Breast cancer in the era of integrating "Omics" approaches

Abstract

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput "omics" technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of "omics" strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different "omics" datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

Fig. 1. Upstream Regulator Analysis Results.

A Venn diagram for significant upstream (both activated and inhibited) from the single “Core Analysis” using IPA tool based on Proteomics (in green), or Metabolomics (in light red). B Venn Diagram for significant upstream (both activated and inhibited) from integrating “Omics” approaches (in violet) vs the sum of the significant upstream obtained by each single approach (Proteomics + Metabolomics, in yellow).

Fig. 2. Upstream Regulator Analysis, based on “omics” integration using the Ingenuity Pathway Analysis software.

Orange and blue shapes represent predicted activation or inhibition, respectively. The predicted relationship between genes may lead to direct activation (orange solid lines) or direct inhibition (blue solid lines). Red and green color indicate genes, proteins, and metabolites increased and decreased in expression, respectively, while the numbers represent the measurements of their expression. A1 shows the proteins and metabolites of the loaded dataset involved in the activation of the upstream regulator Interferon alpha. A2 shows the mechanistic network, theoretically reconstructed that underlies the activation of the upstream Interferon alpha. B1 shows the proteins and metabolites of the loaded dataset involved in the activation of the upstream regulator Fibroblast growth factor 7 (FGF7). B2 shows the mechanistic network, theoretically reconstructed that underlies the activation of the upstream FGF7. C1 shows the proteins and metabolites of the loaded dataset involved in the activation of the upstream regulator Insulin (INS). C2 shows the mechanistic network, theoretically reconstructed that underlies the activation of the upstream INS. D shows the proteins and metabolites of the loaded dataset involved in the down-regulation of the upstream regulator Histone deacetylase 5 (HDAC5).