Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients

Abstract

Immune checkpoint inhibitors, especially PD-1/PD-L1 blockade, have revolutionized cancer treatment and brought tremendous benefits to patients who otherwise would have had a limited prognosis. Nonetheless, only a small fraction of patients respond to immunotherapy, and the costs and side effects of immune checkpoint inhibitors cannot be ignored. With the advent of machine and deep learning, clinical and genetic data have been used to stratify patient responses to immunotherapy. Unfortunately, these approaches have typically been "black-box" methods that are unable to explain their predictions, thereby hindering their responsible clinical application. Herein, we developed a "white-box" Bayesian network model that achieves accurate and interpretable predictions of immunotherapy responses against nonsmall cell lung cancer (NSCLC). This tree-augmented naïve Bayes (TAN) model accurately predicted durable clinical benefits and distinguished two clinically significant subgroups with distinct prognoses. Furthermore, our state-of-the-art white-box TAN approach achieved greater accuracy than previous methods. We hope that our model will guide clinicians in selecting NSCLC patients who truly require immunotherapy and expect our approach to be easily applied to other types of cancer.

Keywords: Bayesian network; immune checkpoint inhibitors; nonsmall cell lung cancer; tree-augmented naïve Bayes.