Expression of the lncRNA Maternally Expressed Gene 3 (MEG3) Contributes to the Control of Lung Cancer Cell Proliferation by the Rb Pathway.

Maternally expressed gene 3 (MEG3, mouse homolog Gtl2) encodes a long noncoding RNA (lncRNA) that is expressed in many normal tissues, but is suppressed in various cancer cell lines and tumors, suggesting it plays a functional role as a tumor suppressor. Hypermethylation has been shown to contribute to this loss of expression. We now demonstrate that MEG3 expression is regulated by the retinoblastoma protein (Rb) pathway and correlates with a change in cell proliferation. Microarray analysis of mouse embryonic fibroblasts (MEFs) isolated from mice with genetic deletion of all three Rb family members (TKO) revealed a significant silencing of Gtl2/MEG3 expression compared to WT MEFs, and re-expression of Gtl2/MEG3 caused decrease in cell proliferation and increased apoptosis. MEG3 levels also were suppressed in A549 lung cancer cells compared with normal human bronchial epithelial (NHBE) cells, and, similar to the TKO cells, re-constitution of MEG3 led to a decrease in cell proliferation and elevated apoptosis. Activation of pRb by treatment of A549 and SK-MES-1 cells with palbociclib, a CDK4/6 inhibitor, increased the expression of MEG3 in a dose-dependent manner, while knockdown of pRb/p107 attenuated this effect. In addition, expression of phosphorylation-deficient mutant of pRb increased MEG3 levels in both lung cancer cell types. Treatment of these cells with palbociclib also decreased the expression of pRb-regulated DNA methyltransferase 1 (DNMT1), while conversely, knockdown of DNMT1 resulted in increased expression of MEG3. As gene methylation has been suggested for MEG3 regulation, we found that palbociclib resulted in decreased methylation of the MEG3 locus similar to that observed with 5-aza-deoxycytidine. Anti-sense oligonucleotide silencing of drug-induced MEG3 expression in A549 and SK-MES-1 cells partially rescued the palbociclib-mediated decrease in cell proliferation, while analysis of the TCGA database revealed decreased MEG3 expression in human lung tumors harboring a disrupted RB pathway. Together, these data suggest that disruption of the pRb-DNMT1 pathway leads to a decrease in MEG3 expression, thereby contributing to the pro-proliferative state of certain cancer cells.

Characterization of estrogen response element binding proteins as biomarkers of breast cancer behavior.

BACKGROUND: While investigating estrogen response element (ERE) binding properties of human estrogen receptor-α (hERα) in breast cancer cytosols, other ERE-binding proteins (ERE-BP) were observed. DESIGN AND METHODS: Recognition properties of ERE-BP were evaluated by electrophoretic mobility shift assays (EMSA) with ERE sequences of the 5'-flanking region of the estrogen responsive gene vitellogenin A2 (VitA2). Cytosols were incubated 16 h, 4 °C with [32P]ERE sequences and separated by EMSA. A method of estimating ERE-BP levels was developed by measuring band intensity from EMSA profiles, expressed in digital light units (DLU)/µg protein and normalized to total DLU. ERE-BP were purified by affinity chromatography and EMSA, and then identified by mass spectrometry. RESULTS: ERE-BP in cytosols did not supershift in the presence of anti-hERα or anti-hERß antibodies recognizing different ER epitopes suggesting that they are not fragments of either receptor isoform. ERE-BP competed with hERα for binding to VitA2-ERE. Increased levels of ERE-BP DNA-binding activities measured in 310 cytosols prepared from breast cancer biopsies correlated with decreased patient survival. Strikingly, breast cancer patients with ER negative status and high ERE-BP expression exhibited the poorest disease-free and overall survival. After purification, ERE-BP were identified as Ku70 (XRCC6) and Ku80 (XRCC5) using mass spectrometry. ERE-BP were confirmed to be Ku70/80 by supershift assay. CONCLUSION: Presence of these novel ERE-binding proteins in a breast carcinoma is a strong predictor of poor prognosis. Our results suggest that ERE-BP, identified as Ku70/Ku80, in cytosols prepared from breast carcinoma biopsies are useful biomarkers for assessing risk of breast cancer recurrence.

Angiogenesis-associated sequence variants relative to breast cancer recurrence and survival.

INTRODUCTION: Breast cancer (BrCA) risk stratification using clinico-pathological biomarkers helps improve disease prognosis prediction. However, disease recurrence rates remain unfavorable and individualized clinical management strategies are needed. Consequently, we evaluated the influence of 14 sequence variants detected in IL-10, TGF-ß1, VEGF, and their associated receptors as effective predictors of BrCA clinical outcomes. METHODS: Tumor DNA samples collected from 441 BrCA patients were genotyped using TaqMan-PCR. Most selected targets alter cytokine serum/plasma levels or signaling pathways. Relationships between genetic profiles and recurrence as well as disease-related mortality were evaluated using cumulative incidence curves and competing risk regression models. RESULTS: The VEGF(-2578)C allele was associated with a 1.3- to 1.6-fold increase in BrCA recurrence (HR(trend) = 1.28; 95% CI = 0.96-1.72) and disease-related mortality (HR(trend) = 1.56; 95% CI = 0.93-2.56). Although this marker was marginally significant relative to BrCA outcomes, there were substantial gains in the 5- and 8-year predictive accuracy compared to standard prognostic indicators. Among ER(+)/PR(+) status patients, there was a significant impact of the VEGF(-2578)CC genotype on disease recurrence and predictive accuracy. CONCLUSIONS: Our findings suggest inheritance of the VEGF(-2578)C allele could serve as an independent prognostic indicator of BrCA prognosis. The VEGF(-2578) marker may have clinical implications among a subset of ER(+)/PR(+) patients with an aggressive phenotype. Because the VEGF(-2578)C allele is linked to high VEGF expression, this cytokine is a potential prognostic and targeted clinical management tool.

Molecular signatures of estrogen receptor-associated genes in breast cancer predict clinical outcome.

Our goal is to identify new molecular targets for drug design and improve understanding of the molecular basis of clinical behavior and therapeutic response of breast cancer (BC). Pure populations of BC cells were procured by laser capture microdissection (LCM) from deidentified tissue specimens. RNA from either LCM-procured cells or whole tissue sections was extracted, purified, and quantified by RT-qPCR using beta-actin for relative quantification. RNA was amplified, Cy5-labeled, and hybridized for microarray. Spectrophotometric and BioAnalyzer analyses evaluated aRNA yield, purity, and transcript length for gene microarray. Unsupervised and supervised methods selected 7,000 genes with significant variation. Expression profiles of BC cells were dominated by genes associated with estrogen receptor-alpha (ERalpha) status; over 3,000 genes were identified as differentially expressed between ERalpha+ and ERalpha(-) BC cells. Other prominent gene expression patterns divided ERalpha+ BCs into subgroups, which were associated with significantly different clinical outcomes (p < 0.01). While exploiting larger gene sets derived from LCM-cells and reports using whole tissues, a preliminary 14 gene subset was selected by UniGene Cluster analysis. Additionally, ERE-binding proteins (ERE-BP) were detected by EMSA, which were not recognized by ERa antibodies. Kaplan-Meier analysis indicated that patients with ERE-BP positive BCs had lower over-all survival than those with ERE-BP negative cancers. Collectively, these results will establish molecular signatures for assessing clinical features of BC and aid in the selection of molecular targets for drug development.

A three-tiered approach for calibration of a biosensor to detect estrogen mimics.

A three-tiered approach was developed to determine the influence of a chemically-diverse group of compounds exhibiting estrogen mimicry using recombinant human estrogen receptor (rhER) activity to calibrate a receptor protein-based biosensor. In the initial tier, a ligand competition array was developed to evaluate compounds inhibiting [3H]estradiol-17beta binding to rhER. Each of six different concentrations of [3H]estradiol-17beta was mixed with increasing concentrations of an unlabeled putative mimic. Each of these mixtures was incubated with a constant amount of rhERalpha and then receptor-bound [[3H]estradiol-17beta was measured. This array protocol analyzes ligand binding affinities of hERalpha with a potential inhibitor over the entire range of receptor protein saturation. When either hERalpha or hERbeta binds to an estrogenic ligand, the receptor monomer forms both homo- and hetero-dimers. Then the ligand-receptor dimer complex activates transcription by associating with an estrogen response element (ERE), which is a specific DNA sequence located upstream of estrogen-responsive genes. The second tier for ligand evaluation utilized an electrophoretic mobility shift assay (EMSA), which was performed with an ERE sequence labeled with [alpha[32]P]dATP and incubated with rhER in the presence or absence of unlabeled ligand. ERE-hER complexes were separated by electrophoresis and analyzed using phosphor imaging technology. To assess biological effects of an estrogen mimic on expression of an ER-target gene, a yeast cell-based bioassay was constructed with recombinant DNA technology using Saccharomyces cerevisiae. Each of these engineered yeast cells contained a rhERalpha expression plasmid (YEpE12) and a separate reporter plasmid (YRG2) containing an ERE sequence upstream of a beta-galactosidase reporter gene. Incubation of these yeast cells with an estrogenic compound allows formation of ligand-hERalpha complexes, which recognize the ERE sequence regulating beta-galactosidase expression. Estrogenic compounds, which were evaluated as calibrators for ligand-based and ERE-based biosensors, elicit varying responses in each of the three tiers of the protocol.

Biosensors for detecting estrogen-like molecules and protein biomarkers.

A novel evanescent-based biosensor (Endotect, ThreeFold Sensors, Inc.) was developed with laser-based fiber optics using fluorescent dye-labeled recombinant human estrogen receptor-alpha (rhERalpha) and hERbeta as probes. A three-tiered approach evaluating various steps in the formation of the estrogen-receptor complex and its subsequent activity was developed for instrument calibration to detect estrogen mimics in biological samples, water and soil. Using this approach, binding affinities and activities of certain known estrogen mimics were determined for their use as calibrator molecules. Results indicated rhERalpha and rhERbeta may be employed as probes to distinguish estrogen mimics with a broad range of affinities. In addition, application of the biosensor for detecting DNA-binding proteins in human tissue extracts was demonstrated. The later studies suggest the biosensor may be used as a clinical laboratory tool for assessing tumor marker proteins.