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Updated: Aug 10, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Lung Immune Therapy Evaluation (LITE) Risk, a Novel Prognostic Model for Patients With Advanced Non-Small Cell Lung
Vishal Navani1, Daniel E Meyers2, Yibing Ruan3
1Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada; Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Introduction/Background:
Immune checkpoint inhibitors (ICI) have revolutionized non-small cell lung cancer (NSCLC). We aimed to identify baseline characteristics, that are prognostic factors for overall survival (OS) in patients with NSCLC treated with ICI monotherapy, in order to derive the Lung Immune Therapy Evaluation (LITE) risk, a prognostic model.
Materials And Methods:
Multi-center observational cohort study of patients with advanced NSCLC that received ≥1 dose of ICI monotherapy. The training set (n=342) consisted of patients with NSCLC who received first line ICI. The test set (n=153) used for external validation was a discrete cohort of patients who received second line ICI. 20 candidate prognostic factors were examined. Penalized Cox regression was used for variable selection. Multiple imputation was used to address missingness.
Results:
Three baseline characteristics populated the final model: ECOG (0, 1 or ≥2), lactate dehydrogenase>upper limit of normal, and derived neutrophil to lymphocyte ratio ≥3. Patients were parsed into 3 risk groups; favorable (n=146, risk score 0-1), intermediate (n=101, risk score 2) and poor (n=95, risk score ≥3). The c-statistic of the training cohort was 0.702 and 0.694 after bootstrapping. The test cohort c-statistic was 0.664. The median OS for favorable, intermediate and poor LITE risk were; 28.3 months, 9.1 months and 2.1 months respectively. Improving LITE risk group was associated with improved OS, intermediate vs favorable HR 2.08 (95%CI 1.46-2.97, P < .001); poor vs favorable HR 5.21 (95%CI 3.69-7.34, P < .001).
Conclusion:
A simple prognostic model, utilizing accessible clinical data, can discriminate survival outcomes in patients with advanced NSCLC.
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