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.
Subject(s)
Caenorhabditis elegans/metabolism , Immunoglobulin Light Chains/genetics , Immunoglobulin Light Chains/toxicity , Immunoglobulin Light-chain Amyloidosis/diagnosis , Immunoglobulin Light-chain Amyloidosis/genetics , Machine Learning , Algorithms , Amino Acid Sequence , Animals , Antibodies/genetics , Caenorhabditis elegans/genetics , Databases, Genetic , Gene Expression , Humans , Immunoglobulin Light Chains/chemistry , Models, Molecular , Mutation , Recombinant ProteinsABSTRACT
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