Lung cancer is a commonly diagnosed human malignancy worldwide, ranking the first in cancer-related mortality among all the cancer types (1). The traditional therapy paradigm for lung cancer includes surgery, chemoradiotherapy and targeted therapy, while its five-year relative survival is only approximately 23% (2). The introduction of immune checkpoint inhibitor (ICI) based immunotherapy holds promise for advanced cancer patients, and its durable efficacy with controllable adverse events is observed in accumulating clinical trials (3). The pharmacological mechanism of ICIs is inhibition of cellular receptors including programmed cell death protein 1 (PD-1), programmed death ligand-1 (PD-L1) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), known as representative drugs such as Pembrolizumab, Durvalumab and Ipilimumab respectively (4). Despite encouraging achievements, some evidences have suggested only less than half of patients benefit from ICI therapy and few patients even experience tumor hyperprogression at the initial stage (5). Currently, various factors are found to affect the clinical efficacy of ICI drugs such as PD-L1 expression, tumor mutation burden (TMB), microbiome and immune infiltration (6). Previously, our team has proved the detrimental effect of several concomitant drugs on ICI efficacy (7–9). A further investigation into the factors affecting ICI efficacy will contribute to developing novel predictive strategies for patient outcome, leading to more tailored therapy decision.

Recently, emerging studies have suggested some clinical parameters could be utilized as non-invasive and low-cost biomarkers for predicting ICI-treated lung cancer patients. For instance, the neutrophil-to-lymphocyte ratio (NLR) has been identified as a prognostic predictor for non-small-cell lung cancer (NSCLC) patients receiving combined immunotherapy and chemotherapy, regardless of PD-L1 expression (10). The early treatment levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH), instead of their pretreatment baseline levels, are found to significantly associate with one-year OS of ICI-treated NSCLC patients (11). The modified Glasgow prognostic score (mGPS), determined by CRP and albumin (ALB) level, could predict the OS and progression-free survival (PFS) of NSCLC patients receiving ICI consolidation after chemoradiotherapy (12). The systemic immune-inflammation index (SII), combining platelet count and NLR, is an unfavorable factor affecting the PFS of NSCLC patients receiving PD-1 inhibitors (13). Our team has previously proved prognostic nutritional index (PNI) as a reliable prognostic indicator for both ICI-treated NSCLC and small cell lung cancer (SCLC) patients (14). However, these prognostic indexes are mainly based on laboratory tests, ignoring individual status such as performance status (PS) and smoking status, which may be also crucial for ICI efficacy (15, 16). Therefore, one or two parameters are far from sufficient to precisely stratify the outcome of ICI-treated NSCLC patients and novel predictive methods are urgently needed.

A recent multicenter retrospective study has developed a comprehensive scoring system that integrates CRP, LDH, ALB, dNLR and PS (named as C-PLAN index) to predict the prognosis of NSCLC patients receiving immunotherapy combined with other anti-cancer therapies (17). In this study, an independent cohort was utilized to further investigate the prognostic value of C-PLAN index in ICI-treated lung cancer patients. The study will not only contribute to validating a novel prognostic index for patient management, but also highlight the potential utility of integrating multiple clinical parameters in outcome prediction.

Although a great paradigm shift has been made by ICIs in the anti-cancer therapeutic scenario, their real benefit remains controversial and limited predictive biomarkers can be used for therapy decisions (18). PD-L1 is currently identified as a powerful molecular biomarker to guide ICI use, however, its accuracy may vary greatly with sample type and immunohistochemistry evaluation (19, 20). The advancing sequencing technique has provided TMB as a promising predictive biomarker for ICI therapy, but methodological differences and high cost limit its actual utilization (21). Accumulating evidences have closely linked gut microbiome with immunotherapy efficacy in lung cancer, while identifying prognosis-related bacteria and developing unified standards for detection methods are still challenging (22). Moreover, emerging studies have highlighted the potential utility of blood inflammatory or nutritional markers as prognostic markers, but their actual predictive performance may be far from satisfactory in metastatic NSCLC patients receiving first-line ICI therapy (23). Therefore, development and validation of novel, cost-effective and non-invasive biomarkers for ICI-treated lung cancer patients is an urgent task for clinicians. A recent multicenter study for the first time has integrated PS status, inflammatory and nutritional markers into a novel biomarker named as C-PLAN index, which was found to effectively stratify the outcome of NSCLC patients receiving immunochemotherapy (17). In this study, a single-center retrospective study including both NSCLC and SCLC patients was performed to further validate whether C-PLAN could act as a reliable prognostic biomarker for ICI-treated lung cancer patients.

