Abstract Breast cancer in premenopausal women (preM) is frequently associated with worse prognosis compared to that in postmenopausal women (postM) even when controlling for prognostic variables. In particular, preM ER+ tumors have a poor prognosis on endocrine therapy. There is, however, a paucity of studies characterizing molecular alterations in premenopausal tumors, a potential avenue for finding personalized therapies for this group of women. We analyzed gene expression, CNV, methylation, and somatic mutations in tumors from preM (≤45; ER+ n = 110, and ER- n = 39) and postM (≥55, ER+ n = 392, and ER- n = 102) women in The Cancer Genome Atlas (TCGA). Unbiased hierarchical clustering of 2,900 most variably expressed genes (using both RNA-seq and Agilent expression array data) in the whole dataset (n = 643) identified four major subtypes which correlated highly with the PAM50 defined subtypes LumA, LumB, Basal and HER2; however, there wasn't any separation between preM and postM samples. Similarly, principal component analysis using 10,000 genes with the highest inter-quartile range (IQR) demonstrated high similarity across preM and postM samples. Direct examination of gene expression differences between PreM and PostM ER+ tumors using unpaired t-test (5% FDR) identified 3,044 differentially expressed genes. The genes most upregulated in premenopausal tumors included AREG, TFPI2, MSMB, TCN1, and GLRA3. Ingenuity Pathway Analysis revealed a highly significant enrichment for TGFb (p<1.9E-16) pathway activity in preM tumors. Intriguingly, no significant gene expression differences between preM and postM ER- tumors were identified. We thus then focused on genetic and epigenetic alterations that may underlie these transcriptomic changes in ER+ preM tumors. Comparison of methylation (450K Illumina array) between preM and postM ER+ tumors showed a difference in 1% (n = 1,738) of the probes. Genes with the largest difference included ESR1, SIM2, and KLF6. Significant differences in DNA copy number variation (Affymetrix SNP 6.0 array) were also identified in ER+ preM tumors. A number of somatic mutations were significantly enriched in preM ER+ tumors including DSPP and GATA3. Integrated analysis also showed that approximately half of the observed differences in gene expression are driven by CNVs. Conclusion: Our in silico study has identified a number of genes and pathways which are significantly altered between preM and postM ER+ breast cancer. Distinct genetic and epigenetic differences suggest unique etiology for some preM tumors. Currently ongoing Paradigm analysis, and confirmatory studies using METABRIC data are expected to further identify pathways that could specifically be targeted in premenopausal breast cancer. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-03.
Regulatory T cells (Treg cells) are critical mediators of self-tolerance, but they can also limit effective anti-tumor immunity. Although under homeostasis a small fraction of Treg cells in lymphoid organs express the putative checkpoint molecule Tim-3, this protein is expressed by a much larger proportion of tumor-infiltrating Treg cells. Using a mouse model that drives cell-type-specific inducible Tim-3 expression, we show that expression of Tim-3 by Treg cells is sufficient to drive Treg cells to a more effector-like phenotype, resulting in increases in suppressive activity, effector T cell exhaustion, and tumor growth. We also show that T-reg-cell-specific inducible deletion of Tim-3 enhances anti-tumor immunity. Enhancement of Treg cell function by Tim-3 is strongly correlated with increased expression of interleukin-10 (IL-10) and a shift to a more glycolytic metabolic phenotype. Our data demonstrate that Tim-3+ Treg cells may be a relevant therapeutic target cell type for the treatment of cancer.
e20597 Background: Brain metastases occur in over 40% of non-small cell lung cancer (NSCLC) patients leading to a poor prognosis. c-Met (MET) is a receptor tyrosine kinase that upon binding hepatocyte growth factor (HGF), mediates proliferation, epithelial-mesenchymal transition (EMT), invasion, angiogenesis and metastasis. We have previously shown that the EMT transcription factor, TWIST1 is required for proliferation in MET driven NSCLC. Therefore, the HGF/MET/TWIST1 pathway may be a significant determinant of metastatic potential to the brain. Methods: We evaluated 125 lung adenocarcinoma (LUAC) brain metastases for MET amplification by FISH as well as other molecular alterations using targeted next generation sequencing in a subset of brain metastases (N = 74) and primary LUAC (N = 171) samples including 13 paired primary and brain sets. MET activation was examined in paired tumors using a HGF-MET proximity binding, dual-antibody assay (VeraTag; Monogram Biosciences). TWIST1 and EMT markers in the paired sets were measured by immunohistochemistry. Results: Compared to primary LUAC, we found that 17 pathogenic variants including TP53, SMAD4, RB1, RET, APC, ALK, FGFR3, EGFR, STK11 and MET alterations were significantly more common in LUAC brain metastases (adj. p values ≤ 0.02). Specifically, MET amplification was significantly enriched in LUAC brain metastases (23/125, 19%) compared to 2-4% in non-brain metastatic and primary sites. Among paired samples, 2/13 brain metastases had MET amplification that was not found in the primary tumor. MET mutations were also present in 16/74 brain cases (22%) compared to 9% (16/171) observed in the lung. VHL mutations were associated with MET altered cases compared to non- MET altered cases. MET expression was increased in the majority of brain metastases compared to the paired LUAC and there were 3 cases with brain specific MET activation. We found that TWIST1 was induced by HGF and determined response to MET TKIs in vitro. Among paired samples, TWIST1 was increased in brain metastases compared to primary LUAC in a subset of cases. Further analyses of TWIST1 and EMT markers is ongoing. Conclusions: Over a third of brain metastases have MET alterations compared to primary LUAC and may be responsive to MET inhibitors.
<div>Abstract<p>Reactivation of androgen receptor (AR) appears to be the major mechanism driving the resistance of castration-resistant prostate cancer (CRPC) to second-generation antiandrogens and involves AR overexpression, AR mutation, and/or expression of AR splice variants lacking ligand-binding domain. There is a need for novel small molecules targeting AR, particularly those also targeting AR splice variants such as ARv7. A high-throughput/high-content screen was previously reported that led to the discovery of a novel lead compound, 2-(((3,5-dimethylisoxazol-4-yl)methyl)thio)-1-(4-(2,3-dimethylphenyl)piperazin-1-yl)ethan-1-one (IMTPPE), capable of inhibiting nuclear AR level and activity in CRPC cells, including those resistant to enzalutamide. A novel analogue of IMTPPE, JJ-450, has been investigated with evidence for its direct and specific inhibition of AR transcriptional activity via a pulldown assay and RNA-sequencing analysis, PSA-based luciferase, qPCR, and chromatin immunoprecipitation assays, and xenograft tumor model 22Rv1. JJ-450 blocks AR recruitment to androgen-responsive elements and suppresses AR target gene expression. JJ-450 also inhibits ARv7 transcriptional activity and its target gene expression. Importantly, JJ-450 suppresses the growth of CRPC tumor xenografts, including ARv7-expressing 22Rv1. Collectively, these findings suggest JJ-450 represents a new class of AR antagonists with therapeutic potential for CRPC, including those resistant to enzalutamide.</p></div>
Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.
Two morphologically distinct subpopulations of GT1-7 cells have been characterized and examined for their responsiveness to glucocorticoids. Type I cells have a neuronal phenotype, extending many lengthy processes, and express neuronal, but not glial, markers. Type II cells show weaker or negative immunostaining for neuronal markers and exhibit fewer processes. The effect of glucocorticoids on gonadotropin-releasing hormone (GnRH) secretion and gene expression was compared in type I and type II GT1-7 cells. For secretion studies, cells were attached to Cytodex beads and perifused with control medium or medium containing dexamethasone (dex). The high level of GnRH secreted by type I cells was slightly enhanced in the presence of dex, whereas dex rapidly and profoundly decreased the already low level of GnRH secreted by type II cells. Immunocytochemistry for GnRH showed dark reaction product in the cell bodies and processes of type I cells and little or no immunoreactivity in type II cells. Both the endogenous mouse GnRH mRNA and the transcriptional activity of a mouse GnRH promoterluciferase reporter gene plasmid were suppressed to a greater extent in type II cells than in type I. In electrophoretic mobility shift assays, there was no difference between type I and type II nuclear extracts in the pattern of protein-DNA complexes formed on two previously identified negative glucocorticoid response elements located at −237 to −201 and −184 to −150 bp of the mouse promoter. Both cell types contained glucocorticoid receptors (GR) by Western blot analysis. Cytosols from type I or type II cells were incubated with [3H]dex to obtain GR binding parameters. Binding data were consistent with a one-site model for dex binding in each case. Small differences in Kd (1.7 nM, type I; 3.1 nM, type II) or Bmax (∼3600 sites/cell, type I; ∼1800 sites/cell, type II) were not likely to account for the differential sensitivity to dex treatment. In conclusion, nuclear alterations in type II cells leading to greater transcriptional susceptibility to dex, coupled with low GnRH storage levels, may be reflected in exquisite sensitivity of GnRH secretion to glucocorticoid repression. This represents the first example of a steroid hormone acting directly on GnRH-producing cells to alter GnRH secretion.
Copy number variation (CNV) GISTIC The Cancer Genome Atlas (TCGA). List of significant (q <0.25) amplification and deletion peaks identified by GISTIC 2.0, uniquely in premenopausal (preM) and postmenopausal (postM) groups. The columns correspond to the sample group in which the peak is uniquely identified (preM or postM), type of peak (amplification or deletion), cytoband, q value, residual q value, wide peak boundaries, and number of genes in the region, followed by the list of genes falling under the wide peak boundaries. (XLSX 73 kb)
