Prognostic impact of baseline blood biomarkers in SCLC first-line treatment

Abstract

Background:

Small cell lung cancer (SCLC) is an aggressive malignancy with poor prognosis despite available treatment options. Although immunotherapy now constitutes the standard of care for extensive-stage SCLC (ES-SCLC), reliable biomarkers for patient stratification remain scarce.

Objectives:

This study aimed to evaluate the prognostic value of baseline inflammatory, metabolic, and nutritional blood biomarkers and to construct an integrated dynamic prediction model for patient stratification.

Design:

This was a retrospective analysis conducted at a single center.

Methods:

We analyzed 191 SCLC patients treated between 2021 and 2024. Primary endpoints were overall survival (OS) and progression-free survival (PFS). The prognostic utility of inflammatory, metabolic, and nutritional blood biomarkers (neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), and prognostic nutritional index (PNI)) was systematically evaluated with quantitative comparisons across stage-treatment subgroups. We subsequently developed a novel composite metric, the LDH/Age (LA) ratio. Multivariable Cox regression was used to identify independent predictors, facilitating the construction and bootstrap validation of a nomogram and accompanying web-based calculator.

Results:

In the ES-SCLC cohort (n = 73) receiving first-line chemoimmunotherapy (CIT), elevated baseline LDH (⩾240 U/L) and low PNI (<46.7) were independent risk factors for reduced OS and PFS. High NLR (⩾2.94) correlated significantly with diminished PFS and, among programmed death-ligand 1 inhibitor users, shorter OS. The novel LA ratio demonstrated superior predictive power for PFS compared to LDH alone (hazard ratio = 2.80, p < 0.001). Leveraging these factors, an integrated prognostic model combining Age, NLR, LDH, and PNI successfully stratified patients into high- and low-risk groups (log-rank p < 0.001).

Conclusion:

LDH, PNI, and NLR are established prognostic biomarkers for ES-SCLC patients undergoing CIT. Crucially, the novel LA ratio demonstrates enhanced predictive value for disease progression. The resulting web-based nomogram constitutes a practical, cost-effective mechanism for precise clinical risk stratification.

Plain language summary

Using routine blood tests to predict treatment outcomes for patients with small cell lung cancer

Why was the study done?

Small cell lung cancer is a fast-growing disease that is often difficult to treat. While newer treatments that combine chemotherapy with drugs that boost the immune system have improved patient care, doctors still struggle to predict which patients will respond best. We wanted to see if simple, everyday blood tests could help predict how well a patient’s treatment would work and how long they might live.

What did the researchers do?

We looked back at the medical records of 191 patients treated for small cell lung cancer between 2021 and 2024. We examined their routine blood tests taken before starting treatment—specifically looking at markers related to inflammation, nutrition, and general cell health. We also created a new, simple score by combining one of these blood markers with the patient’s age.

What did the researchers find?

We found that patients who had higher levels of inflammation or poorer nutritional markers in their blood before treatment tended to have shorter survival times, and their cancer worsened more quickly. Importantly, our newly created score (combining a specific blood marker with age) was very effective at predicting if the disease would progress. Using these findings, we built an online calculator that successfully groups patients into high-risk or low-risk categories.

What do the findings mean?

These results show that doctors can use standard, low-cost blood tests to better understand a patient’s individual risk before starting treatment. By using our online calculator, healthcare teams can easily identify patients who might need closer monitoring or different care strategies. This provides a practical and affordable way to personalize treatment and improve care for people with small cell lung cancer.

Keywords

hematological indicators lactate dehydrogenase (LDH)neutrophil-to-lymphocyte ratio (NLR)prognostic indicators prognostic nutritional index (PNI)risk stratification small-cell lung cancer (SCLC)the innovative Age-corrected LDH ratio

Introduction

Lung cancer remains the leading cause of global cancer-related mortality. Constituting approximately 14% of all cases, small cell lung cancer (SCLC) exhibits the most aggressive phenotype and poorest prognosis among its subtypes. The disease is characterized by an almost universal smoking history (95%) and is predominantly diagnosed at the extensive-stage (ES-SCLC) in 70% of patients.^1,2 Survival outcomes reflect this severity: the 3-year overall survival (OS) rate for limited-stage SCLC (LS-SCLC) is 56.5%, in stark contrast to 17.6% for ES-SCLC, whose 5-year OS rate is below 7%.³

Despite the finding that first-line chemoimmunotherapy in ES-SCLC, as demonstrated by phase III trials (IMpower133 and CASPIAN), achieves high initial tumor shrinkage (60%–70%) and extends median OS to 12–13 months, this success is often transient: approximately 60% of patients progress within 3 months of initiation.^3,4 This challenge underscores the persistently limited long-term improvements in overall response rate, progression-free survival (PFS), and OS, with durable benefit from immune checkpoint inhibitors (ICIs) confined to only 10%–15% of SCLC cases.^5–7

SCLC, unlike non-SCLC (NSCLC), currently lacks validated biomarkers to guide immunotherapy decisions.⁸ While programmed death-ligand 1 (PD-L1) expression, Tumor mutational burden (TMB), and Microsatellite instability-high predict response to ICIs in NSCLC and other malignancies, no such indicator correlates consistently with clinical outcomes in ES-SCLC.⁹ This is paradoxical, as SCLC often exhibits high TMB yet shows poor immunotherapy responsiveness.¹⁰ This diminished efficacy is likely due to the disease’s unique immunosuppressive microenvironment and low immunogenicity.⁷ A key mechanism is antigen presentation defects: approximately 71% of SCLC tumors exhibit absent or dysfunctional MHC Class I expression.¹¹ Since PD-L1 blockade relies mechanistically on antigen presentation and CD8+ T-cell activation, these defects compromise its effectiveness. The fact that all approved ICIs for SCLC are administered independently of PD-L1 testing^12,13 underscores its ambiguous predictive utility and presents a core obstacle to precision oncology. Therefore, identifying robust prognostic indicators—those reflecting inherent disease biology and survival heterogeneity irrespective of treatment modality—is essential for optimizing patient stratification, clinical decision-making, and trial design.