For our entire cohort, patients with high C-PLAN index had a significantly worse PFS and OS than those with low C-PLAN index. The high C-PLAN index was associated with lower DCR as compared with low C-PLAN index. More importantly, the negative correlation of C-PLAN index with clinical outcome remained statistically significant in the multivariate analysis excluding other known risk factors such as tumor stage. These findings collectively confirmed C-PLAN index as an actionable prognostic predictor for ICI-treated lung cancer patients. The C-PLAN index includes following parameters: CRP, LDH, ALB, dNLR and PS score, all of which have been proved to associate with the clinical efficacy of ICI-based therapy. A meta-analysis enrolling 4698 patients has suggested higher pretreatment CRP level is correlated with worse OS and PFS in NSCLC patients receiving ICI therapy (24). The LDH level is found to significantly affect OS and PFS of NSCLC patients receiving nivolumab, although it fails to be proved as an independent prognostic factor in the multivariate analysis (25). In a real-world study, albumin <3.5 g/dL is identified as a crucial unfavorable prognostic factor in stage IV NSCLC patients receiving the first-line immunotherapy (26). High pre-treatment NLR level predicts poor outcome in advanced NSCLC patients receiving first-line pembrolizumab therapy (27). In KRASG12C-Mutant NSCLC patients receiving chemo-immunotherapy, poor PS is an independent unfavorable factor affecting OS and PFS (28). The PS ≥2 is also correlated with poor outcome in 70+ year-old lung cancer patients receiving pembrolizumab alone or with chemotherapy (29). Considering their potential prognostic value, the C-PLAN index combined and optimized these different factors to help clinicians effectively select patients who may benefit from ICI-based therapy.

For further clarifying the prognostic significance of C-PLAN index, the subgroup analysis was performed based on several major clinical features including pathological type, therapy strategy, TNM stage and age. The result identified C-PLAN index as an independent adverse prognostic factor for OS and PFS, regardless of pathological type and TNM stage, strongly supporting its clinical utilization. It is worth mentioning that the C-PLAN index fails to stratify the clinical outcome of patients receiving ICIs alone and patients with age ranging from 18 to 65 years old, which may be partly attributed to the limited sample size. To our knowledge, some recent studies have suggested potential novel prognostic biomarkers for these patients. For instance, RNA-binding motif protein 17 (RBM17) expression in tumor samples could be used to predict the clinical efficacy of ICI monotherapy in NSCLC patients with a low PD-L1 expression (30). Pre-treatment plasma levels of cachexia related immune mediators (such as osteopontin and pentraxin‐3) are found to associate with outcome of advanced or recurrent NSCLC patients receiving PD‐1/PD‐L1 inhibitor monotherapy (31). Increased non-classical monocytes are correlated with favorable outcome in NSCLC patients receiving anti-PD-1 antibody monotherapy, while the opposite was observed for PD-L1-expressing classical monocytes (32). In future, these biomarkers need to be validated in more retrospective cohorts and may be considered to combine with the C-PLAN index in outcome prediction.

Some recent mechanism investigations can be used to partly explain the correlation between C-PLAN index and ICI efficacy. Elevated pretreatment CRP may contribute to the production of adenosine 2a receptor that subsequently suppresses antitumor immune cell functions and upregulates immunosuppressive genes (33). High LDH level usually indicates enhanced glycolytic activity in tumors, resulting in glucose deprivation and tumor acidity to hamper antitumor immunity (34). In terms of drug pharmacokinetics, ICI drugs are mainly developed from IgG antibodies, and albumin participates in the catabolism and recycling of IgG antibodies through its binding with neonatal Fc receptor (35). The transcriptomic analysis reveals low baseline NLR level is correlated with increased expression of CD3, SH2D1A, ZAP70 and CD45RA, all of which are involved in immune activation (36). With regard to PS, lung cancer patients with PS > 2 are more likely to have serious comorbidities such as chronic obstructive pulmonary disease (COPD) that needs long-term corticosteroid use. Dexamethasone as a representative corticosteroid drug is found to inhibit the functions of activated T lymphocytes through upregulating PD-1 expression, finally diminishing ICI efficacy (37). Considering their potential regulatory role in tumor immunity, whether some interventions such as nutrition support and physical exercise could benefit ICI efficacy through improving these markers is worth further investigations.

Several limitations in our present study should be noted. Firstly, the sample size is relatively small and multicenter validations are essential in future. Secondly, due to the retrospective nature of the study, various factors such as patient selection, ICI drug type and treatment strategy may lead to heterogeneity. For instance, the sample size of the female patients in our study was limited (n=19), which may serve as a confounding variable. Therefore, more attention should be paid to the actual prognostic value of C-PLAN in subgroups such as female patients or patients receiving ICIs alone. Thirdly, whether C-PLAN index is correlated with the adverse events of combined therapy or ICI monotherapy remains unclear. Finally, the study mainly focused on the prognostic value of pretreatment C-PLAN index and whether its dynamic evaluation during therapy has any benefit for patient management also needs to be clarified.

In summary, our study identified C-PLAN index as a novel prognostic predictor for advanced lung cancer patients receiving ICI-based anti-cancer therapies. In future, more retrospective validations are essential and the clinical utility of C-PLAN index in ICI-treated patients with other cancers needs to be investigated.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

The studies involving humans were approved by Affiliated Hospital of Yangzhou University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

JW: Conceptualization, Writing – original draft. HJG: Data curation, Formal analysis, Writing – original draft. JY: Resources, Software, Writing – review & editing. JM: Validation, Visualization, Writing – review & editing. YW: Data curation, Writing – review & editing. XY: Funding acquisition, Writing – review & editing. HG: Conceptualization, Writing – review & editing.