Machine learning-guided identification of determinants of PD-L1 expression and tumor mutational burden in lung squamous cell carcinoma
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Hongwei Shang1, Chao Li2, Jundong Wang3
1Department of Cardiothoracic Surgery, the People's Liberation Army Joint Logistic, Support Force No 983rd Hospital, Tianjin, 300142, China. shanghongwei2018@126.com.
Background:
Programmed death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB) are widely used immunotherapy biomarkers, yet their variability and determinants in lung squamous cell carcinoma (LUSC) remain incompletely characterized in real-world testing settings.
Methods:
We analyzed a retrospective real-world LUSC cohort with clinical PD-L1 immunohistochemistry and next-generation sequencing. PD-L1 tumor proportion score (TPS) was modeled as an ordered endpoint using three clinically used categories (< 1%, 1-49%, ≥ 50%) via proportional-odds ordinal regression; TMB was modeled as a continuous outcome after log transformation using multivariable linear regression. To address the practical question of whether imperfect real-world biomarker data can still support hypothesis generation, we additionally applied random forest and XGBoost as exploratory tools for prediction-oriented feature prioritization and evaluated uncertainty using repeated resampling and permutation-based perturbation analyses.
Results:
Regression models provided an interpretable adjusted effect landscape but yielded limited statistically significant associations after adjustment. In contrast, tree-based models prioritized a small subset of clinicogenomic features with predictive information for PD-L1 TPS and TMB variability. Resampling and permutation analyses provided an internal assessment of uncertainty, rank stability, and sensitivity of these feature-prioritization signals.
Conclusions:
In small, heterogeneous real-world LUSC cohorts where conventional regression-based inference may provide limited resolution, machine learning-guided feature prioritization combined with resampling- and permutation-based uncertainty assessment may help identify candidate clinicogenomic signals associated with PD-L1 TPS category and TMB variability. These findings should be interpreted as hypothesis-generating and require validation in larger independent cohorts.