In this context, peripheral blood parameters have emerged as easily accessible indicators of host immune, metabolic, and nutritional status. Neutrophil-to-lymphocyte ratio (NLR) captures the systemic inflammatory response and reflects the balance between pro-tumor inflammation and anti-tumor immunoreaction; elevated neutrophils can promote tumor progression via cytokine release, whereas decreased lymphocytes indicate weakened adaptive immunity against tumor cells.¹⁴ Lactate dehydrogenase (LDH) is a key enzyme in the glycolytic pathway, facilitating the reversible conversion of pyruvate to lactate. It serves as a critical biomarker of tumor metabolic reprogramming, with elevated serum levels directly correlating with tumor burden, hypoxia-induced necrosis, and the generation of an acidic, immunosuppressive microenvironment.¹⁵ Prognostic nutritional index (PNI) is a composite metric derived from serum albumin (ALB) and total lymphocyte count. It acts as a robust marker of both overall nutritional condition and systemic immune health, effectively reflecting the patient’s physiological reserve.¹⁶

Although prior SCLC research established the prognostic utility of NLR, LDH, and PNI^15,17–20—with elevated NLR, elevated LDH, or low PNI consistently predicting adverse outcomes^{14,16,21–24}—the predictive power of single biomarkers is constrained in the era of chemoimmunotherapy. This limitation stems from several factors: (i) marked heterogeneity in optimal cutoffs across studies (threshold inconsistency); (ii) reliance on dichotomized analyses that ignore the continuous biological risk gradient; (iii) a narrow correlational focus on OS that minimizes assessment of short-term efficacy or immune-related adverse events (irAEs); and (iv) the absence of integrated multi-parameter frameworks encompassing inflammatory, metabolic, and nutritional dimensions.

The urgency for such integrated models is further amplified by the shifting therapeutic landscape of SCLC. Prospective trials such as RISE are currently evaluating the integration of consolidative and metastasis-directed radiotherapy (RT) in ES-SCLC treated with chemoimmunotherapy (CIT), highlighting the need for robust prognostic stratification tools in this evolving treatment paradigm.²⁵ To address these clinical needs, this study retrospectively analyzed baseline data from 191 SCLC patients, assessing the prognostic value of inflammatory, metabolic, and nutritional blood biomarkers (NLR, LDH, and PNI) and quantitatively comparing their predictive efficacy across stage and treatment subgroups. This analysis subsequently yielded an integrated risk stratification model, offering a precise supplementary tool for prognostic assessment in SCLC.

Methods

Patient selection

We retrospectively analyzed 278 patients with histologically confirmed SCLC treated at Nanjing Drum Tower Hospital between April 2021 and October 2024. The final analysis included 191 patients (138 ES, 53 LS) who met the following inclusion criteria: (i) histological confirmation of SCLC; (ii) documented VALSG staging; (iii) treatment-naïve status with no history of anticancer therapy (including curative surgery); (iv) completion of ⩾2 standardized treatment cycles with ⩾1 on-site imaging review; and (v) complete pretreatment baseline hematological data (complete blood count, biochemical profiles, and tumor markers). Exclusion criteria included: (i) diagnostic biopsy without subsequent treatment; (ii) loss to follow-up or unevaluable response; (iii) history of other malignant tumors; and (iv) transformation from NSCLC. All patients underwent pretreatment diagnostic biopsy and comprehensive baseline imaging assessment (contrast-enhanced neck/chest/abdominal CT, brain MRI, bone scintigraphy, and FDG-PET). Systematic surveillance consisted of bimonthly CT scans; supplementary targeted imaging (e.g., contrast-enhanced brain MRI) was requested based on clinical indications. The reporting of this study conforms to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement²⁶ (Supplemental File 1).

Data collection

Retrospective data collection focused on four distinct categories: Epidemiological factors (age, sex, smoking history, alcohol consumption, family history of cancer); baseline tumor variables (disease stage per VALSG classification, Ki-67 proliferation index, tumor markers such as NSE, CYFRA21-1, CEA, ProGRP, CA125, CA15-3, and distant metastatic target organs); immunoinflammatory/nutritional indices (NLR, platelet-to-lymphocyte ratio, PNI (PNI = Albumin (g/L) +5 × Lymphocyte count (×10⁹/L)), LDH, serum sodium, C-reactive protein (CRP), ALB, CRP-to-albumin ratio, and serum ferritin); and treatment and survival outcomes (the first-line therapeutic regimen, OS, and PFS).

Survival outcome assessment

OS was defined as the time from treatment initiation until death from any cause. PFS measured the interval from first-line treatment initiation to the first occurrence of objectively confirmed disease progression (PD) or death from any cause, whichever occurred first. Efficacy evaluation utilized the Response Evaluation Criteria in Solid Tumors, version 1.1. Survival status was validated through hospital medical records, outpatient follow-up visits, and telephone interviews. For censored cases (without an observed event at the last follow-up), survival time was calculated from treatment initiation to the date of the last follow-up.

