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Review
. 2025 Mar;29(2):145-151.
doi: 10.1007/s40291-024-00757-3. Epub 2024 Nov 18.

Leveraging Multi-omics to Disentangle the Complexity of Ovarian Cancer

Shijuan Lin  1 , Lily L Nguyen  1 , Alexandra McMellen  2 , Michael S Leibowitz  2 , Natalie Davidson  1 , Daniel Spinosa  3 , Benjamin G Bitler  4
Affiliations
Review

Leveraging Multi-omics to Disentangle the Complexity of Ovarian Cancer

Shijuan Lin et al. Mol Diagn Ther. 2025 Mar.

Abstract

To better understand ovarian cancer lethality and treatment resistance, sophisticated computational approaches are required that address the complexity of the tumor microenvironment, genomic heterogeneity, and tumor evolution. The ovarian cancer tumor ecosystem consists of multiple tumors and cell types that support disease growth and progression. Over the last two decades, there has been a revolution in -omic methodologies to broadly define components and essential processes within the tumor microenvironment, including transcriptomics, metabolomics, proteomics, genome sequencing, and single-cell analyses. While most of these technologies comprehensively characterize a single biological process, there is a need to understand the biological and clinical impact of integrating multiple -omics platforms. Overall, multi-omics is an intriguing analytic framework that can better approximate biological complexity; however, data aggregation and integration pipelines are not yet sufficient to reliably glean insights that affect clinical outcomes.

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Conflict of interest statement

Declarations. Funding: The authors are supported by The Department of Defense (BGB, OC170228, OC200302, OC200225), The American Cancer Society (BGB, RSG-19-129-01-DDC), The Ovarian Cancer Research Alliance (BGB), and the National Institutes of Health (BGB, National Cancer Institute R37CA261987 and R01CA285446; ND, R00HG012945) Conflicts of interest/competing interests: Shijuan Lin, Lily L. Nguyen, Alexandra McMellen, Michael S. Leibowitz, Natalie Davidson, Daniel Spinosa, and Benjamin G. Bitler have no conflicts of interest that are directly relevant to the content of this article. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and material: Not applicable. Code availability: Not applicable. Authors’ contributions: SL completed the literature review, wrote the manuscript, and revised the manuscript. LLN completed the literature review and reviewed the manuscript. AMcM provided critical insight on ovarian cancer dissemination and edited the manuscript. MSL provided critical insight on clinical decision making and edited the manuscript. ND provided critical insight on machine learning approaches, and wrote and edited the manuscript. DS provided critical gynecologic oncology insight, and wrote and edited the manuscript. BGB oversaw the development of the manuscript, and wrote, edited, and revised the manuscript.

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