ArticleNature communications2021
Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 60 citations in OpenAlex.
- Mass Spectrometry-Based De Novo Sequencing of N-Glycosylated Light Chains: Linking Urinary Proteoforms to In Situ Renal Amyloid Deposits.Molecular & cellular proteomics : MCP · 2026Article
- Investigating Local Sequence-Structural Attributes of Amyloidogenic Light Chain Variable Domains.Proteins · 2025Article
- Modeling immunoglobulin light chain amyloidosis in Caenorhabditis elegans.Disease models & mechanisms · 2025Article
- The Role of Artificial Intelligence in Cardiac Amyloidosis: A Focus on Diagnosis and Clinical Application.Journal of cardiovascular development and disease · 2025Review
- An updated AL-base reveals ranked enrichment of immunoglobulin light chain variable genes in AL amyloidosis.Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis · 2025Article
- Article
- Clone-specific residue changes at multiple positions are associated with amyloid formation by antibody light chains.Frontiers in immunology · 2025Article
- An updated AL-Base reveals ranked enrichment of immunoglobulin light chain variable genes in AL amyloidosis.bioRxiv : the preprint server for biology · 2024Article
- Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center.BMC psychiatry · 2024Article
- Review
- How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.Journal of cardiovascular development and disease · 2024Review
- SMaRT M-Seq: an optimized step-by-step protocol for M protein sequencing in monoclonal gammopathies.Biology methods & protocols · 2024Review
- Cardiac and renal AL amyloidosis controlled by autologous stem cell transplantation for 17 years accompanying late onset atrial fibrillation and complete atrioventricular block.CEN case reports · 2023Article
- AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains.Antibody therapeutics · 2023Article
- Widespread amyloidogenicity potential of multiple myeloma patient-derived immunoglobulin light chains.BMC biology · 2023Article
- Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe.Frontiers in immunology · 2023Review
- Complete variable domain sequences of monoclonal antibody light chains identified from untargeted RNA sequencing data.Frontiers in immunology · 2023Article
- Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Cu(II) Binding Increases the Soluble Toxicity of Amyloidogenic Light Chains.International journal of molecular sciences · 2022Article
- Understanding AL amyloidosis with a little help fromFrontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieves a prediction accuracy of 83%. Furthermore, we are able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR, and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.