XL1R-Net: Explainable AI-driven improved LI-regularized deep neural architecture for NSCLC biomarker identification

Background and Objective: Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes - Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data.Methods: An eXplainable AI (XAI)-driven LI-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard LI-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results: The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug-Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K-M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients' likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List.Conclusion: A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC's genetic diversity, using only one omics data type may not adequately capture the tumor's complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

Automated summary

This study aims to identify biomarkers for non-small cell lung cancer (NSCLC) using copy number variation (CNV) data. A novel deep learning architecture, XL1R-Net, is proposed to improve the classification accuracy for NSCLC subtyping. Twenty NSCLC-relevant biomarkers are uncovered using explainable AI (XAI)-based feature identification. The results show that the identified biomarkers have high classification performance and clinical relevance. Additionally, twelve of the biomarkers are potentially druggable and eighteen of them have a high probability of predicting NSCLC patients' survival likelihood according to the Drug-Gene Interaction Database and the K-M Plotter tool, respectively. This research suggests that investigating these seven novel biomarkers can contribute to NSCLC therapy, and the integration of multiomics data and other sources will help better understand NSCLC heterogeneity.