Statistical analysis

Statistical analyses were performed using R software version 4.5.0 (R Foundation for Statistical Computing, Vienna, Austria) and RStudio version 2025.09.2+418 (Posit Software; PBC, Boston, MA, USA). Optimal biomarker cutoffs were defined via maximally selected rank statistics (minprop=0.20) to stratify patients for Kaplan–Meier analysis of OS and PFS. Restricted cubic splines (RCS) confirmed linearity for NLR and PNI (nonlinearity p > 0.05), justifying their inclusion as continuous variables in Cox regression. A multivariate Cox model incorporating Age, NLR, LDH, and PNI was used to calculate risk scores and construct a prognostic nomogram. Risk stratification thresholds were determined by maximally selected statistics and validated via Kaplan–Meier curves (log-rank p < 0.001). Internal validation (1000 bootstraps) yielded a bias-corrected C-index of 0.634. Finally, a dynamic web-based nomogram was developed for clinical application.

Results

Baseline characteristics of study patients

Baseline characteristics for the 191 patients (predominantly male (87.4%), elderly (>65 years), and heavy smokers) are detailed in Table 1. Within this cohort, 138 patients (72.3%) were diagnosed with ES disease (ES-SCLC). Among ES-SCLC patients, first-line outcomes were stratified by regimen: The chemoimmunotherapy combination group (EP/EC + ICI, n = 73) achieved a median OS of 20.4 months and a median PFS of 6.0 months. In contrast, the chemotherapy-only group (EP/EC; n = 46) yielded an OS of 16.3 months and a PFS of 5.9 months. The “Other treatment” group (n = 19), receiving non-standard therapies such as monotherapy or oral anti-angiogenic agents, showed significantly lower metrics (OS: 9.6 months; PFS: 5.7 months). The remaining 53 patients presented with LS disease (LS-SCLC); of these, 24 received first-line chemoradiotherapy (EP/EC + radiotherapy), resulting in an OS of 25.3 months and a PFS of 7.0 months. The other 29 LS-SCLC patients underwent alternative therapeutic regimens, including combination chemotherapy alone (n = 13), chemoimmunotherapy (n = 6), chemoimmunotherapy plus radiotherapy (n = 3), and other non-standard combinations (n = 7; comprising single-agent chemotherapy (n = 3), immunotherapy plus targeted therapy (n = 2), and chemotherapy plus targeted therapy (n = 2)). Regarding RT exposure, 59 patients received RT, while the remaining 132 patients did not. The RT cohort consisted of 27 LS-SCLC patients (24 receiving chemoradiotherapy and 3 receiving chemoimmunotherapy plus RT) and 32 ES-SCLC patients. Notably, the 32 ES-SCLC patients received only palliative RT for metastatic lesions. Although documented, these palliative treatments were not incorporated into the primary systemic treatment regimens, as our data collection focused on initial therapy, and prior studies²⁷ have indicated that palliative RT does not significantly impact OS in ES-SCLC.

Table 1.

Median survival time analysis (small cell lung cancer).

Clinical classification	Patient characteristics		Survival outcomes
Category	Subgroup	n	Median OS (95% CI)	Median PFS (95% CI)
Sex	Male	167	20.8 (16–26.8)	6.3 (5.7–7.3)
	Female	24	22.8 (12.2–43.6)	6.7 (5.8–10.4)
Age	<65	68	24.9 (20.5–41.7)	7.1 (5.8–9.3)
	⩾65	123	16.9 (13.5–25.2)	6.3 (5.4–6.9)
Smoking status	Former	138	20.8 (14.5–27.1)	6.5 (5.8–8.2)
	Never	53	22.2 (19.1–26.2)	6.3 (5.3–7.8)
Stage	Extensive stage	138	19.1 (13.6–23.6)	5.9 (5.4–6.9)
	Limited stage	53	29.4 (22.2–47.1)	7.9 (6.3–10.8)
ES	Chemoimmunotherapy	73	20.4 (14.8–26.8)	6 (5.3–7.8)
	Chemo-only	46	16.3 (11.8–26.2)	5.9 (5.3–8)
	Other treatment	19	9.6 (9.6–124.1)	5.7 (3.9–12.1)
ES patients developing metastasis	Liver metastasis	37	14.8 (14.8–51.9)	5.6 (4.2–6.6)
	Bone metastasis	45	12.2 (9.2–26.2)	5.3 (4–6.6)
LS	Chemo-only	13	28.5 (28.5–42.5)	6.6 (6.6–29.4)
	Chemo radiotherapy	24	25.3 (25.3–60.9)	7 (5.9–10.5)
	Other treatment	16	43.6 (43.6–58)	16.5 (16.5–54.1)
Radiotherapy	ES patients receiving radiotherapy	31	24.9 (20.8–NA)	7.8 (6.3–10.3)
	ES patients not receiving radiotherapy	107	14.5 (10.4–20.4)	5.6 (4.6-6.5)
	LS patients receiving radiotherapy	28	41.7 (22.2–NA)	8.3 (6.8–12.1)
	LS patients not receiving radiotherapy	25	29.4 (13.4–NA)	6.8 (4.7–17.7)

CI, confidence interval; ES, extensive-stage; LS, limited-stage; OS, overall survival; PFS, progression-free survival; NA, not available. Upper CI limit incalculable as median OS not attained (<50% events at data cutoff).

