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Cancer

Machine Learning Model Improves Survival in Advanced Non-Small Cell Lung Cancer

Strategic Insights

— 1 min read

Researchers from the University of Texas MD Anderson Cancer Center have developed a machine learning model that improves survival in advanced non-small cell lung cancer (NSCLC) patients. The model, called A-STEP, uses clinicogenomic data from four cohorts to predict individual benefit from adding chemotherapy to immunotherapy. The study found that A-STEP estimates heterogeneous treatment effects and achieves the largest reduction in 3-month progression risk, improving weighted risk reduction by 13-23% over stand-alone models. The model recommends treatment changes for over 50% of patients, most often favoring immunotherapy combined with chemotherapy.

Key Takeaways:

  • Researchers developed a machine learning model, A-STEP, to predict individual benefit from adding chemotherapy to immunotherapy in advanced NSCLC patients.
  • A-STEP uses clinicogenomic data from four cohorts, including 750 patients from MD Anderson, to estimate treatment effects and achieve a 13-23% reduction in 3-month progression risk.
  • The model recommends treatment changes for over 50% of patients, most often favoring immunotherapy combined with chemotherapy.
  • Simulation on an external cohort showed that patients treated in accordance with A-STEP recommendations had improved 2-year progression-free survival (HR = 0.60 for ICI-Mono treatment arm; HR = 0.58 for ICI-Chemo treatment arm).
  • Predictive features of the model include FBXW7, APC, and PD-L1.

Statistics:

  • 750 patients from MD Anderson were included in the clinicogenomic data used to develop A-STEP.
  • 80 patients from the Mayo Clinic, 1077 patients from Dana-Farber, and 393 patients from Stand Up To Cancer were also included in the data.
  • A-STEP estimates a 13-23% reduction in 3-month progression risk versus stand-alone models.
  • 2-year progression-free survival rates improved from 0.60 for ICI-Mono treatment arm to 0.58 for ICI-Chemo treatment arm.

Sources:

  • Machine-learning Driven Strategies for Adapting Immunotherapy In Metastatic Nsclc. Nature Communications, 2025;16(1).
  • NewsRx. New Machine Learning Findings from University of Texas MD Anderson Cancer Center Reported (Machine-learning Driven Strategies for Adapting Immunotherapy In Metastatic Nsclc). Cancer Weekly. August 26, 2025; p 2751.

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