Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.
Nat Commun
; 12(1): 3532, 2021 06 10.
Article
en En
| MEDLINE
| ID: mdl-34112780
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
6_ODS3_enfermedades_notrasmisibles
Problema de salud:
6_endocrine_disorders
Asunto principal:
Cadenas Ligeras de Inmunoglobulina
/
Caenorhabditis elegans
/
Aprendizaje Automático
/
Amiloidosis de Cadenas Ligeras de las Inmunoglobulinas
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Suiza