectiveness of immune checkpoint inhibitor therapy in oncology. The article details detection methods, strengths, limitations, and applications, guiding researchers and clinicians in selecting appropriate tools for precision medicine.
Hou et al. reviewed the potential of circulating T cells as a promising biomarker for predicting and monitoring the effectiveness of anti-PD-(L)1 therapy in cancer patients. While traditional tumor-based biomarkers face limitations due to tumor heterogeneity, liquid biopsies, particularly analyzing different subsets of T cells from peripheral blood (memory, exhausted, effector T cells) offer a less invasive and potentially more representative approach. The authors discuss existing research on various T cell populations and their correlation with treatment outcome, highlighting both advantages and current limitations of using these biomarkers to inform therapy decisions.
Schlicher et al., extensively review the current status of small molecule inhibitors for CIT, focusing on a new class of drugs designed to overcome limitations of existing treatments. These molecules target intracellular negative regulators of anti-tumor immune responses, emphasizing mechanisms that either directly block suppressive feedback loops within immune cells or counteract immunosuppressive signals in the tumor microenvironment. Key targets covered include enzymes involved in T-cell receptor signaling (e.g. MAP4K1, DGKa/z, and CBL-B), phosphatases (e.g. PTPN2, PTPN22, and SHP-2), and modulators of adenosine and cGAS-STING pathways. The authors meticulously outline preclinical and early clinical trial data for numerous drug candidates, highlighting their potential in combination with existing immune checkpoint inhibitors and the ongoing efforts to identify predictive and pharmacodynamic biomarkers for patient selection and treatment efficacy.
Bargiela et al. explores how the factor inhibiting hypoxia-inducible factor (FIH), an enzyme that senses oxygen levels, regulates T cell function and their ability to fight tumors. The authors found that FIH’s effects on T cells, including their growth, differentiation, and cancer cell killing, depends on both hypoxia-inducible factor protein levels and oxygen concentration. Notably, removing FIH specifically in mouse T cells improved their effectiveness in CIT, suggesting that targeting this enzyme could be a promising strategy for treating cancer.
Papaevangelou et al. explored the synergistic effects of cytotopically modified interleukin-15, which allows IL-15 to anchor to cell membranes (cyto-IL-15), combined with the STING agonist ADU-S100, both administered directly into tumors. This combination effectively eliminated prostate tumors, prolonged survival, and conferred durable systemic immunity against tumor recurrence in a mouse model. The synergy was driven by activating both innate and adaptive immune responses, conferring resistance to subsequent tumor challenges.
Several studies in this Research Topic investigated biomarkers associated with resistance to checkpoint inhibitors and their potential utilization to enrich for responders or as candidates for standalone or combination therapies in CIT.
Nie et al. investigated Glycyl-tRNA synthetase 1 (GARS1) expression across multiple cancer indications, its prognostic value, and its relationship with the tumor immune microenvironment. GARS1 expression was found to be significantly upregulated in most indications (29 out of 33) compared to non-cancerous tissues and was associated with unfavorable survival. Through computational analyses and in vitro experiments on bladder cancer cells, the study identified GARS1 as a potential biomarker for predicting patient outcomes and informing therapeutic strategies effectiveness for GARS1-upregulated tumors.
Rodriguez et al. investigated a new strategy for overcoming resistance to anti-PD-1/CTLA-4 immunotherapy in lung cancer, a significant clinical challenge. While combination immunotherapy initially shows promise, tumors often develop acquired resistance, in part due to the accumulation of immunosuppressive cells, particularly Ly6C+ classical monocytes. The study demonstrates that targeting these Ly6C+ monocytes with an anti-Ly6C antibody can completely reverse this resistance, promoting the differentiation of these monocytes into anti-tumor dendritic cells and thereby reinvigorating the T-cell response for complete transplantable tumor eradication and control of autochthonous lung tumors growth.
Garman et al., performed comprehensive immunophenotyping of LAG-3 expression in tumor microenvironment (TME) and found that it is predominantly expressed by tumor-infiltrating CD8 memory T cells and frequently co-expressed with PD-1. These PD-1+ LAG-3+ CD8 memory T cells exhibit increased expression of activation and inhibitory markers, often accompanied by TOX, suggesting an exhausted state. In contrast, LAG-3 expression was more limited in circulating immune cells. The study also identified abundant expression of LAG-3 ligands, particularly MHC-II and galectin-3, across diverse tumor-infiltrating immune cells. Finally, elevated baseline and on-treatment levels of circulating LAG3 transcript-expressing CD8 memory T cells were associated with disease progression in melanoma patients treated with combination immune checkpoint inhibition. These insights support dual PD-1 and LAG-3 blockade.
Reis et al., investigated the role of tumor beta-2-microglobulin (B2M) and HLA-A protein expression in predicting responses to CIT. Using immunohistochemistry, the study found that the loss of these crucial proteins is a frequent event in various tumor types, particularly in metastatic cancer. A key finding is that immunotherapy can often reverse this protein loss, leading to increased B2M and HLA-A expression. While baseline levels of B2M or HLA-A alone were not definitive predictors of treatment success, their expression after treatment, particularly when analyzed in conjunction with other established biomarkers like CD8 and PD-L1, significantly improved the ability to predict positive responses to CIT.
Two studies investigated the role of TIGIT and its ligands in immunosuppression and their potential role as targets for CIT.
Liu et al., investigated the clinical significance of TIGIT ligand CD155 expression and its relationship with TME in gastric adenocarcinoma (GAC). Data from analyzing patient samples and public datasets demonstrated that increased CD155 expression correlates with GAC progression, poorer patient survival, and immunosuppressive CD68+ macrophages infiltrations. The authors suggest that CD155 could be a promising target for novel immunotherapies in GAC patients, given its role in disease progression.
Li et al., studied the immunosuppressive TME in hepatocellular carcinoma (HCC) using single-cell RNA sequencing (scRNA-seq). The authors investigated how various immune cells within the TME interact to promote tumor growth and specifically identified the TIGIT-PVR/PVRL2 axis as a key co-inhibitory signaling pathway, influencing the interaction between HCC cells, Treg cells, and exhausted CD8+ T cells. Tumor cell lysis and Granzyme B secretion significantly increased by blocking PVR or PVRL2 on HCC cells or TIGIT on immune cells. These findings propose the TIGIT-PVR/PVRL2 axis as a promising target for CIT in HCC.
Fibroblast Activation Protein (FAP) is both a prognostic marker and a potential target for CIT. Two articles in this Research Topic explored its prevalence and interaction with immune cells in the TME as well as its correlation with outcome in CIT.
Dziadek et al. investigated FAP as a biomarker for CIT across multiple tumor indications. They analyzed FAP expression and its correlation with patient outcomes in atezolizumab standalone or combination clinical trials. The study found FAP’s impact on therapy response to be complex, requiring more research to assess its value in predicting which patients would benefit from FAP-targeted treatments, particularly immunotherapy.
To further explore this, Kraxner et al. investigated FAP-expressing fibroblasts interaction in the TME and their relevance to CIT outcome. Immunohistochemistry and transcriptomics analyses showed that higher levels of FAP-positive fibroblasts correlate with increased T cell infiltration in some cancers, such as renal cell carcinoma, contradicting the notion that these fibroblasts exclude immune cells. These fibroblasts are associated with specific immune cells and cytokine signaling, indicating their influence on the TME and immunotherapy effectiveness. These findings underscore the significance of understanding the context of FAP-positive fibroblasts in the TME for designing more efficient personalized cancer treatments.
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