Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide
[1]. Surgical resection remains the standard treatment for early-stage NSCLC, yet postoperative recurrence rates range from 25% to 70%, resulting in 5-year overall survival (OS) rates of only 35% to 65%
[2-3]. Currently, stage I and II NSCLC are typically treated with surgery, while stage III lung cancer remains a challenging disease with significant heterogeneity, with a 5-year OS rate of approximately 13% to 36%. Surgical intervention faces inherent limitations in early and intermediate stages due to tumor characteristics such as large size, critical anatomical location, or mediastinal lymph node involvement
[4]. Historically, neoadjuvant chemotherapy offered minimal survival benefits, with only a 5% OS improvement
[5]. Currently, immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have become standard treatments for advanced or metastatic NSCLC, demonstrating good clinical efficacy, and significantly transforming NSCLC management
[6-8]. In contrast, perioperative therapy for resectable NSCLC has long relied primarily on chemotherapy. Updated 5-year follow-up data from the CA209-159 study
[9] demonstrate promising outcomes with immunotherapy, showing that patients treated with the PD-1 inhibitor nivolumab monotherapy achieved a remarkable 80% 5-year OS rate and a 60% 5-year recurrence-free survival (RFS) rate.Furthermore, the CheckMate 816 study
[10-11] confirmed durable survival benefits from neoadjuvant nivolumab plus chemotherapy for patients with resectable NSCLC with its 5-year follow-up data. With a median follow-up of 68.4 months, the combination therapy group achieved a 65.4% 5-year OS rate. Building on these findings, recent advances in neoadjuvant immunotherapy for stage II-III NSCLC show particular promise. Phase I/II trials reveal differential efficacy between treatment modalities: Monotherapy achieves major pathological response (MPR) rates of 19% to 45%, whereas combination strategies (e.g., chemotherapy combined with immunotherapy or dual immunotherapy) demonstrate substantially higher MPR rates of 33% to 83%
[12-15].
Although several biomarkers—such as PD-L1 expression, tumor mutational burden (TMB), circulating tumor DNA (ctDNA), and gut microbiota—have been explored to predict the efficacy of ICIs, their clinical application remains limited by tumor heterogeneity, invasive sampling procedures, and high cost
[16-20]. In contrast, peripheral blood-based inflammatory indices offer a minimally invasive, dynamic, and cost-effective alternative for monitoring systemic immune activation. Parameters such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and eosinophil counts have shown promise in reflecting the host’s inflammatory status and predicting therapeutic outcomes. Elevated NLR and SII, for instance, correlate with immunosuppressive microenvironments and poorer survival in advanced NSCLC treated with ICIs. Conversely, early eosinophil recovery post-treatment may signify favorable immune activation
[21-23]. Although a few studies
[22, 24-26] have investigated the role of peripheral blood-based inflammatory markers in the context of neoadjuvant immunotherapy, most were limited by small sample sizes, lack of dynamic evaluation, or insufficient integration of multiple markers into predictive models.
The study aims to evaluate therapeutic outcomes and identify prognostic biomarkers in stage IB-IIIB NSCLC patients who received neoadjuvant chemo-immunotherapy as first-line therapy. The endpoint is to assess the associations between baseline and preoperative peripheral blood parameters—particularly inflammatory markers such as interleukin-6 (IL-6), NLR, PLR, lymphocyte-to-monocyte ratio (LMR), and SII—and key clinical outcomes, including objective response rate (ORR), pathological complete response (pCR), MPR, and disease-free survival (DFS). By evaluating inflammation-related markers at both baseline and preoperative timepoints, this study develops a predictive model with strong discriminatory performance. The study also aims to identify clinicopathological features and prognostic factors that influence the efficacy of neoadjuvant immunotherapy in NSCLC.
1 Materials and methods
1.1 Ethics statement
This retrospective clinical study was approved by the Medical Ethics Committee of the Second Xiangya Hospital, Central South University (Ethics approval number: LYF20220092).
