Fibroblast-derived CXCL12 increases vascular permeability in a 3-D microfluidic model independent of extracellular matrix contractility.

Cancer-associated fibroblasts (CAFs) play an active role in remodeling the local tumor stroma to support tumor initiation, growth, invasion, metastasis, and therapeutic resistance. The CAF-secreted chemokine, CXCL12, has been directly implicated in the tumorigenic progression of carcinomas, including breast cancer. Using a 3-D in vitro microfluidic-based microtissue model, we demonstrate that stromal CXCL12 secreted by CAFs has a potent effect on increasing the vascular permeability of local blood microvessel analogues through paracrine signaling. Moreover, genetic deletion of fibroblast-specific CXCL12 significantly reduced vessel permeability compared to CXCL12 secreting CAFs within the recapitulated tumor microenvironment (TME). We suspected that fibroblast-mediated extracellular matrix (ECM) remodeling and contraction indirectly accounted for this change in vessel permeability. To this end, we investigated the autocrine effects of CXCL12 on fibroblast contractility and determined that antagonistic blocking of CXCL12 did not have a substantial effect on ECM contraction. Our findings indicate that fibroblast-secreted CXCL12 has a significant role in promoting a leakier endothelium hospitable to angiogenesis and tumor cell intravasation; however, autocrine CXCL12 is not the primary upstream trigger of CAF contractility.

cPLA2 blockade attenuates S100A7-mediated breast tumorigenicity by inhibiting the immunosuppressive tumor microenvironment.

BACKGROUND: Molecular mechanisms underlying inflammation-associated breast tumor growth are poorly studied. S100A7, a pro-inflammatory molecule has been shown to enhance breast cancer growth and metastasis. However, the S100A7-mediated molecular mechanisms in enhancing tumor growth and metastasis are unclear. METHODS: Human breast cancer tissue and plasma samples were used to analyze the expression of S100A7, cPLA2, and PGE2. S100A7-overexpressing or downregulated human metastatic breast cancer cells were used to evaluate the S100A7-mediated downstream signaling mechanisms. Bi-transgenic mS100a7a15 overexpression, TNBC C3 (1)/Tag transgenic, and humanized patient-derived xenograft mouse models and cPLA2 inhibitor (AACOCF3) were used to investigate the role of S100A7/cPLA2/PGE2 signaling in tumor growth and metastasis. Additionally, CODEX, a highly advanced multiplexed imaging was employed to delineate the effects of S100A7/cPLA2 inhibition on the recruitment of various immune cells. RESULTS: In this study, we found that S100A7 and cPLA2 are highly expressed and correlate with decreased overall survival in breast cancer patients. Further mechanistic studies revealed that S100A7/RAGE signaling promotes the expression of cPLA2 to mediate its oncogenic effects. Pharmacological inhibition of cPLA2 suppressed S100A7-mediated tumor growth and metastasis in multiple pre-clinical models including transgenic and humanized patient-derived xenograft (PDX) mouse models. The attenuation of cPLA2 signaling reduced S100A7-mediated recruitment of immune-suppressive myeloid cells in the tumor microenvironment (TME). Interestingly, we discovered that the S100A7/cPLA2 axis enhances the immunosuppressive microenvironment by increasing prostaglandin E2 (PGE2). Furthermore, CO-Detection by indEXing (CODEX) imaging-based analyses revealed that cPLA2 inhibition increased the infiltration of activated and proliferating CD4+ and CD8+ T cells in the TME. In addition, CD163+ tumor associated-macrophages were positively associated with S100A7 and cPLA2 expression in malignant breast cancer patients. CONCLUSIONS: Our study provides new mechanistic insights on the cross-talk between S100A7/cPLA2 in enhancing breast tumor growth and metastasis by generating an immunosuppressive TME that inhibits the infiltration of cytotoxic T cells. Furthermore, our studies indicate that S100A7/cPLA2 could be used as novel prognostic marker and cPLA2 inhibitors as promising drugs against S100A7-overexpressing aggressive breast cancer.

Lipopolysaccharide from the commensal microbiota of the breast enhances cancer growth: role of S100A7 and TLR4.

