Mechanistic insights into the immune biomarker of perioperative immune checkpoint inhibitors for non-small cell lung cancer

Introduction

Lung cancer was the most common cancer in 2022, with nearly 2.5 million new cases, accounting for one in eight cancer cases globally (1). Among these, non-small cell lung cancer (NSCLC) represents approximately 85% of all lung cancer cases (2). About 30% of newly diagnosed patients with NSCLC are suitable for surgical treatment. Complete surgical resection remains the basis for the treatment of early NSCLC. Nevertheless, postoperative recurrence remains a key challenge in lung cancer surgery, as approximately 45% to 76% of patients experience recurrence after undergoing lung cancer surgery (3). Since the 2008 Lung Adjuvant Cisplatin Evaluation (LACE) meta-analysis demonstrated a 5.4% improvement in 5-year survival [hazard ratio (HR), 0.89; 95% confidence interval (CI): 0.82 to 0.96; P=0.005] for patients receiving adjuvant chemotherapy (4), platinum-based doublet adjuvant chemotherapy has become the standard treatment for patients with resected lung cancer. However, for a long time, perioperative chemotherapy has shown limited survival benefits, highlighting the urgent need for new therapeutic strategies.

In recent years, as immunotherapy—particularly immune checkpoint inhibitors (ICIs)—has played an increasingly important role in the treatment of advanced NSCLC (5,6), attention has shifted toward whether resectable NSCLC could benefit from perioperative ICIs treatment. Currently, the two most well-known approaches for ICIs are the blockade of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4 or CD152) and the blockade of programmed cell death protein 1 (PD-1 or CD279) or programmed death-ligand 1 (PD-L1 or CD274 or B7-H1) (7). Among these, anti-PD-1/PD-L1 therapy is a key focus in the research of perioperative ICIs treatment for resectable NSCLC. CheckMate 159, LCMC3, and AEGEAN respectively evaluated the effectiveness of anti-PD-1/PD-L1 agents such as nivolumab, atezolizumab, and durvalumab in neoadjuvant or adjuvant treatment for early-stage NSCLC, all achieving quite satisfactory results (8-12). However, as more clinical trials delve deeper into this area, several challenges have begun to emerge. The two main issues are the low response rate and the occurrence of immune-related adverse events (irAEs) (13,14). Recently, some clinical trials have identified certain specific biomarkers with significant predictive potential in NSCLC perioperative ICIs treatment (15,16) prompting a focus on the evaluation of existing biomarkers and the discovery of new ones to address the aforementioned challenges (17).

The term “biomarker” is a synthesis of “biological” and “marker”, defined as a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions, allowing for accurate and reproducible measurement (18,19). Suitable biomarkers can guide treatment strategies by identifying suitable patients and predicting outcomes. Therefore, it has become a key area of research in early-stage NSCLC perioperative immunotherapy. Several biomarkers have already been approved by the Food and Drug Administration (FDA) to predict the efficacy of ICIs in cancer treatment, including PD-L1 expression, tumor mutation burden (TMB), and DNA repair defects such as deficient mismatch repair (dMMR) and microsatellite instability-high (MSI-H) (20). In addition, emerging biomarkers, including tumor-infiltrating lymphocytes (TILs), circulating tumor DNA (ctDNA), and the gut microbiome, hold great promise (21). Current trials categorize biomarkers into tissue-, blood-, and host-based groups, assessing their predictive value (22,23). The mechanisms underlying each biomarker and their interconnections are highly complex. Summarizing these mechanisms can enhance our understanding of the underlying principles, providing valuable insights that may inspire the discovery of novel predictive biomarkers.

This review begins by providing a brief introduction to the mechanisms of anti-PD-1/PD-L1 therapies, their overall application in NSCLC treatment, and the current status of clinical research in perioperative therapy for early-stage resectable NSCLC. Subsequently, we summarize the potential predictive biomarkers identified in recent studies and their associated mechanisms, including their interrelationships, limitations, and prospects. By offering insights into these mechanisms and relationships, we seek to advance the combined predictive utility of biomarkers and drive the discovery of novel candidates.

Anti-PD-1/PD-L1 in perioperative NSCLC treatment

Mechanism of anti-PD-1/PD-L1

Before explaining the predictive mechanisms of biomarkers, understanding the mechanisms of anti-PD-1/PD-L1 therapy provides crucial insight into its potential associations. PD-1 is a type I membrane protein expressed on immune cells, such as activated T cells, macrophages, B cells, dendritic cells (DCs), and natural killer (NK) cells (5). PD-1 has two ligands, PD-L1 (B7-H1 or CD274) and PD-L2 (B7-DC or CD273). PD-L1 is broadly expressed on tumor cells, immune-infiltrating cells, and antigen-presenting cells (APCs), whereas PD-L2 expression is relatively limited (24,25). The PD-1/PD-L1 pathway plays a dual role: physiologically, it modulates immune responses to prevent excessive inflammation and maintain self-tolerance, preventing immune cells from attacking normal tissues (26). Pathologically, tumor cells and APCs expressing PD-L1 bind to PD-1 on TILs, resulting in T cell exhaustion, increased release of immunosuppressive cytokines, and tumor immune evasion (26,27).

Anti-PD-1/PD-L1 monoclonal antibodies block interactions between T cells and tumor cells, effectively ‘releasing the brakes’ on the immune system. By inhibiting the recruitment of Src homologous phosphatase 2 (SHP-2), these ICIs restore lymphocyte-specific protein tyrosine kinase (LCK)-induced zeta-chain-associated protein kinase 70 (ZAP70) phosphorylation, enabling the reactivation of downstream signaling pathways. This process reactivates TILs, facilitating their proliferation, targeting, and elimination of tumor cells (7,28-30). Additionally, restored T cell activity amplifies immune memory, establishing durable anti-tumor effects (7,31). Furthermore, this blockade enhances the anti-tumor functions of other immune cells in the tumor microenvironment (TME), such as macrophages and NK cells (32). In summary, anti-PD-1/PD-L1 monoclonal antibodies achieve therapeutic effects by modulating the TME, where changes in components like PD-L1 expression, TILs, and tertiary lymphoid structures (TLSs) can influence ICIs efficacy. Such variations provide opportunities for biomarker development. Investigating the correlations between these changes and ICIs responses enables the identification of predictive biomarkers with potential clinical utility (Figure 1).

