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This research aims to evaluate whether APOBEC mutational signatures can independently predict immune checkpoint inhibitor response in TMB-low metastatic NSCLC patients.

July 3, 2026Open Access

APOBEC mutational signatures predict immune checkpoint inhibitor benefit in TMB-low metastatic NSCLC independent of PD-L1 status

AYAdar YaacovHebrew University of Jerusalem AGAlbert GrinshpunHebrew University of Jerusalem RGRoni GillisHebrew University of Jerusalem

Key Points

This research aims to evaluate whether APOBEC mutational signatures can independently predict immune checkpoint inhibitor response in TMB-low metastatic NSCLC patients.
Analyzed 857 ICI-treated and 1328 ICI-untreated TMB-low patients from the MSK-CHORD cohort.
Classified APOBEC status using MESiCA, a machine-learning algorithm.
Conducted treatment interaction testing and multivariate adjustment for predictive value.
APOBEC-positive patients had a median overall survival of 33.7 months versus 17.4 months for others; HR 0.60, 95% CI 0.42-0.85, P = 0.004.
No overall survival benefit in ICI-untreated patients (HR 0.98, P = 0.85); significant treatment interaction (P = 0.032).
Benefits were evident in PD-L1-negative patients; adjusted HR 0.51, P = 0.002.

Abstract

BACKGROUND: ∼60%-70% of patients with metastatic non-small cell lung cancer (NSCLC) have relatively low tumor mutational burden (TMB), with limited biomarker-guided immunotherapy options beyond programmed death-ligand 1 (PD-L1) expression. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) mutational signatures, defined by characteristic genomic footprints, are associated with higher TMB and a better response to immune checkpoint inhibitors (ICIs). However, whether APOBEC independently predicts ICI response in TMB-low tumors has not been established. MATERIALS AND METHODS: We analyzed 857 ICI-treated and 1328 ICI-untreated TMB-low (<10 mut/Mb) patients with metastatic NSCLC from the MSK-CHORD cohort. APOBEC status was classified using MESiCA, a machine-learning algorithm for mutational signature detection from targeted gene panels. Predictive value was established through treatment interaction testing, multivariate adjustment, propensity score weighting, and landmark analyses. External validation included 82 TMB-low patients from published whole-exome sequencing studies. RESULTS: APOBEC-positive patients (n = 52, 6.1%) had significantly improved overall survival median 33.7 versus 17.4 months; hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.42-0.85, P = 0.004. No such benefit was observed in ICI-untreated patients (HR 0.98, P = 0.85), with significant treatment interaction (P = 0.032), confirming a predictive rather than prognostic effect. APOBEC remained independently predictive after adjustment for PD-L1 status and age (adjusted HR 0.57, P = 0.002). The benefit was particularly pronounced in PD-L1-negative ICI-treated patients (HR 0.51, P = 0.002). The effect was robust across propensity score, landmark, and bootstrap analyses. External validation confirmed independent APOBEC benefit (adjusted HR 0.23, P = 0.020). CONCLUSIONS: APOBEC mutational signatures represent a robust predictive biomarker for ICI response in TMB-low metastatic NSCLC, identifying responders particularly among PD-L1-negative patients where biomarker guidance is most needed, detectable from targeted gene panels used in routine clinical practice.

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