Comparative analysis of immunotherapy treatments in non-small cell lung cancer (NSCLC) using novel causal machine learning approaches.

e20598 Background: While immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancer (NSCLC) treatment, predicting immune-related adverse events (irAEs) remains challenging. We conducted a retrospective analysis comparing pembrolizumab (Pembro) versus ipilimumab plus nivolumab (Ipi/Nivo) to evaluate treatment effect heterogeneity and identify factors driving differential irAE risk. Methods: We analyzed data from 174 advanced NSCLC patients treated with chemotherapy plus either Pembro (n=71) or Ipi/Nivo (n=103). Using overlap weighting with logistic regression and k-fold cross-validation, we addressed potential bias from non-randomized treatment assignment. We estimated both average treatment effects (ATE) and conditional average treatment effects (CATE) using causal machine learning models. Heterogeneity in treatment effect (HTE) was assessed using the ABC-D (Area Between Curves - Double) test, with subsequent SHAP (SHapley Additive exPlanations) analysis to identify key factors driving heterogeneity. Results: After propensity adjustment reducing average imbalance from 28% to <3%, the efficacy analysis showed a non-significant ATE of 0.11 months [95% CI: -2.88, 3.10] in restricted mean survival time at 24 months favoring Ipi/Nivo. For irAEs, observed rates were 72% with Ipi/Nivo versus 51% with Pembro. The adjusted irAE analysis demonstrated significantly lower risk with Pembro (absolute risk reduction: 0.21 [95% CI: 0.06, 0.35]). The ABC-D test revealed significant heterogeneity in irAE occurrence (p=0.046). SHAP analysis identified smoking history and contralateral lung metastases as factors associated with greater Pembro benefit, while lymph node involvement was associated with reduced Pembro advantage. Conclusions: Our retrospective analysis using advanced causal machine learning techniques demonstrated significant patient-level heterogeneity in irAE risk between Pembro and Ipi/Nivo treatments in advanced NSCLC. While Pembro showed lower overall irAE risk, specific clinical factors modified the magnitude of this benefit. These findings may help inform personalized immunotherapy selection, though validation in prospective studies is warranted.

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Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e20598
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
0.00
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article

Comparative analysis of immunotherapy treatments in non-small cell lung cancer (NSCLC) using novel causal machine learning approaches.

Dan Goldstaub, Keren Peri-Hanania, Raviv Pryluk, Walid Shalata et al.
Journal of Clinical Oncology
Lung Cancer Treatments and Mutations
article

Comparative analysis of immunotherapy treatments in non-small cell lung cancer (NSCLC) using novel causal machine learning approaches.

Dan Goldstaub, Keren Peri-Hanania, Raviv Pryluk, Walid Shalata, Oshri Machluf, Tzviel Frostig, Elad Berkman, Gal Shoham Levin
article en

Abstract

e20598 Background: While immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancer (NSCLC) treatment, predicting immune-related adverse events (irAEs) remains challenging. We conducted a retrospective analysis comparing pembrolizumab (Pembro) versus ipilimumab plus nivolumab (Ipi/Nivo) to evaluate treatment effect heterogeneity and identify factors driving differential irAE risk. Methods: We analyzed data from 174 advanced NSCLC patients treated with chemotherapy plus either Pembro (n=71) or Ipi/Nivo (n=103). Using overlap weighting with logistic regression and k-fold cross-validation, we addressed potential bias from non-randomized treatment assignment. We estimated both average treatment effects (ATE) and conditional average treatment effects (CATE) using causal machine learning models. Heterogeneity in treatment effect (HTE) was assessed using the ABC-D (Area Between Curves - Double) test, with subsequent SHAP (SHapley Additive exPlanations) analysis to identify key factors driving heterogeneity. Results: After propensity adjustment reducing average imbalance from 28% to <3%, the efficacy analysis showed a non-significant ATE of 0.11 months [95% CI: -2.88, 3.10] in restricted mean survival time at 24 months favoring Ipi/Nivo. For irAEs, observed rates were 72% with Ipi/Nivo versus 51% with Pembro. The adjusted irAE analysis demonstrated significantly lower risk with Pembro (absolute risk reduction: 0.21 [95% CI: 0.06, 0.35]). The ABC-D test revealed significant heterogeneity in irAE occurrence (p=0.046). SHAP analysis identified smoking history and contralateral lung metastases as factors associated with greater Pembro benefit, while lymph node involvement was associated with reduced Pembro advantage. Conclusions: Our retrospective analysis using advanced causal machine learning techniques demonstrated significant patient-level heterogeneity in irAE risk between Pembro and Ipi/Nivo treatments in advanced NSCLC. While Pembro showed lower overall irAE risk, specific clinical factors modified the magnitude of this benefit. These findings may help inform personalized immunotherapy selection, though validation in prospective studies is warranted.

Journal of Clinical OncologyVol. 43(16_suppl)
Soroka Medical Center (IL), Phasex (United States) (US)
Openalex Percentile: Top 80%
Lung Cancer Treatments and Mutations
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