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Background Eukaryotic Initiation Factor 4E-Binding Protein ( EIF4EBP1 , 4EBP1) is overexpressed in many human cancers including breast cancer, yet the role of 4EBP1 in breast cancer remains understudied. Despite the known role of 4EBP1 as a negative regulator of cap-dependent protein translation, 4EBP1 is predicted to be an essential driving oncogene in many cancer cell lines in vitro, and can act as a driver of cancer cell proliferation. EIF4EBP1 is located within the 8p11-p12 genomic locus, which is frequently amplified in breast cancer and is known to predict poor prognosis and resistance to endocrine therapy. Methods Here we evaluated the effect of 4EBP1 targeting using shRNA knock-down of expression of 4EBP1, as well as response to the mTORC targeted drug everolimus in cell lines representing different breast cancer subtypes, including breast cancer cells with the 8p11-p12 amplicon, to better define a context and mechanism for oncogenic 4EBP1. Results Using a genome-scale shRNA screen on the SUM panel of breast cancer cell lines, we found 4EBP1 to be a strong hit in the 8p11 amplified SUM-44 cells, which have amplification and overexpression of 4EBP1. We then found that knock-down of 4EBP1 resulted in dramatic reductions in cell proliferation in 8p11 amplified breast cancer cells as well as in other luminal breast cancer cell lines, but had little or no effect on the proliferation of immortalized but non-tumorigenic human mammary epithelial cells. Kaplan-Meier analysis of EIF4EBP1 expression in breast cancer patients demonstrated that overexpression of this gene was associated with reduced relapse free patient survival across all breast tumor subtypes. Conclusions These results are consistent with an oncogenic role of 4EBP1 in luminal breast cancer and suggests a role for this protein in cell proliferation distinct from its more well-known role as a regulator of cap-dependent translation.

Background The gene desert on human chromosomal band 8q24 harbors multiple genetic variants associated with common cancers, including breast cancer. The locus, including the gene desert and its flanking genes, MYC , PVT1 and FAM84B , is also frequently amplified in human breast cancer. We generated a megadeletion (MD) mouse model lacking 430-Kb of sequence orthologous to the breast cancer-associated region in the gene desert. The goals were to examine the effect of the deletion on mammary cancer development and on transcript level regulation of the candidate genes within the locus. Methods The MD allele was engineered using the MICER system in embryonic stem cells and bred onto 3 well-characterized transgenic models for breast cancer, namely MMTV-PyVT , MMTV-neu and C3(1)-TAg. Mammary tumor growth, latency, multiplicity and metastasis were compared between homozygous MD and wild type mice carrying the transgenes. A reciprocal mammary gland transplantation assay was conducted to distinguish mammary cell-autonomous from non-mammary cell-autonomous anti-cancer effects. Gene expression analysis was done using quantitative real-time PCR. Chromatin interactions were evaluated by 3C. Gene-specific patient outcome data were analysed using the METABRIC and TCGA data sets through the cBioPortal website. Results Mice homozygous for the MD allele are viable, fertile, lactate sufficiently to nourish their pups, but maintain a 10% lower body weight mainly due to decreased adiposity. The deletion interferes with mammary tumorigenesis in mouse models for luminal and basal breast cancer. In the MMTV-PyVT model the mammary cancer-reducing effects of the allele are mammary cell-autonomous. We found organ-specific effects on transcript level regulation, with Myc and Fam84b being downregulated in mammary gland, prostate and mammary tumor samples. Through analysis using the METABRIC and TCGA datasets, we provide evidence that MYC and FAM84B are frequently co-amplified in breast cancer, but in contrast with MYC , FAM84B is frequently overexpressed in the luminal subtype, whereas MYC activity affect basal breast cancer outcomes. Conclusion Deletion of a breast cancer-associated non-protein coding region affects mammary cancer development in 3 transgenic mouse models. We propose Myc as a candidate susceptibility gene, regulated by the gene desert locus, and a potential role for Fam84b in modifying breast cancer development.