1.2 Participants
This retrospective study collected data on NSCLC patients who underwent neoadjuvant immunotherapy at our hospital between April 2019 and October 2022. The inclusion criteria were: Age 18 to 75 years; histologically confirmed primary bronchogenic lung cancer; clinical stage IB to IIIB according to the American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system; at least one measurable lesion evaluable by RECIST 1.1 criteria; an ECOG performance status of 0-1; receipt of neoadjuvant immunotherapy combination with chemotherapy for no more than 10 months (either PD-1 or PD-L1 inhibitor); and sufficient cardiopulmonary function and laboratory parameters to tolerate the treatment. Exclusion criteria included: Inability to tolerate surgery; positivity for epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), or ROS proto-oncogene 1, receptor tyrosine kinase (ROS1) mutations; presence of locally advanced or metastatic disease; coexisting other malignancies; severe primary diseases affecting the heart, liver, kidneys, or hematopoietic system; inability to understand or comply with the study procedures or refusal to sign informed consent; pregnancy or lactation; and any other malignancy requiring systemic treatment.
1.3 Data collection and evaluation
The study retrospectively analyzed electronic medical records of NSCLC patients meeting inclusion criteria to collect clinical, pathological, and laboratory data. This included patient demographics (gender, age, height, and weight), smoking history (including smoking index: Daily cigarettes×years of smoking), tumor characteristics (location, TNM stage, programmed PD-L1 expression), treatment details, imaging results, and survival outcomes (DFS, OS). DFS refers to the time from initiation of neoadjuvant therapy to disease recurrence, metastasis, or death from any cause, whichever occurs first. OS is defined as the time from treatment initiation to death from any cause. Peripheral blood parameters (neutrophils, monocytes, lymphocytes, platelets, IL-6, and albumin) were evaluated at baseline (pre-immunotherapy) and pre-surgery timepoints. From these data, we calculated several ratios and indices: the PLR; the LMR (defined as the absolute lymphocyte count divided by the absolute monocyte count); the prognostic nutritional index [PNI; calculated as albumin (g/L) plus 5 times the total lymphocyte count (×109/L)]; the SII (defined as the platelet count multiplied by the lymphocyte count divided by the white blood cell count); the NLR (defined as the absolute neutrophil count divided by the absolute lymphocyte count); and the NLR rate (defined as the ratio of preoperative NLR to baseline NLR).
Receiver operating characteristic (ROC) curves were employed to determine optimal cut-off values for peripheral blood parameters. In this study, DFS served as the binary classification label. ROC curves were constructed to identify the optimal thresholds for peripheral blood-related indicators, including baseline PLR, LMR, PNI, SII, neutrophil count, NLR, platelet count, monocyte count, albumin level, IL-6 level, preoperative SII, preoperative LMR, preoperative NLR, and NLR rate. ROC curves systematically evaluate classifier performance by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) across varying thresholds. The optimal threshold was defined as the point on the ROC curve closest to the top-left corner (maximizing combined sensitivity and specificity). Further validation utilized the Youden index to confirm the critical values for stratifying peripheral blood parameters into high and low groups.
1.4 Construction of a predictive model for neoadjuvant immunotherapy efficacy in NSCLC
Clinical, pathological, and post-immunotherapy DFS data from NSCLC patients undergoing neoadjuvant immunotherapy were analyzed. Variables strongly associated with DFS outcomes were selected using least absolute shrinkage and selection operator (Lasso) regression under the minimum lambda (λ) constraint. A multivariate Cox proportional hazards model with stepwise regression was subsequently constructed to establish a scoring equation for predicting recurrence or mortality risk. Patients were stratified into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival analysis compared outcomes between groups, while ROC curves and area under the curve (AUC) calculations assessed the model’s predictive accuracy.
