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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A predictive model for immunotherapy in extensive-stage small cell lung cancer based on peripheral blood
Jiacheng Wang1, Heng Li1, Lanjun Li1
1Department of Thoracic Surgery II, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Background:
Extensive-stage small cell lung cancer (ES-SCLC) has a dismal prognosis, with first-line immunochemotherapy benefiting only a minority of patients. Existing inflammatory biomarkers show inconsistent predictive utility, and accessible tools for individualized survival estimation remain lacking. This study aimed to develop and validate a nomogram incorporating the serum lactate dehydrogenase to albumin ratio (LAR) to predict overall survival (OS) and progression-free survival (PFS) in ES-SCLC patients receiving first-line immunotherapy.
Methods:
A retrospective cohort of 223 pathologically confirmed ES-SCLC patients treated at Yunnan Cancer Hospital (November 2021-August 2025) was analyzed. Ten inflammatory-metabolic markers [including LAR, systemic immune-inflammation index (SII), prognostic nutritional index (PNI)] and clinicopathological variables were extracted. Patients were randomly assigned in a 7:3 ratio to the training (n=156) and internal validation (n=67) cohorts. Optimal cutoff values for continuous variables were determined using the surv_cutpoint function. Independent prognostic factors were identified using univariate/multivariate Cox regression, and nomograms for OS and PFS were constructed. Model performance was evaluated via concordance index (C-index), calibration curves, time-dependent receiver operating characteristic (TimeROC), and decision curve analysis (DCA). Risk stratification was performed using median model-derived scores.
Results:
Median follow-up was 22 months. Multivariate analysis identified six independent OS predictors: LAR >6.27 [hazard ratio (HR) =2.794], adrenal metastasis (HR =3.725), PNI <50.5 (HR =0.622), smoking history (HR =1.596), white blood cell count (WBC) >8.30×109/L (HR=1.692), and age >58 years (HR =1.97). Eight independent PFS predictors: tumor-node-metastasis (TNM) stage IV disease (HR =1.407) LAR >6.36 (HR =1.482), SII <342.25 (HR =0.574), albumin (ALB) <42.3 g/L (HR =0.684), neutrophil count (NEUT) <2.64×109/L (HR =0.554), chlorine concentration <96 mmol/L (HR =0.613), male (HR =1.772) and liver metastasis (HR =1.762). The OS nomogram demonstrated superior discriminative ability compared with TNM staging (C-index: 0.728 vs. 0.519, P<0.001). The C-indices for the training and validation cohorts were 0.728 and 0.648 for OS, and 0.668 and 0.627 for PFS, respectively. Time-dependent area under the curve (AUC) for 12-month OS reached 0.793 (training) and 0.742 (validation). Calibration curves and DCA confirmed strong agreement and clinical utility. Low-risk patients (via median score) had significantly longer OS/PFS than high-risk patients (log-rank P<0.05).
Conclusions:
We developed the first LAR-integrated nomogram for ES-SCLC immunotherapy outcomes, demonstrating robust predictive accuracy using routine peripheral blood parameters. This accessible tool enables individualized risk stratification and treatment optimization, particularly in resource-limited settings. Prospective multicenter validation is warranted to advance clinical adoption.