Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective

Abstract

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

Keywords: Cancer subtypes; Gene expression; Integrated analysis; Modeling systems; Mouse models; Sequencing.

Fig. 1

Multi-omics techniques have been uncovering the intrinsic features and parallels between human breast cancer and research models

Fig. 2

Simplified representation of breast cancer (BC) models generation. (a) Transgenic mice: one common approach for BC genetically engineered mice model generation is to overexpress an oncogene driven by a specific promoter targeting the mammary gland, such as MMTV. (b) 3D culture: the combination of a supporting scaffold (scaffold-dependent model), such as hydrogels and inert matrices, and different cell types allow cell growth and cell-extracellular matrix and cell-cell interactions. (c) Mammospheres (MM): these spheroids can be originated either from breast cancer cell lines (BCCL) or from BC biopsy. A single-cell suspension is obtained from the material, cell phenotypes are sorted for stem and progenitor cells, followed by culture in an ultra-low adherent surface for MM formation. (d) Patient-derived xenograft (PDX): tissue fragments from patient’s tumor are directly transplanted onto the immunodeficient mice heterotopically or orthotopically, with no need of an in vitro preparation step (F0). Once tumor reaches appropriate size, it can be dissected and expanded by reimplanting it onto another mice recipient (F1). The tumor expansion can go on for multiple generations (Fn). Patient-derived organoid (PDO): tissue fragments from patient’s tumor are digested and cultured in a 3D extracellular matrix hydrogel, giving rise to organoids