Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences
ObjectiveIndividuals 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 cli...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Pediatrics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2023.1171920/full |
| _version_ | 1797681937222991872 |
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| author | Tania Cuppens Manpreet Kaur Ajay A. Kumar Julie Shatto Andy Cheuk-Him Ng Mickael Leclercq Marek Z. Reformat Arnaud Droit Ian Dunham François V. Bolduc François V. Bolduc François V. Bolduc |
| author_facet | Tania Cuppens Manpreet Kaur Ajay A. Kumar Julie Shatto Andy Cheuk-Him Ng Mickael Leclercq Marek Z. Reformat Arnaud Droit Ian Dunham François V. Bolduc François V. Bolduc François V. Bolduc |
| author_sort | Tania Cuppens |
| collection | DOAJ |
| description | ObjectiveIndividuals 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.MethodsUsing 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.ResultsHAC 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.ConclusionOur 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. |
| first_indexed | 2024-03-11T23:52:14Z |
| format | Article |
| id | doaj.art-85fcf976a6ac49d09ed2369e8d5cc585 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-03-11T23:52:14Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-85fcf976a6ac49d09ed2369e8d5cc5852023-09-19T06:11:22ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602023-09-011110.3389/fped.2023.11719201171920Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differencesTania Cuppens0Manpreet Kaur1Ajay A. Kumar2Julie Shatto3Andy Cheuk-Him Ng4Mickael Leclercq5Marek Z. Reformat6Arnaud Droit7Ian Dunham8François V. Bolduc9François V. Bolduc10François V. Bolduc11Département de Médecine Moléculaire de L'Université Laval, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, CanadaDepartment of Pediatric Neurology, University of Alberta, Edmonton, AB, CanadaEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United KingdomDepartment of Pediatric Neurology, University of Alberta, Edmonton, AB, CanadaDepartment of Pediatric Neurology, University of Alberta, Edmonton, AB, CanadaDépartement de Médecine Moléculaire de L'Université Laval, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaDépartement de Médecine Moléculaire de L'Université Laval, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, CanadaEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United KingdomDepartment of Pediatric Neurology, University of Alberta, Edmonton, AB, CanadaDepartment of Medical Genetics, University of Alberta, Edmonton, AB, CanadaNeuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, CanadaObjectiveIndividuals 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.MethodsUsing 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.ResultsHAC 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.ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fped.2023.1171920/fullneurodevelopmental differencesglobal developmental delayphenotypewhole exome sequencinghierarchical agglomerative clusteringk-means clustering |
| spellingShingle | Tania Cuppens Manpreet Kaur Ajay A. Kumar Julie Shatto Andy Cheuk-Him Ng Mickael Leclercq Marek Z. Reformat Arnaud Droit Ian Dunham François V. Bolduc François V. Bolduc François V. Bolduc Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences Frontiers in Pediatrics neurodevelopmental differences global developmental delay phenotype whole exome sequencing hierarchical agglomerative clustering k-means clustering |
| title | Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences |
| title_full | Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences |
| title_fullStr | Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences |
| title_full_unstemmed | Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences |
| title_short | Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences |
| title_sort | developing a cluster based approach for deciphering complexity in individuals with neurodevelopmental differences |
| topic | neurodevelopmental differences global developmental delay phenotype whole exome sequencing hierarchical agglomerative clustering k-means clustering |
| url | https://www.frontiersin.org/articles/10.3389/fped.2023.1171920/full |
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