Maria Rosaria De Filippo

Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation

BackgroundEstrogen receptors alpha (ERα) and beta (ERβ) are transcription factors (TFs) that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (BC). The two receptors can be found co-expressed and play specific, often opposite, roles, with ERβ being able to modulate the effects of ERα on gene transcription and cell proliferation. ERβ is frequently lost in BC, where its presence generally correlates with a better prognosis of the disease. The identification of the genomic targets of ERβ in hormone-responsive BC cells is thus a critical step to elucidate the roles of this receptor in estrogen signaling and tumor cell biology.ResultsExpression of full-length ERβ in hormone-responsive, ERα-positive MCF-7 cells resulted in a marked reduction in cell proliferation in response to estrogen and marked effects on the cell transcriptome. By ChIP-Seq we identified 9702 ERβ and 6024 ERα binding sites in estrogen-stimulated cells, comprising sites occupied by either ERβ, ERα or both ER subtypes. A search for TF binding matrices revealed that the majority of the binding sites identified comprise one or more Estrogen Response Element and the remaining show binding matrixes for other TFs known to mediate ER interaction with chromatin by tethering, including AP2, E2F and SP1. Of 921 genes differentially regulated by estrogen in ERβ+ vs ERβ- cells, 424 showed one or more ERβ site within 10 kb. These putative primary ERβ target genes control cell proliferation, death, differentiation, motility and adhesion, signal transduction and transcription, key cellular processes that might explain the biological and clinical phenotype of tumors expressing this ER subtype. ERβ binding in close proximity of several miRNA genes and in the mitochondrial genome, suggests the possible involvement of this receptor in small non-coding RNA biogenesis and mitochondrial genome functions.ConclusionsResults indicate that the vast majority of the genomic targets of ERβ can bind also ERα, suggesting that the overall action of ERβ on the genome of hormone-responsive BC cells depends mainly on the relative concentration of both ERs in the cell.

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

BackgroundMassively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations.ResultsLiterature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values.ConclusionsThe information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.

Genomic landscape of adenoid cystic carcinoma of the breast.

Adenoid cystic carcinoma (AdCC) is a rare type of triple‐negative breast cancer (TNBC) characterized by the presence of the MYB–NFIB fusion gene. The molecular underpinning of breast AdCCs other than the MYB–NFIB fusion gene remains largely unexplored. Here we sought to define the repertoire of somatic genetic alterations of breast AdCCs. We performed whole‐exome sequencing, followed by orthogonal validation, of 12 breast AdCCs to determine the landscape of somatic mutations and gene copy number alterations. Fluorescence in situ hybridization and reverse‐transcription PCR were used to define the presence of MYB gene rearrangements and MYB–NFIB chimeric transcripts. Unlike common forms of TNBC, we found that AdCCs have a low mutation rate (0.27 non‐silent mutations/Mb), lack mutations in TP53 and PIK3CA and display a heterogeneous constellation of known cancer genes affected by somatic mutations, including MYB, BRAF, FBXW7, SMARCA5, SF3B1 and FGFR2. MYB and TLN2 were affected by somatic mutations in two cases each. Akin to salivary gland AdCCs, breast AdCCs were found to harbour mutations targeting chromatin remodelling, cell adhesion, RNA biology, ubiquitination and canonical signalling pathway genes. We observed that, although breast AdCCs had rather simple genomes, they likely display intra‐tumour genetic heterogeneity at diagnosis. Taken together, these findings demonstrate that the mutational burden and mutational repertoire of breast AdCCs are more similar to those of salivary gland AdCCs than to those of other types of TNBCs, emphasizing the importance of histological subtyping of TNBCs. Furthermore, our data provide direct evidence that AdCCs harbour a distinctive mutational landscape and genomic structure, irrespective of the disease site of origin. Copyright

Massively Parallel Sequencing-Based Clonality Analysis of Synchronous Endometrioid Endometrial and Ovarian Carcinomas

Synchronous early-stage endometrioid endometrial carcinomas (EECs) and endometrioid ovarian carcinomas (EOCs) are associated with a favorable prognosis and have been suggested to represent independent primary tumors rather than metastatic disease. We subjected sporadic synchronous EECs/EOCs from five patients to whole-exome massively parallel sequencing, which revealed that the EEC and EOC of each case displayed strikingly similar repertoires of somatic mutations and gene copy number alterations. Despite the presence of mutations restricted to the EEC or EOC in each case, we observed that the mutational processes that shaped their respective genomes were consistent. High-depth targeted massively parallel sequencing of sporadic synchronous EECs/EOCs from 17 additional patients confirmed that these lesions are clonally related. In an additional Lynch Syndrome case, however, the EEC and EOC were found to constitute independent cancers lacking somatic mutations in common. Taken together, sporadic synchronous EECs/EOCs are clonally related and likely constitute dissemination from one site to the other.