The role of commensal bacterial microbiota in the pathogenesis of human malignancies has been a research field of incomparable progress in recent years. Although breast tissue is commonly assumed to be sterile, recent studies suggest that human breast tissue may contain a bacterial microbiota. In this study, we used an immune-competent orthotopic breast cancer mouse model to explore the existence of a unique and independent bacterial microbiota in breast tumors. We observed some similarities in breast cancer microbiota with skin; however, breast tumor microbiota was mainly enriched with Gram-negative bacteria, serving as a primary source of lipopolysaccharide (LPS). In addition, dextran sulfate sodium (DSS) treatment in late-stage tumor lesions increased LPS levels in the breast tissue environment. We also discovered an increased expression of S100A7 and low level of TLR4 in late-stage tumors with or without DSS as compared to early-stage tumor lesions. The treatment of breast cancer cells with LPS increased the expression of S100A7 in breast cancer cells in vitro. Furthermore, S100A7 overexpression downregulated TLR4 and upregulated RAGE expression in breast cancer cells. Analysis of human breast cancer samples also highlighted the inverse correlation between S100A7 and TLR4 expression. Overall, these findings suggest that the commensal microbiota of breast tissue may enhance breast tumor burden through a novel LPS/S100A7/TLR4/RAGE signaling axis.

Slit2 Inhibits Breast Cancer Metastasis by Activating M1-Like Phagocytic and Antifibrotic Macrophages.

Tumor-associated macrophages (TAM) are heterogeneous in nature and comprise antitumor M1-like (M1-TAM) or pro-tumor M2-like (M2-TAM) TAMs. M2-TAMs are a major component of stroma in breast tumors and enhance metastasis by reducing their phagocytic ability and increasing tumor fibrosis. However, the molecular mechanisms that regulate phenotypic plasticity of TAMs are not well known. Here we report a novel tumor suppressor Slit2 in breast cancer by regulating TAMs in the tumor microenvironment. Slit2 reduced the in vivo growth and metastasis of spontaneous and syngeneic mammary tumor and xenograft breast tumor models. Slit2 increased recruitment of M1-TAMs to the tumor and enhanced the ability of M1-TAMs to phagocytose tumor cells in vitro and in vivo. This Slit2-mediated increase in M1-TAM phagocytosis occurred via suppression of IL6. Slit2 was also shown to diminish fibrosis in breast cancer mouse models by increasing the expression of matrix metalloproteinase 13 in M1-TAMs. Analysis of patient samples showed high Slit2 expression strongly associated with better patient survival and inversely correlated with the abundance of CD163+ TAMs. Overall, these studies define the role of Slit2 in inhibiting metastasis by activating M1-TAMs and depleting tumor fibrosis. Furthermore, these findings suggest that Slit2 can be a promising immunotherapeutic agent to redirect TAMs to serve as tumor killers for aggressive and metastatic breast cancers. In addition, Slit2 expression along with CD163+ TAMs could be used as an improved prognostic biomarker in patients with breast cancer. SIGNIFICANCE: This study provides evidence that the antitumor effect of Slit2 in breast cancer occurs by activating the phagocytic activity of M1-like tumor-associated macrophages against tumor cells and diminishing fibrosis.

Molecular and Cellular Factors Associated with Racial Disparity in Breast Cancer.

Recent studies have demonstrated that racial differences can influence breast cancer incidence and survival rate. African American (AA) women are at two to three fold higher risk for breast cancer than other ethnic groups. AA women with aggressive breast cancers show worse prognoses and higher mortality rates relative to Caucasian (CA) women. Over the last few years, effective treatment strategies have reduced mortality from breast cancer. Unfortunately, the breast cancer mortality rate among AA women remains higher compared to their CA counterparts. The focus of this review is to underscore the racial differences and differential regulation/expression of genetic signatures in CA and AA women with breast cancer. Moreover, immune cell infiltration significantly affects the clinical outcome of breast cancer. Here, we have reviewed recent findings on immune cell recruitment in the tumor microenvironment (TME) and documented its association with breast cancer racial disparity. In addition, we have extensively discussed the role of cytokines, chemokines, and other cell signaling molecules among AA and CA breast cancer patients. Furthermore, we have also reviewed the distinct genetic and epigenetic changes in AA and CA patients. Overall, this review article encompasses various molecular and cellular factors associated with breast cancer disparity that affects mortality and clinical outcome.

A new backpropagation neural network classification model for prediction of incidence of malaria.

Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified  as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.