Genetically Engineered Mice as Experimental Tools to Dissect the Critical Events in Breast Cancer

Menezes, Mitchell E.; Das, Swadesh K.; Emdad, Luni; Windle, Jolene J.; Wang, Xiangyang; Sarkar, Devanand; Fisher, Paul B.

doi:10.1016/b978-0-12-800249-0.00008-1

Cited by 28 publications

(24 citation statements)

References 138 publications

(183 reference statements)

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“…Mechanisms of metastatic breast tumor progression are studied widely in two-dimensional (2D) cultures by exposing them to external stimulus like hypoxia and in xenograft models by manipulating various signaling pathways and molecular targets (2,3), which do not recapitulate natural events of breast tumor progression (4,5). Cells in 2D monolayers lack microenvironmental context, cell-cell contacts in 3D, integrated cell signaling and cell-matrix interaction, making them physiologically different from the cells in solid tumors in vivo (4,5).…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Breast Cancer Models Mimic Hallmarks of Size-Induced Tumor Progression

et al. 2016

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Tumor size is strongly correlated with breast cancer metastasis and patient survival. Increased tumor size contributes to hypoxic and metabolic gradients in the solid tumor and to an aggressive tumor phenotype. Thus, it is important to develop three-dimensional (3D) breast tumor models that recapitulate size-induced microenvironmental changes and consequently, natural tumor progression in real time without the use of artificial culture conditions or gene manipulations. Here, we developed size-controlled multicellular aggregates (“microtumors”) of subtype-specific breast cancer cells by using non-adhesive polyethylene glycol dimethacrylate hydrogel microwells of defined sizes (150–600 μm). These 3D microtumor models faithfully represent size-induced microenvironmental changes such as hypoxic gradients, cellular heterogeneity and spatial distribution of necrotic/proliferating cells. These microtumors acquire hallmarks of tumor progression in the same cell lines within 6 days. Of note, large microtumors of hormone receptor positive cells exhibited an aggressive phenotype characterized by collective cell migration and upregulation of mesenchymal markers at mRNA and protein level, which was not observed in small microtumors. Interestingly, triple negative breast cancer (TNBC) cell lines did not show size-dependent upregulation of mesenchymal markers. In conclusion, size-controlled microtumor models successfully recapitulated clinically observed positive association between tumor size and aggressive phenotype in hormone receptor positive breast cancer while maintaining clinically proven poor correlation of tumor size with aggressive phenotype in TNBC. Such clinically relevant 3D models generated under controlled experimental conditions can serve as precise preclinical models to study mechanisms involved in breast tumor progression as well as antitumor drug effects as a function of tumor progression.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Breast Cancer Models Mimic Hallmarks of Size-Induced Tumor Progression

et al. 2016

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“…Since then, derivative approaches are now able to model metastasis due to expression of co-operating oncogenes (Sinn et al, 1987;Guy et al, 1992;Podsypanina et al, 2008;Adams et al, 2011) and assess mechanisms of pathway perturbation including TGF-b and WNT (Pierce et al, 1995;Li et al, 2000). The introduction of conditional and inducible systems to drive the expression of transgenes has enabled these models to be further refined (Sandgren et al, 1995;Moody et al, 2002;Podsypanina et al, 2008;Menezes et al, 2014;Rutkowski et al, 2014), including a model in mice of invasive lobular breast cancer created using CRISPR/Cas9-mediated disruption of PTEN (Annunziato et al, 2016).…”

Section: Transcriptional Deregulation During the Initiation Of Breastmentioning

confidence: 99%

Transcriptional regulation of normal human mammary cell heterogeneity and its perturbation in breast cancer

et al. 2019

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The mammary gland in adult women consists of biologically distinct cell types that differ in their surface phenotypes. Isolation and molecular characterization of these subpopulations of mammary cells have provided extensive insights into their different transcriptional programs and regulation. This information is now serving as a baseline for interpreting the heterogeneous features of human breast cancers. Examination of breast cancer mutational profiles further indicates that most have undergone a complex evolutionary process even before being detected. The consequent intra‐tumoral as well as inter‐tumoral heterogeneity of these cancers thus poses major challenges to deriving information from early and hence likely pervasive changes in potential therapeutic interest. Recently described reproducible and efficient methods for generating human breast cancers de novo in immunodeficient mice transplanted with genetically altered primary cells now offer a promising alternative to investigate initial stages of human breast cancer development. In this review, we summarize current knowledge about key transcriptional regulatory processes operative in these partially characterized subpopulations of normal human mammary cells and effects of disrupting these processes in experimentally produced human breast cancers.

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“…Several different in vivo models of metastasis have been developed to model human metastatic disease (Khanna & Hunter, 2005; Menezes et al, 2014). …”

Section: Preclinical In Vitro and In Vivo Models Of Metastasismentioning

confidence: 99%

“…Researchers have also developed genetically engineered or transgenic mouse models that spontaneously develop tumors and metastases (Menezes et al, 2014; Smith & Muller, 2013). In these models, the genetic makeup of the mouse is altered to either inhibit the expression of a tumor suppressor gene or overexpress an oncogene or combinations of the two, so that mice will spontaneously develop tumors and metastases over their normal lifespan, sometimes as rapidly as a couple of months (Eklund, Bry, & Alitalo, 2013; Fantozzi & Christofori, 2006; Husemann & Klein, 2009).…”

Section: Preclinical In Vitro and In Vivo Models Of Metastasismentioning

confidence: 99%

Detecting Tumor Metastases

Menezes

Das

Minn

et al. 2016

Advances in Cancer Research

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Metastasis is the complex process by which primary tumor cells migrate and establish secondary tumors in an adjacent or distant location in the body. Early detection of metastatic disease and effective therapeutic options for targeting these detected metastases remain impediments to effectively treating patients with advanced cancers. If metastatic lesions are identified early, patients might maximally benefit from effective early therapeutic interventions. Further, monitoring patients whose primary tumors are effectively treated for potential metastatic disease onset is also highly valuable. Finally, patients with metastatic disease can be monitored for efficacy of specific therapeutic interventions through effective metastatic detection techniques. Thus, being able to detect and visualize metastatic lesions is key and provides potential to greatly improve overall patient outcomes. In order to achieve these objectives, researchers have endeavored to mechanistically define the steps involved in the metastatic process as well as ways to effectively detect metastatic progression. We presently overview various preclinical and clinical in vitro and in vivo assays developed to more efficiently detect tumor metastases, which provides the foundation for developing more effective therapies for this invariably fatal component of the cancerous process.

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Genetically Engineered Mice as Experimental Tools to Dissect the Critical Events in Breast Cancer

Cited by 28 publications

References 138 publications

Three-Dimensional Breast Cancer Models Mimic Hallmarks of Size-Induced Tumor Progression

Three-Dimensional Breast Cancer Models Mimic Hallmarks of Size-Induced Tumor Progression

Transcriptional regulation of normal human mammary cell heterogeneity and its perturbation in breast cancer

Detecting Tumor Metastases

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