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.