Figure 1 Mechanisms of anti-PD-1/PD-L1 and associated predictive biomarkers. The expression of PD-L1 on the tumor cell surface is regulated by multiple factors, including genes, micro-RNAs, and transcription factors. One mechanism that promotes PD-L1 expression is the activation of STATs transcription factors induced by IFN-γ. When PD-L1 on the surface of tumor cells or APCs (such as dendritic cells) binds to PD-1 on T cells, the PD-1/PD-L1 signaling pathway is activated, leading to the recruitment of phosphatase SHP-2 to the C-terminal ITSM. This reduces LCK-induced ZAP70 phosphorylation and downregulates the RAS and PI3K pathways, thereby inhibiting T cell proliferation and activation. Anti-PD-1/PD-L1 antibodies block the PD-1/PD-L1 signaling pathways, preventing SHP-2 aggregation, reactivating LCK-induced ZAP70 phosphorylation, and restoring TCR signaling. This process reactivates tumor-infiltrating lymphocytes, allowing them to proliferate, target, and eliminate tumor cells. Factors involved in this pathway, such as PD-L1 expression, have the potential as predictive biomarkers (indicated by green diamonds). This figure was created by Figdraw with ID: UPOOO145b5. CBC, complete blood count; ctDNA, circulating tumor DNA; DC, dendritic cell; ITSM, immunoreceptor tyrosine-based switch motif; ITIM, immunoreceptor tyrosine-based inhibition motif; MHC, major histocompatibility complex; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; PD-L1 Ex, PD-L1 expression; PICs, peripheral immune cells; STATs, signal transducer and activator of transcription proteins; TCR, T cell receptor; TILs, tumor-infiltrating lymphocytes; TMB, tumor mutation burden; TLSs, tertiary lymphoid structures.

Current landscape of clinical trials

Based on the above mechanisms, ICIs represent a monumental breakthrough in cancer treatment and have become one of the most promising approaches in oncology, including for NSCLC. Given the extensive research on the PD-1/PD-L1 axis in T cell exhaustion and tumor immune evasion, clinical trials and applications for these pathways are relatively advanced (33). Positive results from clinical trials like KEYNOTE-024 and CheckMate-057 have led to the approval of PD-1/PD-L1-based checkpoint therapies as first- and second-line treatments for advanced NSCLC, significantly improving outcomes in late-stage disease (34-36). Recently, research has expanded to evaluate ICIs in perioperative settings (neoadjuvant and adjuvant therapies) for early-stage NSCLC, aiming to address high postoperative recurrence rates and improve long-term outcomes (14). The following section summarizes current clinical research progress across three approaches: adjuvant, neoadjuvant, and combined neoadjuvant-adjuvant therapies (Table 1).

Adjuvant ICIs therapies

The landmark phase III IMpower010 trial demonstrated a significant improvement in disease-free survival (DFS) in the overall population with adjuvant atezolizumab compared to best supportive care (BSC) (HR 0.79; 95% CI: 0.64–0.96; P=0.02) (37). Similarly, in the phase III PEARLS/KEYNOTE-091 trial, median DFS was significantly prolonged in the pembrolizumab group compared to the placebo group (53.6 vs. 42.0 months; HR 0.76; 95% CI: 0.63–0.91; P=0.001) (38). Collectively, these findings highlight the substantial potential of adjuvant ICIs therapy in the postoperative management of NSCLC. Notably, subgroup analyses stratified by PD-L1 expression in the two trials revealed divergent results. In IMpower010, a more pronounced DFS benefit from atezolizumab was observed in patients whose tumors expressed PD-L1 on ≥1% of tumor cells (HR 0.66; 95% CI: 0.50–0.88; P=0.003). In long-term overall survival (OS) follow-up, this benefit was also more significant in the subgroup with PD-L1 tumor cell (TC) expression ≥50% (HR 0.43; 95% CI: 0.24–0.78) compared to those with PD-L1 TC ≥1% (HR 0.71; 95% CI: 0.49–1.03) (39). However, in the KEYNOTE-091 trial, pembrolizumab did not show a significant DFS benefit in patients with PD-L1 tumor proportion score (TPS) ≥50% (HR 0.82; 95% CI: 0.57–1.18; P=0.14). These inconsistent findings may be attributed to multiple factors, including differences in trial design, therapeutic agents, and treatment regimens. Nevertheless, two important insights can be drawn: first, PD-L1 expression remains a highly promising predictive biomarker; second, PD-L1 expression alone is insufficient to guide perioperative immunotherapy decision-making. There is an urgent need to explore and validate additional biomarkers to improve predictive accuracy.

Neoadjuvant ICIs therapies

Since the groundbreaking results of the first clinical trial assessing neoadjuvant ICIs, CheckMate 159, a plethora of neoadjuvant treatment trials for NSCLC have emerged. The main treatment approaches include neoadjuvant anti-PD-1/anti-PD-L1 monotherapy, combinations with chemotherapy, anti-CTLA-4 antibodies, and other immune modulators such as ramucirumab (anti-VEGFR-2), relatlimab (anti-LAG-3), and oleclumab (anti-CD73) (10,22,40-43). Notably, the CheckMate 816 trial made significant strides and received FDA approval; it evaluated the efficacy of neoadjuvant nivolumab combined with platinum-based chemotherapy in 358 patients with stage IB-IIIA NSCLC. The results demonstrated a significant improvement in event-free survival (EFS) (31.6 vs. 20.8 months, P=0.005) and pathological complete response (pCR) (24.0% vs. 2.2%, P<0.001) in the combination group compared to the chemotherapy-only group. It is also worth noting that the majority of the benefits were observed primarily in patients with PD-L1 expression levels of ≥50% (22).

