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 datTabke a from two independent cohorts. The CART-OMC
model developed on the discovery dataset achieved the highest performance, with
validation set Matthews correlation coefficient (MCC) of 0.437, using the expressions of
just 29 ML-selected genesincluding CXCL9 and IFNG. Univariate biomarkers like TMB, TNB,
and PD-L1 were less predictive with MCC 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.