Núñez-Carpintero I, Petrizzelli M, Zinovyev A, Cirillo D, Valencia A. The multilayer community structure of medulloblastoma.
iScience 2021;
24:102365. [PMID:
33889829 PMCID:
PMC8050854 DOI:
10.1016/j.isci.2021.102365]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/17/2021] [Accepted: 03/24/2021] [Indexed: 01/20/2023] Open
Abstract
Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.
The molecular interpretation of rare diseases is a challenging task
Multilayer networks allow patient stratification and explainability
We identify subgroup-specific genes and multilayer associations in medulloblastoma
Multilayer community analysis enables the molecular interpretation of rare diseases
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