Perioperative ICIs therapies

The sandwich approach of neoadjuvant plus adjuvant ICIs is increasingly being evaluated in randomized clinical trials. The global AEGEAN study assessed the effectiveness of preoperative neoadjuvant chemotherapy combined with durvalumab, followed by adjuvant durvalumab, compared to preoperative chemotherapy followed by placebo in 802 patients with stage IIA–IIIB NSCLC. The results showed significant improvements in both pCR (17.2% vs. 4.3%; P<0.001) and EFS (HR 0.68; 95% CI: 0.53–0.88; P=0.004) (11,12). The latest results from AEGEAN reported at WCLC indicated significant improvements in EFS [median, not reached (NR) vs. 30.0 months; HR 0.69] and DFS (NR vs. NR; HR 0.66) with durvalumab versus placebo, while OS showed a trend toward improvement (NR vs. 53.2 months; HR 0.89) (44). Another noteworthy study is KEYNOTE-671, a global phase 3 trial conducted at 189 medical centers, which evaluated neoadjuvant pembrolizumab or placebo in combination with cisplatin-based chemotherapy, followed by postoperative pembrolizumab or placebo. The results also showed that after a median follow-up of 36.6 months, the 4-year OS rate with pembrolizumab (HR =0.72) and the 2-year EFS rate (HR =0.59) were significantly improved compared to the placebo group (45,46).

The role of biomarkers

Key clinical trials, including IMpower010, CheckMate 816, and KEYNOTE-671, have demonstrated significant improvements in DFS and OS, underscoring the pivotal role of anti-PD-1/PD-L1 therapies in both adjuvant and neoadjuvant settings. However, two key challenges associated with ICIs therapy remain unresolved. The first is the variability in response rates. This issue could be effectively addressed by identifying predictive biomarkers to select patients who are most likely to benefit from treatment. The second is irAEs. A multicenter retrospective study reported that pulmonary irAEs are the most common form of toxicity during ICIs therapy in NSCLC, regardless of whether ICIs are used as monotherapy or in combination regimens (47). Among these, checkpoint inhibitor pneumonitis (CIP) is particularly prevalent, with an incidence ranging from 1.1% to 6.4% during neoadjuvant therapy. CIP can lead to symptoms such as cough, dyspnea and hypoxemia. Upon diagnosis, ICIs treatment is typically discontinued, and only a small proportion of patients may be re-challenged with ICIs therapy after resolution, substantially limiting future immunotherapy options for individuals who develop irAEs (48). Previous studies have shown that specific biomarkers such as immune cell profiles, autoantibodies, and genetic factors may have predictive value for developing irAEs during ICIs therapy (49-51). Therefore, reliable biomarkers capable of predicting both treatment efficacy and the risk of irAEs are crucial for optimizing patient selection and promoting personalized therapeutic strategies.

While trials such as LCMC3 and NEOSTAR have shown that PD-L1 expression predicts the efficacy of neoadjuvant ICIs in resectable NSCLC (10,40), and the NADIM trial demonstrated improved progression-free survival (PFS) and OS in patients with low ctDNA levels (mutant allele fraction, MAF <1%) (52), these findings remain insufficient. The problem still persists. For example, the NADIM trial found that although a PD-L1 TPS of 25% or more was significantly associated with major pathological response (MPR) or pCR, 58% of patients with a PD-L1 TPS below 25% also achieved MPR or pCR (53). Furthermore, in the OS analysis of the NADIM study, neither TMB nor PD-L1 expression was predictive of long-term survival. In addition, there is currently no standardized method for quantifying ctDNA (52). Therefore, further research is required to identify novel biomarkers and explore multimodal biomarker combinations to enhance predictive accuracy. Moreover, larger cohort studies and clinical trials are needed to standardize biomarker detection methodologies and establish unified evaluation metrics. The subsequent section will explore the current landscape of potential biomarkers for NSCLC perioperative ICIs therapy, examining their mechanisms and clinical correlations to inspire the discovery of innovative predictive biomarkers (Figure 2).

Figure 2 Biomarkers for evaluating the efficacy of perioperative ICI therapy in patients with early resectable NSCLC. The biomarkers are classified into three categories based on their source: blood, tumor tissue, and feces. The figure summarizes the current potential biomarkers for predicting the efficacy of ICIs therapy in patients with early resectable NSCLC, among which PD-L1 expression and TMB have been FDA-approved. This figure was created by Figdraw with ID: PAPIS51160. FDA, Food and Drug Administration; ICI, immune checkpoint inhibitor; MRD, minimal or molecular residual disease; NSCLC, non-small cell lung cancer; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; TCR, T cell receptor; TMB, tumor mutation burden.

Biomarkers for NSCLC perioperative ICIs

Tissue-based biomarkers and mechanism

PD-L1 expression

The mechanisms of the PD-1/PD-L1 axis in tumor immunity have been detailed in the previous sections. In the TME, PD-L1 expressed on tumor cells and APCs binds to PD-1 on TILs, inducing tumor immune evasion (26,27). PD-1 is widely expressed and present during both the priming and expansion of T-cell activation, but PD-L1 expression in tumor and immune cells is heterogeneous and dynamic, regulated by both intracellular and extracellular factors. Therefore, PD-L1 expression possesses the fundamental conditions necessary to predict tumor immune status and the efficacy of ICIs responses (54,55).

Early studies evaluating the safety and efficacy of nivolumab and atezolizumab confirmed the potential utility of PD-L1 expression as a predictive biomarker (56,57). Following these studies, the FDA approved PD-L1 expression as a predictive biomarker for ICIs response (20). Recently, an increasing number of clinical trials have confirmed the predictive value of PD-L1 expression. For instance, the phase II LCMC3 study reported an association between MPR and high PD-L1 TPS (10). Similarly, the NEOSTAR study observed elevated baseline tumor PD-L1 expression in responding patients (58). Moreover, the CheckMate 816 trial demonstrated that patients with tumor PD-L1 expression ≥1% experienced greater EFS benefit compared to those with PD-L1 expression <1% (22). Additionally, study has also shown that exosomal PD-L1 (exoPD-L1) and soluble PD-L1 (sPD-L1) levels in peripheral tissues are negatively correlated with CD3⁺ and CD8⁺ T-lymphocyte infiltration in tumors (59). Shimada et al.’s study also demonstrated a significant correlation between exoPD-L1 levels and tumor PD-L1 expression. High levels of exoPD-L1 often indicate high PD-L1 expression in tumor tissue (60).