1.5 Statistical analysis
Analyses were performed using GraphPad Prism 9, RStudio, R (v4.3.3), and SPSS 27.0. Categorical data were described as No.(%), while continuous variables were described as median (25th percentile, 75th percentile) for non-normally distributed data or means±standard deviations (SDs) for normally distributed data. Group comparisons employed chi-square or Fisher’s exact tests (for categorical variables) and parametric/non- parametric tests (t-tests, Mann-Whitney U, or Wilcoxon tests for continuous variables, based on normality and variance homogeneity). Spearman’s correlation assessed variable relationships. Univariate/multivariate Logistic regression identified associations between clinicopathological features, peripheral blood parameters, and treatment outcomes (ORR, pCR, and MPR), while Cox proportional hazards regression analyzed DFS predictors. Survival differences were evaluated via Kaplan-Meier curves with log-rank tests. Statistical significance was set at P<0.05 (two-tailed).
2 Results
2.1 Patient baseline characteristics and peripheral blood biomarkers
This study included 144 patients with stage IB-IIIB NSCLC who received neoadjuvant chemo-immunotherapy. The cohort was predominantly male (131/144, 91.0%), smokers (101/144, 70.1%), with squamous cell carcinoma (107/144, 74.3%), stage IIIA (77/144, 53.5%), treated with 3 to 4 cycles of immunotherapy (85/144, 59.0%), and preoperative RECIST evaluations indicating partial response (PR) (95/144, 66.0%). Among the patients, 107 (74.3%) were under 65 years old at diagnosis, 74 (51.4%) had a BMI<24 kg/m
2, 11 (7.6%) had tumor PD-L1 expression <1%, 75 (52.1%) had 1%-49%, 41 (28.5%) had ≥50%, and 17 (11.8%) were not tested. Additionally, 95 (66.0%) had a smoking index≥400. The pathological differentiation was low to moderate in 80 (55.6%), moderate to high in 54 (37.5%), and unknown in 10 (6.9%) patients. Regarding the types of immunotherapy administered, the majority received pembrolizumab (48/144, 33.3%) and toripalimab (46/144, 31.9%). Other immunotherapies included sintilimab (27/144, 18.8%), camrelizumab (5/144, 3.5%), nivolumab (8/144, 5.6%), and unknown types (4/144, 2.8%). Surgical approaches varied, with thoracoscopic resection being the most common (73/144, 50.7%), followed by thoracotomy (51/144, 35.4%) and robotic-assisted resection (20/144, 13.9%;
Table 1). The specific numerical distributions of peripheral blood-related indicators are presented in the following
Table 2.
2.2 Clinical outcomes of neoadjuvant immunotherapy in NSCLC
This study enrolled 144 stage IB-IIIB NSCLC patients who received neoadjuvant immunotherapy, with pathological response evaluable in 139 patients. The ORR in the cohort was 69.8%. Compared with non-MPR patients, MPR patients exhibited a significantly higher ORR (
P=0.031). However, no significant correlations were observed between ORR and pCR or DFS (Supplementary Figure
1A and
1B,
https://doi. org/10.57760/sciencedb.36384). Among evaluable patients, 59 (42.4%) achieved pCR and demonstrated significantly longer DFS versus non-pCR patients (
P=0.039, Supplementary
Figure 1C;
https://doi.org/10.57760/sciencedb. 36384), while 90 (64.7%) attained MPR without significant DFS improvement (
P=0.079, Supplementary
Figure 1D;
https://doi.org/10.57760/sciencedb.36384).
After median follow-up of 28.8 months (95% CI 26.1 to 31.0) through February 2024, 27 DFS events occurred. Overall median DFS was not reached, with 12- and 24-month DFS rates of 92.3% and 84.1% respectively. Kaplan-Meier analysis confirmed superior DFS in pCR versus non-pCR groups (P=0.039). For MPR versus non-MPR comparison, 12-month DFS rates were 95.5% versus 89.1% and 24-month rates 87.8% versus 75.4%, showing a non-significant trend toward reduced recurrence/death risk in MPR patients (HR=0.509, 95% CI 0.236 to 1.098, P>0.05).
