Abstract
Purpose
The Scottish Inflammatory Prognostic Score (SIPS), derived from serum albumin and neutrophil count integrates inflammatory and nutritional status. However, its prognostic role in advanced non–small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors remains unclear.
Methods
This multicenter, retrospective study included 816 patients with advanced or metastatic NSCLC who received nivolumab following platinum-based chemotherapy between January 2015 and January 2025 at 19 oncology centers in Turkey. The SIPS was calculated at baseline by assigning one point each for albumin < 35 g/L and neutrophil count > 7.5 × 10⁹/L, classifying patients as favorable (0), intermediate (1), or poor (2). Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan–Meier analysis and compared using the log-rank test. Cox regression models were used to identify independent prognostic factors.
Results
The median OS decreased across the SIPS groups: 15.0 months (95% CI 12.9–17.1), 8.0 months (95% CI 5.3–10.7), and 5.0 months (95% CI 3.5–6.5) for the favorable, intermediate, and poor groups, respectively (p < 0.001). The median PFS was 7.0, 4.0, and 2.0 months (p < 0.001). In the multivariate analysis, SIPS remained independently associated with both OS (HR = 3.19, 95% CI 1.90–5.35, p < 0.001) and PFS (HR = 3.02, 95% CI 1.89–4.84, p < 0.001).
Conclusions
Baseline SIPS is a simple, cost-effective, and independent prognostic biomarker in nivolumab-treated NSCLC. Prospective studies are needed to validate these findings and evaluate its integration with other markers to improve clinical risk stratification and may be useful for prognostic assessment.
Keywords: Non-small cell lung cancer, Nivolumab, Systemic inflammation, SIPS, Immunotherapy
Introduction
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases [1]. Despite therapeutic advances, the prognosis of patients with advanced or metastatic NSCLC remains poor, with 5-year survival rates typically below 20% [2]. The introduction of immune checkpoint inhibitors (ICIs) targeting the programmed death-1 (PD-1) receptor and its ligand (PD-L1) has markedly improved clinical outcomes in a subset of patients with NSCLC. Nivolumab, a fully human IgG4 monoclonal antibody against PD-1, demonstrated a significant survival benefit compared with docetaxel in previously treated NSCLC in the pivotal CheckMate 017 and CheckMate 057 trials, leading to its regulatory approval as a second-line therapy [3, 4]. However, the expanding number of second- and later-line treatment options has increased the need for reliable prognostic tools to interpret outcomes across heterogeneous patient populations [5].
Nevertheless, only a proportion of patients derive durable benefits from PD-1 blockade, and reliable predictive and prognostic biomarkers are needed. Although PD-L1 expression and tumour mutational burden (TMB) are widely used to guide immunotherapy decisions, their predictive performance is suboptimal because of inter-assay variability, intratumoural heterogeneity, and limited tissue availability [6–8]. Consequently, interest has shifted toward systemic inflammation and nutritional indices that reflect the host immune–inflammatory state and tumour microenvironment, and new potential predictive and prognostic biomarkers are under investigation [9, 10].
Systemic inflammation plays a crucial role in cancer progression and immune evasion. Biomarkers such as the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum albumin levels have been investigated as prognostic indicators in patients treated with ICIs [11, 12]. The Systemic Inflammation and Prognosis Score (SIPS), which integrates two easily measurable laboratory parameters—serum albumin and absolute neutrophil count—has recently emerged as a simple and objective prognostic index [13]. Among the available inflammation-based prognostic indices, such as the NLR, Lung Immune Prognostic Index (LIPI), and modified Glasgow Prognostic Score (mGPS), SIPS offers several advantages [14, 15]. Unlike multicomponent indices that require complex calculations or less routinely measured parameters, SIPS is derived solely from two universally available laboratory markers—serum albumin and absolute neutrophil count—allowing for rapid, inexpensive, and reproducible assessments in community oncology settings. Moreover, SIPS integrates the nutritional and inflammatory dimensions of the host response, providing a more comprehensive reflection of the tumour–host interaction than purely inflammation-based ratios. Its prognostic value has been validated in multiple malignancies, including hepatocellular and oesophageal cancers, supporting its generalisability [16–19]. However, evidence regarding its prognostic significance in NSCLC remains scarce, particularly in real-world cohorts of patients treated with nivolumab.
Therefore, this multicentre, retrospective study aimed to evaluate the prognostic value of the SIPS score in patients with advanced or metastatic NSCLC treated with nivolumab following platinum-based chemotherapy. We hypothesised that higher SIPS scores would be independently associated with shorter progression-free survival (PFS) and overall survival (OS) in this population.
Materials and methods
Study design
This multicenter, retrospective cohort study was conducted at 19 oncology centers across Turkey. The study included patients diagnosed with advanced or metastatic NSCLC who were treated with nivolumab between January 2015 and January 2025. The study adhered to the principles of the Declaration of Helsinki and received approval from the ethics committees of all participating institutions.