Effects of Oestrogen on MicroRNA Expression in Hormone-Responsive Breast Cancer Cells

Oestrogen receptor alpha (ERα) is a ligand-dependent transcription factor that mediates oestrogen effects in hormone-responsive cells. Following oestrogenic activation, ERα directly regulates the transcription of target genes via DNA binding. MicroRNAs (miRNAs) represent a class of small noncoding RNAs that function as negative regulators of protein-coding gene expression. They are found aberrantly expressed or mutated in cancer, suggesting their crucial role as either oncogenes or tumour suppressor genes. Here, we analysed changes in miRNA expression in response to oestrogen in hormone-responsive breast cancer MCF-7 and ZR-75.1 cells by microarray-mediated expression profiling. This led to the identification of 172 miRNAs up- or down-regulated by ERα in response to 17β-oestradiol, of which 52 are similarly regulated by the hormone in the two cell models investigated. To identify mechanisms by which ERα exerts its effects on oestrogen-responsive miRNA genes, the oestrogen-dependent miRNA expression profiles were integrated with global in vivo ERα binding site mapping in the genome by ChIP-Seq. In addition, data from miRNA and messenger RNA (mRNA) expression profiles obtained under identical experimental conditions were compared to identify relevant miRNA target transcripts. Results show that miRNAs modulated by ERα represent a novel genomic pathway to impact oestrogen-dependent processes that affect hormone-responsive breast cancer cell behaviour. MiRNome analysis in tumour tissues from breast cancer patients confirmed a strong association between expression of these small RNAs and clinical outcome of the disease, although this appears to involve only marginally the oestrogen-regulated miRNAs identified in this study.

iMir: An integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq

BackgroundQualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once.ResultsWe designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deep-sequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNA-Seq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells.ConclusionThe integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNA-Seq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir.

Uterine adenosarcomas are mesenchymal neoplasms

Uterine adenosarcomas (UAs) are biphasic lesions composed of a malignant mesenchymal (ie stromal) component and an epithelial component. UAs are generally low‐grade and have a favourable prognosis, but may display sarcomatous overgrowth (SO), which is associated with a worse outcome. We hypothesized that, akin to breast fibroepithelial lesions, UAs are mesenchymal neoplasms in which clonal somatic genetic alterations are restricted to the mesenchymal component. To characterize the somatic genetic alterations in UAs and to test this hypothesis, we subjected 20 UAs to a combination of whole‐exome (n = 6), targeted capture (n = 13) massively parallel sequencing (MPS) and/or RNA sequencing (n = 6). Only three genes, FGFR2, KMT2C and DICER1, were recurrently mutated, all in 2/19 cases; however, 26% (5/19) and 21% (4/19) of UAs harboured MDM2/CDK4/HMGA2 and TERT gene amplification, respectively, and two cases harboured fusion genes involving NCOA family members. Using a combination of laser‐capture microdissection and in situ techniques, we demonstrated that the somatic genetic alterations detected by MPS were restricted to the mesenchymal component. Furthermore, mitochondrial DNA sequencing of microdissected samples revealed that epithelial and mesenchymal components of UAs were clonally unrelated. In conclusion, here we provide evidence that UAs are genetically heterogeneous lesions and mesenchymal neoplasms. Copyright

The repertoire of somatic genetic alterations of acinic cell carcinomas of the breast: an exploratory, hypothesis-generating study.

