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<p>Atezolizumab is a treatment for metastatic urothelial carcinoma (mUC), yet only 23% of mUC patients benefit from it. Worse yet, accurately predicting such responders remains challenging, despite existing biomarkers. Here we employed eight machine learning (ML) algorithms to predict mUC patient response to atezolizumab using tumours’ gene expression profiling and clinical data from two independent cohorts. The CART-OMC model developed on the discovery dataset achieved the highest performance, with a validation set Matthews correlation coefficient (MCC) of 0.437, using the expressions of just 29 ML-selected genes, including CXCL9 and IFNG. Univariate biomarkers like TMB, TNB, and PD-L1 were less predictive with MCCs of 0, 0.316, and 0, respectively. Upon merging these datasets, the best-performing model (LGBM-OMC; MCC of 0.252) also outperformed top modelling approaches such as EaSIeR (MCC ~ 0) and JADBio (MCC of 0.179). We make these promising ML models freely available to predict atezolizumab response in other mUC patients.</p>

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Predicting atezolizumab response in metastatic urothelial carcinoma patients using machine learning on integrated tumour gene expression and clinical data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月10日

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  • Chayanit Piyawajanusorn,
  • Ghita Ghislat,
  • Pedro J. Ballester

摘要

Atezolizumab is a treatment for metastatic urothelial carcinoma (mUC), yet only 23% of mUC patients benefit from it. Worse yet, accurately predicting such responders remains challenging, despite existing biomarkers. Here we employed eight machine learning (ML) algorithms to predict mUC patient response to atezolizumab using tumours’ gene expression profiling and clinical data from two independent cohorts. The CART-OMC model developed on the discovery dataset achieved the highest performance, with a validation set Matthews correlation coefficient (MCC) of 0.437, using the expressions of just 29 ML-selected genes, including CXCL9 and IFNG. Univariate biomarkers like TMB, TNB, and PD-L1 were less predictive with MCCs of 0, 0.316, and 0, respectively. Upon merging these datasets, the best-performing model (LGBM-OMC; MCC of 0.252) also outperformed top modelling approaches such as EaSIeR (MCC ~ 0) and JADBio (MCC of 0.179). We make these promising ML models freely available to predict atezolizumab response in other mUC patients.