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Updated: May 8, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Immunotherapy Response and Survival Outcome by Immunophenotypic Signature in Non-Small Cell Lung Cancer
Dhruv Bansal1, Ben Ponvilawan2, Dmitrii Grachev3
1Endeavor Health, Evanston, IL.
Purpose:
Current predictive biomarkers for immune checkpoint inhibitor (ICI)-based therapy in patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), such as PD-L1 protein expression or tumor mutation burden, are still suboptimal. We aim to explore immunophenotypic factors as potential biomarkers in this patient population.
Methods:
Clinical, genomic, and transcriptomic data from five patient cohorts, consisting of three publicly available data and two retrospective cohorts, were included. Immune tumor microenvironment (TME) subtype and tertiary lymphoid structure (TLS) signature were evaluated using RNA expression data, and deconvolutional cellular decomposition of tumor samples was performed using the Kassandra algorithm. Survival analysis was performed using a log-rank test and multivariate Cox regression. All statistical analyses were performed using Python version 3.10.
Results:
In total, 514 patients were included in this analysis. A minority of LUAD (40.8%) had an immune-hot phenotype, which corresponded to better overall survival (OS) and progression-free survival (PFS) than the immune-cold phenotype. TLS-high signature was also associated with a superior ICI response rate and improved PFS, even with multivariate adjustments. Increased T-cell and macrophage infiltration and trafficking were predictive of ICI response. PD-L1 status and KEAP1 or STK11 mutations did not affect the response rates but were associated with poorer OS and PFS.
Conclusion:
Dynamic and active immune recruitment, indicated by immune-hot TME and high TLS score, was predictive of ICI benefit in patients with LUAD. Further prospective studies are warranted to expand to other treatment combinations with PD-(L)1 inhibitors.