Patient selection
Eligible participants were adults (≥ 18 years) with histologically confirmed advanced or metastatic NSCLC who had received at least one cycle of nivolumab following prior platinum-based chemotherapy. Inclusion required the availability of baseline laboratory data, specifically albumin and absolute neutrophil count values obtained within 14 days before nivolumab initiation, and sufficient follow-up information for survival analysis. Patients were excluded if they had concurrent active malignancies, lacked essential clinical data, or discontinued nivolumab after the first cycle for non-medical reasons.
Data collection
Demographic, clinical, and treatment-related data were extracted from electronic medical records. The variables collected included age, sex, Eastern Cooperative Oncology Group Performance Status (ECOG PS), tumor histology (either squamous or non-squamous), PD-L1 expression, and metastasis pattern (either de novo or metachronous). Additionally, the presence of brain, liver, or bone metastases and smoking status were recorded for each patient. Information on previous systemic treatments and the treatment line at which nivolumab was initiated were also documented. Given the long accrual period (2015–2025), potential calendar-time confounding was considered. Although treatment patterns in Türkiye remained largely stable because of reimbursement policies, patients were grouped by nivolumab initiation era (2015–2020, 2021–2025) to account for temporal differences in diagnostics, supportive care, and subsequent therapies. Data completeness was cross-verified at each center, and cases with missing essential variables were excluded from the multivariate analysis.
SIPS calculation
Baseline laboratory values were obtained from routine blood tests conducted within 14 days before nivolumab therapy initiation, consistent with the methodology used in the original study [13]. The SIPS was determined by assigning one point each for albumin levels below 35 g/L and neutrophil counts exceeding 7.5 × 10⁹/L, thereby categorising patients into three prognostic groups: 0 (favourable), 1 (intermediate), and 2 (poor) [12].
Disease evaluation (response assessment)
Baseline imaging was performed using CT or PET-CT and repeated in accordance with institutional protocols. Disease progression was evaluated based on radiology reports and, when available, was defined using the immune Response Evaluation Criteria in Solid Tumors (iRECIST) to capture immune-related response patterns.
Outcome measures
The primary endpoint was OS, defined as the interval between nivolumab initiation and death from any cause. Patients who were still alive at the time of the analysis were censored at their last recorded follow-up. The secondary endpoint was PFS, measured from the start of nivolumab treatment to either the first documented disease progression or death from any cause. Patients without progression at the data cutoff were censored at their most recent follow-up. The duration of follow-up was calculated using the reverse Kaplan–Meier method.
Follow-up time was estimated using the reverse Kaplan–Meier method.
Statistical analysis
All analyses were conducted using IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp. Armonk, NY, USA). Continuous variables were summarized as mean ± standard deviation (SD) or median (interquartile range, IQR), and categorical variables as frequencies and percentages. A formal power calculation was not performed because of the retrospective nature of the study; however, the final sample size (n = 816) provided sufficient statistical power for multivariate modelling. A sensitivity analysis was performed to assess the potential impact of missing PD-L1 data by comparing the baseline characteristics and survival outcomes between PD-L1–evaluable and non-evaluable patients. Survival curves were generated using the Kaplan-Meier approach, and the log-rank test was applied to assess the differences. For survival analysis, Cox’s proportional hazards model was employed, and hazard ratios (HRs) were calculated. Multivariate survival analysis including all covariates in the initial model (gender, age, histological subtype, ECOG PS, PD-L1 status, metastatic disease, brain metastasis, liver metastasis, bone metastasis, smoking history, SIPS score, treatment era) was performed using a stepwise backward procedure to derive a final model of the variables that had a significant independent relationship with survival outcomes. To remove a variable from the model, the corresponding p-value had to be > 0.10. This study adheres to the Reporting Recommendations for Tumor Marker Prognostic Studies guideline. Statistical significance was set at p < 0.05.
Results
Patient characteristics
Comprehensive baseline clinical and demographic details are presented in Table 1. A total of 816 patients with advanced or metastatic NSCLC were included in the study. The mean age was 63.7 (± 8.9) years (range, 31–89), with 52.7% aged < 65 years. Most patients were male (86.4%) and had a history of smoking (83.3%). Non-squamous (53.7%) and squamous cell carcinoma (46.3%). ECOG PS was 0–1 in 87,1% of cases. PD-L1 expression data were available for 355 patients (43.5% of the cohort), whereas the remaining 56.5% had missing values. 67.2% of patients were de-novo metastatic. 20.3% of patients had brain metastasis, 17.5% of patients have liver metastasis, and 42.7% of patients have bone metastasis. The median follow-up duration, estimated using the reverse Kaplan–Meier method, was 20.0 months (95% CI: 18.5–21.5). At the time of data cut-off, 65.8% of the patients had disease progression and 57.8% had died.
Table 1.
