ELF5 suppresses estrogen sensitivity and underpins the acquisition of antiestrogen resistance in luminal breast cancer.

We have previously shown that during pregnancy the E-twenty-six (ETS) transcription factor ELF5 directs the differentiation of mammary progenitor cells toward the estrogen receptor (ER)-negative and milk producing cell lineage, raising the possibility that ELF5 may suppress the estrogen sensitivity of breast cancers. To test this we constructed inducible models of ELF5 expression in ER positive luminal breast cancer cells and interrogated them using transcript profiling and chromatin immunoprecipitation of DNA followed by DNA sequencing (ChIP-Seq). ELF5 suppressed ER and FOXA1 expression and broadly suppressed ER-driven patterns of gene expression including sets of genes distinguishing the luminal molecular subtype. Direct transcriptional targets of ELF5, which included FOXA1, EGFR, and MYC, accurately classified a large cohort of breast cancers into their intrinsic molecular subtypes, predicted ER status with high precision, and defined groups with differential prognosis. Knockdown of ELF5 in basal breast cancer cell lines suppressed basal patterns of gene expression and produced a shift in molecular subtype toward the claudin-low and normal-like groups. Luminal breast cancer cells that acquired resistance to the antiestrogen Tamoxifen showed greatly elevated levels of ELF5 and its transcriptional signature, and became dependent on ELF5 for proliferation, compared to the parental cells. Thus ELF5 provides a key transcriptional determinant of breast cancer molecular subtype by suppression of estrogen sensitivity in luminal breast cancer cells and promotion of basal characteristics in basal breast cancer cells, an action that may be utilised to acquire antiestrogen resistance

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

Abstract: Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM−/− patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

Cell cycle control by ID1 and WT1 in breast cancer cells

Loss of proliferative control is a cornerstone of cancer development, induced by deregulation of mitogenic signalling, insensitivity to anti-proliferative signals and direct changes in cell cycle proteins. In breast cancer these alterations are frequently targeted through cyclins D1 and E, leading to defects in G1/S transition. I have investigated the role of two potential pro-proliferative oncogenes in breast cancer, id1 andwt1. Each protein promotes proliferation in distinct contexts, with unique consequences for breast cancer cells.Using a 3D culture model of non-transformed mammary epithelial cells, I identified that id1 undergoes downregulation via rapid proteosomal degradation and cytoplasmic relocalisation during mammary epithelial morphogenesis. Overexpression of Id1 led to an increase in acinar size via an increase in S phase, and was dependent on the presence of an intact HLH domain in Id1. Co-expression with the proto-oncogene Bcl2 led to a more disorganised acinar structure, indicating that Id1 overexpression primed the cells for further oncogenic insult. Further, Id1 overexpression was unable to increase acinar size in cyclin D1-/- acini, indicating that Id1 is dependent on cyclin D1 for its proliferative effects. Overall these data identified Id1 as capable of altering the proliferation of normal mammary epithelial cells, a crucial step in early breast carcinogenesis.Wt1 was originally identified as a tumour suppressor, but our data lends support to Wt1 acting as an oncogene in breast cancer. Wt1 is expressed highly in a range of breast cancer cell lines, and is strongly regulated by progestins. Using siRNA, we identified that Wt1 is likely to be a molecular intermediary of progestin as the downregulation of Wt1 mimics a subset of progestin effects on cell proliferation and lipid synthesis. Conversely, the overexpression of the major Wt1 isoform, Wt1 (+/+), led to attenuation of progestin-induced differentiation and growth arrest via maintenance of cyclin D1 levels. The effects of Wt1 overexpression were specific to progestins, and did not affect the actions of anti-estrogens or androgens. Consequently the overexpression of Wt1 (+/+) may disrupt the endocrine response in mammary epithelial cells, and contribute to excess proliferation and failure to differentiate during breast oncogenesis

