Regulation of growth, invasion and metabolism of breast ductal carcinoma through CCL2/CCR2 signaling interactions with MET receptor tyrosine kinases

Expression of immune-related genes and breast cancer recurrence in women with ductal carcinoma in situ

BACKGROUND AND AIM

Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer with highly variable clinical behavior, but risk stratification is still challenging. We sought to identify immune-related gene expression signatures of pure DCIS associated with different risks of breast cancer recurrence.

METHODS

A retrospective nested case-control study of 143 pure DCIS was performed including 70 women with subsequent ipsilateral breast event (IBE, in situ or invasive; cases) and 73 DCIS women with no IBE and matched for age, tumor size, treatment, hormone receptors/HER2 status, and follow-up time (controls). RNA was extracted from DCIS samples and subjected to next-generation sequencing gene expression analysis of 395 immune-related genes. Correlations between DCIS immune-related gene expression and IBE were analyzed using weighted Cox regression for nested case-control data.

RESULTS

Eight immune-related genes were differentially expressed between cases and controls. MAGEA10 expression (present vs. absent) and high expression levels of IFNA17 and CBLB (Q4 vs. Q1) were observed more frequently in DCIS of women with subsequent IBE, mainly invasive (p-valueFDR < 0.05). Conversely, expression of IL3RA1, TAGAP, TNFAIP8, and high expression levels of CCL2 and LRP1 were associated with a lower risk of IBE (p-valueFDR < 0.05).

CONCLUSION

This exploratory analysis of pure DCIS showed significant differences in immune-related gene expression profiles between women with and with no subsequent IBE, particularly as invasive IBE. These results, after additional validation, could improve risk stratification and management of DCIS patients.

How chemokines organize the tumour microenvironment

For our immune system to contain or eliminate malignant solid tumours, both myeloid and lymphoid haematopoietic cells must not only extravasate from the bloodstream into the tumour tissue but also further migrate to various specialized niches of the tumour microenvironment to functionally interact with each other, with non-haematopoietic stromal cells and, ultimately, with cancer cells. These interactions regulate local immune cell survival, proliferative expansion, differentiation and their execution of pro-tumour or antitumour effector functions, which collectively determine the outcome of spontaneous or therapeutically induced antitumour immune responses. None of these interactions occur randomly but are orchestrated and critically depend on migratory guidance cues provided by chemokines, a large family of chemotactic cytokines, and their receptors. Understanding the functional organization of the tumour immune microenvironment inevitably requires knowledge of the multifaceted roles of chemokines in the recruitment and positioning of its cellular constituents. Gaining such knowledge will not only generate new insights into the mechanisms underlying antitumour immunity or immune tolerance but also inform the development of biomarkers (or 'biopatterns') based on spatial tumour tissue analyses, as well as novel strategies to therapeutically engineer immune responses in patients with cancer. Here we will discuss recent observations on the role of chemokines in the tumour microenvironment in the context of our knowledge of their physiological functions in development, homeostasis and antimicrobial responses.

Mdwgan-gp: data augmentation for gene expression data based on multiple discriminator WGAN-GP

BACKGROUND

Although gene expression data play significant roles in biological and medical studies, their applications are hampered due to the difficulty and high expenses of gathering them through biological experiments. It is an urgent problem to generate high quality gene expression data with computational methods. WGAN-GP, a generative adversarial network-based method, has been successfully applied in augmenting gene expression data. However, mode collapse or over-fitting may take place for small training samples due to just one discriminator is adopted in the method.

RESULTS

In this study, an improved data augmentation approach MDWGAN-GP, a generative adversarial network model with multiple discriminators, is proposed. In addition, a novel method is devised for enriching training samples based on linear graph convolutional network. Extensive experiments were implemented on real biological data.

CONCLUSIONS

The experimental results have demonstrated that compared with other state-of-the-art methods, the MDWGAN-GP method can produce higher quality generated gene expression data in most cases.

TNF-Alpha Promotes an Inflammatory Mammary Microenvironment That Favors Macrophage and Epithelial Migration in a CCL2- and Mitochondrial-ROS-Dependent Manner

The influence of an inflammatory microenvironment on tumorigenesis has been widely accepted. Systemic conditions that favor the onset of an inflammatory landscape predispose the progression of breast cancer. Under obesity conditions, the endocrine function of adipose tissue is one of the main determinants of the production of local and systemic inflammatory mediators. Although these mediators can stimulate tumorigenesis and recruit inflammatory cells, as macrophages, the mechanism involved remains poorly understood. In the present work, we describe that the TNFα treatment of mammary preadipocytes from human normal patients blocks adipose differentiation and promotes the generation of pro-inflammatory soluble factors. The latter stimulate the mobilization of THP-1 monocytes and MCF-7 epithelial cancer cells in an MCP1/CCL2- and mitochondrial-ROS-dependent manner. Together, these results reaffirm the contribution of an inflammatory microenvironment and mtROS in the progression of breast cancer.