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Updated: Jul 20, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Clinical-genomic nomogram for predicting sensitivity to second-line immunotherapy for advanced non-small cell lung
Ying Hua1,2,3, Ai Wang4, Chao Xie3
1Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Background:
The introduction of immune checkpoint inhibitors (ICIs) has significantly improved the outcomes of patients with advanced non-small cell lung cancer (NSCLC). However, ICIs only benefit a subset of patients. The study aimed to identify genomic biomarkers and construct models to predict the response to second-line ICI therapy.
Methods:
We retrospectively collected clinical data and genetic testing results from patients with NSCLC treated with second-line ICI at a single medical center between August 2018 and June 2021. We reanalyzed the raw sequence data of clinical genetic testing and defined the common detection region among the different testing panels. Immunotherapy sensitivity was evaluated using the immune-based Response Evaluation Criteria in Solid Tumors.
Results:
We included 102 patients as a training cohort and 46 as a test cohort. In the training cohort, we examined the relationship between ICI response and the mutation status of 343 genes. Mutations in the EGFR gene were significantly more common in the resistant group than in the sensitive group (41.0% vs. 20.6%; P=0.04), while mutations in the EP300 gene were associated with greater sensitivity to ICIs (39.7% vs. 15.4%; P=0.01). A nomogram was built based on clinical variables, genomic data, and programmed death-ligand 1 (PD-L1) expression. The total nomogram points were significantly higher in the sensitive group than in the resistance group in both cohorts, and the areas under the receiver operating characteristic curve were 0.780 in the training cohort and 0.720 in the test cohort. The higher nomogram points also indicated better progression-free survival.
Conclusions:
Based on real-world clinical settings, the clinical genomic nomogram, which involved limited input variables that were economical and easy to obtain, demonstrated a good ability to predict the response to second-line ICI treatment in advanced NSCLC.