These results may provide us with the following insights for PD-L1 expression: (I) the primary mechanism by which PD-L1 expression serves as a predictive biomarker for ICIs therapy lies in its crucial role in tumor immune evasion. Overexpression of PD-L1 in tumor cells indicates that ICIs can more effectively block this pathway. (II) ICIs restore anti-tumor immunity primarily by reactivating tumor-infiltrating T lymphocytes that were suppressed by the PD-1/PD-L1 signaling pathway blockade. (III) Since exoPD-L1 in the peripheral blood is positively correlated with PD-L1 expression in tumor tissue, could the detection and analysis of exoPD-L1 or sPD-L1 assist or replace the predictive role of tumor tissue PD-L1 expression? This could enable more convenient and non-invasive monitoring. (IV) PD-L1 expression is induced by various factors in the TME and is regulated at multiple levels, including genomic, post-transcriptional, translational, and post-translational modifications (61). This indicates that PD-L1 expression has dynamic variability and the potential for dynamic monitoring. This further supports its potential as a suitable predictive biomarker for perioperative ICIs treatment in NSCLC.

TILs

Lymphocytes, comprising B cells and T cells, are pivotal in adaptive immune responses. During tumor progression, they recognize abnormal tumor cells and infiltrate tumor tissues. These lymphocytes within and around the tumor are known as TILs, with CD8⁺ T cells being the most abundant subset (62,63). In most types of malignancies, CD4⁺ and CD8⁺ T cells, as well as T helper cells 1, are associated with a favorable prognosis, whereas regulatory T cells are linked to a poor prognosis (64).

Given the important role of TILs in tumor prognosis, their predictive value in perioperative NSCLC immunotherapy has been explored. The phase II NEOSTAR trial found that neoadjuvant nivolumab plus ipilimumab treatment led to higher levels of CD3⁺ TILs in resected tumor tissues, along with increased levels of memory T cells, compared to nivolumab alone (15,40). The LCMC3 trial also demonstrated a significant expansion of peripheral CD8⁺ T cells in patients treated with neoadjuvant atezolizumab who achieved an MPR. Furthermore, high pre-treatment levels of NK-like T cells and NK cells in the peripheral blood were significantly associated with a lack of response to ICIs (10). Numerous studies have shown that the outcome of ICIs therapy in cancer patients is related to the quality and quantity of lymphocytes in the TME (65,66), meaning that the status of TILs directly influences the effectiveness of ICIs.

The underlying mechanisms of the above phenomenon can be explained from several perspectives: (I) immune checkpoints like PD-1/PD-L1 and CTLA-4 facilitate immune evasion primarily by inhibiting TILs within the TME, making the quantity and quality of TILs critical to the restoration of TIL-mediated tumor immunity after immune checkpoint blockade (67,68). (II) The presence of non-exhausted T cells in the TME is key to ICIs efficacy, as they can regain immune activity once checkpoint inhibition is blocked (69). The term ‘exhaustion’ was originally used to describe the hyporesponsive state of T cells in chronic infections. In tumors, exhausted T cells refer to a hypofunctional state where long-term tumor antigen stimulation impairs T cell effector functions. Several factors contribute to T cell exhaustion, including myeloid-derived suppressor cells (MDSCs), the PD-1/PD-L1 and CTLA-4 immune checkpoint pathways, and chronic tumor antigen stimulation (70-72). Therefore, the balance between exhausted and non-exhausted T cells in the tumor tissue can predict the efficacy of ICIs therapy and disease prognosis in NSCLC patients. (III) Central memory T cells (TCM) and effector memory T cells (TEM) within TILs have the capacity for self-renewal and long-term survival. A higher abundance of TCM and TEM in the TME is often associated with better outcomes from ICIs therapy and improved prognosis (73,74).

TLSs

Traditionally, an effective anti-cancer adaptive immune response was thought to originate in secondary lymphoid organs (SLOs), where DCs, B cells, and CD4⁺ T cells collaborate to form germinal centers, generating effector and memory T cells that mount anti-tumor responses. However, further research into the TME has revealed that anti-tumor immunity also occurs directly within the tumor in organized cellular aggregates similar to SLOs, known as TLSs (75,76). TLSs are characterized by a central zone of CD20⁺ B cells surrounded by CD3⁺ T cells, resembling the lymphoid follicles in SLOs. While TLSs in different tumors may vary in T cell composition, one common feature is the presence of CD4⁺ T follicular helper (TFH) cells. Additionally, B lymphocytes, DCs, CD68⁺ macrophages, and peripheral node addressin (PNAd)-positive high endothelial venules (HEV) contribute to TLS formation. TLSs play a significant role in tumor immunity by enabling faster, more effective immune responses, fine-tuning immune regulation and secreting survival factors that enhance lymphocyte stability and survival (77).

Emerging research has shown that in tumors such as melanoma, the presence of more mature and dense TLSs, as well as B-cell signatures within TLSs, correlates with better responses to ICIs (78,79). In the context of perioperative ICIs for NSCLC, several studies have explored the predictive role of TLSs. For example, research by Sun et al. (80) demonstrated that NSCLC patients who achieved a MPR after neoadjuvant immunochemotherapy exhibited higher TLS maturation and abundance. Mature TLSs, characterized by germinal centers and the presence of CD21⁺ and CD23⁺ follicular dendritic cells (FDCs), were also associated with improved DFS, highlighting TLSs’ potential as predictive biomarkers (23,80,81).

Although the molecular and cellular mechanisms underlying TLSs’ predictive value are not fully understood, several factors may contribute: (I) mature DCs and B cells within TLSs enhance tumor antigen presentation and T cell activation, boosting anti-tumor immunity and facilitating the restoration of T cell function after ICIs block immune checkpoints (76,81). (II) TLSs also promote T cell differentiation into memory T cells, enabling long-term immune responses and self-renewal, which enhances the efficacy of ICIs. Additionally, studies suggest that modulating TLSs could improve ICIs’ efficacy. For example, chemokines and cytokines like CXCL13 and LIGHT can be used to induce HEV formation and recruit endogenous T and B cells. Immunoadjuvants such as Toll-like receptor 9 (TLR9) or Stimulator of interferon genes (STING) agonists can also be used to promote APC maturation, enhancing TLSs function. Furthermore, KRAS^G12C inhibitors can suppress tumor-driven myelocytomatosis (MYC) inhibition mediated by interferon (IFN), thereby promoting the formation of tumor-associated TLSs (82-85). This could offer new insights into addressing ICIs resistance and improving treatment efficacy.

