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Updated: Jun 7, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Explainable AI reveals squamous histology and U-shaped PD-L1 patterns as primary subgroup predictors of neoadjuvant
Fangfang Shen1,2,3, Zheqing Zhu4,5, Junge Liu1,2,3
1Shanxi Province Cancer Hospital, Taiyuan, Shanxi, China.
Background:
The introduction of neoadjuvant and perioperative immunotherapy has broadened treatment options for resectable non-small cell lung cancer (NSCLC). However, clinical benefit varies across subpopulations, and standard linear models cannot fully capture the complex feature interactions and trial-level differences found in aggregate data.
Methods:
We applied an integrated framework combining multilevel meta-regression with an inverse-variance weighted eXtreme Gradient Boosting (XGBoost) algorithm. SHapley Additive exPlanations (SHAP) were used to interpret the model and identify efficacy variance drivers across seven randomized controlled trials.
Results:
Multilevel meta-regression demonstrated that event-free survival (EFS) benefit correlated positively with PD-L1 expression, peaking in the 50% subgroup (adjusted HR = 0.44, 95% CI: 0.33--0.57). XGBoost-SHAP analysis revealed trial-level variance as the dominant driver of heterogeneity. Among subgroup-level clinical covariates, a non-linear U-shaped PD-L1 predictive pattern (<1% and 50%), squamous cell carcinoma (SCC) histology (HR = 0.53, 95% CI: 0.43--0.65), and smoking history emerged as primary predictors. Synthesizing these signatures into a hypothesis-generating subgroup stratification framework indicated that SCC and PD-L1-negative (<1%) non-squamous cohorts benefit from continuous perioperative blockade. Conversely, exploratory analyses suggested that PD-L1-positive ( 1%) non-squamous tumors achieved maximal observed benefit from exclusively neoadjuvant regimens (HR = 0.50).
Conclusions:
Our results suggest that for PD-L1-positive non-squamous cases, the added benefit of extended adjuvant therapy may be limited. However, given our reliance on aggregate data and trial-level imbalances, these findings remain hypothesis-generating and should not alter current clinical practice. Rather, they offer an exploratory framework to inform patient selection for future de-escalation trials.