2.3 Peripheral blood parameters
2.3.1 Correlation with clinical outcomes
Statistical analysis of baseline hematologic indicators revealed that, except for preoperative lymphocyte count (
P=0.010) and preoperative neutrophil count (
P=0.014), which were positively correlated with ORR, no significant associations were observed between ORR and baseline PLR, LMR, PNI, SII, NLR, platelet count, monocyte count, albumin level, or IL-6 (all
P>0.05,
Table 3).
Comparative analysis using the Mann-Whitney
U test for non-normally distributed continuous variables revealed distinct inflammatory biomarker profiles associated with pathological response. In the pCR cohort, significant associations were observed between pathological complete response and baseline LMR (
P=0.031), SII (
P=0.038), IL-6 (
P=0.043), platelet count (
P=0.031), and albumin levels (
P=0.043,
Table 4). Similarly, MPR correlated significantly with baseline SII (
P=0.010), platelet count (
P=0.011), albumin (
P=0.019), and IL-6 (
P=0.046) when compared to non-MPR counterparts (
Table 5). These findings systematically demonstrate that peripheral blood inflammation markers exhibit differential expression patterns predictive of depth of pathological regression.
2.3.2 Predictive value for pathological response
In univariate logistic regression, MPR was significantly associated with higher ORR (OR=2.31, 95% CI 1.07 to 4.97, P=0.033). Squamous histology was also more likely to achieve CR or PR compared with adenocarcinoma (OR=3.12, 95% CI 1.37 to 7.13, P=0.007). Variables with P<0.20 were entered into multivariate analysis—including histology, PD-L1 expression, agent type, surgical approach, baseline and preoperative LMR, and preoperative neutrophil count—and only low baseline LMR remained an independent predictor of ORR (OR=0.21, 95% CI 0.05 to 0.92, P=0.04). For pCR, univariate regression identified squamous versus adenocarcinoma histology (OR=2.47, 95% CI 1.12 to 6.62, P=0.027), high baseline PLR (OR=2.51, 95% CI 1.21 to 5.21, P=0.014), and low baseline PNI (OR=0.31, 95% CI 0.12 to 0.78, P=0.013) as significant correlates; none retained significance in multivariate analysis. In the MPR cohort, univariate analysis showed squamous histology (OR=2.63, 95% CI 1.17 to 5.93, P=0.019), high baseline PLR (OR=2.31, 95% CI 1.03 to 5.21, P=0.043), high baseline SII (OR=2.80, 95% CI 1.23 to 6.39, P=0.015), high baseline IL-6 (OR=3.20, 95% CI 1.04 to 9.85, P=0.043), and low preoperative neutrophil count (OR=2.31, 95% CI 1.12 to 4.78, P=0.024) as significant. Only squamous histology remained an independent predictor in multivariate analysis (OR 7.34, 95% CI 1.02 to 52.51, P=0.047).
ROC analysis of baseline PLR, LMR, PNI, SII, NLR, IL-6 and preoperative LMR, SII, NLR, NLR ratio showed no association with ORR. For pCR, elevated baseline SII (AUC=0.607,
P=0.038), reduced baseline LMR (AUC=0.613,
P=0.030), and elevated baseline IL-6 (AUC=0.634,
P=0.042) were predictive. For MPR, elevated baseline SII (AUC=0.639,
P=0.010) and elevated baseline IL-6 (AUC=0.651,
P=0.045) were significant (
Figure 1).
2.3.3 Prognostic value for DFS
To further explore the association between peripheral blood immune and inflammatory markers and DFS, cutoff values derived from ROC curves were used to dichotomize these markers into high/low groups, followed by Kaplan-Meier survival analysis. The results demonstrated that baseline low NLR (
HR=0.363,
P=0.014), preoperative low LMR (
HR=0.260,
P=0.018), and preoperative high SII (
HR=0.278,
P=0.003) were significantly associated with reduced DFS risk. We further examined associations between other peripheral blood markers (baseline neutrophil count, platelet count, monocyte count, albumin level, cortisol level; preoperative lymphocyte count, and neutrophil count) and DFS. After similar dichotomization (ROC-based cutoffs), Kaplan-Meier analysis revealed that baseline low neutrophil count conferred reduced DFS risk versus high neutrophil count (
HR=0.458,
P=0.042;
Figure 2). Conversely, baseline low platelet count (
HR=0.552,
P=0.152), high monocyte count (
HR=0.427,
P=0.083), low albumin (
HR=0.041,
P=0.072), low cortisol (
HR=0.458,
P=0.068), and preoperative high lymphocyte count (
HR=0.535,
P=0.073) showed non-significant trends toward reduced DFS risk.
