Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer

Abstract

Lung cancer is responsible for the most cancer deaths worldwide, with non-small-cell lung cancer (NSCLC) making up 80% of cases. Some genetic factors leading to NSCLC development include genetic mutations and Programmed Cell Death Ligand 1 (PD-L1) expression. PD-L1 proteins are targeted in an NSCLC treatment called PD-L1 blockade therapy (immune therapy). However, this treatment is effective in a low percentage of patients. This study aimed to create machine learning models to use features, like the number of mutations and the number of PD-L1 proteins in cancer cells, along with others, to predict whether a patient will receive clinical benefits from immune therapy. This was carried out by downloading and merging datasets from cbioportal.org to create a sample size for the model. Features that were highly correlated with clinical benefits were identified. Three machine learning models (Gaussian naïve Bayes, decision tree, and logistic regression) were created using these features to predict clinical benefits in patients, and each model’s accuracy was evaluated. All three models had accuracy rates between 55 and 85%, with two of the models averaging an accuracy rate of around 75%. Doctors can use these models to more accurately predict whether immune therapy treatment is likely to work in a patient before prescribing it to them.

Keywords

• non-small-cell lung cancer
• PD-L1 blockade therapy
• immune therapy
• immune checkpoint inhibitors
• PD-L1 proteins
• Gaussian naïve Bayes