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Article

Integrated Multi-Omics Maps of Lower-Grade Gliomas

1
Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, 04107 Leipzig, Germany
2
Armenian Bioinformatics Institute (ABI), 0014 Yerevan, Armenia
3
Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 0014 Yerevan, Armenia
4
Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Jerome Solassol
Cancers 2022, 14(11), 2797; https://doi.org/10.3390/cancers14112797
Received: 12 April 2022 / Revised: 18 May 2022 / Accepted: 31 May 2022 / Published: 4 June 2022
(This article belongs to the Special Issue Multi-Omics Approaches in Oncology)
Data from multiple omics domains were increasingly generated in large-scale tumour studies to enhance our understanding of molecular mechanisms of cancer. We present an integrated cartography of three omics layers combining the transcriptome, methylome, and genome (copy number variations) into a unique mapping scheme which enabled us to decipher functional links within and between the omics domains. Application to lower grade gliomas reveals distinct networks governed either by methylation or copy number variations, both affecting transcriptomics modes of cell activity. The integrated maps provide an intuitive view on tumour heterogeneity across the omics layers distinguishing, e.g., astrocytoma- and oligodendroglioma-like glioma types. In a wider sense, multi-omics cartography deciphers the effect of different omes on tumour phenotypes and their molecular hallmarks with individual resolution.
Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers. View Full-Text
Keywords: integrative cancer bioinformatics; transcriptome; DNA methylome and copy number variation data; lower grade gliomas; self-organizing maps machine learning; modes of genomics regulation integrative cancer bioinformatics; transcriptome; DNA methylome and copy number variation data; lower grade gliomas; self-organizing maps machine learning; modes of genomics regulation
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MDPI and ACS Style

Binder, H.; Schmidt, M.; Hopp, L.; Davitavyan, S.; Arakelyan, A.; Loeffler-Wirth, H. Integrated Multi-Omics Maps of Lower-Grade Gliomas. Cancers 2022, 14, 2797. https://doi.org/10.3390/cancers14112797

AMA Style

Binder H, Schmidt M, Hopp L, Davitavyan S, Arakelyan A, Loeffler-Wirth H. Integrated Multi-Omics Maps of Lower-Grade Gliomas. Cancers. 2022; 14(11):2797. https://doi.org/10.3390/cancers14112797

Chicago/Turabian Style

Binder, Hans, Maria Schmidt, Lydia Hopp, Suren Davitavyan, Arsen Arakelyan, and Henry Loeffler-Wirth. 2022. "Integrated Multi-Omics Maps of Lower-Grade Gliomas" Cancers 14, no. 11: 2797. https://doi.org/10.3390/cancers14112797

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