Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity

Maura Garofalo, Luca Piccoli, Margherita Romeo, Maria Monica Barzago, Sara Ravasio, Mathilde Foglierini, Milos Matkovic, Jacopo Sgrignani, Raoul Gasparo, Marco Prunotto, Luca Varani, Luisa Diomede, Olivier Michielin, Antonio Lanzavecchia and Andrea Cavalli ()
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Maura Garofalo: Università della Svizzera italiana
Luca Piccoli: Università della Svizzera italiana
Margherita Romeo: Istituto di Ricerche Farmacologiche Mario Negri IRCCS
Maria Monica Barzago: Istituto di Ricerche Farmacologiche Mario Negri IRCCS
Sara Ravasio: Università della Svizzera italiana
Mathilde Foglierini: Università della Svizzera italiana
Milos Matkovic: Università della Svizzera italiana
Jacopo Sgrignani: Università della Svizzera italiana
Raoul Gasparo: Università della Svizzera italiana
Marco Prunotto: University of Geneva
Luca Varani: Università della Svizzera italiana
Luisa Diomede: Istituto di Ricerche Farmacologiche Mario Negri IRCCS
Olivier Michielin: University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipôle
Antonio Lanzavecchia: Università della Svizzera italiana
Andrea Cavalli: Università della Svizzera italiana

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23880-9

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DOI: 10.1038/s41467-021-23880-9

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