Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer

Background: Lung cancer is the leading cause of death compared to other cancers in the USA. The overall survival rate of lung cancer is not satisfactory even though there are cutting-edge treatment methods for cancers. Genomic profiling and biomarker gene identification of lung cancer patients may play a role in the therapeutics of lung cancer patients. The biomarker genes identified by most of the existing methods (statistical and machine learning based) belong to the whole cohort or population. That is why different people with the same disease get the same kind of treatment, but results in different outcomes in terms of success and side effects. So, the identification of biomarker genes for individual patients is very crucial for finding efficacious therapeutics leading to precision medicine. Methods: In this study, we propose a pipeline to identify lung cancer class-specific and patient-specific key genes which may help formulate effective therapies for lung cancer patients. We have used expression profiles of two types of lung cancers, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), and Healthy lung tissues to identify LUADand LUSC-specific (class-specific) and individual patient-specific key genes using an explainable machine learning approach, SHaphley Additive ExPlanations (SHAP). This approach provides scores for each of the genes for individual patients which tells us the attribution of each feature (gene) for each sample (patient). Result: In this study, we applied two variations of SHAP- tree explainer and gradient explainer for which tree-based classifier, XGBoost, and deep learning-based classifier, convolutional neural network (CNN) were used as classification algorithms, respectively. Our results showed that the proposed approach successfully identified class-specific (LUAD, LUSC, and Healthy) and patient-specific key genes based on the SHAP scores. Conclusion: This study demonstrated a pipeline to identify cohort-based and patient-specific biomarker genes by incorporating an explainable machine learning technique, SHAP. The patient-specific genes identified using SHAP scores may provide biological and clinical insights into the patient’s diagnosis.