TMB

TMB refers to the number of somatic nonsynonymous mutations or all mutations detected per megabase of sequenced DNA in a tumor sample, either through whole-exome sequencing (WES) or targeted sequencing (86). WES is the gold standard for comprehensive TMB assessment, though its cost and complexity remain high. Next-generation sequencing (NGS) panels have now become an accepted alternative for traditional TMB measurement. To date, the FDA has approved four methods for measuring TMB (87-89).

Several clinical trials have investigated the predictive value of TMB in perioperative immunotherapy for resectable NSCLC. In one trial involving 21 resectable early-stage NSCLC patients treated with neoadjuvant nivolumab, a significant correlation was observed between pretreatment TMB and pathological response (8). Similarly, a phase Ib study evaluating the efficacy of neoadjuvant low-dose radiotherapy (LDRT) in combination with durvalumab and chemotherapy in patients with potentially resectable stage III NSCLC also reported that higher TMB was associated with an increased likelihood of achieving pCR or MPR (90). However, the LCMC3 trial did not identify a significant correlation between TMB levels and pathological response (10). Collectively, these findings underscore the potential utility of TMB as a predictive biomarker for perioperative ICIs therapy in NSCLC. Nonetheless, they also highlight the limitations of using TMB as a standalone predictor, reinforcing the need for integrated, multimodal biomarker strategies.

The primary mechanisms underpinning the association between high TMB and improved ICIs efficacy include the following: (I) higher TMB indicates more tumor mutations, leading to the generation of more neoantigens and tumor-associated antigens. This increases the likelihood of generating an immunogenic response by triggering T cell activation. After ICIs block PD-1/PD-L1 or CTLA-4 checkpoints, T cells are more likely to reactivate, resulting in a stronger immune response (91-93). (II) High TMB may upregulate antigen processing genes or type I interferon transcription and expression while downregulating proliferation markers. This enhances antigen presentation, regulates both innate and adaptive immune responses, and promotes anti-tumor activity, thereby improving ICIs outcomes (94).

Blood-based biomarkers and mechanism

ctDNA and minimal or molecular residual disease (MRD)

MRD is defined as the detection of remaining tumor cells in the blood following the administration of initial therapy (95). Liquid biopsy techniques that analyze circulating tumor cells (CTCs), exosomes, RNA, and ctDNA in blood or other fluids can be used to identify MRD in NSCLC (96). As a biomarker for MRD, ctDNA was first identified in 1994 and has since been recognized as a highly specific indicator of malignancy (97-99). ctDNA in peripheral blood is primarily released through tumor cell necrosis and apoptosis, but it can also be released via active mechanisms like exosomes and microvesicles (100). A variety of techniques are available for the detection and quantification of ctDNA, including droplet digital PCR (dPCR) based on microfluidic platforms and targeted sequencing technologies (99,101). MRD detection and monitoring have been widely used in patients with hematologic malignancies, playing a significant role in post-therapy risk stratification and disease prognosis (102). However, due to the low concentration of CTCs or factors shed into the bloodstream from solid tumors, MRD analysis in solid tumor patients remains challenging. The widespread use of NGS technology allows for more extensive mutation analysis of ctDNA in plasma, providing very high sensitivity, and making the measurement of MRD in solid tumors possible (96,103). Related studies have shown that ctDNA detection can reflect postoperative lung cancer MRD levels (104), and ctDNA-based MRD is highly correlated with recurrence-free survival (RFS) and disease relapse in early resectable NSCLC (105), positioning ctDNA-based MRD as a research focus for predictive biomarkers in the perioperative setting of resectable NSCLC treated with ICIs.

Several clinical trials have evaluated ctDNA’s predictive value in perioperative immunotherapy for NSCLC. In the NADIM phase II trial, the Oncomine pan-cancer cell-free assay was used to detect and analyze ctDNA, and the results showed that patients with low ctDNA levels (MAF <1%) at baseline had significantly improved PFS and OS compared to those with high pre-treatment ctDNA levels. The MAF refers to the ratio of mutated DNA molecules to the total DNA molecules (mutant and wild-type), with a detection limit of MAF ≥0.1% (52). Dongsheng Yue and colleagues used an ultra-deep lung cancer-specific MRD (LC-MRD) sequencing panel to analyze ctDNA profiles in 22 NSCLC patients before, after, and during the follow-up of neoadjuvant immunotherapy. They found that dynamic changes in ctDNA were highly correlated with pathological responses, with detectable preoperative ctDNA being associated with lower RFS [hazard ratio (HR) 7.41, 95% confidence interval (CI): 0.91–60.22, P=0.03], and ctDNA detected 3–8 days postoperatively was an independent risk factor for recurrence (HR 5.37, 95% CI: 1.27–22.67, P=0.01) (106).

These trials consistently reveal a common finding: high ctDNA levels in peripheral blood are often associated with poorer ICIs treatment outcomes and worse prognoses. Although research is still ongoing, several mechanisms can help explain this phenomenon: (I) ctDNA levels closely correlate with disease burden. Magbanua et al. demonstrated that ctDNA levels significantly correlate with functional tumor volume (FTV) (107), suggesting that higher ctDNA levels may indicate larger, more aggressive tumors or advanced disease stages, which are often linked to a more complex immune microenvironment and poorer ICIs treatment outcomes. It’s important to note that the relationship between ctDNA and tumor volume varies within the same cancer type and across different cancer types (108,109), warranting further investigation. (II) ctDNA analysis can capture tumor heterogeneity and related gene mutations (such as KRAS gene mutations) (100,109), meaning that higher ctDNA levels may be associated with genomic instability, leading to faster clonal evolution and immune evasion, thereby contributing to poorer ICIs outcomes. Additionally, the rapid clonal evolution of tumors may also contribute to the development of resistance to ICIs (100,110), explaining the correlation between high ctDNA levels and poorer treatment responses. This also suggests that ctDNA may have the potential to predict ICIs resistance in certain cancer patients.