Survival analysis of pretreatment laboratory parameters

Prognostic value of pretreatment LDH

Using maximally selected rank statistics, the optimal baseline LDH cutoff was established at 240 U/L within the ES-SCLC chemoimmunotherapy cohort (n = 73). High baseline LDH (⩾240 U/L, n = 36) significantly correlated with reduced OS (hazard ratio (HR) = 1.03, 95% CI: 1.01–1.05, p = 0.003) and PFS (HR = 1.04, 95% CI: 1.02–1.06, p < 0.001) relative to the low LDH group (<240 U/L, n = 37). Per 10 U/L increase in LDH, the associated risk of death increased by 3%, and the risk of first-line disease progression by 4%. Crucially, this association was stronger in the PD-L1 inhibitor users subgroup (n = 55; OS: HR = 1.04, p < 0.001; PFS: HR = 1.04, p < 0.001). Conversely, LDH lacked prognostic significance in the ES chemotherapy-only group and all treatment subgroups of LS-SCLC (Figure 1).

Figure 1.

Prognostic value of baseline LDH in ES-SCLC treated with chemoimmunotherapy. (a, b) Kaplan–Meier curves for OS and PFS in the total chemoimmunotherapy cohort (n = 73). Patients were stratified into low LDH (<240 U/L) and high LDH (⩾240 U/L) groups. High baseline LDH was significantly associated with inferior OS (p = 0.003) and PFS (p < 0.001). (c, d) Kaplan–Meier curves for OS and PFS in the subgroup treated with PD-L1 inhibitors (n = 55). Consistent with the overall cohort, elevated LDH levels significantly predicted shorter survival outcomes (p < 0.001 for both). p-Values were calculated using the log-rank test.

Prognostic value of pretreatment NLR

Maximally selected rank statistics identified an NLR cutoff of 2.94 within the ES-SCLC cohort receiving chemoimmunotherapy (n = 73). Patients in the high NLR group (⩾2.94, n = 37) demonstrated significantly shorter PFS (HR = 1.22, p = 0.039). Furthermore, each 1-unit increase in NLR was associated with a 22% higher risk of first-line progression. While OS showed only a non-significant trend toward reduction in the full chemoimmunotherapy cohort (HR = 1.20, p = 0.079), the PD-L1 inhibitor user subgroup (n = 55) exhibited a significant OS decrement (HR = 1.33, p = 0.031), where each 1-unit NLR increase conferred a 33% elevated mortality risk. The NLR lacked prognostic significance in all other ES treatment subgroups and LS cohorts. Methodologically, linearity testing (RCS test) confirmed a linear relationship between NLR and survival risk, justifying its inclusion as a continuous variable (Figure 2).

Figure 2.

Prognostic value of baseline NLR in ES-SCLC treated with chemoimmunotherapy. (a) Kaplan–Meier curve for PFS in the total chemoimmunotherapy cohort (n = 73). Patients stratified to the high NLR group (⩾2.9) demonstrated significantly shorter PFS compared to those with low NLR (HR = 1.22, p = 0.039). (b) Kaplan–Meier curve for OS in the subgroup of patients treated with PD-L1 inhibitors (n = 55). Elevated baseline NLR was significantly associated with inferior survival outcomes in this specific treatment subset (HR = 1.33, p = 0.031).

Prognostic value of pretreatment PNI

In the ES-SCLC cohort receiving chemoimmunotherapy (n = 73), a PNI cutoff of 46.7 was established via median-based dichotomization. The high PNI group demonstrated significantly prolonged OS (HR = 0.69, p = 0.009) and PFS (HR = 0.78, p = 0.021). As a continuous measure, each 5-unit PNI increase further correlated with a 31% reduced mortality risk, and 22% decreased risk of first-line disease progression. This utilization of PNI as a continuous variable was validated through RCS testing, which confirmed linearity with survival outcomes (p > 0.05 for nonlinearity; Figure 3).

Figure 3.

Prognostic value of baseline PNI in ES-SCLC treated with chemoimmunotherapy. (a, b) Kaplan–Meier curves for OS and PFS in the chemoimmunotherapy cohort (n = 73). Upper panels display the distribution of PNI values with the median cutoff of 46.7. Patients in the high PNI group (⩾46.7, yellow line) demonstrated significantly prolonged OS (HR = 0.69, p = 0.009) and PFS (HR = 0.78, p = 0.021) compared to the low PNI group (<46.7, blue line).

Prognostic value of the novel composite biomarker LDH/age

The prognostic utility of the novel composite biomarker, LDH/age (LA), was assessed. Visual analysis using a point-line plot and heatmap demonstrated a significant multiplicative interaction, wherein age modified the prognostic effect of LDH. This interaction justified integrating age and LDH into the LA score for SCLC prognostic stratification.

Utilizing maximally selected rank statistics, cohort-specific prognostic LA cutoffs were established in ES-SCLC. In the chemoimmunotherapy cohort (n = 73), patients with LA ⩾3.18 exhibited significantly worsened PFS (HR = 2.80, p < 0.001), although only a non-significant trend toward reduced OS was observed (HR = 1.71, p = 0.084). Conversely, within the chemotherapy-only cohort (n = 46), high LA (⩾4.14) significantly predicted inferior OS (HR = 2.32, p = 0.028), while suggesting a comparable tendency for PFS reduction that did not achieve statistical significance (HR = 1.83, p = 0.068). Critically, forest plot analysis of multivariable Cox regression further confirmed that elevated LA (⩾3.18) maintained robust independence as a PFS predictor in the chemoimmunotherapy subgroup after adjustment for established clinical confounders, including metastatic sites, inflammation markers, nutritional indices, proliferation status, and sex (Figure 4).

