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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87681 |
| Date | 26 October 2023 |
| Creators | Binder, Hans, Schmidt, Maria, Hopp, Lydia, Davitavyan, Suren, Arakelyan, Arsen, Loeffler-Wirth, Henry |
| Publisher | MDPI |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 2797 |
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