Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences Article Swipe
Tania Cuppens
,
Manpreet Kaur
,
Ajay Kumar
,
Julie Shatto
,
Andy Cheuk‐Him Ng
,
Mickaël Leclercq
,
Marek Reformat
,
Arnaud Droit
,
Ian Dunham
,
François V. Bolduc
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fped.2023.1171920
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fped.2023.1171920
Objective Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering. Methods Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach. Results HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3–12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC. Conclusion Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.3389/fped.2023.1171920
- OA Status
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- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386860285
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386860285Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fped.2023.1171920Digital Object Identifier
- Title
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Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differencesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-18Full publication date if available
- Authors
-
Tania Cuppens, Manpreet Kaur, Ajay Kumar, Julie Shatto, Andy Cheuk‐Him Ng, Mickaël Leclercq, Marek Reformat, Arnaud Droit, Ian Dunham, François V. BolducList of authors in order
- Landing page
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https://doi.org/10.3389/fped.2023.1171920Publisher landing page
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fped.2023.1171920Direct OA link when available
- Concepts
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Phenotype, Genetic heterogeneity, Hierarchical clustering, Computational biology, Biology, Bioinformatics, Genetics, Gene, Cluster analysis, Machine learning, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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