Baseline clinical and demographic characteristics stratified by SIPS
| Characteristics | Total (N = 816) | SIPS 0 (n = 544) | SIPS 1 (n = 215) | SIPS 2 (n = 57) | p-value |
|---|---|---|---|---|---|
| Age | |||||
| < 65 | 430 (52.7) | 286 (52.6) | 111 (51.6) | 33 (57.9) | 0.54 |
| 65–74 | 303 (37.1) | 200 (36.8) | 81 (37.7) | 22 (38.6) | |
| ≥ 75 | 83 (10.2) | 58 (10.7) | 23 (10.7) | 2 (3.5) | |
| Sex | |||||
| Female | 111 (13.6) | 80 (14.7) | 22 (10.2) | 9 (15.8) | 0.23 |
| Male | 705 (86.4) | 464 (85.3) | 193 (89.8) | 48 (84.2) | |
| ECOG PS | |||||
| 0 | 170 (20.8) | 121 (22.2) | 36 (16.7) | 13 (22.8) | < 0.05 |
| 1 | 541 (66.3) | 368 (67.6) | 142 (66.0) | 31 (54.4) | |
| 2 | 90 (11.0) | 47 (8.6) | 34 (15.8) | 9 (15.8) | |
| 3 | 15 (1.8) | 8 (1.5) | 3 (1.4) | 4 (7.0) | |
| Histology | |||||
| Non-squamous | 438 (53.7) | 300 (55.1) | 111 (51.6) | 27 (47.4) | 0.41 |
| Squamous | 378 (46.3) | 244 (44.9) | 104 (48.4) | 30 (52.6) | |
| PD-L1 | 355* | 251 (70.7) | 83 (23.4) | 21 (5.9) | 0.30 |
| 0 | 133 (37.5) | 97 (38.6) | 26 (31.3) | 10 (47.6) | |
| ≥ 1% | 222 (62.5) | 154 (61.4) | 57 (68.7) | 11 (52.4) | |
| Metastasis | |||||
| De-novo | 549 (67.2) | 360 (66.1) | 149 (69.3) | 40 (70.2) | 0.58 |
| Metachronous | 267 (32.8) | 184 (33.9) | 66 (30.7) | 17 (29.8) | |
| Brain metastases | |||||
| Absent | 650 (79.7) | 436 (80.1) | 168 (78.1) | 46 (80.7) | 0.80 |
| Present | 166 (20.3) | 108 (19.9) | 47 (21.9) | 11 (19.3) | |
| Liver metastases | |||||
| Absent | 673 (82.4) | 444 (81.6) | 186 (86.5) | 43 (75.4) | 0.152 |
| Present | 143(17.5) | 100 (18.3) | 29 (13.4) | 14 (24.5) | |
| Bone metastases | |||||
| Absent | 467 (57.2) | 309 (56.8) | 128 (59.5) | 30 (52.6) | 0.63 |
| Present | 349 (42.7) | 235 (28.7) | 87 (40.4) | 27 (47.3) | |
| Smoking | |||||
| No | 136 (16.7) | 95 (17.5) | 31 (14.4) | 10 (17.5) | 0.58 |
| Yes | 680 (83.3) | 449 (82.5) | 184 (85.6) | 47 (82.5) | |
| Treatment era | |||||
| 2015–2020 | 219 (26.8) | 145 (26.6) | 58 (26.9) | 16 (28) | 0.98 |
| 2021–2025 | 597 (73.2) | 399 (73.4) | 157 (73.1) | 41 (72) | |
SIPS Systemic Inflammation Prognostic Score; ECOG PS Eastern Cooperative Oncology Group Performance Status
*PD-L1 expression was evaluable in 355 patients
Among the 816 patients (SIPS 0 = 66%, SIPS 1 = 26%, SIPS 2 = 6%), the age and sex distributions were similar across the groups. However, ECOG PS showed significant variation (p < 0.05), with poorer functional status noted in higher SIPS categories. There were no significant differences in histology, metastasis patterns, smoking status, treatment era or the presence of brain, liver, or bone metastases. It should be noted that PD-L1 expression data were available for only 355 of the 816 patients (43.5%), with more than half of the cohort lacking evaluable PD-L1 results. To assess whether missing PD-L1 data might have influenced the survival analyses, exploratory comparisons were conducted between patients with evaluable and non-evaluable PD-L1 status. No significant differences were observed in the baseline characteristics or survival outcomes between these two subgroups (p > 0.05), suggesting that the missing data were unlikely to introduce major selection bias. However, the high rate of missing data warrants a cautious interpretation of PD-L1–related findings. Overall, the groups were comparable, except for ECOG PS, which deteriorated with increasing SIPS, indicating a higher systemic burden among patients with elevated inflammatory scores.
SIPS and overall survival
The median survival time was 13.0 months (95% CI: 11.4–14.6) for the overall cohort (Table 2). The median survival time significantly decreased across the SIPS categories ( p < 0.001): 15.0 months (95% CI: 12.9–17.1) for the favorable group, 8.0 months (95% CI: 5.3–10.7) for the intermediate group, and 5.0 months (95% CI: 3.5–6.5) for the poor group. This stepwise decline demonstrates the robust prognostic discrimination of the SIPS score.
Table 2.