2.3.4 Construction of a predictive model
This study employed Lasso regression with the minimum lambda criterion to identify and consolidate 12 optimal variables significantly associated with DFS, namely age, smoking status, pathological type, baseline NLR, baseline neutrophil count, baseline IL-6, baseline monocyte count, baseline cortisol, pre-treatment lymphocyte count, pre-treatment SII, pre-treatment LMR, and NLR ratio (Figures
3A and
3B). Subsequent stepwise multivariable Cox proportional hazards regression analysis incorporated nine key factors (
Figures 3C)—age (
P=0.096), pathological type (
P=0.264), baseline NLR (
P=0.018), baseline neutrophil count (
P=0.051), baseline IL-6 (
P=0.094), baseline monocyte count (
P=0.093), pre-treatment lymphocyte count (
P=0.108), pre-treatment SII (
P=0.006), and pre-treatment LMR (
P=0.029)—into a predictive model for disease progression risk, defined by the formula: Risk score =(0.666×age)+(0.457×pathological type)+(1.012×baseline NLR)+(0.782×baseline neutrophil count)+(1.723×baseline IL-6)-(0.850×baseline monocyte count)-(1.638×pre-treatment lymphocyte count)-(1.280×pre-treatment SII)+(1.346×pre-treatment LMR), where variables were coded as: Pathological type (1: Squamous carcinoma, 2: Adenocarcinoma), Age (1: <65 years, 2: ≥65 years), Baseline NLR, Baseline neutrophil count, Baseline IL-6, Baseline monocyte count, Pre-treatment lymphocyte count, Pre-treatment SII, and Pre-treatment LMR (all 1: Low expression, 2: High expression); notably, pre-treatment SII (
HR=0.256, 95%
CI 0.087 to 0.752,
P=0.013) and pre-treatment LMR (
HR=3.982, 95%
CI 1.126 to 14.081,
P=0.032) emerged as independent predictors of DFS in this NSCLC neoadjuvant immunotherapy cohort. Individual risk scores were calculated, and model accuracy was validated via ROC curve analysis (AUC=0.818,
Figure 3D) using Kaplan-Meier-estimated survival probabilities; stratification by median risk score revealed significantly reduced disease recurrence or death risk in the low-risk versus high-risk group (
HR=0.18, 95%
CI 0.073 to 0.446,
P<0.001), despite median DFS not being reached, confirming strong predictive value (
Figure 4).
3 Discussion
Although surgery remains the standard of care for early and locally advanced NSCLC, postoperative recurrence and poor long-term survival continue to limit clinical outcomes
[27-30]. In recent years, neoadjuvant PD-1/PD-L1 ICIs have demonstrated promising rates of pCR and MPR, yet substantial response heterogeneity persists. This study addresses an unmet need by validating the predictive potential of peripheral blood inflammation-related biomarkers for treatment response and survival.
Our findings show that elevated baseline SII is significantly associated with higher pCR and MPR rates. This observation presents an intriguing paradox: although SII has historically been linked to tumor-promoting inflammation and poor prognosis
[31-32]. In our study, it may reflect an immunologically active tumor microenvironment (TME) more likely to respond to ICIs. This is supported by previous evidence showing that patients with low SII exhibit more active B-cell receptor signaling and increased immune cell infiltration. Furthermore, dynamic SII changes during treatment were independently associated with MPR
[22], reinforcing the utility of SII as a biomarker for predicting ICI response. These findings suggest that a pre-treatment inflammatory state may support immune priming within the TME.IL-6 also emerged as a robust predictive factor. While serum IL-6 has been shown to predict ICI efficacy, especially in NSCLC patients with low or absent PD-L1 expression
[33], elevated plasma IL-6 levels have been associated with poorer outcomes
[34]. In our study, low baseline plasma IL-6 correlated with higher pCR and MPR, aligning with these prior findings. This highlights potential biological distinctions between serum and plasma compartments and raises questions about IL-6’s temporal role in modulating immunity—whether plasma IL-6 better reflects real-time immune status, and whether neoadjuvant treatment capitalizes on IL-6’s immunostimulatory effects before immune exhaustion occurs. These considerations warrant further study into the timing and optimal biological compartment for IL-6 assessment.