Peripheral TCR repertoire

T cell-mediated antigen recognition relies on the interaction between the T cell receptor (TCR) and the major histocompatibility complex (MHC) molecules expressed on APCs. A complete TCR chain consists of a constant (C) region and a variable (V) region. The V region undergoes V/D/J gene rearrangement to generate functional TCR sequences, enabling specific recognition of diverse antigens (111-113). It is this random rearrangement of TCR gene segments that produces the vast diversity of TCRs capable of recognizing a wide array of antigens, forming what is known as the TCR repertoire (23,114). The peripheral TCR repertoire, defined by the diversity and composition of all TCRs in peripheral blood, can be profiled using high-throughput sequencing technologies and reflects the immune system’s capacity to recognize a wide range of antigens (112,114,115).

Several studies have explored the correlation between the peripheral TCR repertoire and the efficacy of ICIs in NSCLC patients. Key parameters analyzed include the density, diversity, and clonality of the peripheral TCR repertoire (8,58,116,117). In phase II randomized NEOSTAR trial, patients received neoadjuvant nivolumab or nivolumab + ipilimumab, and TCR sequencing performed in a small subset of samples revealed that pre-treatment peripheral TCR repertoire richness was positively correlated with T cell richness in resected tumors after neoadjuvant ICIs treatment (Spearman’s correlation coefficient, R=0.82; P=0.02) (58). The study also found that resected lung tumors from patients who received ICIs treatment had higher TCR richness and clonality compared to adjacent uninvolved lung tissue. Similarly, In the phase II NEOTIDE trial, Zhang et al. (118) evaluated the safety and efficacy of neoadjuvant sintilimab + chemotherapy in patients with EGFR-mutant NSCLC. The study revealed that patients resistant to immunochemotherapy exhibited a high CCR8⁺ regulatory T cell (Treg) ratio and TCR clonal expansion, along with a low CXCL13⁺ exhausted T cell TCR clonal expansion (118). These findings suggest that varying subtypes of TCR may reflect differential ICIs treatment responses. Incorporating multiple TCR-related parameters into predictive models may enhance accuracy, and further research is warranted to explore the predictive potential of novel, yet uncharacterized, TCR subtypes.

In summary, the possible mechanisms that explain why the peripheral TCR repertoire can serve as a biomarker for predicting ICIs efficacy are as follows: (I) greater peripheral TCR repertoire diversity and clonality indicate a more diverse T cell pool and a better capacity for antigen recognition. This suggests that after ICIs lift the immune suppression in tumor tissues, T cells are better able to recognize tumor antigens and activate an immune response (116,119). (II) ICIs treatment enhances the recruitment of dominant T cell clones from the periphery to the tumor. Higher peripheral TCR repertoire diversity and clonality imply a richer and more diverse pool of T cells available for recruitment to the tumor, leading to better treatment outcomes (58,120).

Peripheral immune cells

Peripheral immune cells are immune cells circulating in the blood, including various T cells, B cells, NK cells, and other immune cells like macrophages and APCs. These cells collectively identify tumor-associated antigens and mediate the immune response against tumors. Peripheral blood immune cells can be analyzed via methods such as single-cell sequencing, NGS, and flow cytometry (121,122). Research indicates dynamic changes in peripheral immune cells after neoadjuvant immunotherapy in resectable NSCLC patients, which correlates with ICIs treatment effectiveness, suggesting their potential as biomarkers (15,123-125).

Huai and colleagues analyzed peripheral blood samples from 189 NSCLC patients before and after neoadjuvant therapy [including eosinophil fraction, absolute neutrophil count (ANC), and absolute lymphocyte count (ALC)]. They found that patients with an increased eosinophil fraction post-treatment had a higher MPR rate, but there was no significant association with OS or EFS (124). Additionally, survival analysis indicated that elevated post-treatment neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte (PLR) ratios were significantly associated with poorer OS and EFS (124). These findings may be explained by mechanisms where activated eosinophils recruit CD8⁺ T cells, while neutrophils secrete cytokines that dampen NK and lymphocyte activity (124,126-128). Moreover, in the NADIM trial, pre- and post-neoadjuvant nivolumab plus chemotherapy blood samples from NSCLC patients showed lower activated CD4⁺ T and NK cell density, along with downregulated PD-1 expression in pCR patients post-treatment (125). Both trials validated the predictive potential of peripheral immune cells and highlighted the differential impact of distinct immune cell subsets on the antitumor immunity. These findings also underscore the importance of multiparametric predictive models. However, further research across larger cohorts is needed due to cellular complexity and variability.

Although the mechanisms by which peripheral immune cells predict ICIs efficacy are complex, they can be summarized as follows: (I) their composition and activity reflect the patient’s immune status—higher T cell density and a more diverse TCR repertoire typically indicate a robust immune state. Additionally, their dynamic nature allows for real-time monitoring of immunotherapy effectiveness (121,123,129,130). (II) Peripheral immune cells can migrate to tumor tissue in response to immune signals [e.g., mucosal-associated invariant T (MAIT) cells migrate to NSCLC tumor sites via the CCR6-CCL20 axis (131)], reflecting the TME status and indirectly indicating the effect of ICIs on tumors (128,131,132).

Complete blood count (CBC)

CBC is based on measuring absolute values and ratios of circulating blood cells and is considered a potential predictor of tumor response to ICIs. A patient’s CBC includes several parameters, such as the absolute counts of white blood cells, eosinophils, monocytes, neutrophils, hemoglobin, and platelets, as well as ratios like the NLR, monocyte-to-lymphocyte ratio (M:L), and PLR. Other specific metrics include the prognostic nutritional index (PNI), composed of serum albumin levels and peripheral blood lymphocyte counts. These parameters have been analyzed in numerous studies on NSCLC immunotherapy, with some showing correlations with treatment efficacy and prognosis (125,130,133-136).

