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

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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Journal
ESMO Open
Published
2026-06-30
DOI
https://doi.org/10.1016/j.esmoop.2026.108305
Primary Topic
Cancer Immunotherapy and Biomarkers
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article
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article

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

Roni Gillis, A. Pomerantz, Albert Grinshpun, Noam Asna et al.
ESMO Open
Cancer Immunotherapy and Biomarkers
article

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

Roni Gillis, A. Pomerantz, Albert Grinshpun, Noam Asna, Adar Yaacov, R. Basheer, N. Peled
article en

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.

ESMO OpenVol. 11(7)
Sheba Medical Center (IL), Shaare Zedek Medical Center (IL)
Zero hunger
Openalex Percentile: Top 9%
Cancer Immunotherapy and Biomarkers
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