In addition to inflammation indices, this study identified several cellular and protein biomarkers with prognostic significance. Low baseline LMR independently predicted ORR, while changes in preoperative LMR (
HR=0.260) and SII (
HR=0.278) were more predictive of DFS than baseline values, underscoring the importance of monitoring dynamic immune changes during therapy. Elevated baseline platelet counts were associated with improved pCR and MPR (AUC=0.611 to 0.640), challenging the traditional view of platelets as purely pro-metastatic
[35]. Increasing evidence suggests a close association between platelets and immune activity, particularly in lung cancer. Platelet levels may reflect immune cell trafficking and tumor proliferation, and declines in platelet counts may signal effective immunotherapy. Moreover, an elevated postoperative/preoperative platelet ratio has been linked to poor prognosis and may serve as a reliable predictor of long-term outcomes in NSCLC
[36]. Conversely, low baseline albumin levels were associated with inferior pathological response, further supporting the link between nutritional status, systemic inflammation, and ICI efficacy. Albumin has strong prognostic value in patients undergoing immunotherapy
[37], and prior studies suggest that only patients with normal or high albumin levels benefit from ICIs, supporting its use as a predictive biomarker
[38].
Most variables included in our final model were derived from baseline or preoperative peripheral blood parameters. Neutrophils and lymphocytes played particularly important roles, consistent with previous studies showing that elevated NLR and PLR at baseline are associated with shorter OS, PFS, and lower response rates in metastatic NSCLC treated with ICIs
[24, 39-40]. In addition, post-treatment increases in NLR, PLR, and SII have also been linked to worse OS and EFS
[22-23]. These results highlight the important role of immune-inflammatory biomarkers in predicting both response and prognosis in neoadjuvant immunotherapy and validate the feasibility of DFS prediction models.
Overall, peripheral blood biomarkers offer a practical and dynamic window into the tumor-immune interface. Rather than being viewed as isolated predictors, SII, IL-6, LMR, and albumin together form a systemic immune profile: SII may indicate “hot” tumors more likely to respond, platelet counts may reflect immune cell trafficking efficiency, and albumin levels may represent the host’s metabolic reserve. For clinical application, several challenges remain—most notably, establishing standardized cutoffs across populations, harmonizing sampling schedules, and developing multivariable prediction models that incorporate PD-L1, TMB, and other established factors.
Future prospective studies should focus on elucidating the mechanisms linking pre-treatment inflammation and ICI susceptibility and evaluate whether nutritional or anti-inflammatory interventions can modulate these biomarkers. Such efforts could facilitate the transition of neoadjuvant immunotherapy from empirical use to a precision-driven approach, ultimately improving outcomes for patients with resectable or borderline-resectable NSCLC.
In conclusion, peripheral-blood inflammation-related parameters show promise as prognostic and predictive biomarkers for neoadjuvant immunotherapy efficacy in NSCLC. Their integration into clinical practice could enable more personalized treatment planning, reduce unnecessary toxicity, and ultimately improve outcomes for patients with resectable and borderline-resectable lung cancer.
the Key Scientific Research Project of the Hunan Provincial Department of Science and Technology(2024PT5102)
the Outstanding Youth Program under the Scientific Research Project of the Hunan Provincial Department of Education(23B0011)
the Wu Jieping Medical Foundation(320.6750.2023-05-39)
the Postgraduate Innovative Project of Central South University(2024XQLH151)
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