SIPS patients survival outcomes
| SSIPS | Patients n (%) |
Median OS | p | Survival at 3 Months n (%) |
Survival at 6 Months n (%) |
Survival at 12 Months n (%) |
|---|---|---|---|---|---|---|
| 0 | 544 (44.2%) | 15 | < 0.001 | 445 (81%) | 357 (65%) | 240 (44%) |
| 1 | 215 (32,2%) | 8 | 138 (64%) | 103 (48%) | 59 (27%) | |
| 2 | 57 (14.3%) | 5 | 32 (56%) | 17 (29%) | 8 (14%) |
| SSIPS | Patients n (%) |
Median PFS | p | Survival at 3 Months n (%) |
Survival at 6 Months n (%) |
Survival at 12 Months n (%) |
|---|---|---|---|---|---|---|
| 0 | 544 (44.2%) | 7 | 0.38 | 354 (65.0%) | 249 (45.7%) | 138 (25.3%) |
| 1 | 215 (32.2%) | 4 | 106 (49.3%) | 77 (35.8%) | 42 (19.5%) | |
| 2 | 57 (14.3%) | 2 | 16 (28%) | 7 (12.2%) | 4 (7.0%) |
SIPS Systemic Inflammation, OS overall survival, PFS progression-free survival
Pairwise comparisons confirmed significant survival differences among the SIPS groups (Fig. 1). Survival was significantly longer in the favorable group than in the intermediate (p < 0.001) and poor (p < 0.001) groups, and in the intermediate group compared with the poor group (p = 0.002). Collectively, these findings reinforce the prognostic validity of the SIPS classification in stratifying patient outcomes.
Fig. 1.
Kaplan–Meier survival curves examining the association between the Scottish Inflammatory Prognostic Score (SIPS) and overall survival (A) and progression free survival (B)
In the univariate Cox regression analysis, ECOG PS (HR = 1.39, 95% CI: 1.01–1.91, p = 0.04), albumin score (HR = 0.56, 95% CI: 0.46–0.69, p < 0.001), neutrophil score (HR = 1.76, 95% CI: 1.42–2.18, p < 0.001), SIPS score (HR = 2.67, 95% CI: 1.96–3.64, p < 0.001), and presence of liver metastasis (HR = 1.38, 95% CI: 1.22–1.72, p = 0.003) were significantly associated with overall survival (OS). These results indicate that a poorer performance status, higher inflammatory burden, and presence of liver metastasis were linked to an increased risk of death (Table 3). Other variables, including sex, age, histological subtype, PD-L1 status, metastatic pattern, brain metastasis, bone metastasis, smoking history, and treatment era were not significantly associated with OS in the univariate model.
Table 3.
Univariate and multivariate cox regression analysis for overall survival
| Variable | Univariate HR (95% CI) | p-value | Multivariate HR (95% CI) | p-value |
|---|---|---|---|---|
| Gender (male, female) | 0.89 (0.69–1.15) | 0.39 | – | – |
| Age (≤ 64, 65–74, ≥ 75) | 0.96 (0.72–1.29) | 0.82 | – | – |
| Histological Subtype (non-squamous, squamous) | 1.12 (0.94–1.34) | 0.17 | – | - |
| ECOG PS (0, 1, 2, 3) | 1.39 (1.01–1.91) | 0.04 | – | – |
| PD-L1 (0, ≥ 1%) | 0.92 (0.68–1.24) | 0.59 | – | – |
| Metastatic disease (denovo, metochronic) | 0.85 (0.71–1.03) | 0.10 | – | – |
| Brain metastasis (absent, present) | 1.05 (0.85–1.30) | 0.63 | – | – |
| Liver metastasis (absent, present) | 1.38 (1.11–1.72) | 0.003 | 1.39 (1.11–1.73) | 0.004 |
| Bone metastasis (absent, present) | 1.09 (0.90–1.30) | 0.35 | – | – |
| Smoking history (no, yes) | 1.16 (0.91–1.48) | 0.20 | – | – |
| Albumin score (0, 1) | 0.56 (0.46–0.69) | < 0.001 | – | – |
| Neutrophil score (0, 1) | 1.76 (1.42–2.18) | < 0.001 | – | – |
| Sips score (0, 1, 2) | 2.67 (1.96–3.64) | < 0.001 | 3.19 (1.90–5.35) | < 0.001 |
| Treatment era(2015–2020,2021–2025) | 0.87 (0.72–1.06) | 0.18 |
HR hazard ratio, CI confidence interval, ECOG PS Eastern Cooperative Oncology Group Performance, SIPS Systemic Inflammation Prognostic Score
In the multivariate analysis, ECOG PS was included as a covariate to adjust for functional status; however, it was not statistically significant after accounting for SIPS and other covariates and was excluded during the backward elimination process. Variance inflation factor (VIF) assessment revealed a moderate overlap between ECOG PS and SIPS, supporting this exclusion to avoid multicollinearity. Importantly, the prognostic effect of SIPS persisted after the initial adjustment for ECOG PS, confirming its independent prognostic significance. In the final model, SIPS (HR = 3.19, 95% CI, 1.90–5.35; p < 0.001) and liver metastasis (HR = 1.39, 95% CI, 1.11–1.73; p = 0.004) remained significant independent predictors of OS among the study population.
