Identification of Jun loss promotes resistance to histone deacetylase inhibitor entinostat through Myc signaling in luminal breast cancer

Abstract Background Based on promising phase II data, the histone deacetylase inhibitor entinostat is in phase III trials for patients with metastatic estrogen receptor-positive breast cancer. Predictors of sensitivity and resistance, however, remain unknown. Methods A total of eight cell lines and nine mouse models of breast cancer were treated with entinostat. Luminal cell lines were treated with or without entinostat at their IC50 doses, and MMTV/Neu luminal mouse tumors were untreated or treated with entinostat until progression. We investigated these models using their gene expression profiling by microarray and copy number by arrayCGH. We also utilized the network-based DawnRank algorithm that integrates DNA and RNA data to identify driver genes of resistance. The impact of candidate drivers was investigated in The Cancer Genome Atlas and METABRIC breast cancer datasets. Results Luminal models displayed enhanced sensitivity to entinostat as compared to basal-like or claudin-low models. Both in vitro and in vivo luminal models showed significant downregulation of Myc gene signatures following entinostat treatment. Myc gene signatures became upregulated on tumor progression in vivo and overexpression of Myc conferred resistance to entinostat in vitro. Further examination of resistance mechanisms in MMTV/Neu tumors identified a portion of mouse chromosome 4 that had DNA copy number loss and low gene expression. Within this region, Jun was computationally identified to be a driver gene of resistance. Jun knockdown in cell lines resulted in upregulation of Myc signatures and made these lines more resistant to entinostat. Jun-deleted samples, found in 17–23% of luminal patients, had significantly higher Myc signature scores that predicted worse survival. Conclusions Entinostat inhibited luminal breast cancer through Myc signaling, which was upregulated by Jun DNA loss to promote resistance to entinostat in our models. Jun DNA copy number loss, and/or high MYC signatures, might represent biomarkers for entinostat responsiveness in luminal breast cancer

A Review of FOXI3 Regulation of Development and Possible Roles in Cancer Progression and Metastasis

Development and cancer share a variety of functional traits such as EMT, cell migration, angiogenesis, and tissue remodeling. In addition, many cellular signaling pathways are noted to coordinate developmental processes and facilitate aspects of tumor progression. The Forkhead box superfamily of transcription factors consists of a highly conserved DNA binding domain, which binds to specific DNA sequences and play significant roles during adult tissue homoeostasis and embryogenesis including development, differentiation, metabolism, proliferation, apoptosis, migration, and invasion. Interestingly, various studies have implicated the role of key Fox family members such as FOXP, FOXO, and FOXA during cancer initiation and metastases. FOXI3, a member of the Forkhead family affects embryogenesis, development, and bone remodeling; however, no studies have reported a role in cancer. In this review, we summarize the role of FOXI3 in embryogenesis and bone development and discuss its potential involvement in cancer progression with a focus on the bone metastasis. Moreover, we hypothesize possible mechanisms underlying the role of FOXI3 in the development of solid tumor bone metastasis

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

The E2F Transcription Factors Regulate Tumor Development and Metastasis in a Mouse Model of Metastatic Breast Cancer

While the E2F transcription factors (E2Fs) have a clearly defined role in cell cycle control, recent work has uncovered new functions. Using genomic signature methods, we predicted a role for the activator E2F transcription factors in the mouse mammary tumor virus (MMTV)-polyomavirus middle T oncoprotein (PyMT) mouse model of metastatic breast cancer. To genetically test the hypothesis that the E2Fs function to regulate tumor development and metastasis, we interbred MMTV-PyMT mice with E2F1, E2F2, or E2F3 knockout mice. With the ablation of individual E2Fs, we noted alterations of tumor latency, histology, and vasculature. Interestingly, we noted striking reductions in metastatic capacity and in the number of circulating tumor cells in both the E2F1 and E2F2 knockout backgrounds. Investigating E2F target genes that mediate metastasis, we found that E2F loss led to decreased levels of vascular endothelial growth factor (Vegfa), Bmp4, Cyr61, Nupr1, Plod 2, P4ha1, Adamts1, Lgals3, and Angpt2. These gene expression changes indicate that the E2Fs control the expression of genes critical to angiogenesis, the remodeling of the extracellular matrix, tumor cell survival, and tumor cell interactions with vascular endothelial cells that facilitate metastasis to the lungs. Taken together, these results reveal that the E2F transcription factors play key roles in mediating tumor development and metastasis in addition to their well-characterized roles in cell cycle control

ssGSEA testing of signatures shows key molecular traits histological tumor classes.