Acinic cell carcinoma (ACC) of the breast is a rare form of triple‐negative (that is, oestrogen receptor‐negative, progesterone receptor‐negative, HER2‐negative) salivary gland‐type tumour displaying serous acinar differentiation. Despite its triple‐negative phenotype, breast ACCs are reported to have an indolent clinical behaviour. Here, we sought to define whether ACCs have a mutational repertoire distinct from that of other triple‐negative breast cancers (TNBCs). DNA was extracted from microdissected formalin‐fixed, paraffin‐embedded sections of tumour and normal tissue from two pure and six mixed breast ACCs. Each tumour component of the mixed cases was microdissected separately. Tumour and normal samples were subjected to targeted capture massively parallel sequencing targeting all exons of 254 genes, including genes most frequently mutated in breast cancer and related to DNA repair. Selected somatic mutations were validated by targeted amplicon resequencing and Sanger sequencing. Akin to other forms of TNBC, the most frequently mutated gene found in breast ACCs was TP53 (one pure and six mixed cases). Additional somatic mutations affecting breast cancer‐related genes found in ACCs included PIK3CA, MTOR, CTNNB1, BRCA1, ERBB4, ERBB3, INPP4B, and FGFR2. Copy number alteration analysis revealed complex patterns of gains and losses similar to those of common forms of TNBCs. Of the mixed cases analysed, identical somatic mutations were found in the acinic and the high‐grade non‐acinic components in two out of four cases analysed, providing evidence of their clonal relatedness. In conclusion, breast ACCs display the hallmark somatic genetic alterations found in high‐grade forms of TNBC, including complex patterns of gene copy number alterations and recurrent TP53 mutations. Furthermore, we provide circumstantial genetic evidence to suggest that ACCs may constitute the substrate for the development of more aggressive forms of triple‐negative disease. Copyright

Molecular mechanisms of selective estrogen receptor modulator activity in human breast cancer cells: identification of novel nuclear cofactors of antiestrogen-ERα complexes by interaction proteomics.

Estrogen receptor alpha (ERα) is a ligand-activated transcription factor that controls key cellular pathways via protein-protein interactions involving multiple components of transcriptional coregulator and signal transduction complexes. Natural and synthetic ERα ligands are classified as agonists (17β-estradiol/E(2)), selective estrogen receptor modulators (SERMs: Tamoxifen/Tam and Raloxifene/Ral), and pure antagonists (ICI 182,780-Fulvestrant/ICI), according to the response they elicit in hormone-responsive cells. Crystallographic analyses reveal ligand-dependent ERα conformations, characterized by specific surface docking sites for functional protein-protein interactions, whose identification is needed to understand antiestrogen effects on estrogen target tissues, in particular breast cancer (BC). Tandem affinity purification (TAP) coupled to mass spectrometry was applied here to map nuclear ERα interactomes dependent upon different classes of ligands in hormone-responsive BC cells. Comparative analyses of agonist (E(2))- vs antagonist (Tam, Ral or ICI)-bound ERα interacting proteins reveal significant differences among ER ligands that relate with their biological activity, identifying novel functional partners of antiestrogen-ERα complexes in human BC cell nuclei. In particular, the E(2)-dependent nuclear ERα interactome is different and more complex than those elicited by Tam, Ral, or ICI, which, in turn, are significantly divergent from each other, a result that provides clues to explain the pharmacological specificities of these compounds.

Genetic alterations of triple negative breast cancer by targeted next-generation sequencing and correlation with tumor morphology

Triple negative breast cancer represents a heterogeneous group of breast carcinomas, both at the histologic and genetic level. Although recent molecular studies have comprehensively characterized the genetic landscape of these tumors, few have integrated a detailed histologic examination into the analysis. In this study, we defined the genetic alterations in 39 triple negative breast cancers using a high-depth targeted massively parallel sequencing assay and correlated the findings with a detailed morphologic analysis. We obtained representative frozen tissue of primary triple negative breast cancers from patients treated at our institution between 2002 and 2010. We characterized tumors according to their histologic subtype and morphologic features. DNA was extracted from paired frozen primary tumor and normal tissue samples and was subjected to a targeted massively parallel sequencing platform comprising 229 cancer-associated genes common across all experiments. The average number of non-synonymous mutations was 3 (range 0–10) per case. The most frequent somatic alterations were mutations in TP53 (74%) and PIK3CA (10%) and MYC amplifications (26%). Triple negative breast cancers with apocrine differentiation less frequently harbored TP53 mutations (25%) and MYC gains (0%), and displayed a high mutation frequency in PIK3CA and other PI3K signaling pathway-related genes (75%). Using a targeted massively parallel sequencing platform, we identified the key somatic genetic alterations previously reported in triple negative breast cancers. Furthermore, our findings show that triple negative breast cancers with apocrine differentiation constitute a distinct subset, characterized by a high frequency of PI3K pathway alterations similar to luminal subtypes of breast cancer.