Deep Learning to Discover Genomic Signatures for Racial Disparity in Lung Cancer

Background: In the United States, African American Males (AAM) have the highest lung cancer incidence and mortality rate compared to European American Males (EAM). Cigarette is considered the major risk factor for lung cancer, but smoking alone fails to interpret the rationale for developing lung cancer between AAM and EAM. The higher rates of lung cancer among AAM occur even though they have lower smoking rates, smoke fewer cigarettes per day, and are less likely to be heavy smokers than EAM. Identifying genomic signatures such as key genes that can differentiate lung cancers between AAM and EAM will be a stepping stone to comprehend the disparity of lung cancer between AAM and EAM.Method: The gene expression profiles of whole blood samples from AAM and EAM patients were used to identify the key genes that can differentiate the lung cancers between AAM and EAM. Due to the US population's imbalanced nature between AAM and EAM, the distribution of samples for the present study is also highly imbalanced (AAM: 15 and EAM: 153). Here, we developed a computational framework using a deep learning-based unsupervised feature selection approach, concrete autoencoder (CAE), which can select actual features rather than latent features. First, we showed that features such as differentially expressed genes (DEGs) discovered by a supervised statistical approach LIMMA could not differentiate lung cancers between AAM and EAM. Then we showed that the CAE could isolate essential features capable of differentiating lung cancers between AAM and EAM.Results: The proposed framework using CAE was able to detect 34 key features/genes, which outperforms all sets of DEGs identified using three different thresholds on fold change. Using the selected 34 genes, the Random Forest classifier was able to classify lung cancers among AAM and EAM with 99% accuracy and only one false negative.Conclusion: The proposed framework using CAE reveals the key genes that can differentiate lung tumors between AAM and EAM. These key genes can be used as biomarkers to understand the difference in lung cancer development between AAM and EAM. This study also showed that the CAE is capable of extracting relevant features from a highly imbalanced dataset.