Cited by 18Bioinformatics and Genomic NetworksGene expression and cancer classificationGlioma Diagnosis and Treatment

Detecting molecular subtypes from multi-omics datasets using SUMO

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Sienkiewicz, Karolina, Chen, Jinyu, Chatrath, Ajay, Lawson, John T., Sheffield, Nathan C., Zhang, Louxin, Ratan, Aakrosh·2022·Cell Reports Methods

Abstract

We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for “ IDH wild type, with molecular features of glioblastoma” by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.

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Crossref · 10.1016/j.crmeth.2021.100152

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  • SUMO slightly outperformed NEMO in robustness to varying levels of missing samples, except at extreme missingness of 80% and greater.

    “These comparisons indicated a slight outperformance by SUMO, except for in the cases of extreme missingness (80% and greater).”

    From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Sienkiewicz et al., 2022

  • SUMO's range of adjusted rand index values was typically narrower than NEMO's, suggesting less variability in expected performance.

    “As well, the range of adjusted rand index values (the performance metric used) was typically observed to be narrower for SUMO, suggesting less variability in expected performance.”

    From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Sienkiewicz et al., 2022

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Authors
Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., & Ratan, A.
Affiliations
  • University of Virginia (US)
  • National University of Singapore (SG)
  • University of Virginia Cancer Center (US)
Corresponding author
Ratan, Aakrosh
Venue
Cell Reports Methods
Publisher
Elsevier BV
Published
2022-01
Volume
2
Issue
1
Pages
100152
ISSN
2667-2375 (ISSN-L)
OpenAlex
W4210728747
Cited by
18
References
51
Citations by year
2026: 2 · 2025: 2 · 2024: 7 · 2023: 5 · 2022: 2
FWCI
1.31
Type
journal article
Language
en
Open access
Locations
  • Cell Reports Methods (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (P30CA044579)
  • National Institute of General Medical Sciences (GM128636)
  • National Research Foundation Singapore (NRF2016NRF-NSFC001\u2013026, NRF2016NRF-NSFC001)
  • UVA Cancer Center (T32 GM007267)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
Keywords
molecular subtyping, multi-omics integration, non-negative matrix factorization, patient similarity networks, missing data, tumor subtypes, prognosis, lower-grade glioma, DNA methylation, distal enhancers, chromosome 7 gain, chromosome 10 loss, IDH-wildtype, SUMO
MeSH terms
Multiomics, Chromosome Aberrations, Glioblastoma, Glioma, Humans, Prognosis

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