SIPS and secondary outcomes
The median PFS differed significantly across the SIPS score categories, demonstrating a distinct prognostic gradient (Table 2). Patients in the favorable group had the longest median PFS of 7.0 months (95% CI: 6.0–8.0), whereas those in the intermediate group had a median PFS of 4.0 months (95% CI: 3.2–4.8). The poor group showed the shortest median PFS of 2.0 months (95% CI: 1.4–2.6). For the overall cohort, the median PFS was 5.0 months (95% CI: 4.3–5.7). These findings indicate a clear and progressive decline in disease control with worsening SIPS categories, confirming the prognostic value of the SIPS score for progression-free survival outcomes.
Pairwise comparisons confirmed significant differences in PFS among the SIPS categories (Fig. 1). Survival was significantly longer in the favorable group than in both the intermediate (p = 0.042) and poor (p < 0.001) groups, while the intermediate group also outperformed the poor group (p < 0.001). These findings confirm the prognostic significance of the SIPS score in relation to progression-free survival. These results indicate a clear stepwise decline in PFS across worsening SIPS categories, confirming the strong prognostic utility of the SIPS score for disease progression.
In the univariate Cox regression analysis for PFS, the albumin score (HR = 1.39, 95% CI, 1.15–1.67; p < 0.001), neutrophil score (HR = 1.42, 95% CI, 1.16–1.74; p = 0.001), SIPS score (HR = 2.23, 95% CI, 1.66–2.99; p < 0.001), ECOG PS (HR = 0.58, 95% CI, 0.34–1.00; p = 0.004), and the presence of liver metastasis (HR = 1.35, 95% CI, 1.11–1.65; p = 0.003) were significantly associated with disease progression (Table 4). These findings indicate that lower albumin levels, higher neutrophil counts, poorer performance status, and liver involvement are associated with an increased risk of earlier progression. Other variables, including sex, age, histological subtype, PD-L1 expression, metastatic pattern, brain metastasis, bone metastasis, smoking history, and treatment era were not significantly associated with PFS in the univariate model.
Table 4.
Univariate and multivariate cox regression analysis of PFS
| Variable | Univariate HR (95% CI) | p-value | Multivariate HR (95% CI) | p-value |
|---|---|---|---|---|
| Gender (male, female) | 1.08 (0.87–1.35) | 0.47 | – | – |
| Age (≤ 64, 65–74, ≥ 75) | 0.98 (0.87–1.10) | 0.80 | – | – |
| Histological Subtype (non-squamous, squamous) | 1.02 (0.87–1.19) | 0.77 | – | – |
| ECOG PS (0, 1, 2, 3) | 0.58 (0.34–1.00) | 0.04 | 1.31 (0.97–1.75) | 0.072 |
| PD-L1 (0, ≥ 1%) | 1.13 (0.88–1.45) | 0.33 | – | – |
| Metastatic disease (denovo, metochronic) | 1.09 (0.92–1.29) | 0.28 | – | – |
| Brain metastasis (absent, present) | 0.99 (0.81–1.20) | 0.91 | – | – |
| Liver metastasis (absent, present) | 1.35 (1.10–1.65) | 0.003 | 1.33 (1.08–1.63) | 0.006 |
| Bone metastasis (absent, present) | 1.15 (0.98–1.35) | 0.086 | – | – |
| Smoking history (no, yes) | 1.01 (0.82–1.24) | 0.91 | – | – |
| Albumin score (0, 1) | 1.39 (1.15–1.67) | < 0.001 | – | – |
| score (0, 1) | 1.42 (1.16–1.74) | 0.001 | – | – |
| Sips score (0, 1, 2) | 2.23 (1.66–2.99) | < 0.001 | 3.02 (1.89–4.84) | < 0.001 |
| Treatment era(2015–2020,2021–2025) | 0.90 (0.75–1.07) | 0.23 |
PFS progression-free survival, HR hazard ratio, CI confidence interval, ECOG PS Eastern Cooperative Oncology Group Performance, SIPS Systemic Inflammation Prognostic Score
In the multivariate Cox regression analysis, ECOG PS was initially included to adjust for performance status but did not retain statistical significance after adjustment for SIPS and other covariates. Collinearity diagnostics demonstrated a moderate overlap between ECOG PS and SIPS, suggesting that both variables reflect overlapping aspects of the host’s systemic status. Consequently, ECOG PS was excluded from the backward elimination process to avoid multicollinearity. In the final model, SIPS (HR = 3.02, 95% CI: 1.89–4.84, p < 0.001) and the presence of liver metastasis (HR = 1.33, 95% CI: 1.08–1.63, p = 0.006) remained independent prognostic factors, confirming the robust and independent value of SIPS for disease progression.
PD-L1 expression data were available for 355 patients (43.5%), of whom 133 (37.5%) had PD-L1 expression < 1%, 222 (62.5%) had PD-L1 expression ≥ 1%, and 461 (56.5%) had unknown PD-L1 status (Table 1). SIPS demonstrated prognostic stratification within the available PD-L1 subgroups; however, given the high rate of missing PD-L1 data, conclusions regarding biological independence from PD-L1 should be interpreted cautiously (Table 5). In both the PD-L1-negative and PD-L1-positive subgroups, survival outcomes exhibited a consistent stepwise decline with increasing SIPS categories. Higher SIPS scores were significantly associated with shorter PFS and OS, independent of the PD-L1 status. Comparisons between the PD-L1 < 1% and ≥ 1% groups revealed no significant differences in either OS or PFS (p > 0.6). SIPS remained independently associated with outcomes, whereas PD-L1 did not show an association in this cohort. However, the interpretation of these subgroup analyses should be made with caution because of the high proportion of missing PD-L1 data (> 50%), which may have reduced the statistical power to detect potential differences between groups.
