Machine and Deep Learning Methods for Predicting Immune Checkpoint Blockade Response

Immune checkpoint blockade (ICB) therapy has improved treatment options in various cancer malignancies and holds promise for increasing the overall survival of treated patients. However, only a small proportion of patients benefit from ICB treatment. Furthermore, ICB therapy has been known to induce adverse autoimmunity reactions in certain patients. These two reasons motivate the clinical need to identify factors that predict a patient's response to ICB treatment. In our study, we developed several machine and deep learning-based models to predict response to ICB treatment, using a real-world tabular dataset across sixteen cancer types. We showed that our best model CB16, which is based on gradient boosting, outperforms all-known published results for this task, with sensitivity and specificity scores of 80.6% and 78.8% respectively. Our model also offers insights to clinical interpretability through the use of the SHAP explanation framework, which are consistent with known important predictors. Next, in order to see if deep learning can improve performance, we propose a methodology for the design of deep neural networks that addresses the lack of spatial and temporal structure in tabular data. Our approach is based on a combination of learning ordered representations and ensembling techniques. We show that, for the ICB prediction problem, current SOTA deep-learning architectures such as TabNet and Tab-Transformer do not perform well while our method achieves good performance. Our method achieves an F1 score 12.4 percentage points beyond that of Tab-Transformer, and sensitivity and specificity scores of 77.3% and 62.2% respectively. Through our work, we hope to improve the task of predicting ICB response, and contribute towards the creation of high-performance and interpretable AI models for real-world tabular data.