Multiomics Insights Into AL Amyloidosis

Abstract

Light chain amyloidosis is a systemic or localized protein conformational disorder triggered by misfolded immunoglobulin light chains, leading to amyloid fibril deposition. The disease is characterized by multiorgan involvement and delayed diagnosis, contributing to poor prognosis and high mortality rates. Recent advances in multiomics technologies have substantially improved our understanding of the molecular pathogenesis of AL amyloidosis. Genomic studies have identified susceptibility loci, cytogenetic features, and heterogeneous somatic alterations. Transcriptomic analyses have delineated key genes and pathways underlying clonal plasma cell expansion. Proteomic analyses have revealed the organ involvement preferences and the mechanisms of tissue injury. Immunomics has revealed immune repertoire alterations associated with disease progression. Metabolomics and microbiomics have identified metabolic and microbial signatures with diagnostic and prognostic values. Furthermore, clinlabomics, through the integration of sequence, clinical, and other data, has established machine learning models that improve risk prediction and stratification in AL amyloidosis. However, pronounced disease heterogeneity, which hampers robust biomarker discovery, together with insufficient cross‐omics integration, fragmented datasets, and persistent barriers to the clinical implementation of artificial intelligence, continue to limit the translational impact of multiomics research in AL amyloidosis. This review systematically summarizes the key applications of multiomics technologies in AL amyloidosis research, evaluates recent advances, and provides an analysis of current limitations and challenges, laying a theoretical foundation for molecular subtype‐based diagnostics and therapeutics.