Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data.
Amyotrophic lateral sclerosis
Biomarkers
Patient stratification
Precision medicine
Transcriptomics
Unsupervised and supervised machine learning
Journal
Acta neuropathologica communications
ISSN: 2051-5960
Titre abrégé: Acta Neuropathol Commun
Pays: England
ID NLM: 101610673
Informations de publication
Date de publication:
21 Dec 2023
21 Dec 2023
Historique:
received:
19
09
2023
accepted:
10
11
2023
medline:
22
12
2023
pubmed:
22
12
2023
entrez:
22
12
2023
Statut:
epublish
Résumé
Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .
Identifiants
pubmed: 38129934
doi: 10.1186/s40478-023-01686-8
pii: 10.1186/s40478-023-01686-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
208Subventions
Organisme : Motor Neurone Disease Association
ID : Motor Neurone Disease Association
Pays : United Kingdom
Informations de copyright
© 2023. The Author(s).
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