Figure 4.

Development and prognostic evaluation of the novel LA ratio in ES-SCLC. (a, b) Interaction analysis between age and baseline LDH levels. Point-line plots and heatmaps visualize the significant multiplicative interaction of age on the prognostic impact of LDH for OS and PFS. The “Multiplier Index” quantifies the modification effect, justifying the construction of the composite LA ratio. (c) Kaplan–Meier curve for OS in the chemotherapy-only cohort (n = 46). Patients stratified to the high LA group (⩾4.14) exhibited significantly inferior survival compared to the low LA group (HR = 2.32, p = 0.028). (d) Kaplan–Meier curve for PFS in the chemoimmunotherapy cohort (n = 73). A high LA ratio (⩾3.18) was strongly associated with worsened PFS (HR = 2.80, p < 0.001). (e) Forest plot illustrating subgroup analysis of the association between the LA ratio and PFS in the chemoimmunotherapy cohort. The elevated LA ratio (⩾3.18) remained a robust risk factor across stratifications by sex, metastatic sites, and inflammatory markers.

Forest plot: Multivariable analysis

Multivariate analysis of the LDH model indicated that elevated LDH independently predicted both OS (HR = 1.03, p = 0.030) and PFS (HR = 1.04, p < 0.001) in ES-SCLC patients receiving chemoimmunotherapy. Additionally, after adjustment, Ki67 independently predicted OS (HR = 2.56, p = 0.019) and PFS (HR = 1.78, p = 0.043). Within the LA model, the composite LA score independently predicted worse PFS (HR = 2.55, p = 0.001) in the frontline setting. Conversely, PNI retained independent prognostic significance for both OS (HR = 0.67, p = 0.009) and PFS (HR = 0.81, p = 0.029; Figure 5).

Figure 5.

Multivariate analysis of prognostic factors in ES-SCLC chemoimmunotherapy. (a, b) Forest plots displaying multivariable Cox regression results for the LDH model regarding OS (a) and PFS (b). Baseline LDH (per 10 U/L increase) and Ki67 were identified as independent risk factors for both survival endpoints. (c, d) Forest plots for the novel LA model regarding OS (c) and PFS (d). In this composite model, the high LA ratio (⩾3.18) served as a robust independent predictor for disease progression (HR = 2.55, p = 0.001). Conversely, PNI consistently maintained independent prognostic significance for both OS (HR = 0.67, p = 0.009) and PFS (HR = 0.81, p = 0.029).

Multivariate prognostic model

A nomogram, derived via multivariable Cox regression, integrated four independent prognostic indicators—Age, NLR, LDH, and PNI—to predict PFS in ES-SCLC. In this scoring system, LDH served as the dominant prognostic factor, assigning 100 points to the High-Risk group (>274 U/L) and 71 points to the Medium Risk group (204–274 U/L). High-Risk PNI (<46.7) contributed a substantial 59 points, while both High Age (⩾65 years) and High-Risk NLR (>2.94) added supplementary weights of 5 points, respectively. Total risk scores (ranging from 0 to 220) directly indicated increased short-term progression risk. Internal validation, conducted using 1000 bootstrap resampling iterations, confirmed strong model discrimination and robustness (original C-index: 0.683; calibrated C-index: 0.634). Subsequent stratification into high- and low-risk groups demonstrated a statistically significant difference in PFS via Kaplan–Meier analysis (log-rank p < 0.0001, Figure 6; accessible at: https://wybwzj.shinyapps.io/SCLC_Prognostic_Calculator_v4/, Figure 7).

Figure 6.

Construction and validation of a prognostic nomogram for PFS in ES-SCLC. Kaplan–Meier curves for PFS stratified by the nomogram-derived total risk score. The model successfully discriminated patients into high-risk (red line) and low-risk (blue line) groups, with the high-risk group demonstrating significantly inferior survival outcomes (p < 0.0001).

Figure 7.

Demonstration of the web-based calculator interface. (1) Interface functionality: The input panel is located on the left side of the webpage, where clinicians can select corresponding categories from the drop-down menus based on the patient’s baseline characteristics and click “Predict” to generate the prognosis. The output panel is displayed on the right main interface, where the X-axis (Time) represents the follow-up duration (months), and the Y-axis (Survival Probability) represents the probability of PFS. (2) Result presentation: Survival plot (Dynamic Survival Curve): The black stepped line represents the predicted PFS probability curve for the individual patient. Numerical summary: This section provides precise probabilities at specific time points, outputting the exact percentage and CIs for the “n-month progression-free survival rate.” Furthermore, survival curves of different patients can be visualized in distinct colors on the same page for comparative risk assessment.

