Abstract
Simple SummaryBreast cancer (BC) is a heterogeneous tumor type and has become the leading cause of cancer worldwide, with 685,000 deaths forecast in 2020. The clinical management of BC patients remains challenging, and there exists an urgent need for improved diagnostic, prognostic, and therapeutic strategies. Multi-omics platforms represent a promising tool for discovering novel biomarkers and identifying new therapeutic targets. In addition, the ongoing development of multi-omics approaches may foster the identification of more robust and accurate algorithms for data analysis. This review aims to summarize the results of recent multi-omics-based studies focused on the characterization of the metabolic phenotype of BC.Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.
Highlights
Breast cancer (BC) is the most frequently diagnosed tumor and the leading cause of cancer deaths in women worldwide [1]
Ried out in a reduced number of studies, which probably reflects limited sample availability have been described in genomic [42], epigenomic [52,53], transcriptomic [54], proteomi and/or technical difficulties associated with generating complete multi-omics datasets
Metabolomics represents a non-invasive strategy that could facilitate the development of non-invasive biomarkers with enormous potential for high-risk population screening, patient stratification, and treatment follow-up
Summary
Breast cancer (BC) is the most frequently diagnosed tumor and the leading cause of cancer deaths in women worldwide [1]. Systems based on different molecular features related to tumor biology, including histological type, grade, lymphovascular invasion, and marker status, do not accurately reflect BC subtype heterogeneity or specific patient subtypes [23]. A range of studies employing multi-omics-based approaches, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have noted the existence of characteristic metabolic profiles for BC subtypes. This review summarizes the results obtained in recently published multi-omics-based biomarkers [50] or metabolic pathways related to specific subgroups of BC patients [51]. Biomarkers for the detection andanalyses stratification of BC ried out in a reduced number of studies, which probably reflects limited sample availability have been described in genomic [42], epigenomic [52,53], transcriptomic [54], proteomi and/or technical difficulties associated with generating complete multi-omics datasets. A range studiesOverall, has compared the metabolic profiles of BC differences between BC and healthy tissues has revealed alterations in metabolites and/or enzymes involved in glycolysis, amino acid, lipid, and nucleotide metabolism
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