A Comprehensive Targeted Panel of 282 Genes: Unveiling Key Biomarkers in Multiple Myeloma

Abstract

AbstractMultiple myeloma (MM) is a hematological cancer that evolves from the benign precursor stage termed monoclonal gammopathy of undetermined significance (MGUS). Understanding the pivotal biomarkers, genomic events, and gene interactions distinguishing MM from MGUS can significantly contribute to early detection and an improved understanding of MM’s pathogenesis. In this study, we present a curated comprehensive targeted sequencing panel focusing on 282 MM-relevant genes and employing clinically oriented NGS targeted sequencing approaches. To identify these 282 MM-relevant genes, we designed an innovative AI-basedbio-inspired graph network learning based gene gene interactions (Bio-DGI)model for detecting biomarkers and gene interactions that can potentially differentiate MM from MGUS. The Bio-DGI model leverages gene interactions from nine protein-protein interaction (PPI) networks and analyzes the genomic features from 1154 MM and 61 MGUS samples, respectively. The proposed model out-performed baseline machine learning (ML) and deep learning (DL) models, demonstrating quantitative and qualitative superiority by identifying the highest number of MM-relevant genes in the post-hoc analysis. The pathway analysis underscored the importance of top-ranked genes by highlighting the MM-relevant pathways as the top-significantly altered pathways. The 282-gene panel encompasses 9272 coding regions and has a length of 2.577 Mb. Additionally, the 282-gene panel showcased superior performance compared to previously published panels, excelling in detecting both genomic and transformative events. Notably, the proposed gene panel also highlighted highly influential genes and their interactions within gene communities in MM. The clinical relevance is confirmed through a two-fold univariate survival analysis. The study’s findings shed light on essential gene biomarkers and their interactions, providing valuable insights into disease progression.