Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

Abstract

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

Keywords: breast; cancer; genomics; ovarian; proteomics.

Figure 1

Gene expression-based classification of breast and ovarian cancers. The major molecular classifications of breast and ovarian cancers are depicted here. Further highlighted are the molecular similarities between high grade serous ovarian and basal-like breast cancer.

Figure 2

Use of single platform and integrative omics in cancer biology and medicine. The major contributions of single and multi-platform omics studies as well as single cell omics are summarized here. Single platform studies enable cataloging of mutation or alteration patterns, identifying signaling networks of interest and defining certain molecular subtypes. Multiplatform studies can further expand single platform-defined molecular subtypes and identify signaling pathways by identifying mutations in multiple genes representing multiple levels of pathway dysregulation. Single cell analyses allow for analyses of tumor cell subpopulations, identify cell transition states, map molecular markers and cell populations and identify circulating tumor cell populations. Orthogonal analysis of these data provides further context to genomic studies. These approaches contribute to a greater understanding of tumor biology as well as clinical advancements in treating cancer.