BackgroundUrothelial cancer patients treated with immune checkpoint inhibitor (ICI) therapy have varied response and survival.1 Clinical and immunogenomic biomarkers could help predict ICI response and survival to inform decisions about patient selection for ICI treatment.MethodsThe association of clinical metadata and immunogenomic signatures with response and survival was analyzed in a set of 347 urothelial cancer patients treated with the PD-L1 inhibitor atezolizumab as part of the IMVigor210 study.1 Data were divided into a discovery set (2/3 of patients) and validation set (1/3 of patients). We analyzed as potential predictors 70 total variables, of which 16 were clinical metadata and 54 were immunogenomic signatures. Categorical variables were converted to dummy variables (89 total variables: 35 clinical, 54 immunogenomic). Using the discovery set, elastic net regression with Monte Carlo cross-validation was used to build optimal models for response (logistic regression) and survival (Cox proportional-hazards). Model performance was evaluated using the validation set.ResultsIn the optimal model of response, 17 variables (10 clinical, 7 immunogenomic) were selected as informative predictors, including Baseline Eastern Cooperative Oncology Group (ECOG) Score = 0, Neoantigen Burden, Lymph Node Metastases, and Tumor Mutation Burden (figure 1). The final model predicted patient response with good performance (Area Under Curve = 0.828, pAUC = 2.38e-3; True Negative Rate = 91.7%, True Positive Rate = 87.5%, pconfusion matrix = 0.0252). In the optimal model of survival, 32 variables (17 clinical, 15 immunogenomic) were selected as informative predictors, including baseline ECOG Score = 0, IC Level 2+, Race = Asian, and Consensus Tumor Subtype = Neuroendocrine (figure 2). The final model predicted patient survival with good performance (c-indexmodel = 0.652, pc-index = 0.0290).Abstract 662 Figure 1Elastic Net Logistic Regression with Monte Carlo Cross-Validation to Predict Response to Atezolizumab in Urothelial Cancer. (A) Predictive variables with beta coefficient 95% confidence intervals that exclude 0, derived from Monte Carlo cross-validation. (B) Confusion matrix of actual vs. predicted response data in the validation set. (C) Total response proportions of actual and predicted response data in the validation setAbstract 662 Figure 2Elastic Net Cox Proportional-Hazards Regression with Monte Carlo Cross-Validation to Predict Survival. (A) Predictor variables with beta coefficient 95% confidence intervals that exclude 0, derived from Monte Carlo cross-validation. (B) Predictions vs. survival outcomes in the validation set. (C) Loess models of density curves for survival outcomes in the validation set. 95% confidence intervals were generated through bootstrapping with replacement. (D) Loess fit of predictions vs. survival outcomes in the validation set. 95% confidence interval indicates strength of fitConclusionsModels incorporating clinical metadata and immunogenomic signatures can predict response and survival for urothelial cancer patients treated with atezolizumab. Among predictors in those models, baseline performance status is the greatest and most positive predictor of response and survival.ReferenceMariathasan S, Turley S, Nickles D, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 2018;554:544–548.
Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays
