The detection of ESR1/PGR/ERBB2 mRNA levels by RT-QPCR: a better approach for subtyping breast cancer and predicting prognosis

Relationship between ER expression by IHC or mRNA with Ki67 response to aromatase inhibition: a POETIC study

BACKGROUND

In clinical practice, oestrogen receptor (ER) analysis is almost entirely by immunohistochemistry (IHC). ASCO/CAP recommends cut-offs of < 1% (negative) and 1-10% (low) cells positive. There is uncertainty whether patients with ER low tumours benefit from endocrine therapy. We aimed to assess IHC and mRNA cut-points for ER versus biological response of primary breast cancer to 2 weeks' aromatase inhibitor treatment as measured by change in Ki67.

METHODS

Cases were selected from the aromatase inhibitor treatment group of POETIC. We selected the 15% with the poorest Ki67 response (PR, < 40% Ki67 suppression, n = 230) and a random 30% of the remainder categorised as intermediate (IR, 40-79% Ki67 suppression, n = 150) and good-responders (GR, ≥ 80% Ki67 suppression, n = 230) from HER2 - group. All HER2 + cases available were selected irrespective of their response category (n = 317). ER expression was measured by IHC and qPCR.

RESULTS

ER IHC was available from 515 HER2 - and 186 HER2 + tumours and ER qPCR from 367 HER2 - and 171 HER2 + tumours. Ninety-one percentage of patients with ER IHC < 10% were PRs with similar rates in HER2 - and HER2 + cases. At or above ER IHC 10% substantial numbers of patients showed IR or GR. Similar proportions of patients were defined by cut-points of ER IHC < 10% and ER mRNA < 5 units. In addition, loss of PgR expression altered ER anti-proliferation response with 92% of PgR - cases with ER IHC < 40% being PRs.

CONCLUSIONS

There was little responsiveness at IHC < 10% and no distinction between < 1% and 1-10% cells positive. Similar separation of PRs from IR/GRs was achieved by IHC and mRNA.

Stimuli-Mediated Specific Isolation of Exosomes from Blood Plasma for High-Throughput Profiling of Cancer Biomarkers

Exosomes, ranging from 30-150 nm in diameter, have emerged as promising non-invasive biomarkers for the diagnosis and prognosis of numerous diseases. However, current research on exosomes is largely restricted by the lack of an efficient method to isolate exosomes from real samples. Herein, the first stimuli-mediated enrichment and purification system to selectively and efficiently extract exosomes from clinical plasma for high-throughput profiling of exosomal mRNAs as cancer biomarkers is presented. This novel isolation system relies on specific installation of the stimuli-responsive copolymers onto exosomal phospholipid bilayers, by which the enrichment and purification are exclusively achieved for exosomes rather than the non-vesicle counterparts co-existing in real samples. The stimuli-mediated isolation system outperforms conventional methods such as ultracentrifugation and polyethylene glycol-based precipitation in terms of isolation yield, purity, and retained bioactivity. The high performance of the isolation system is demonstrated by enriching exosomes from 77 blood plasma samples and validated the clinical potentials in profiling exosomal mRNAs for cancer diagnosis and discrimination with high accuracy. This simple isolation system can boost the development of extracellular vesicle research, not limited to exosomes, in both basic and clinical settings.

Multiplexed analysis of small extracellular vesicle-derived mRNAs by droplet digital PCR and machine learning improves breast cancer diagnosis

Breast cancer has become the leading cause of global cancer incidence and a serious threat to women's health. Accurate diagnosis and early treatment are of great importance to prognosis. Although clinically used diagnostic approaches can be used for cancer screening, accurate diagnosis of breast cancer is still a critical unmet need. Here, we report a 4-plex droplet digital PCR technology for simultaneous detection of four small extracellular vesicle (sEV)-derived mRNAs (PGR, ESR1, ERBB2 and GAPDH) in combination with machine learning (ML) algorithms to improve breast cancer diagnosis. We evaluate the diagnsotic results with and without the assistance of the ML models. The results indicate that ML-assisted analysis exhibits higher diagnostic performance even using a single marker for breast cancer diagnosis, and demonstrate improved diagnostic performance under the best combination of biomarkers and suitable ML diagnostic model. Therefore, multiple sEV-derived mRNAs analysis coupled with ML not only provides the best combination of markers for breast cancer diagnosis, but also significantly improves the diagnostic efficiency of breast cancer.