Discussion

This study systematically evaluated the prognostic value of pretreatment blood biomarkers across different SCLC stages and treatment regimens. In the ES-SCLC subgroup receiving chemoimmunotherapy, both categorical (LDH ⩾ 240 U/L, NLR ⩾ 2.94, PNI < 46.7) and continuous variable analyses consistently predicted adverse outcomes. Specifically, high LDH and low PNI emerged as independent risk factors after multivariate adjustment, while elevated NLR demonstrated a strong univariate association with survival. Notably, our novel composite LA ratio (LA) exhibited significantly superior predictive efficacy for PFS compared to LDH alone, effectively integrating tumor metabolic burden with host physiological reserve. In contrast, none of these biomarkers showed significant survival associations in LS-SCLC cohorts treated with platinum-etoposide chemoradiation. Given this stage-specific divergence, we developed an integrated prognostic model exclusively for ES-SCLC by incorporating demographic (Age), inflammatory (NLR), metabolic (LDH), and nutritional (PNI) parameters. This model achieved robust risk stratification (log-rank p < 0.001) and was successfully translated into a web-based dynamic calculator for clinical application.

Lactate significantly remodels the tumor microenvironment (TME) through the modulation of various cellular components.^28,29 This remodeling process drives proliferation, metastasis, immune suppression,³⁰ and treatment resistance. LDH is the key enzyme of the Warburg effect and catalyzes lactate production.³¹ Accordingly, its elevated serum levels correlate with tumor lactate load across several advanced malignancies, such as melanoma, NSCLC, and pancreatic carcinoma.³² Consequently, LDH is not only a critical target for developing metabolic cancer therapies but also a widely utilized clinical prognostic biomarker.^33–35

Elevated baseline LDH remains a robust independent adverse prognostic factor in ES-SCLC across the chemotherapy and current CIT eras.^{15,21,36–38} This suggests that the addition of ICIs (PD-1/PD-L1 inhibitors) does not mitigate this risk. While prior studies broadly link high LDH to poor outcomes, prognostic cutoff values remain inconsistent due to laboratory-specific upper limits of normal.^27,39 Furthermore, how incremental increases in LDH precisely impact survival or progression risk in the CIT era remains poorly characterized. Our large-scale cohort study addresses these deficiencies: Multivariable analysis confirmed that in ES-SCLC patients receiving first-line CIT, each 10-U/L increment in baseline LDH was independently associated with a 3% increase in mortality risk and a 4% increase in progression risk. These results validate the clinical use of a binary LDH threshold (240 U/L). More importantly, they highlight its potential as a continuous biomarker for the precise quantification of incremental biological risk.

Although LDH proved to be an independent prognostic factor in multivariable analysis, its utility is tempered by physiological drift. Specifically, age-related changes, such as skeletal muscle atrophy and reduced hepatic metabolism, can induce non-tumor-related serum elevation, thereby compromising its specificity for tumor burden.^40–43 Crucially, interaction analysis revealed a significant age-dependent modification of LDH’s prognostic effect (interaction p < 0.01). Both line plots and heatmaps confirmed that patients aged ⩾66 years exhibit substantially higher progression risk at equivalent LDH levels, with a steep escalation beyond that threshold. Quantitatively, the progression risk per 50 U/L LDH increment was 1.10-fold greater in the elderly subgroup. This differential impact suggests that senescence may amplify the pathological consequences of LDH through microenvironmental alterations, including chronic inflammation and metabolic dysregulation. Furthermore, diminished repair capacity in these patients heightens their vulnerability to tumor aggression. Therefore, the observed synergy between attenuated metabolic reserve and tumor invasiveness strongly underscores the inherent limitations of employing standalone LDH for precise risk stratification.

We thus developed the LA ratio to standardize LDH levels against age, correcting for physiological variance. High LA indicates an elevation of age-normalized LDH. Its strong association with early progression during chemoimmunotherapy likely stems from its role as a surrogate for lactate-driven immune suppression. LDH drives elevated glycolytic flux, which rapidly triggers lactate accumulation. This creates a self-amplifying acidic TME that induces comprehensive immune paralysis.^44,45 Specifically, this acidosis directly impedes cytotoxic T-cell effector functions through IFN-γ suppression and T-cell receptor desensitization.^46–48 It also expands immunosuppressive networks by increasing Treg infiltration and MDSC activation.^49,50 Together, these processes form a dominant immunosuppressive niche.

The biological plausibility of the LA ratio lies in its capacity to reflect the dynamic competition between tumor-mediated metabolic pressure and host immune plasticity. LDH-driven lactate accumulation directly impairs effector cell fitness via intracellular acidification.^45,46 By incorporating age as a denominator, the LA ratio further accounts for the progressive decline in host immune-reconstitution potential, a hallmark of immunosenescence. This synergy is especially relevant under the systemic immune perturbations caused by chemotherapy. Treatment-induced lymphopenia necessitates a sufficient “immunological headroom” for the compensatory expansion of anti-tumor lymphocytes. However, in high LA patients, this recovery is doubly hampered. First, lactate-induced MOESIN lactylation at the Lys72 residue reinforces Treg stability.⁴⁹ Second, advanced age diminishes the host’s overall regenerative capacity. Consequently, these patients fail to re-establish functional immune surveillance following chemotherapy-induced depletion, providing a mechanistic basis for the rapid disease kinetics observed in this subgroup. This niche critically nullifies the efficacy of checkpoint blockade and drives early progression regardless of tumor PD-L1 status.⁵¹ Ultimately, this immune resistance accelerates the disease kinetics seen in high-LA patients.

