Can a dysfunctional T-cell gene signature predict nonresponse to PD-1 blockade in non-small cell lung cancer?

The development of immune checkpoint inhibitors (ICIs) such as anti-programmed cell death-1 (PD-1) antibodies, anti-programmed death-ligand 1 (PD-L1) antibodies, and anti-cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) antibodies has brought about a significant therapeutic revolution for many cancers, including non-small cell lung cancer (NSCLC). In NSCLC, nivolumab, an anti-PD-1 antibody, demonstrated superiority over docetaxel, one of the standard treatments for second-line therapy, and was approved as a second-line treatment (1,2). Subsequently, several ICIs such as pembrolizumab, atezolizumab, durvalumab, ipilimumab, and tremelimumab have been approved, enabling their use in practical clinical settings in various patterns including monotherapy, combination therapy with chemotherapy, and combination therapy with other ICIs (3). While treatment with ICIs offers benefits to patients such as prolonged survival compared to conventional therapies in terms of progression-free survival (PFS) and overall survival (OS), it does not show clinical benefit for all cases. Furthermore, it can also lead to adverse events known as immune-related adverse events (irAEs), which are thought to be caused by the activation of immune cells (4,5). Some irAEs include conditions affecting endocrine function such as hypothyroidism, adrenal insufficiency, as well as serious conditions like pneumonitis, myocarditis, cytokine release syndrome, encephalitis among others. Due to irAEs, it is essential to carefully consider cases for administering ICIs. In addition, ICIs are highly expensive drugs, and indiscriminate use in cases who they are ineffective can pose social and economic issues. Therefore, biomarkers predicting the therapeutic efficacy of ICIs become crucial to avoid administering unnecessary ICIs to whom ICIs are ineffective.

Several biomarkers have been reported to predict the efficacy of ICIs so far. The most widely utilized is the expression of PD-L1 in tumor cells, known as PD-L1 tumor proportion score (TPS). In the pooled analysis of the CheckMate 017 and 057 trials comparing docetaxel to nivolumab in second-line treatment for patients with NSCLC, long-term survival rates were analyzed. The median OS for nivolumab was 11.1 months [95% confidence interval (CI): 9.2 to 13.1], while for docetaxel it was 8.1 months (95% CI: 7.2 to 9.2). Five-year pooled OS rates were 13.4% in nivolumab group and 2.6% in docetaxel, demonstrating statistically significant efficacy of nivolumab in long-term survival (6). In subgroup analysis of pooled analysis, the median survival OS with nivolumab was 13.4 months in the group with PD-L1 TPS of 1% or higher, compared to 9.7 months in the group with less than 1% PD-L1 expression, suggesting a potential difference in efficacy based on PD-L1 expression. In a phase III trial, KEYNOTE-024, comparing pembrolizumab monotherapy with platinum-based combination therapy for patients with NSCLC with PD-L1 TPS 50% or higher in first-line treatment, PFS showed a hazard ratio (HR) of 0.50 (95% CI: 0.37–0.68; P<0.001; median: 10.3 vs. 6.0 months), and OS had a HR of 0.62 (95% CI: 0.48–0.81; median: 26.3 vs. 13.4 months). This demonstrated that in cases with high PD-L1 expression, pembrolizumab monotherapy significantly prolongs PFS and OS compared to platinum-based combination therapy (7,8). In a phase III trial, KEYNOTE-042, comparing pembrolizumab monotherapy with platinum-based combination therapy for patients with PD-L1 TPS 1% or higher, subgroup analysis of patients with PD-L1 TPS 50% or higher demonstrated that pembrolizumab significantly prolonged OS compared to chemotherapy, achieving reproducibility of the results seen in the KEYNOTE-024 trial. However, in subgroup analysis of patients with PD-L1 TPS 1–49%, the HR was 0.88 (95% CI: 0.75–1.04; median: 13.4 vs. 12.1 months), failing to demonstrate statistical significance, with overlapping survival curves (9,10). These results underscore the utility of PD-L1 TPS as one of the biomarkers for monotherapy with ICIs, however, there are cases where the effectiveness of ICIs is observed even in patients with low PD-L1 TPS. Additionally, due to tumor heterogeneity, biopsy specimens may not accurately represent the overall expression of PD-L1. Therefore, PD-L1 TPS is not considered a perfect biomarker at present.