The Hypoxic Response Expression as a Survival Biomarkers in Treatment-Naive Advanced Breast Cancer

OBJECTIVE

Hypoxia-associated biomarkers profiling may provide information for prognosis, staging, and subsequent therapy. We aim to evaluate whether the quantitative gene and protein expression of hypoxic response tumor markers - carbonic anhydrase IX (CAIX) and hypoxia- inducible factor 1 alpha (HIF1A) - may have a role in predicting survival in advanced breast cancer of Indonesian population.

METHODS

Tumor tissues and peripheral blood samples were collected from treatment - naïve locally advanced (LABC) or metastatic breast cancer patients (MBC) at Wahidin Sudirohusodo General Hospital (Makassar, South Sulawesi) and its referral network hospitals from July 2017 to March 2019. The level of mRNA (of blood and tumor tissue samples) and soluble protein (of blood samples) of CAIX and HIF1A were measured by RT-qPCR and ELISA methods, respectively, besides the standard histopathological grading and molecular subtype assessment. The CAIX and HIF1A expression, patients' age, tumor characteristics, surgery status, and neoadjuvant chemotherapy drug classes were further involved in survival analyses for overall survival (OS) and progression-free survival (PFS).

RESULTS

Forty (30 LABC, 10 MBC) eligible patients examined were 21 hormone-receptors positives (15 Luminal A, 6 Luminal B) and 19 hormone-receptors negatives (10 HER2-enriched, 9 triple-negative). The CAIX blood mRNA and CAIX soluble protein levels in hormone-receptors negative patients were higher than in hormone-receptor-positive patients (p < 0.05). In univariate analysis, both CAIX and HIF1A levels predict OS (except HIF1A protein) with CAIX tissue mRNA has the highest hazard ratio (HR 8.04, 95%CI:2.45-26.39), but not PFS. Cox proportional hazard model confirmed that CAIX tissue mRNA is the independent predictor of OS (HR 6.10, 95%CI: 1.16-32.13) along with surgical status and tumor advancement type (LABC or MBC).

CONCLUSIONS

CAIX mRNA expression of tumor tissue in treatment-naïve advanced breast cancer has a predictive value for OS.
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DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.

Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer

PURPOSE

To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer.

METHODS

Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set.

RESULTS

Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively.

CONCLUSIONS

Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.

Breast cancer cells obtain an osteomimetic feature via epithelial-mesenchymal transition that have undergone BMP2/RUNX2 signaling pathway induction

Bone is one of the most common organs of breast cancer metastasis. Cancer cells that mimic osteoblasts by expressing bone matrix proteins and factors have a higher likelihood of metastasizing to bone. However, the molecular mechanisms of osteomimicry formation of cancer cells remain undefined. Herein, we identified a set of bone-related genes (BRGs) that are ectopically co-expressed in primary breast cancer tissues and determined that osteomimetic feature is obtained due to the osteoblast-like transformation of epithelial breast cancer cells that have undergone epithelial-mesenchymal transition (EMT) followed by bone morphogenetic protein-2 (BMP2) stimulation. Furthermore, we demonstrated that breast cancer cells that transformed into osteoblast-like cells with high expression of BRGs showed enhanced chemotaxis, adhesion, proliferation and multidrug resistance in an osteoblast-mimic bone microenvironment in vitro. During these processes, runt-related transcription factor 2 (RUNX2) functioned as a master mediator by suppressing or activating the transcription of BRGs that underlie the dynamic antagonism between the TGF-β/SMAD and BMP/SMAD signaling pathways in breast cancer cells. Our findings suggest a novel mechanism of osteomimicry formation that arises in primary breast tumors, which may explain the propensity of breast cancer to metastasize to the skeleton and contribute to potential strategies for predicting and targeting breast cancer bone metastasis and multidrug resistance.

ESR1 and PGR polymorphisms are associated with estrogen and progesterone receptor expression in breast tumors

Hormone receptor-positive (HR+) breast cancers express the estrogen (ERα) and/or progesterone (PgR) receptors. Inherited single nucleotide polymorphisms (SNPs) in ESR1, the gene encoding ERα, have been reported to predict tamoxifen effectiveness. We hypothesized that these associations could be attributed to altered tumor gene/protein expression of ESR1/ERα and that SNPs in the PGR gene predict tumor PGR/PgR expression. Formalin-fixed paraffin-embedded breast cancer tumor specimens were analyzed for ESR1 and PGR gene transcript expression by the reverse transcription polymerase chain reaction based Oncotype DX assay and for ERα and PgR protein expression by immunohistochemistry (IHC) and an automated quantitative immunofluorescence assay (AQUA). Germline genotypes for SNPs in ESR1 (n = 41) and PGR (n = 8) were determined by allele-specific TaqMan assays. One SNP in ESR1 (rs9322336) was significantly associated with ESR1 gene transcript expression (P = 0.006) but not ERα protein expression (P > 0.05). A PGR SNP (rs518162) was associated with decreased PGR gene transcript expression (P = 0.003) and PgR protein expression measured by IHC (P = 0.016), but not AQUA (P = 0.054). There were modest, but statistically significant correlations between gene and protein expression for ESR1/ERα and PGR/PgR and for protein expression measured by IHC and AQUA (Pearson correlation = 0.32-0.64, all P < 0.001). Inherited ESR1 and PGR genotypes may affect tumor ESR1/ERα and PGR/PgR expression, respectively, which are moderately correlated. This work supports further research into germline predictors of tumor characteristics and treatment effectiveness, which may someday inform selection of hormonal treatments for patients with HR+ breast cancer.

Serum deprivation response inhibits breast cancer progression by blocking transforming growth factor-β signaling

Serum deprivation response (SDPR), a key substrate for protein kinase C, play a critical role in inducing membrane curvature and participate in the formation of caveolae. However, the function of SDPR in cancer development and progression is still not clear. Here, we found that SDPR is downregulated in human breast cancer. Overexpression of SDPR suppresses cell proliferation and invasion in MDA‐MB‐231 cells, while depletion of SDPR promotes cell proliferation and invasion in MCF10A cells. Subsequently, SDPR depletion induces epithelial–mesenchymal transition (EMT)‐like phenotype. Finally, knockdown of SDPR activates transforming growth factor‐β (TGF‐β) signaling by upregulation of TGF‐β1 expression. In conclusion, our results showed that SDPR inhibits breast cancer progression by blocking TGF‐β signaling.

Serum deprivation response suppresses cell proliferation and invasion in breast cancer cells. SDPR depletion induces epithelial–mesenchymal transition by activation of TGF‐β signaling.

Sex Differences in Plasmacytoid Dendritic Cell Levels of IRF5 Drive Higher IFN-α Production in Women

Increased IFN-α production contributes to the pathogenesis of infectious and autoimmune diseases. Plasmacytoid dendritic cells (pDCs) from females produce more IFN-α upon TLR7 stimulation than pDCs from males, yet the mechanisms underlying this difference remain unclear. In this article, we show that basal levels of IFN regulatory factor (IRF) 5 in pDCs were significantly higher in females compared with males and positively correlated with the percentage of IFN-α-secreting pDCs. Delivery of recombinant IRF5 protein into human primary pDCs increased TLR7-mediated IFN-α secretion. In mice, genetic ablation of the estrogen receptor 1 (Esr1) gene in the hematopoietic compartment or DC lineage reduced Irf5 mRNA expression in pDCs and IFN-α production. IRF5 mRNA levels furthermore correlated with ESR1 mRNA levels in human pDCs, consistent with IRF5 regulation at the transcriptional level by ESR1. Taken together, these data demonstrate a critical mechanism by which sex differences in basal pDC IRF5 expression lead to higher IFN-α production upon TLR7 stimulation in females and provide novel targets for the modulation of immune responses and inflammation.

Anti-Inflammatory Effects of a Methanol Extract from the Marine Sponge Geodia cydonium on the Human Breast Cancer MCF-7 Cell Line

Many research groups are working to find new possible anti-inflammatory molecules, and marine sponges represent a rich source of biologically active compounds with pharmacological applications. In the present study, we tested different concentrations of the methanol extract from the marine sponge, Geodia cydonium, on normal human breast epithelial cells (MCF-10A) and human breast cancer cells (MCF-7). Our results show that this extract has no cytotoxic effects on both cell lines whereas it induces a decrease in levels of VEGF and five proinflammatory cytokines (CCL2, CXCL8, CXCL10, IFN-γ, and TNF-α) only in MCF-7 cells in a dose-dependent manner, thereby indicating an anti-inflammatory effect. Moreover, interactomic analysis suggests that all six cytokines are involved in a network and are connected with some HUB nodes such as NF-kB subunits and ESR1 (estrogen receptor 1). We also report a decrease in the expression of two NFKB1 and c-Rel subunits by RT-qPCR experiments only in MCF-7 cells after extract treatment, confirming NF-kB inactivation. These data highlight the potential of G. cydonium for future drug discovery against major diseases, such as breast cancer.

Identifying Etiologically Distinct Sub-Types of Cancer: A Demonstration Project Involving Breast Cancer

With the advent of increasingly detailed molecular portraits of tumor specimens, much attention has been directed toward identifying clinically distinct subtypes of cancer. Subtyping of tumors can also be accomplished with the goal of identifying distinct etiologies. We demonstrate the use of new methodologies to identify genes that distinguish etiologically heterogeneous subtypes of breast cancer using data from the case-control Cancer and Steroid Hormone Study. Tumor specimens were evaluated using a breast cancer expression panel of 196 genes. Using a statistical measure that distinguishes the degree of etiologic heterogeneity in tumor subtypes, each gene is ranked on the basis of its ability to distinguish etiologically distinct subtypes. This is accomplished independently using case-control comparisons and by examining the concordance odds ratios in double primaries. The estrogen receptor gene, and others in this pathway with expression levels that correlated strongly with estrogen receptor levels, demonstrate high degrees of etiologic heterogeneity in both methods. Our results are consistent with a growing literature that confirms the distinct etiologies of breast cancers classified on the basis of estrogen receptor expression levels. This proof-of-principle project demonstrates the viability of new strategies to identify genomic features that distinguish subtypes of cancer from an etiologic perspective.

Pharmacodynamic imaging guides dosing of a selective estrogen receptor degrader

PURPOSE

Estrogen receptor (ER) targeting is key in management of receptor-positive breast cancer. Currently, there are no methods to optimize anti-ER therapy dosing. This study assesses the use of 16α-(18)F-fluoroestradiol ((18)F-FES) PET for fulvestrant dose optimization in a preclinical ER(+) breast cancer model.

EXPERIMENTAL DESIGN

In vitro, (18)F-FES retention was compared with ERα protein expression (ELISA) and ESR1 mRNA transcription (qPCR) in MCF7 cells (ER(+)) after treatment with different fulvestrant doses. MCF7 xenografts were grown in ovariectomized nude mice and assigned to vehicle, low- (0.05 mg), medium- (0.5 mg), or high-dose (5 mg) fulvestrant treatment groups (5-7 per group). Two and 3 days after fulvestrant treatment, PET/CT was performed using (18)F-FES and (18)F-FDG, respectively. ER expression was assessed by immunohistochemistry, ELISA, and qPCR on xenografts. Tumor proliferation was assessed using Ki67 immunohistochemistry.

RESULTS

In vitro, we observed a parallel graded reduction in (18)F-FES uptake and ER expression with increased fulvestrant doses, despite enhancement of ER mRNA transcription. In xenografts, ER expression significantly decreased with increased fulvestrant dose, despite similar mRNA expression and Ki67 staining among the treatment groups. We observed a significant dose-dependent reduction of (18)F-FES PET mean standardized uptake value (SUV(mean)) with fulvestrant treatment but no significant difference among the treatment groups in (18)F-FDG PET SUV(mean).

CONCLUSIONS

We demonstrated that (18)F-FES uptake mirrors the dose-dependent changes in functional ER expression with fulvestrant resulting in ER degradation and/or blockade; these precede changes in tumor metabolism and proliferation. Quantitative (18)F-FES PET may be useful for tracking early efficacy of ER blockade/degradation and guiding ER-targeted therapy dosing in patients with breast cancer.

Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples

Breast cancers are categorized into three subtypes based on protein expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/ERBB2). Patients enroll onto experimental clinical trials based on ER, PR, and HER2 status and, as receptor status is prognostic and defines treatment regimens, central receptor confirmation is critical for interpreting results from these trials. Patients enrolling onto experimental clinical trials in the metastatic setting often have limited available archival tissue that might better be used for comprehensive molecular profiling rather than slide-intensive reconfirmation of receptor status. We developed a Random Forests-based algorithm using a training set of 158 samples with centrally confirmed IHC status, and subsequently validated this algorithm on multiple test sets with known, locally determined IHC status. We observed a strong correlation between target mRNA expression and IHC assays for HER2 and ER, achieving an overall accuracy of 97 and 96 %, respectively. For determining PR status, which had the highest discordance between central and local IHC, incorporation of expression of co-regulated genes in a multivariate approach added predictive value, outperforming the single, target gene approach by a 10 % margin in overall accuracy. Our results suggest that multiplexed qRT-PCR profiling of ESR1, PGR, and ERBB2 mRNA, along with several other subtype associated genes, can effectively confirm breast cancer subtype, thereby conserving tumor sections and enabling additional biomarker data to be obtained from patients enrolled onto experimental clinical trials.

Potential anti-inflammatory effects of the hydrophilic fraction of pomegranate (Punica granatum L.) seed oil on breast cancer cell lines

In this work, we characterized conjugated linolenic acids (e.g., punicic acid) as the major components of the hydrophilic fraction (80% aqueous methanol extract) from pomegranate (Punica granatum L.) seed oil (PSO) and evaluated their anti-inflammatory potential on some human colon (HT29 and HCT116), liver (HepG2 and Huh7), breast (MCF-7 and MDA-MB-231) and prostate (DU145) cancer lines. Our results demonstrated that punicic acid and its congeners induce a significant decrease of cell viability for two breast cell lines with a related increase of the cell cycle G0/G1 phase respect to untreated cells. Moreover, the evaluation of a great panel of cytokines expressed by MCF-7 and MDA-MB-231 cells showed that the levels of VEGF and nine pro-inflammatory cytokines (IL-2, IL-6, IL-12, IL-17, IP-10, MIP-1α, MIP-1β, MCP-1 and TNF-α) decreased in a dose dependent way with increasing amounts of the hydrophilic extracts of PSO, supporting the evidence of an anti-inflammatory effect. Taken together, the data herein suggest a potential synergistic cytotoxic, anti-inflammatory and anti-oxidant role of the polar compounds from PSO.

Inhibition of KIF22 suppresses cancer cell proliferation by delaying mitotic exit through upregulating CDC25C expression

KIF22 is a microtubule-dependent molecular motor protein with DNA-binding capacity. It is well known that KIF22 plays a critical role in cell mitosis as a motor protein; however, the role of altered KIF22 expression and its transcriptional regulatory function in cancer development have not yet been defined. This study showed that KIF22 was overexpressed in human cancer tissues, and inhibition of KIF22 significantly led to accumulation of cells in the G2/M phases, resulting in suppression of cancer cell proliferation. The investigation of the molecular mechanisms demonstrated that cell division cycle 25C (CDC25C) is a direct transcriptional target of KIF22, and inhibition of KIF22 increased CDC25C expression and cyclin-dependent kinase 1 (CDK1) activity, resulting in delayed mitotic exit. Phosphorylation of KIF22 was required for its transcriptional regulatory function and the reduction of CDK1 activity. Thus, we conclude that inhibition of KIF22 suppresses cancer cell proliferation by delaying mitotic exit through the transcriptional upregulation of CDC25C.

Cancer-associated fibroblasts induce epithelial-mesenchymal transition of breast cancer cells through paracrine TGF-β signalling

Background:

Cancer-associated fibroblasts (CAFs) activated by tumour cells are the predominant type of stromal cells in breast cancer tissue. The reciprocal effect of CAFs on breast cancer cells and the underlying molecular mechanisms are not fully characterised.

Methods:

Stromal fibroblasts were isolated from invasive breast cancer tissues and the conditioned medium of cultured CAFs (CAF-CM) was collected to culture the breast cancer cell lines MCF-7, T47D and MDA-MB-231. Neutralising antibody and small-molecule inhibitor were used to block the transforming growth factor-β (TGF-β) signalling derived from CAF-CM, which effect on breast cancer cells.

Results:

The stromal fibroblasts isolated from breast cancer tissues showed CAF characteristics with high expression levels of α-smooth muscle actin and SDF1/CXCL12. The CAF-CM transformed breast cancer cell lines into more aggressive phenotypes, including enhanced cell–extracellular matrix adhesion, migration and invasion, and promoted epithelial–mesenchymal transition (EMT). Cancer-associated fibroblasts secreted more TGF-β1 than TGF-β2 and TGF-β3, and activated the TGF-β/Smad signalling pathway in breast cancer cells. The EMT phenotype of breast cancer cells induced by CAF-CM was reversed by blocking TGF-β1 signalling.

Conclusion:

Cancer-associated fibroblasts promoted aggressive phenotypes of breast cancer cells through EMT induced by paracrine TGF-β1. This might be a common mechanism for acquiring metastatic potential in breast cancer cells with different biological characteristics.