Conversely, in the chemotherapy-only cohort, distinct prognostic patterns emerged. In this chemotherapy-only context, high LA primarily reflects systemic metabolic decompensation.^52,53 It correlates with mitochondrial dysfunction^54,55 and diminished cellular stress responses. In elderly patients, these issues are exacerbated by age-related declines in hepatic clearance and protein synthesis. In younger patients, hypermetabolic tumors compromise antioxidant defenses. These impairments reduce therapeutic tolerance and repair capacity,⁵⁶ ultimately manifesting as diminished OS rather than the immediate progression driven by immune resistance. By controlling for age-based LDH variance, the LA metric isolates these distinct biological dimensions. Specifically, it distinguishes metabolic immunosuppression—which is actionable with immunotherapy—from the pervasive host catabolic decline typically observed under conventional chemotherapy.

Notably, the optimal cutoff values for LA (PFS: 3.18; OS: 4.14) were uniformly determined across all cohorts using the Maximally Selected Rank Statistics method. Although survival analyses for OS in the chemoimmunotherapy cohort and PFS in the chemotherapy-alone cohort did not reach statistical significance, both demonstrated clinically relevant adverse prognostic trends. These borderline results are potentially attributable to limited statistical power resulting from sample size constraints. Critically, multivariate forest plot analysis confirmed that LA maintained significant prognostic value for PFS after adjusting for established confounders. This suggests LA may function as an independent prognostic biomarker in ES-SCLC irrespective of therapeutic modality. Future studies with expanded cohorts are warranted to validate its universality across treatment regimens. Of particular interest, LA lacked prognostic significance in the LS-SCLC cohort. This may be explained by disease-intrinsic mechanisms; specifically, the lower tumor burden in LS disease diminishes the magnitude of LDH elevation, thereby reducing discriminatory power. Additionally, the relatively small cohort size likely further compromised statistical detection capability.

The critical interaction between systemic inflammation and nutritional status drives key processes in tumor development and progression. This synergistic relationship supports the use of readily available blood-derived indicators for effective prognostic prediction across diverse malignancies.^37,57

The PNI, derived from serum albumin and absolute lymphocyte count, was originally conceived to assess nutritional and immunological status in gastrointestinal diseases. Its prognostic utility is well-established across numerous solid malignancies, including esophageal,⁵⁸ gastric,⁵⁸ colorectal,⁵⁹ renal cell,⁶⁰ hepatocellular,⁵⁸ gynecological,⁶¹ and biliary tract carcinomas.⁶² Nevertheless, its relevance in SCLC remains ambiguous. This investigation represents the first systematic study of PNI in SCLC. Our results reveal that a high pretreatment PNI (>46.7) significantly correlates with prolonged OS and PFS in ES-SCLC patients receiving chemoimmunotherapy. Quantitatively, each 5-unit PNI increment was associated with a 31% reduction in mortality risk and a 22% reduction in the risk of frontline progression. Multivariate analysis further established PNI as an independent protective prognostic factor. These findings not only reinforce PNI’s known function in solid tumors but also constitute its initial validation specifically within ES-SCLC immunotherapy cohorts. As an accessible and cost-effective serological biomarker, PNI shows promise for refining SCLC risk stratification. Specifically, high PNI patients may better tolerate intensified regimens, while low PNI individuals may warrant early supportive interventions. Crucially, this current cutoff (46.7), derived from a single-center median, necessitates external validation in larger multicenter cohorts. Future research should investigate the correlation of PNI kinetics with treatment response and develop integrated prognostic models incorporating other immune-related biomarkers.

NLR, an established systemic inflammation marker, is a known predictor of poor prognosis in SCLC.^14,21,24 Our analysis validated this prognostic value in ES-SCLC patients receiving platinum-based chemotherapy plus immunotherapy. Utilizing maximally selected rank statistics, we determined 2.94 as the optimal NLR cutoff. A high NLR (⩾2.94) was significantly associated with shorter PFS (HR = 1.22, p = 0.039), whereby each 1-unit NLR increase correlated with a 22% higher risk of disease progression. Although the overall cohort demonstrated no statistically significant OS impact (HR = 1.20, p = 0.079), a starker survival detriment emerged specifically in the PD-L1 inhibitor subgroup. Here, each unit elevation in NLR dramatically increased mortality risk by 33% (HR = 1.33, p = 0.031)—a pattern contrasting with the consistent OS effects of PNI and LDH. Notwithstanding its limited independent prognostic value in multivariate analysis (potentially constrained by cohort size), the strong univariate associations with PFS and OS outcomes in the PD-L1-treated subset confirm NLR’s clinical utility as a stratification biomarker.

The biological rationale for this observation is supported by evidence in other malignancies. While direct mechanistic evidence in SCLC remains limited, studies in NSCLC demonstrate that neutrophils actively drive tumor immune evasion and resistance to PD-1/PD-L1 blockade. Specifically, tumor-derived factors such as G-CSF activate STAT3 signaling, inducing PD-L1 upregulation on neutrophils to directly suppress the anti-tumor activity of effector T cells and NK cells.⁶³ Furthermore, specific subpopulations, such as low-density neutrophils, are strongly associated with primary resistance to monotherapy immune checkpoint inhibition, while their secretion of immunosuppressive cytokines, such as IL-10, further reinforces a hostile TME.^64,65 However, evidence suggests that combining immunotherapy with cytotoxic chemotherapy may help deplete these immunosuppressive neutrophil populations and partially overcome this resistance.⁶⁵ This profound neutrophil-mediated immune paralysis is clinically reflected by an elevated NLR, providing a strong biological rationale for the diminished sensitivity to PD-L1 inhibitors and the subsequent survival detriment observed in our high-NLR SCLC cohort.⁶⁶