Table 5.
Association of the Scottish Inflammatory Prognostic Score and PD-L1 expression with survival outcomes in non–small-cell lung cancer patients receiving second-line nivolumab monotherapy
| SIPS | p | |||||
|---|---|---|---|---|---|---|
| 0 | 1 | 2 | ||||
| PDL-1 | 0 |
OS Median (95% CI) |
n = 96 19 (16.7–21.2) |
n = 26 8 (4.4–11.5) |
n = 10 5 (3.6–6.5) |
0.007 |
| ≥ 1% |
n = 157 18 (13.3–22.6) |
n = 52 10 (4.1–15.8) |
n = 10 6 (0.21 − 11.7) |
0.000 | ||
| p | 0.902 | 0.605 | 0.783 | |||
| SIPS | p | |||||
|---|---|---|---|---|---|---|
| 0 | 1 | 2 | ||||
| PDL-1 | 0 |
PFS Median (95% CI) |
n = 96 8 (5.6–10.3) |
n = 26 5 (1.6–8.3) |
n = 10 2 (1.2–2.7) |
0.008 |
| ≥ 1% |
n = 157 9 (6.9–11.08) |
n = 52 4 (2.4–5.5) |
n = 10 3 (1.6 − 4.3) |
0.001 | ||
| P | 0.65 | 0.65 | 0.69 | |||
SIPS Systemic Inflammation Prognostic Score, OS overall survival, PFS progression-free survival, HR hazard ratio, CI confidence interval
Discussion
In this large, multicentre, real-world cohort of patients with advanced or metastatic NSCLC treated with nivolumab following platinum-based chemotherapy, SIPS emerged as an independent prognostic biomarker for both OS and PFS. Patients with favourable SIPS scores demonstrated markedly improved outcomes compared with those with intermediate and poor scores. These findings confirm that systemic inflammation and nutritional status are strongly associated with survival, reinforcing the prognostic importance of host-related factors in NSCLC patients.
The pivotal CheckMate 017 and CheckMate 057 trials established nivolumab as an effective second-line therapy for both squamous and non-squamous NSCLC, producing durable survival benefit over docetaxel [3, 4]. However, only a subset of patients achieves long-term remission, and predictive and prognostic biomarkers remain insufficient. Tumour-related markers, such as PD-L1 expression and TMB, provide partial guidance but fail to fully capture interpatient heterogeneity [20–22]. Inflammation is a well-recognised hallmark of cancer, playing a pivotal role in tumour initiation, promotion, and progression [23]. Therefore, it is not unexpected that various indicators of systemic inflammation have been linked to poor prognosis in several solid malignancies, including NSCLC. Neutrophils are the most prevalent immune cells in the NSCLC microenvironment [24]. Higher neutrophil counts are associated with decreased survival in NSCLC [25–27]. In this regard, systemic inflammation-based scores have emerged as valuable and cost-effective tools for enhancing prognostic accuracy. Indices such as the NLR, LIPI, and mGPS have shown clinical relevance in patients with NSCLC undergoing immunotherapy [9, 10, 28, 29]. In our cohort, consistent with previous research, a high neutrophil count was associated with unfavourable outcomes for both PFS and OS.
Hypoalbuminemia indicates both systemic inflammation and nutritional deficiency, two interconnected conditions that can impair cellular immune function and diminish therapy tolerance [30]. Serum albumin levels have been found to correlate with survival in various diseases, including cancer [31]. In our cohort, hypoalbuminaemia was significantly associated with poorer PFS and OS.
The integration of these two parameters, including high neutrophil count and hypoalbuminaemia, allows SIPS to capture both immune-inflammatory activation and host nutritional reserve, offering a multidimensional reflection of the tumour–host balance at baseline. The median OS in our cohort (13.0 months overall; 15.0, 8.0, and 5.0 months for SIPS 0, 1, and 2, respectively) and median PFS (7.0, 4.0, and 2.0 months) followed a clear stepwise decline across risk categories. Importantly, the persistence of SIPS as an independent prognostic factor in multivariate analysis, alongside liver metastasis, highlights its robustness even after adjusting for clinical variables. Collectively, the consistency of these results across trials supports the cross-platform reproducibility of the SIPS as a reliable and inexpensive biomarker of immune fitness.