<p>(A) Boxplot of enrichment scores pertaining to key cell signaling pathways. (B) Boxplot of enrichment scores pertaining to gene sets for gene targets of specific transcription factors. (C) Boxplot of enrichment scores pertaining to gene sets for gene targets of specific miRNAs. * = Significant Enrichment detected in standard GSEA (nominal p-value<0.05); see additional <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007135#pgen.1007135.s048" target="_blank">S30 File</a> for additional details and statistics.</p

Gene expression signatures of histology predict known histological associations in mouse models.

<p>Gene expression signatures of histology predict known histological associations in mouse models.</p

Histology signature analysis across human cancers.

<p>(A)Unsupervised hierarchical clustering of human tumors on the basis of mouse mammary tumor histology signatures. Above the heatmap, blue depict the position of individual tumors annotated by cancer type in the dendrogram above and heatmap below. The red bars provide histological and additional information for each sample. The black bars beside the heatmap annotate the position of each signature gene in the heatmap. The green cluster highlights the cluster featuring the majority of squamous tumors while the blue cluster in the dendrogram highlights a mesenchymal cluster largely composed of melanoma. (B) Geneset enrichment analysis shows significant enrichment of mouse derived squamous histology in tumors from the green squamous cluster. NES = 1.92, nominal p-value 0.0, FDR q-value = 0.003 (C) Geneset enrichment analysis shows significant enrichment for high expression expression of mouse derived EMT histology signature (genes highly expressed expressed in EMT tumors) in samples belonging to the blue cluster in A. NES = 1.93, nominal p-value 0.0, FDR q-value = 6.97 E -4.</p

Generation and validation of mouse mammary squamous tumor signature.

<p>(A)Tumor histologies observed in a study MMTV-PyMT tumors[<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007135#pgen.1007135.ref054" target="_blank">54</a>].(B) Venn diagram illustrating the number of genes identified in each comparison using significance analysis of microarrays, 184 genes were commonly identified and proposed as signature genes. (C) Heatmap representation of unsupervised hierarchical clustering of MMTV-PyMT tumors limited to squamous signature genes shows performance of the signature on the training dataset. Levels of RMA normalized median centered expression values are shown according the colorbar. Genes expression data is deposited on GEO datasets GSE104397. (D) Heatmap representation of unsupervised hierarchical clustering of MMTV-Myc tumors limited to squamous signature genes shows performance of the signature on the validation dataset. Levels of RMA normalized median centered expression values are shown according the colorbar. (E) Gene set enrichment analysis testing for enrichment of the proposed squamous signature genes shows significant enrichment in MMTV-Myc squamous tumors (normalized enrichment score, NES = 1.48, nominal p-value = 0.0, FDR q-value = 0.029).</p

Testing mouse mammary tumor histology signatures across a gene expression database of mouse mammary tumor models.

<p>Above the heatmap black bars depict the position of each tumor from a given major oncogenic model of mammary tumorigenesis. Samples marked with red bars are mouse cells lines that are labeled according to their differentiation as being mesenchymal, epithelial, or basal. Next, blue bars depict available histological annotations for individual tumors in the dataset. Beside the middle heatmap, the black bars indicate the position of signature genes row by row. The middle heatmap displays the RMA-normalized median centered expression level of each gene in a given signature across samples; expression levels are depicted by the color bar on the right hand side. The bottom heatmap displays enrichment scores for each sample and signature from single sample gene set enrichment analysis (ssGSEA). Each sample was tested for the signature (consisting of ‘up’ genes) that it scored maximum for. Samples were grouped according to their highest scoring signature and all samples and heatmaps were sorted high to low within each ssGSEA identified group.</p