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Traditionally, identifying predictive biomarkers for treatment efficacy involves evaluating treatment-marker interactions through regression models considering clinical outcomes. This study seeks optimal personalized treatment strategies, known as individualized treatment rules (ITRs) through innovative approach utilizing weighting method and formulation of personalized treatment scoring. Integrating two scoring methods, (linear vs. non-linear) we aim to elucidate clinicogenomic indicators predictive of treatment efficacy. Emphasis is placed on identifying patients at elevated risk for early progression and determining their optimal treatment choice between immune checkpoint inhibitor-monotherapy (ICI-Mono) and ICI-Chemotherapy (ICI-Chemo). A total of 408 non-small cell lung cancer (NSCLC) patients, from MD Anderson and Mayo Clinic were enrolled. Performance was evaluated as average treatment effect of weighted risk reduction for 3-months progression between subgroup of patients who were treated according to vs. against model’s recommendation. The non-linear scoring method shows better performance comparing to the linear scoring method (overall risk reduction: −25.4% vs. −15.5% in training and −14.3 vs. −9.6% in testing cohort). Tobacco exposure and lung adenocarcinoma significantly influences outcomes in the ICI-Mono while stage-IVB and KRAS mutated gene associated with great effect from ICI-Chemo. These findings offer valuable insights for seamlessly integrating precision medicine into real-world clinical scenarios.

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Estimating the Average Treatment Effect Using Weighting Methods in Lung Cancer Immunotherapy

  • 会议论文

  • 发表日期：

    2025年

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  • Maliazurina B. Saad,
  • Qasem Al-Tashi,
  • Lingzhi Hong,
  • Wentao Li,
  • Shenduo Li,
  • John V. Heymach,
  • Yanyan Lou,
  • Natalie I. Vokes,
  • Jianjun Zhang,
  • Jia Wu

摘要

Traditionally, identifying predictive biomarkers for treatment efficacy involves evaluating treatment-marker interactions through regression models considering clinical outcomes. This study seeks optimal personalized treatment strategies, known as individualized treatment rules (ITRs) through innovative approach utilizing weighting method and formulation of personalized treatment scoring. Integrating two scoring methods, (linear vs. non-linear) we aim to elucidate clinicogenomic indicators predictive of treatment efficacy. Emphasis is placed on identifying patients at elevated risk for early progression and determining their optimal treatment choice between immune checkpoint inhibitor-monotherapy (ICI-Mono) and ICI-Chemotherapy (ICI-Chemo). A total of 408 non-small cell lung cancer (NSCLC) patients, from MD Anderson and Mayo Clinic were enrolled. Performance was evaluated as average treatment effect of weighted risk reduction for 3-months progression between subgroup of patients who were treated according to vs. against model’s recommendation. The non-linear scoring method shows better performance comparing to the linear scoring method (overall risk reduction: −25.4% vs. −15.5% in training and −14.3 vs. −9.6% in testing cohort). Tobacco exposure and lung adenocarcinoma significantly influences outcomes in the ICI-Mono while stage-IVB and KRAS mutated gene associated with great effect from ICI-Chemo. These findings offer valuable insights for seamlessly integrating precision medicine into real-world clinical scenarios.