Another biomarker that has recently attracted considerable interest is tumor mutational burden (TMB), which quantifies the quantity of mutations present in the tumor. The initiation of the cancer immunity cycle begins with dendritic cells or antigen-presenting cells recognizing neoantigens, and activated T cells subsequently recognizing and attacking cancer cells via major histocompatibility complex (MHC) class I. Since neoantigens are generated as a result of genetic mutations, a higher number of mutations increases the likelihood that some of the neoantigens presented by MHC proteins will be immunogenic. Consequently, with more immunogenic neoantigens, there is a greater potential for T cell recognition and eradication of cancer cells. Therefore, it is reasonable to consider that a high TMB could serve as a biomarker for the therapeutic efficacy of ICIs (11). In fact, there have been numerous reports showing a correlation between high TMB and the effect of ICIs across many cancers, including NSCLC; however, since neoantigens recognized by T cells can theoretically arise even in environments with few mutations, attempts to dichotomize the predictive ability of TMB are imperfect (12). A meta-analysis revealed that the proportion of infiltrating CD8-positive cytotoxic T cells within the tumor could play a crucial role of the effect of ICIs. Combining the proportion of tumor-infiltrating lymphocytes (TILs) with PD-L1 TPS and TMB could lead to a more robust biomarker (13,14).

In a recent issue of Clinical Cancer Research, Hummelink et al. reported that a gene expression signature demonstrating elevated sensitivity and negative predictive value (NPV), offering potential as a biomarker to predict non-responsiveness to nivolumab in NSCLC (15). In previous studies, the same team had reported that the presence of PD-1-positive TILs (PD-1^T TILs), a subset of CD8-positive TILs, was shown to correlate with treatment response and survival in a small and medium-sized cohort of patients with NSCLC treated with PD-1 inhibitors (16,17). They also had revealed elevated levels of the cytokine C-X-C motif chemokine 13 (CXCL13), which exclusively binds to the chemokine receptor CXCR5 expressed on B cells and CD4-positive follicular helper T cells, and marks a subset of virus-specific CD8-positive T cells responding to PD-1 blockade in lymphoid tissues, can predict the efficacy of PD-1 inhibitors (17). It seems plausible that PD-1, a marker of exhausted T cells, positive TILs undergo immunological reactivation upon administration of anti-PD-1 antibodies, which block the PD-1/PD-L1 immune checkpoint axis, this allows them to function as cytotoxic T cells capable of attacking tumor cells. However, these previous studies were limited by their reliance on immunohistochemistry (IHC) and digital pathology systems for detecting PD-1^T TILs, which raised concerns about variability in evaluation between facilities due to differences in field of view, sample size, and sample heterogeneity. To address this limitation and explore alternative methods for predicting the efficacy of ICIs using the PD-1^T TILs that they established, they conducted further research with an RNA expression signature analysis. In this analysis, they were able to identify high and low groups of PD-1^T TILs by scoring the gene expression of STAT1, OAS1, TAP1, HEY1, CXCL13, IFIT2, IL6, TDO2, CD6, CTLA4, CD274, and LAG3. They reported that this scoring enabled the prediction of the efficacy of nivolumab, suggesting it could serve as a marker for predicting the effectiveness of monotherapy with ICIs. The patients with disease control at 12 months and patients with progressive disease were correctly classified with a NPV of 100% and a positive predictive value of 32%. These findings show that the PD-1^T signature had a higher accuracy for predicting disease control at 12 months and survival compared with PD-L1 TPS (15). These results are made possible by advancements and widespread adoption of RNA sequencing technology, not IHC, enabling the creation of more robust biomarkers based on the expression of numerous genes. This underscores the importance of further exploration, which could lead to the development of even better biomarkers. Due to the limitation of this study in the small number of cases, further validation in large cohort, and to determine the optimal combination of gene expressions for analysis need to be considered. For example, in a phase III clinical trial evaluating the additive effects of atezolizumab and bevacizumab to chemotherapy, analysis was conducted defining the expression of PD-L1, CXCL9, and IFN-γ messenger RNA (mRNA) as the effector T cell gene signature. It was reported that higher expression of the effector T cell gene signature was associated with greater additive effects of atezolizumab (18). There is room for consideration regarding whether a gene set focusing on exhausted T cells or one focusing on effector T cells would be more suitable.

In conclusion, predictive biomarkers for the effect of ICIs are crucial not only for identifying cases who treatment would be highly effective but also for detecting cases who treatment may be ineffective, thereby avoiding unnecessary physical, social, and financial burdens. Unfortunately, at present, there is no single marker that can reliably predict effectiveness. Therefore, it is necessary to consider combining PD-L1 TPS, TMB, the proportion of TILs, gene signatures of immune cells within the tumor, and other factors [such as cancer-testis antigens (19)]. Regarding the report by Hummelink et al., further investigations with an increasing number of cases are awaited to establish it as a robust biomarker.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Lung Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-286/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-286/coif). H.T. has received honoraria for lectures and presentations from AstraZeneca and Chugai Pharmaceutical. K.A. has received honoraria for lectures and presentations from Chugai Pharmaceutical, Merck Sharp & Dohme (MSD), Bristol-Myers Squibb and Ono Pharmaceuticals. H.M. has received honoraria for lectures and presentations from AstraZeneca, Bristol-Myers Squibb, Chugai Pharmaceutical and MSD, and consulting fees for expert testimony from MSD. The authors have no other conflicts of interest to declare.

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