In NSCLC patients undergoing neoadjuvant immunotherapy, CBC has shown notable biomarker potential. Laza-Briviesca and colleagues studied various CBC metrics in NSCLC patients receiving neoadjuvant immunochemotherapy, finding that only PLR showed a significant difference between patients with pCR and those with incomplete response (>10% viable tumor, pIR) (125). Liu et al. conducted a similar study, showing that patients in the MPR group had significantly reduced leukocyte and neutrophil counts after neoadjuvant immunotherapy (137). Liu et al. (138) conducted a retrospective analysis of 116 NSCLC patients receiving neoadjuvant immunochemotherapy and found that a higher baseline NLR was significantly associated with a lower pCR rate (P=0.001) and shorter DFS (P=0.021) (138). These findings support the prognostic value of CBC-derived biomarkers and highlight the potential of integrating multiple CBC parameters in future predictive models.

While CBC includes numerous and complex metrics, current research results support several mainstream views and their possible mechanisms of action: (I) high NLR is generally associated with poorer prognosis in NSCLC patients receiving neoadjuvant ICIs, likely due to neutrophil-mediated inflammatory responses and immune suppression, while lymphocytes play a critical role in anti-tumor immunity, with low lymphocyte levels often indicating a weakened immune response (127,130,133,137). (II) High PLR is also often linked to poor outcomes in NSCLC patients treated with neoadjuvant ICIs (125,133). This may be due to platelets’ role in secreting cytokines and growth factors that inhibit T cell activation and promote metastatic lesions. Additionally, platelets may form complexes with CTCs, facilitating immune evasion (133,139). (III) Eosinophil count is often associated with favorable ICIs outcomes, possibly due to their chemokine-mediated recruitment of CD8⁺ T cells to combat tumor cells (124,126). Finally, PNI is often positively correlated with ICIs treatment efficacy, likely due to serum albumin’s role in nutritional status, which is closely tied to immune function. Therefore, the combined index of lymphocyte and serum albumin levels, PNI, is naturally linked to immune activity (136).

In summary, peripheral blood biomarkers offer the advantages of accessibility and non-invasiveness, but their complexity warrants further research. CBC, peripheral immune cells, and peripheral TCR repertoire biomarkers are interrelated and can be evaluated in combination for a comprehensive assessment.

Host-based biomarkers and mechanism

The gut microbiota, which comprises the microorganisms residing in the human gastrointestinal tract, is considered a key factor in promoting host health. A healthy gut microbiota includes members of the Clostridiales, Bacteroidetes, and Firmicutes phyla, which all play crucial roles in the host. Notably, species such as Akkermansia, Bifidobacterium, Faecalibacterium, Lactobacillus, and Ruminococcaceae have been found to aid in ICIs treatment response (140). Advances in molecular biology are gradually unveiling the complex interactions between the gut microbiome and the host, with growing evidence suggesting that the bacterial composition in the gut may play a critical role in cancer immunotherapy (58,141).

The NEOSTAR phase II trial investigated the differences in gut microbiome composition among patients receiving neoadjuvant ICIs and its associations with treatment response and toxicity. While no significant differences were found in diversity, gene-level Linear Discriminant Analysis Effect Size (LEfSe) results indicated that Paraprevotella and Akkermansia were associated with MPR in nivolumab and nivolumab + ipilimumab groups, respectively. Additionally, Dialister sp. was linked to reduced toxicity with nivolumab, and Bifidobacterium, Enterobacteriaceae, and an unclassified Erythrobacteraceae species were associated with reduced toxicity in the dual therapy group (58). Bredon et al. found that a high baseline abundance of F. prausnitzii was positively associated with favorable clinical responses to ICIs and was correlated with improved OS in patients with advanced NSCLC (P=0.008) (142).

Gut microbial-derived metabolites, synthesized from dietary compounds or host metabolites by microbes, have also been shown to impact systemic immune function. These include short-chain fatty acids (SCFAs), indole metabolites, and polyamines (143,144). Routy and colleagues found that fecal microbiota transplants from responders into germ-free mice improved the anti-tumor effects of PD-1 inhibitors in the mice (145). Collectively, these findings underscore the predictive potential of gut microbiota and their associated metabolites in clinical response to ICIs.

The complex mechanisms underlying gut microbiota and its metabolites as predictive biomarkers in NSCLC perioperative ICIs treatment require further study. Possible mechanisms include: (I) beneficial bacteria like Akkermansia may modulate host immunity, enhancing ICIs effectiveness by activating DCs, recruiting CD8⁺ T cells to the TME, and inducing an IL-12-dependent Th1 immune response for improved tumor immunity (15,146,147); (II) gut microbial-derived metabolites modulate ICIs response by influencing immune function. For example, SCFAs produced by colonic microbial fermentation of dietary fiber enhance forkhead box protein P3 (FOXP3) expression and increase Treg cell numbers by inhibiting histone deacetylase 9 (HDAC9), contributing to immune regulation (143,144). Indole metabolites, derived from tryptophan degradation by gut bacteria, influence TH17 and Treg differentiation by promoting transforming growth factor β (TGF-β)-induced Treg expansion. Polyamines, derived from foods like cheese and soy, also participate in T cell activation, with different Th subsets having varying dependencies on extracellular polyamines (143,144,148).

In summary, gut microbiota and their metabolites significantly influence ICIs treatment outcomes, showing promising potential as predictive markers for ICIs efficacy.

Conclusions

In summary, the advent of ICIs has brought new hope to many cancer patients. For NSCLC patients, those with advanced disease have benefited from ICIs, while perioperative ICIs treatment for early resectable NSCLC is also under active research, aimed at addressing the high postoperative recurrence rates. However, the treatment currently faces two major challenges: the low response rate of ICIs in NSCLC patients and the occurrence of irAEs. To address these issues, researchers are focusing on biomarkers that can predict ICIs efficacy and provide insights for treatment decisions, thereby maximizing the therapeutic benefits of ICIs.

This article first provides a brief overview of the mechanisms of anti-PD-1/PD-L1, revealing its potential connections with certain biomarkers. We then summarize the overall applications of anti-PD-1/PD-L1 in NSCLC and highlight the current clinical research progress in perioperative ICIs treatment, along with achievements in treating early resectable NSCLC. Finally, we summarize the promising biomarkers identified in clinical trials and explore their predictive capabilities from a mechanistic perspective, including the immunobiological principles affecting ICIs efficacy and the potential interconnections among multiple biomarkers (Figure 3).