Beyond isolated clinical tools, the successful implementation of chemoimmunotherapy necessitates refined patient selection. Our model suggests that patients with low baseline inflammation and preserved nutritional status (high PNI) are the optimal candidates for these regimens.^67,68 To achieve superior predictive precision, these systemic inflammatory indices must be integrated with broader liquid biopsy (LBx) modalities. Circulating tumor DNA and blood-based TMB have emerged as critical genomic indicators,^68,69 while advanced tools such as circulating tumor cells and extracellular vesicles provide real-time insights into the evolving TME and chemoresistance mechanisms.^69,70 Ultimately, comprehensive prognostic models that integrate multidimensional biomarkers—combining genomic data with systemic inflammatory profiles and PD-L1 expression—significantly outperform single-parameter assessments, facilitating personalized treatment escalation or de-escalation.^67,70,71

To enhance clinical translation, we created a dynamic, web-based prognostic calculator derived from the integrated Age-NLR-LDH-PNI model (accessible at: https://wybwzj.shinyapps.io/SCLC_Prognostic_Calculator_v4/). Distinct from static nomograms, which offer only fixed-point estimates, this tool enables continuous survival prediction and displays visualized 95% confidence intervals, providing the necessary adaptability for managing rapidly progressing ES-SCLC. Accessible across mobile and desktop platforms, the calculator operationalizes our statistical findings into a practical bedside tool, thereby facilitating clinicians’ ability to conduct real-time, individualized risk assessment and optimize follow-up strategies.

The prognostic significance of baseline systemic inflammation must be evaluated within the broader context of the immune-radiotherapy interface. In SCLC, radiation-induced lymphopenia is a critical biological mediator of clinical outcomes. This condition is driven by the synergy between baseline lymphocyte depletion and low-dose radiation exposure to circulating immune cells in the lungs and heart.⁷² Such treatment-related lymphopenia can negatively impact survival and may attenuate the therapeutic efficacy of ICIs by exhausting the effector cell pool.⁷² Consequently, integrating treatment-related factors and radiation dosimetry with baseline immune-nutritional indices could further refine the precision of prognostic models. As treatment paradigms evolve toward the integration of consolidative and metastasis-directed radiotherapy in ES-SCLC—a shift currently being evaluated in trials such as RISE²⁵—biomarker-driven risk stratification will become increasingly essential for personalizing treatment strategies.

Despite its practical utility, this study has several limitations. As a single-center retrospective analysis, our cohort is subject to selection bias and lacks temporal precision for death dates. Methodologically, we did not analyze patients receiving local radiotherapy as a distinct subgroup, nor did we differentiate standard chemotherapy regimens (EP/EC) or analyze rare conditions like baseline hyponatremia. Furthermore, our model relies exclusively on baseline biomarkers and lacks integration with treatment-related parameters like lung V5 or the evaluation of dynamic fluctuations. Notably, Ki-67 emerged as an independent adverse prognostic factor in multivariate analysis despite univariate non-significance, underscoring its latent value. However, it was excluded from our final integrated model due to the high proportion of missing data across the retrospective cohort. In summary, the absence of multi-center external validation necessitates caution when generalizing the LA ratio. Future prospective multi-center studies featuring longitudinal monitoring are required to determine whether the dynamic kinetics of these markers can further refine therapeutic response prediction and the early identification of irAEs.

Conclusion

This study validates the significant prognostic utility of baseline blood biomarkers in ES-SCLC patients receiving first-line chemoimmunotherapy. Specifically, elevated LDH and low PNI independently predicted poor survival, while NLR was found to be a key indicator of disease progression and treatment-specific outcomes. Crucially, the innovative Age-corrected LDH ratio (calculated as LDH divided by age) refines risk assessment by calibrating metabolic tumor burden for physiological senescence, thereby achieving superior predictive accuracy for disease progression versus isolated LDH levels. To leverage these findings, we integrated age, tumor burden, nutritional status, and systemic inflammation to develop and validate a robust nomogram and an online dynamic calculator. This non-invasive, accessible tool effectively stratifies patients into distinct risk groups, thereby providing clinicians with a means to optimize treatment monitoring and identify high-risk candidates warranting intensified therapeutic strategies or closer surveillance.

Supplemental Material

sj-docx-1-tam-10.1177_17588359261450062 – Supplemental material for Prognostic impact of baseline blood biomarkers in SCLC first-line treatment

Supplemental material, sj-docx-1-tam-10.1177_17588359261450062 for Prognostic impact of baseline blood biomarkers in SCLC first-line treatment by Yibing Wang, Meijin Ren, Jingxin Liu, Ziyue Xiang, Hui Ding, Jiaqi Xie, Naiqing Ding and Yang Yang in Therapeutic Advances in Medical Oncology

Supplemental Material

sj-docx-2-tam-10.1177_17588359261450062 – Supplemental material for Prognostic impact of baseline blood biomarkers in SCLC first-line treatment

Supplemental material, sj-docx-2-tam-10.1177_17588359261450062 for Prognostic impact of baseline blood biomarkers in SCLC first-line treatment by Yibing Wang, Meijin Ren, Jingxin Liu, Ziyue Xiang, Hui Ding, Jiaqi Xie, Naiqing Ding and Yang Yang in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

None.

Declarations

ORCID iDs

Yibing Wang

Meijin Ren

Supplemental material

Supplemental material for this article is available online.

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