A poor ECOG PS is thought to reduce the efficacy of ICIs, likely because of weakened immune function, diminished lymphocyte activity, and shorter OS expectancy. Consequently, patients with an ECOG PS of 2 are often excluded from ICI clinical trials and remain underrepresented in studies focusing on special or frail populations [32]. However, whether ECOG PS serves as a purely prognostic indicator or also has predictive value for ICI response remains an unresolved question. In our cohort, although ECOG PS demonstrated a significant association with OS and PFS in the univariate analysis, it lost statistical significance in the multivariate analysis. This attenuation likely reflects the overlap between ECOG PS and the systemic inflammatory parameters encompassed by the SIPS score. In our cohort, ECOG PS showed significant variation across SIPS categories (p < 0.05), with poorer functional status observed in patients with higher SIPS scores. Because a deteriorating performance status is frequently accompanied by hypoalbuminaemia and elevated neutrophil counts, the SIPS may capture much of the prognostic information conveyed by the ECOG PS. Consequently, SIPS emerged as a strong and independent predictor of OS after adjusting for confounding variables.
Although earlier studies suggested that smoking-related NSCLC shows higher PD-L1 expression and TMB, leading to better responses to ICIs [33, 34], our analysis found no significant association between smoking status and survival rates. This result is consistent with a recent meta-analysis that also reported no meaningful difference in ICI efficacy between smokers and never-smokers across large randomised trials [35]. These findings indicate that smoking history alone may not reliably predict immunotherapy outcomes, as other factors, such as PD-L1 expression, TMB, systemic inflammation, and overall clinical status, play stronger roles. In our cohort, the prognostic effect of systemic inflammation-based indices, such as the SIPS score, and the lack of detailed smoking data (e.g. pack-years, cessation time) may have further reduced the apparent impact of smoking. Overall, our results support the growing evidence that smoking history should not be viewed as a universal predictor of ICI benefit.
It is not yet clear how survival outcomes related to immunotherapy are influenced by the type of metastases present at the start of ICI treatment. However, some studies have indicated that the effectiveness of ICIs differs depending on the metastatic site [36]. Retrospective studies have shown that patients with melanoma or NSCLC with liver metastases who are treated with ICIs tend to have weaker therapeutic responses and shorter PFS. This negative prognostic effect seems to be associated with a lower infiltration of CD8⁺ T cells at the margins of liver metastases, indicating an immunosuppressive microenvironment as a possible reason for poorer outcomes [37]. However, brain metastases have not been definitively identified as negative prognostic factors, as ICIs may cross the blood-brain barrier and elicit a response in certain patients [38]. Our analysis confirmed the negative prognostic impact of liver metastases, whereas the effects of bone and brain metastases on survival remain uncertain.
Although PD-L1 remains the most extensively validated biomarker for predicting the response to anti-PD-1/PD-L1 therapy in NSCLC, its predictive accuracy is limited [39]. CheckMate 017 trial, which included patients with squamous-cell NSCLC, PD-L1 expression was neither prognostic nor predictive of survival outcomes in this subgroup. In contrast, the CheckMate 057 trial, conducted among patients with non-squamous (adenocarcinoma) NSCLC, demonstrated that nivolumab yielded longer OS, improved progression-free survival, and higher objective response rates compared with docetaxel at prespecified PD-L1 expression thresholds of ≥ 1%, ≥ 5%, and ≥ 10%. In our cohort, however, no statistically significant differences in survival outcomes were observed between PD-L1-positive and PD-L1-negative patients, even after stratification by histological subtype (squamous versus non-squamous). These findings reinforce the limited predictive and prognostic utility of PD-L1 expression, particularly in real-world settings. Variability in testing assays, intratumoral heterogeneity, and temporal changes in PD-L1 expression may contribute to these discrepancies. The high proportion of missing PD-L1 expression data (> 50%) is another limitation, which restricts the robustness of the conclusions regarding its prognostic value. Although subgroup analyses comparing PD-L1-evaluable and non-evaluable patients did not show significant differences in survival, the possibility of residual bias due to incomplete biomarker data cannot be ruled out. Furthermore, the interpretation of PD-L1-related outcomes should be approached with caution because of the substantial proportion of patients lacking evaluable results in this real-world cohort. Collectively, these results highlight the need for more comprehensive biomarkers, such as inflammation-based indices or composite immune signatures, to better assess the survival of patients with NSCLC receiving immunotherapy.
The long accrual period of the study raises the possibility of calendar-time confounding. Although treatment standards in our setting remained largely stable, temporal differences in patient selection, diagnostic availability, imaging practices, supportive care, and post-progression therapies may still influence outcomes. To address this, patients were categorized into treatment eras (2015–2020, 2021–2025) and era was included as a covariate in multivariable models, with additional era-stratified analyses performed. Baseline characteristics were comparable across eras, and the prognostic effect of SIPS remained consistent, suggesting that the observed associations were not primarily driven by temporal changes in clinical practice.
The therapeutic landscape of advanced NSCLC beyond platinum failure has evolved substantially in recent years, with multiple systemic options becoming available in later lines of therapy [5]. While nivolumab remains a commonly used treatment after chemotherapy, alternative strategies including immunotherapy combinations, anti-angiogenic approaches, and targeted agents in biomarker-selected populations have been reported with varying efficacy [5]. Recent reports indicate median overall survival ranging approximately between 8 and 14 months in unselected post-platinum populations, with improved outcomes observed in selected molecular or clinical subgroups [5]. In this context, our findings do not aim to demonstrate superiority of a specific treatment but rather to provide clinically meaningful risk stratification within a uniformly treated cohort. Such prognostic classification may assist interpretation of real-world outcomes and facilitate patient stratification in future comparative studies.