Figure 3 Interaction network of biomarkers in predicting anti-PD-1/PD-L1 treatment efficacy. There exists an interconnection between various predictive biomarkers, forming a complex interaction network that collectively modulates tumor immune status and the effect of anti-PD-1/PD-L1 therapies. The two key opposing forces in this network are immune cells (particularly tumor-infiltrating lymphocytes, TILs) and the tumor itself. Factors impacting the strength of each side during this process have the potential to become biomarkers. (I) TIL-associated biomarkers: TMB reflects the tumor’s mutational landscape and neoantigen load. More neoantigens mean a higher likelihood of immune cells recognizing tumor antigens and being activated, which helps activate both peripheral immune cells and TILs. At the same time, the density, richness, and clonality of the TCR repertoire affect immune cells’ ability to recognize antigens. The activity of TILs directly impacts the efficacy of anti-PD-1/PD-L1. Peripheral immune cells, including neutrophils, eosinophils, and lymphocytes, influence the migration of lymphocytes by secreting cytokines and chemokines, which in turn affects the number and activity of TILs, thus influencing the ICIs response. Additionally, gut microbiota, their metabolites, and TLSs also influence TILs quantity and quality, reflecting their pivotal role in anti-tumor immunity. (II) Tumor-associated biomarkers: MRD in circulating blood indicates, to some extent, tumor burden and disease prognosis, with ctDNA being the primary effective parameter for monitoring MRD in solid tumors. High levels of ctDNA suggest greater tumor clonal evolution and malignancy. Furthermore, certain indicators in the CBC, such as platelets, can form complexes that promote tumor escape, contributing to the tumor’s side. PD-L1 expression in tumor tissues directly affects the efficacy of anti-PD-1/PD-L1 therapies. These biomarkers reflect the strength of the tumor. Given the intricate interaction networks between biomarkers, the combined analysis of multiple biomarkers becomes crucial in predicting the outcome of this “war” and the final ICIs response and tumor prognosis. Green background indicates tissue-based biomarkers, red background indicates blood-based biomarkers and yellow background indicates host-based biomarkers. This figure was created by Figdraw with ID: PUAYR80022. CBC, complete blood count; ctDNA, circulating tumor DNA; ICIs, immune checkpoint inhibitors; MRD, minimal or molecular residual disease; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; TCR, T cell receptor; TILs, tumor-infiltrating lymphocytes; TMB, tumor mutation burden; TLSs, tertiary lymphoid structures.

Overall, the predictive mechanisms of these biomarkers can be categorized into two aspects: First, they relate to the host’s immune status. They may directly reflect the host’s immune status and ICIs treatment efficacy and prognosis, or indirectly affect ICIs efficacy by influencing immune responses and anti-tumor immunity in the TME. For example, TILs and TLSs directly reflect the composition and diversity of immune cells in the TME and the status of anti-tumor immunity; peripheral TCR repertoire and peripheral immune cells indicate the host’s immune reserve, affecting TME and ICIs efficacy through the recruitment and migration of immune cells; PD-L1 expression directly indicates the level of ICIs response. Second, they relate to the tumor tissue’s condition. They may directly reflect the malignancy of the tumor to indicate ICIs efficacy, as seen with ctDNA-based MRD reflecting tumor genomic instability and clonal evolution; or they may indirectly influence ICIs efficacy by affecting tumor conditions, such as platelets promoting metastatic lesions and mediating immune evasion. Analyzing these mechanisms from both perspectives could provide starting points for the discovery of new biomarkers and offer fresh insights. Furthermore, interpreting these mechanisms can help us identify connections among multiple biomarkers and establish multimodal biomarker models, thereby improving tumor response assessment and enhancing ICIs treatment efficacy.

It is important to note that some issues have emerged in clinical trials concerning biomarkers. Differences in MPR results and the correlation of biomarkers among various trials remain unclear, potentially due to variations in treatment dosage, scheduling, sample size, or a lack of standardized assessments. Further standardized research protocols and evaluation methods may help mitigate uncertainties to some extent. Moreover, many trials face challenges related to insufficient sample sizes for biomarker measurements, hindering robust and evidence-based evaluation of their predictive value. Measurement methods also require improvement. For instance, the lack of standardization in cut-off values, detection platforms, and sequencing technologies—particularly for ctDNA and TCRβ—hinders cross-study comparability and delays the clinical translation of these biomarkers (149). Furthermore, the absence of matched samples at different treatment time points limits dynamic monitoring of biomarker changes over time (15). High costs associated with sequencing technologies further impede research on biomarkers such as ctDNA, TMB, and TCR.

Notably, artificial intelligence (AI)-based approaches are increasingly playing an important role in biomarker research, expanding the scope of biomarker discovery and showcasing the unique ability to integrate multimodal data from existing datasets to identify new meta-biomarkers. However, these methods remain in the research phase and have yet to demonstrate practical changes (150). More extensive prospective clinical trials, combined with the advancement of accurate, accessible, and cost-effective sequencing technologies, may promote the standardization of detection methods and cut-off values and enable deeper exploration and broader clinical application of predictive biomarkers. The goal is to develop a multimodal, high-throughput, and dynamically monitored predictive scheme for personalized ICIs treatment, providing better therapeutic strategies and precision for the perioperative management of early resectable NSCLC.

Acknowledgments

We sincerely thank all the participants. Some icons or graphic elements in Figures 1-3 are created by us using Figdraw platform (https://www.figdraw.com/#/). Final schematic illustrations were created and integrated by our original design.

Footnote

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-162/prf

Funding: This study was supported by National Natural Science Foundation of China (Nos. 82072557, 82372855 to H.L.), National Key Research and Development Program of China (No. 2021YFC2500900 to H.L.), Interdisciplinary Program of Shanghai Jiao Tong University (No. YG2023ZD04 to H.L.), Novel Interdisciplinary Research Project from Shanghai Municipal Health Commission (No. 2022JC023 to H.L.), Natural Science Foundation of Shanghai Municipal (No. 22ZR1439200 to R.J.), and Shanghai Leading Talent Program from Shanghai Municipal Commission of Commerce.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-162/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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