Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Duan, Ran; Gao, Lin; Gao, Yong; Hu, Yuxuan; Han, Xu; Huang, Ming‐Feng; Song, Kuo; Wang, Hongda; Dong, Yun; Jiang, Chaoqun; Zhang, Chenxing; Jia, Songwei

doi:10.1371/journal.pcbi.1009224

Cited by 61 publications

(54 citation statements)

References 108 publications

(128 reference statements)

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“…More importantly, multi-omics data are mandatory for the comprehensive understanding of the whole repertoire of genomics regulation underlying cancer genesis and development. A similar “Solomonic” conclusion was drawn in a systematic benchmark study comparing multi-omics integration methods on cancer data [ 54 ]. The effect of different omics data types varies and can improve the outcome in terms of both clustering and clinical metrics, but it can have even a negative effect if too many omics layers are integrated, thus refuting the intuition that incorporating more types of omics data always helps produce better results.…”

Section: Discussionsupporting

confidence: 63%

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Binder

Schmidt

Hopp

et al. 2022

Cancers

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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.

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Section: Discussionsupporting

confidence: 63%

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Binder

Schmidt

Hopp

et al. 2022

Cancers

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“…Several computational approaches exist that can help integrate multiple omics datasets. However, the detailed discussion of these approaches and their pros and cons are beyond the scope of this review and have already been review by Duan et al and Subramanian et al [82,83]. Multi-omics data integration can lead towards the identification of diagnostic biomarkers, prognostic biomarkers, screening biomarkers, and potential therapeutic targets for rare ovarian cancers (Figure 1).…”

Section: Multi-omics Dataset Integration Towards a Systems Biology View Of Rare Ovarian Cancersmentioning

confidence: 99%

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

et al. 2021

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Rare ovarian cancers are ovarian cancers with an annual incidence of less than 6 cases per 100,000 women. They generally have a poor prognosis due to being delayed diagnosis and treatment. Exploration of molecular mechanisms in these cancers has been challenging due to their rarity and research efforts being fragmented across the world. Omics approaches can provide detailed molecular snapshots of the underlying mechanisms of these cancers. Omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, can identify potential candidate biomarkers for diagnosis, prognosis, and screening of rare gynecological cancers and can aid in identifying therapeutic targets. The integration of multiple omics techniques using approaches such as proteogenomics can provide a detailed understanding of the molecular mechanisms of carcinogenesis and cancer progression. Further, omics approaches can provide clues towards developing immunotherapies, cancer recurrence, and drug resistance in tumors; and form a platform for personalized medicine. The current review focuses on the application of omics approaches and integrative biology to gain a better understanding of rare ovarian cancers.

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“…Success in creating user-friendly software that facilitates data integration has been limited, as the majority of the available tools are only accessible to researchers with a significant bioinformatics background. In a methodical study to evaluate accuracy, robustness, and computational efficiency of different integration protocols, Duan and coworkers employed 10 integration methods for cancer datasets [ 105 ]. The authors also evaluated the influence of combining different omics data types and concluded that integrating multiple datasets does not always result in a better performance for tumor subtyping [ 105 ].…”

Section: Future Perspectivesmentioning

confidence: 99%

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Gonçalves

Bollwein

Schwamborn

2022

Life

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Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

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Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Cited by 61 publications

References 108 publications

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Integrated Multi-Omics Maps of Lower-Grade Gliomas

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

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