Several limitations warrant consideration. First, its retrospective and multicentre design introduces potential heterogeneity among participating institutions. Differences in imaging intervals, supportive care practices, and reporting standards across centres could have influenced outcome assessments, and centre effects were not adjusted for statistically. Second, inclusion required the availability of baseline laboratory data (albumin and neutrophil counts). Patients lacking these results—often those with poorer clinical status or delayed testing—were excluded, which may have enriched the cohort for fitter individuals and slightly overestimated the survival outcomes. Third, the analysis is subject to potential immortal-time bias, as patients needed to survive long enough to undergo baseline testing and receive at least one cycle of nivolumab before being included in the dataset. This requirement may have artificially prolonged the estimated survival duration.
Fourth, unmeasured confounding cannot be ruled out in this study. Clinically relevant variables, such as corticosteroid or antibiotic exposure, lactate dehydrogenase (LDH) levels, nutritional indices, and comorbidity burden, were not systematically captured and may have influenced both SIPS values and treatment outcomes. Fifth, laboratory parameters were assessed only at baseline, and time-varying covariates and landmark analyses were not performed. Consequently, this study could not evaluate whether dynamic changes in systemic inflammation or nutritional status during treatment correlated with therapeutic response or prognosis. Finally, the non-uniform application of the iRECIST criteria across centres may have introduced minor variability in the progression assessment.
Despite these limitations, the large multicentre sample and consistent direction of associations across analyses strengthen the validity of the findings. Future prospective studies incorporating longitudinal SIPS assessments, standardised response evaluations, and adjustments for centre-level and clinical confounders are warranted to validate and extend these results.
Despite the limitations noted above, this study has several important strengths. The SIPS score uniquely combines indicators of both systemic inflammation and nutritional status, providing an integrated reflection of the host’s immune and metabolic conditions. Another major strength is the large, multicentre design, which included a high number of patients treated across multiple institutions, thereby enhancing generalisability of the findings. Furthermore, given that the SIPS is derived from routinely assessed peripheral blood parameters, it can be easily and rapidly implemented in daily oncology practice. Its simplicity and cost-effectiveness make it a practical tool for assisting clinicians in risk stratification and prognostic classification for patients receiving immune checkpoint inhibitors.
The clinical implications of these findings are substantial. Given its simplicity and universal accessibility, SIPS can readily complement PD-L1 testing and performance status in clinical workflows. Stratifying patients according to inflammatory and nutritional risks before initiating immunotherapy may help identify those who require intensified supportive care, earlier combination regimens, or closer monitoring. Additionally, incorporating SIPS as a stratification factor in future clinical trials could refine risk classification and allow for more balanced treatment comparison. Beyond its prognostic value, further studies should investigate whether dynamic changes in SIPS during therapy correlate with treatment response or immune-related adverse events, potentially expanding its utility as a monitoring biomarker for immunotherapy. Prospective studies integrating SIPS with molecular and radiomic biomarkers are warranted to further refine patient stratification and optimise personalised immunotherapy.
Conclusion
SIPS is a simple, inexpensive, and independent biomarker of survival in advanced NSCLC treated with nivolumab. Higher SIPS scores were consistently associated with shorter PFS and OS, confirming its prognostic validity across PD-1 inhibitor settings. Given its accessibility and reproducibility, SIPS provides clinically meaningful risk stratification and may be useful for prognostic assessment and clinical trial stratification in advanced NSCLC.
Acknowledgements
The authors would like to acknowledge Editage (www.editage.com) for providing pre-submission peer review support. The authors used ChatGPT (OpenAI, San Francisco, CA, USA) and Paperpal (https://paperpal.com) to improve the English language and readability of the manuscript. The authors reviewed and edited the content to ensure accuracy and originality.
Author contributions
Erdem KÖLEMEN: Writing – Original Draft, Data Curation, Investigation, Project Administration, Writing – Review & Editing, Formal Analysis, Methodology, Validation Umut Kefeli: Reviewed and revised the manuscript Devrim Çabuk, Kazım Uygun: Supervision, Conceptualization Mustafa Erman, Saadettin Kılıçkap, Tolga Köseci, Duygu Bayır,Oktay Bozkurt, Deniz Can Güven, Hacer Demir, Muslih Urun, Sedat Yıldız, Teoman Sakalar, EmelMutlu Özkan, Gözde Ağdaş, Berrak Mermit Erçek, Mehmet Mutlu Kıdı, Sedat Biter, Havva Yeşil Cinkir, Latif Karahan, Aslıhan Ezgi Apaydın Rollas: Writing–review&editingNargiz Majidova, Cengiz Akosman, Muhammet Cengiz, Mustaya Ersoy, Elif Şahin, Pervin Can Sancı, Ayşe Nuransoy Cengiz, Erkam Kocaaslan, Ezgi Türkoğlu, Sedat Yıldırım, Anıl Krakayalı, Hayati Arvas: Resources
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author (Erdem Kolemen) upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author (Erdem Kolemen) upon reasonable request.