A subset of triple negative breast cancer (TNBC) is characterized by overexpression of the epidermal growth factor receptor (EGFR) and loss of PTEN, and patients with these determinants have a poor prognosis. We used cell line models of EGFR-positive/PTEN null TNBC to elucidate the signaling networks that drive the malignant features of these cells and cause resistance to EGFR inhibitors. In these cells, amphiregulin (AREG)-mediated activation of EGFR results in up-regulation of fibronectin (FN1), which is known to be a mediator of invasive capacity via interaction with integrin β1. EGFR activity in this PTEN null background also results in Wnt/beta-catenin signaling and activation of NF-κB. In addition, AKT is constitutively phosphorylated in these cells and is resistant to gefitinib. Expression profiling demonstrated that AREG-activated EGFR regulates gene expression differently than EGF-activated EGFR, and functional analysis via genome-scale shRNA screening identified a set of genes, including PLK1 and BIRC5, that are essential for survival of SUM-149 cells, but are uncoupled from EGFR signaling. Thus, our results demonstrate that in cells with constitutive EGFR activation and PTEN loss, critical survival genes are uncoupled from regulation by EGFR, which likely mediates resistance to EGFR inhibitors. •Activation of EGFR by AREG alters signaling and gene expression compared to EGF.•Activation of EGFR by AREG reduces mTORC1 pathway expression and phosphorylation.•EGF-positive, PTEN-null TNBC cells are poised for Wnt/beta-catenin signaling.•Wnt/beta-catenin activity occurs in a subset of cells and is enhanced in mammospheres.•Regulation of growth/survival genes is uncoupled from EGFR in PTEN-null TNBC cells.

Background Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. Results We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. Conclusions Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

The 8p11-p12 amplicon occurs in approximately 15% of breast cancers in aggressive luminal B-type tumors. Previously, we identified WHSC1L1 as a driving oncogene from this region. Here, we demonstrate that over-expression of WHSC1L1 is linked to over-expression of ERα in SUM-44 breast cancer cells and in primary human breast cancers. Knock-down of WHSC1L1, particularly WHSC1L1-short, had a dramatic effect on ESR1 mRNA and ERα protein levels. SUM-44 cells do not require exogenous estrogen for growth in vitro; however, they are dependent on ERα expression, as ESR1 knock-down or exposure to the selective estrogen receptor degrader fulvestrant resulted in growth inhibition. ChIP-Seq experiments utilizing ERα antibodies demonstrated extensive ERα binding to chromatin in SUM-44 cells under estrogen-free conditions. ERα bound to ERE and FOXA1 motifs under estrogen-free conditions and regulated expression of estrogen-responsive genes. Short-term treatment with estradiol enhanced binding of ERα to chromatin and influenced expression of many of the same genes to which ERα was bound under estrogen-free conditions. Finally, knock-down of WHSC1L1 in SUM-44 cells resulted in loss of ERα binding to chromatin under estrogen-free conditions, which was restored upon exposure to estradiol. These results indicate the SUM-44 cells are a good model of a subset of luminal B breast cancers that have the 8p11-p12 amplicon, over-express WHSC1L1, and over-express ERα, but are independent of estrogen for binding to chromatin and regulation of gene expression. Breast cancers such as these, that are dependent on ERα activity but independent of estradiol, are a major cause of breast cancer mortality. •SUM44 is a model cell line for ERα positive breast cancer with the 8p11 amplicon.•WHSC1L1 is a driving oncogene from the 8p11 amplicon in SUM44 cells.•SUM44 breast cancer cells have high ERα expression, regulated by WHSC1L1 knockdown.•ERα is required for growth and survival of SUM44 cells but is estrogen-independent.•WHSC1L1 knock-down re-sensitizes ERα to estradiol for binding to essential genes.

Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.

Translation initiation of the picornavirus genome is regulated by an internal ribosome entry site (IRES). The IRES of a neurovirulent picornavirus, the GDVII strain of Theiler's murine encephalomyelitis virus, requires polypyrimidine tract‐binding protein (PTB) for its function. Although neural cells are deficient in PTB, they express a neural‐specific homologue of PTB (nPTB). We now show that nPTB and PTB bind similarly to multiple sites in the GDVII IRES, rendering it competent for efficient translation initiation. Mutation of a PTB or nPTB site results in a more prominent decrease in nPTB than PTB binding, a decrease in activity of nPTB compared with PTB in promoting translation initiation, and attenuation of the neurovirulence of the virus without a marked effect on virus growth in non‐neural cells. The addition of a second‐site mutation in the mutant IRES generates a new PTB (nPTB) binding site, and restores nPTB binding, translation initiation and neurovirulence. We conclude that the tissue‐specific expression and differential RNA‐binding properties of PTB and nPTB are important determinants of cell‐specific translational control and viral neurovirulence.

Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.