Andy’s Algorithms: new automated digital image analysis pipelines for FIJI

Abstract Quantification of cellular antigens and their interactions via antibody-based detection methods are widely used in scientific research. Accurate high-throughput quantitation of these assays using general image analysis software can be time consuming and challenging, particularly when attempted by users with limited image processing and analysis knowledge. To overcome this, we have designed Andy’s Algorithms, a series of automated image analysis pipelines for FIJI, that permits rapid, accurate and reproducible batch-processing of 3,3′-diaminobenzidine (DAB) immunohistochemistry, proximity ligation assays (PLAs) and other common assays. Andy’s Algorithms incorporates a step-by-step tutorial and optimization pipeline to make batch image analysis simple for the untrained user and adaptable across laboratories. Andy’s algorithms provide a simpler, faster, standardized work flow compared to existing programs, while offering equivalent performance and additional features, in a free to use open-source application of FIJI. Andy’s Algorithms are available at GitHub, publicly accessed at https://github.com/andlaw1841/Andy-s-Algorithm

PLA Figure 2 Positive Series

The second set of images provided is a set of photomicrographs acquired on methanol fixed T47D breast cancer cells and subjected to proximity ligation assays using antibodies to detect interactions between CDK2 and Cyclin E1 and counter stained with ToPro3/DAPI (nuclei) and Phalloidin (cytoplasm

IHC Figure 1 Model 2 Treated Photomicrographs

The first set of photomicrographs provided depict the lungs from mice bearing human breast cancer cell line xenografts subjected to DAB+ immune-histochemistry using antibodies to either anti-human high molecular weight cytokeratin (MDA-MB-468 cells, model 1) or anti-human Vimentin (MDA-MB-231 cells, model 2), a technique that was used to quantify metastatic deposits in the lungs

ELF5 suppresses the estrogen-sensitive phenotype.

<p>(A) Western blot showing reduced expression of key genes involved in the response to estrogens following induction of ELF5 expression. (B) Reduced transcriptional activity of reporters of <i>ER</i> and <i>FOXA1</i> (<i>UGT2B17</i> promoter) transcriptional activity following induction of ELF5 in MCF7 cells. Black bars, -DOX, grey bars +DOX 72 h for <i>ERE</i> and 24 h and 48 h for <i>FOXA1</i>. (C) Cell accumulation in MCF7-V5 cell cultures with (+E) or without (−E) 10 nM estrogen treatment, or following expression of ER (+ER) and 10 nM E in the context of induced ELF5. Black bars, -DOX; grey bars +DOX, 72 h and 144 h, respectively. (D) interaction of ELF5-regulated gene sets with estrogen-regulated gene sets. <i>p</i>-Values and odds ratios derived from hypergeometric tests. Number of genes in brackets. (E) Enrichment of gene sets in ELF5 ChIP targets either down (Dn) or Up in response to forced ELF5-V5 expression in T47D cells with DOX. P-Values for hypergeometric tests from GSEA (upper case) or Oncomine (lower case).</p

ELF5 specifies breast cancer subtype.

<p>(A) Sub network of breast cancer subtype gene sets derived from forced ELF5 expression in MCF7 luminal breast cancer cells (inner node color) and knockdown of ELF5 expression in HCC1937 basal breast cancer cells. Node size is proportional to gene set size; thicker green lines indicate greater gene set overlap. Nodes are positioned according to similarity in gene sets. Labels in bold type indicate the functional significance of the four clusters generated, label is plain type is the gene set name. The full network is shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001461#pbio.1001461.s016" target="_blank">Figure S16</a>. (B–D) expression signature analysis of the ELF5-induced changes in molecular subtype produced by ELF5 knockdown in HCC1937 cells (B), or forced ELF5 expression in MCF7 cells (C), or T47D cells (D). Bars show the indicated comparisons that produce the associated <i>p</i>-values. BS, borderline significance; NS, not significant.</p

Elf5 modulates the adhesion of breast cancer cells.

<p>(A) Quantification of detached cells in cultures treated with DOX (+D) compared to no induction (−D). (B) Ability of DOX-treated cells to replate 4 h after trypsin destruction of attachment proteins, compared to untreated cells. Data are expressed as a percentage of replated untreated cells. (C) Proportion of apoptotic cells in DOX treated (grey bars) compared to untreated (black bars) T47D-ELF5-V5 cells, measured by flow cytometry using the M30 antibody. (D) Expression and activation of key cell adhesion proteins following DOX induction of ELF5-V5 expression.</p