Unsupervised machine-learning identifies clinically distinct subtypes of ALS that reflect different genetic architectures and biological mechanisms
Authors: Spargo Thomas P , Marriott Heather , Hunt Guy P , Pain Oliver , Kabiljo Renata , Bowles Harry , Sproviero William , Gillett Alexandra C , Fogh Isabella ,   Project MinE ALS Sequencing Consortium, Andersen Peter M. , Başak Nazli A. , Shaw Pamela J. , Corcia Philippe , Couratier Philippe , de Carvalho Mamede , Drory Vivian , Glass Jonathan D. , Gotkine Marc , Hardiman Orla , Landers John E. , McLaughlin Russell , Mora Pardina Jesús S. , Morrison Karen E. , Pinto Susana , Povedano Monica , Shaw Christopher E. , Silani Vincenzo , Ticozzi Nicola , Damme Philip Van , van den Berg Leonard H. , Vourc’h Patrick , Weber Markus , Veldink Jan H. , Dobson Richard J.B. , Khleifat Ahmad Al , Cummins Nicholas , Stahl Daniel , Al-Chalabi Ammar , Iacoangeli Alfredo 
Published: June 2023

AbstractBackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by a highly variable clinical presentation and multifaceted genetic and biological bases that translate into great patient heterogeneity. The identification of homogeneous subgroups of patients in terms of both clinical presentation and biological causes, could favour the development of effective treatments, healthcare, and clinical trials. We aimed to identify and characterise homogenous clinical subgroups of ALS, examining whether they represent underlying biological trends.MethodsLatent class clustering analysis, an unsupervised machine-learning method, was used to identify homogenous subpopulations in 6,523 people with ALS from Project MinE, using widely collected ALS-related clinical variables. The clusters were validated using 7,829 independent patients from STRENGTH. We tested whether the identified subgroups were associated with biological trends in genetic variation across genes previously linked to ALS, polygenic risk scores of ALS and related neuropsychiatric traits, and in gene expression data from post-mortem motor cortex samples.ResultsWe identified five ALS subgroups based on patterns in clinical data which were general across international datasets. Distinct genetic trends were observed for rare variants in theSOD1andC9orf72genes, and across genes implicated in biological processes relevant to ALS. Polygenic risk scores of ALS, schizophrenia and Parkinson’s disease were also higher in distinct clusters with respect to controls. Gene expression analysis identified different altered biological processes across clusters reflecting the genetic differences. We developed a machine learning classifier based on our model to assign subgroup membership using clinical data available at first visit, and made it available on a public webserver athttp://latentclusterals.er.kcl.ac.uk.ConclusionALS subgroups characterised by highly distinct clinical presentations were discovered and validated in two large independent international datasets. Such groups were also characterised by different underlying genetic architectures and biology. Our results showed that data-driven patient stratification into more clinically and biologically homogeneous subtypes of ALS is possible and could help develop more effective and targeted approaches to the biomedical and clinical study of ALS.