A pan-cancer integrative pathway analysis of multi-omics data

Abstract

Background: Multi-view -omics datasets offer rich opportunities for integrative analysis across genomic, transcriptomic, and epigenetic data platforms. Statistical methods are needed to rigorously implement current research on functional biology, matching the complex dynamics of systems genomic datasets.

Methods: We apply imputation for missing data and a structural, graph-theoretic pathway model to a dataset of 22 cancers across 173 signaling pathways. Our pathway model integrates multiple data platforms, and we test for differential activation between cancerous tumor and healthy tissue populations.

Results: Our pathway analysis reveals significant disturbance in signaling pathways that are known to relate to oncogenesis. We identify several pathways that suggest new research directions, including the Trk signaling and focal adhesion kinase activation pathways in sarcoma.

Conclusions: Our integrative analysis confirms contemporary research findings, which supports the validity of our findings. We implement an interactive data visualization for exploration of the pathway analyses, which is available online for public access.

Graphical abstract

Keywords

multi-platform data integration / pathway analysis / imputation / cancer genomics / data visualization

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ACKNOWLEDGEMENTS

Yuping Zhang acknowledges Faculty Research Excellence Program Award from University of Connecticut.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Henry Linder and Yuping Zhang declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature