Expression of FOXA1 and GATA-3 in breast cancer: the prognostic significance in hormone receptor-negative tumours

The expression of additional genes, other than oestrogen receptor (ER), may be important to the hormone-responsive phenotype of breast cancer. Microarray analyses have revealed that forkhead box A1 (FOXA1) and GATA binding protein 3 (GATA-3) are expressed in close association with ERalpha, both encoding for transcription factors with a potential involvement in the ERalpha-mediated action in breast cancer. The purpose of this study was to explore if the expression of FOXA1 and GATA-3 may provide an opportunity to stratify subsets of patients that could have better outcome, among the ERalpha-negative/poor prognosis breast cancer group.The present study was supported by a research grant (SFRH/BD/15316/ 2005 to AA) financed by the Portuguese Science and Technology Foundation (FCT). The authors thank Prof. Raquel Seruca ( coordinator from the Cancer Genetics group at IPATIMUP) for scientific assistance, Dr Jose Luis Costa (postdoctorate at IPATIMUP) for critically reading the manuscript before submission, and Dr Nuno Marcos ( PhD student at IPATIMUP) for artwork assistance

CD44(+)/CD24(- )breast cancer cells exhibit enhanced invasive properties: an early step necessary for metastasis

INTRODUCTION: A subpopulation (CD44(+)/CD24(-)) of breast cancer cells has been reported to have stem/progenitor cell properties. The aim of this study was to investigate whether this subpopulation of cancer cells has the unique ability to invade, home, and proliferate at sites of metastasis. METHODS: CD44 and CD24 expression was determined by flow cytometry. Northern blotting was used to determine the expression of proinvasive and 'bone and lung metastasis signature' genes. A matrigel invasion assay and intracardiac inoculation into nude mice were used to evaluate invasion, and homing and proliferation at sites of metastasis, respectively. RESULTS: Five among 13 breast cancer cell lines examined (MDA-MB-231, MDA-MB-436, Hs578T, SUM1315, and HBL-100) contained a higher percentage (>30%) of CD44(+)/CD24(- )cells. Cell lines with high CD44(+)/CD24(- )cell numbers express basal/mesenchymal or myoepithelial but not luminal markers. Expression levels of proinvasive genes (IL-1α, IL-6, IL-8, and urokinase plasminogen activator [UPA]) were higher in cell lines with a significant CD44(+)/CD24(- )population than in other cell lines. Among the CD44(+)/CD24(-)-positive cell lines, MDA-MB-231 has the unique property of expressing a broad range of genes that favor bone and lung metastasis. Consistent with previous studies in nude mice, cell lines with CD44(+)/CD24(- )subpopulation were more invasive than other cell lines. However, only a subset of CD44(+)/CD24(-)-positive cell lines was able to home and proliferate in lungs. CONCLUSION: Breast cancer cells with CD44(+)/CD24(- )subpopulation express higher levels of proinvasive genes and have highly invasive properties. However, this phenotype is not sufficient to predict capacity for pulmonary metastasis

Suppression of apoptosis inhibitor c-FLIP selectively eliminates breast cancer stem cell activity in response to the anti-cancer agent, TRAIL

Introduction It is postulated that breast cancer stem cells (bCSCs) mediate disease recurrence and drive formation of distant metastases - the principal cause of mortality in breast cancer patients. Therapeutic targeting of bCSCs however, is hampered by their heterogeneity and resistance to existing therapeutics. In order to identify strategies to selectively remove bCSCs from breast cancers, irrespective of their clinical subtype, we sought an apoptosis mechanism that would target bCSCs yet would not kill normal cells. Suppression of the apoptosis inhibitor cellular FLICE-Like Inhibitory Protein (c-FLIP) partially sensitizes breast cancer cells to the anti-cancer agent Tumour Necrosis Factor-Related Apoptosis Inducing Ligand (TRAIL). Here we demonstrate in breast cancer cell lines that bCSCs are exquisitely sensitive to the de-repression of this pro-apoptotic pathway, resulting in a dramatic reduction in experimental metastases and the loss of bCSC self-renewal. Methods Suppression c-FLIP was performed by siRNA (FLIPi) in four breast cancer cell lines and by conditional gene-knockout in murine mammary glands. Sensitivity of these cells to TRAIL was determined by complementary cell apoptosis assays, including a novel heterotypic cell assay, while tumour-initiating potential of cancer stem cell subpopulations was determined by mammosphere cultures, aldefluor assay and in vivo transplantation. Results Genetic suppression of c-FLIP resulted in the partial sensitization of TRAIL-resistant cancer lines to the pro-apoptotic effects of TRAIL, irrespective of their cellular phenotype, yet normal mammary epithelial cells remained refractory to killing. While 10%-30% of the cancer cell populations remained viable after TRAIL/FLIPi treatment, subsequent mammosphere and aldefluor assays demonstrated that this pro-apoptotic stimulus selectively targeted the functional bCSC pool, eliminating stem cell renewal. This culminated in an 80% reduction in primary tumours and a 98% reduction in metastases following transplantation. The recurrence of residual tumour initiating capacity was consistent with the observation that post-treated adherent cultures re-acquired bCSC-like properties in vitro. Importantly however this recurrent bCSC activity was attenuated following repeated TRAIL/FLIPi treatment. Conclusions We describe an apoptotic mechanism that selectively and repeatedly removes bCSC activity from breast cancer cell lines and suggest that a combined TRAIL/FLIPi therapy could prevent metastatic disease progression in a broad range of breast cancer subtypes. [PROVISIONAL

Choosing the right cell line for breast cancer research

Breast cancer is a complex and heterogeneous disease. Gene expression profiling has contributed significantly to our understanding of this heterogeneity at a molecular level, refining taxonomy based on simple measures such as histological type, tumour grade, lymph node status and the presence of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines. In the present review we discuss some of the issues surrounding the use of breast cancer cell lines as experimental models, in light of these revised clinical classifications, and put forward suggestions for improving their use in translational breast cancer research

SLUG/SNAI2 and Tumor Necrosis Factor Generate Breast Cells With CD44+/CD24- Phenotype

<p>Abstract</p> <p>Background</p> <p>Breast cancer cells with CD44+/CD24- cell surface marker expression profile are proposed as cancer stem cells (CSCs). Normal breast epithelial cells that are CD44+/CD24- express higher levels of stem/progenitor cell associated genes. We, amongst others, have shown that cancer cells that have undergone epithelial to mesenchymal transition (EMT) display the CD44+/CD24- phenotype. However, whether all genes that induce EMT confer the CD44+/CD24- phenotype is unknown. We hypothesized that only a subset of genes associated with EMT generates CD44+/CD24- cells.</p> <p>Methods</p> <p>MCF-10A breast epithelial cells, a subpopulation of which spontaneously acquire the CD44+/CD24- phenotype, were used to identify genes that are differentially expressed in CD44+/CD24- and CD44-/CD24+ cells. Ingenuity pathway analysis was performed to identify signaling networks that linked differentially expressed genes. Two EMT-associated genes elevated in CD44+/CD24- cells, SLUG and Gli-2, were overexpressed in the CD44-/CD24+ subpopulation of MCF-10A cells and MCF-7 cells, which are CD44-/CD24+. Flow cytometry and mammosphere assays were used to assess cell surface markers and stem cell-like properties, respectively.</p> <p>Results</p> <p>Two thousand thirty five genes were differentially expressed (p < 0.001, fold change ≥ 2) between the CD44+/CD24- and CD44-/CD24+ subpopulations of MCF-10A. Thirty-two EMT-associated genes including SLUG, Gli-2, ZEB-1, and ZEB-2 were expressed at higher levels in CD44+/CD24- cells. These EMT-associated genes participate in signaling networks comprising TGFβ, NF-κB, and human chorionic gonadotropin. Treatment with tumor necrosis factor (TNF), which induces NF-κB and represses E-cadherin, or overexpression of SLUG in CD44-/CD24+ MCF-10A cells, gave rise to a subpopulation of CD44+/CD24- cells. Overexpression of constitutively active p65 subunit of NF-κB in MCF-10A resulted in a dramatic shift to the CD44+/CD24+ phenotype. SLUG overexpression in MCF-7 cells generated CD44+/CD24+ cells with enhanced mammosphere forming ability. In contrast, Gli-2 failed to alter CD44 and CD24 expression.</p> <p>Conclusions</p> <p>EMT-mediated generation of CD44+/CD24- or CD44+/CD24+ cells depends on the genes that induce or are associated with EMT. Our studies reveal a role for TNF in altering the phenotype of breast CSC. Additionally, the CD44+/CD24+ phenotype, in the context of SLUG overexpression, can be associated with breast CSC "stemness" behavior based on mammosphere forming ability.</p

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer