Citations

Abstract The emergence of tumor cells with certain stem-like characteristics, such as high aldehyde dehydrogenase (ALDH) activity due to ALDH1A1 expression, contributes to chemotherapy resistance and tumor relapse. However, clinically applicable inhibitors of ALDH activity have not been reported. There is evidence to suggest that epigenetic regulation of stem-related genes contributes to chemotherapy efficacy. Here, we show that bromodomain and extraterminal (BET) inhibitors suppress ALDH activity by abrogating BRD4-mediated ALDH1A1 expression through a super-enhancer element and its associated enhancer RNA. The clinically applicable small-molecule BET inhibitor JQ1 suppressed the outgrowth of cisplatin-treated ovarian cancer cells both in vitro and in vivo. Combination of JQ1 and cisplatin improved the survival of ovarian cancer–bearing mice in an orthotopic model. These phenotypes correlate with inhibition of ALDH1A1 expression through a super-enhancer element and other stem-related genes in promoter regions bound by BRD4. Thus, targeting the BET protein BRD4 using clinically applicable small-molecule inhibitors, such as JQ1, is a promising strategy for targeting ALDH activity in epithelial ovarian cancer. Cancer Res; 76(21); 6320–30. ©2016 AACR.

Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle, although patients continue to receive treatment for multiple cycles. In addition, the toxicity data from multiple types and grades are typically summarized into a single binary outcome of dose‐limiting toxicity. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades. In this work, we propose to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing‐finding. A linear mixed model was utilized in the Bayesian framework, with addition of Bayesian risk functions for decision‐making in dose assignment. The performance of this design is evaluated using simulation studies and compared with the previously proposed quasi‐likelihood continual reassessment method (QLCRM) design. Twelve clinical scenarios incorporating four different locations of maximum tolerated dose and three different time trends (decreasing, increasing, and no effect) were investigated. The proposed repeated measures design was comparable with the QLCRM when only cycle 1 data were utilized in dose‐finding; however, it demonstrated an improvement over the QLCRM when data from multiple cycles were used across all scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Background: The strongest known risk factor for endometrial cancer is obesity. To determine whether SNPs associated with increased body mass index (BMI) or waist–hip ratio (WHR) are associated with endometrial cancer risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and endometrial cancer in 6,609 cases and 37,926 country-matched controls. Methods: Logistic regression analysis and fixed effects meta-analysis were used to test for associations between endometrial cancer risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for endometrial cancer was assessed using Mendelian randomization, with BMIwGRS as instrumental variable. Results: The BMIwGRS was significantly associated with endometrial cancer risk (P = 3.4 × 10−17). Scaling the effect of the BMIwGRS on endometrial cancer risk by its effect on BMI, the endometrial cancer OR per 5 kg/m2 of genetically predicted BMI was 2.06 [95% confidence interval (CI), 1.89–2.21], larger than the observed effect of BMI on endometrial cancer risk (OR = 1.55; 95% CI, 1.44–1.68, per 5 kg/m2). The association attenuated but remained significant after adjusting for BMI (OR = 1.22; 95% CI, 1.10–1.39; P = 5.3 × 10−4). There was evidence of directional pleiotropy (P = 1.5 × 10−4). BMI SNP rs2075650 was associated with endometrial cancer at study-wide significance (P < 4.0 × 10−4), independent of BMI. Endometrial cancer was not significantly associated with individual WHR SNPs or the WHRwGRS. Conclusions: BMI, but not WHR, is causally associated with endometrial cancer risk, with evidence that some BMI-associated SNPs alter endometrial cancer risk via mechanisms other than measurable BMI. Impact: The causal association between BMI SNPs and endometrial cancer has possible implications for endometrial cancer risk modeling. Cancer Epidemiol Biomarkers Prev; 25(11); 1503–10. ©2016 AACR.

Abstract Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10−8 seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type–specific expression quantitative trait locus and enhancer–gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10−5 in the three-cancer meta-analysis. Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052–67. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 932

Abstract Background: Tumorigenesis involves both genetic and epigenetic changes. Epigenetic alterations are reversible and are promising cancer therapeutic targets. Decitabine (5-aza-2'-deoxycytidine), a DNA methyltransferase inhibitor, is FDA approved for hematological malignancies. However, the effect of decitabine in breast cancer is not completely understood. Previous reports indicated that one decitabine mechanism involves regulation of protein levels for DNMT1, the major DNA methyltransferase that methylates hemimethylated CpG di-nucleotides in DNA. However, the E3 ligase involved in this process has not been identified. Whether decitabine also regulates DNMT3A and 3B in a similar fashion remains unclear. Therefore, our goals were to 1) understand mechanisms underlying decitabine action, 2) test the antitumor activity of decitabine in breast cancer models and 3) identify biomarkers associated with response to decitabine. Methods and Results: Western blots of breast cancer cell lines showed that DNMT1, DNMT3A, and DNMT3B protein levels decreased following decitabine treatment without a reduction in mRNA levels. Bioinformatic analysis of DNA methyltransferase sequences revealed a potential TRAF6 binding motif, and the interaction with TRAF6 (TNF receptor-associated factor 6) was confirmed by IP. TRAF6 functions as an E3 ligase. To determine whether TRAF6 might be the E3 ligase responsible for the degradation of DNMTs after decitabine treatment, we knocked down TRAF6 by RNA interference or knocked out the TRAF6 gene by CRISPR/Cas9. Down regulation of TRAF6 attenuated DNMT ubiquitination and increased DNMT protein levels, suggesting that TRAF6 might mediate proteasome-dependent degradation of all three DNMTs. This was further confirmed by reconstituting the knockout cells with WT and a TRAF6-C70A mutant, followed by assessing DNMT protein levels. Global DNA methylation was also increased after TRAF6 depletion and was confirmed in TRAF6 knock out cells in which DNMT levels were unaffected by decitabine. Cell cytotoxicity and colony forming assays showed that TRAF6 knockout cells were resistant to decitabine, suggesting that a major decitabine mechanism of action is through the regulation of TRAF6 which, in turn, degrades DNMTs, leading to decreased global methylation. Finally, decitabine significantly induced TRAF6 at both mRNA and protein levels, a process that might create positive feedback leading to increased degradation of DNMT proteins upon decitabine treatment. Based on these results, we further hypothesized that levels of the three DNMTs might influence decitabine response. Using 18 breast cancer patient derived xenograft (PDX) models, we found a wide range of DNMT protein levels regardless of ER/HER2 status. DNMT levels in the PDX models were directly associated with sensitivity to decitabine treatment, confirming our hypothesis. Conclusion: Our data showed that decitabine might be an effective agent for treating breast cancer and revealed a novel mechanism underlying decitabine treatment. Baseline DNMT protein levels may serve as a biomarker for predicting decitabine drug response. Citation Format: Yu J, Qin B, Boughey JC, Moyer AM, Visscher DW, Sinnwell JP, Yin P, Thompson KJ, Docter TJ, Kalari KR, Suman VJ, Wieben ED, Felten SJ, Conners AL, Jones KN, McLaughlin SA, Copland JA III, Moreno Aspitia A, Northfelt DW, Gray RJ, Ingle JN, Lou Z, Weinshilboum R, Goetz MP, Wang L. Regulation of DNA methyltransferases via TRAF6 determines breast cancer response to decitabine. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-51.

Background BRCA1 interacting protein C-terminal helicase 1 (BRIP1) is one of the Fanconi Anaemia Complementation (FANC) group family of DNA repair proteins. Biallelic mutations in BRIP1 are responsible for FANC group J, and previous studies have also suggested that rare protein truncating variants in BRIP1 are associated with an increased risk of breast cancer. These studies have led to inclusion of BRIP1 on targeted sequencing panels for breast cancer risk prediction. Methods We evaluated a truncating variant, p.Arg798Ter (rs137852986), and 10 missense variants of BRIP1, in 48 144 cases and 43 607 controls of European origin, drawn from 41 studies participating in the Breast Cancer Association Consortium (BCAC). Additionally, we sequenced the coding regions of BRIP1 in 13 213 cases and 5242 controls from the UK, 1313 cases and 1123 controls from three population-based studies as part of the Breast Cancer Family Registry, and 1853 familial cases and 2001 controls from Australia. Results The rare truncating allele of rs137852986 was observed in 23 cases and 18 controls in Europeans in BCAC (OR 1.09, 95% CI 0.58 to 2.03, p=0.79). Truncating variants were found in the sequencing studies in 34 cases (0.21%) and 19 controls (0.23%) (combined OR 0.90, 95% CI 0.48 to 1.70, p=0.75). Conclusions These results suggest that truncating variants in BRIP1, and in particular p.Arg798Ter, are not associated with a substantial increase in breast cancer risk. Such observations have important implications for the reporting of results from breast cancer screening panels.

Previous genome‐wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine‐mapping study across 2.06 Mb (chr8:127,561,724–129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93–0.97, conditional p = 5.8 × 10−6), rs7815245 (OR = 0.94, 95% CI = 0.91–0.96, conditional p = 1.1 × 10−6) and rs2033101 (OR = 1.05, 95% CI = 1.02–1.07, conditional p = 1.1 × 10−4) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r2 = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.

AbstractThe cranial neural crest and the cranial mesoderm are the source of tissues from which the bone and cartilage of the skull, face and jaws are constructed. The development of the cranial mesoderm is not well studied, which is inconsistent with its importance in craniofacial morphogenesis as a source of precursor tissue of the chondrocranium, muscles, vasculature and connective tissues, mechanical support for tissue morphogenesis, and the signaling activity that mediate interactions with the cranial neural crest. Phenotypic analysis of conditional knockout mouse mutants, complemented by the transcriptome analysis of differentially enriched genes in the cranial mesoderm and cranial neural crest, have identified signaling pathways that may mediate cross-talk between the two tissues. In the cranial mesenchyme, Bmp4 is expressed in the mesoderm cells while its signaling activity could impact on both the mesoderm and the neural crest cells. In contrast, Fgf8 is predominantly expressed in the cranial neural crest cells and it influences skeletal development and myogenesis in the cranial mesoderm. WNT signaling, which emanates from the cranial neural crest cells, interacts with BMP and FGF signaling in monitoring the switch between tissue progenitor expansion and differentiation. The transcription factor Twist1, a critical molecular regulator of many aspects of craniofacial development, coordinates the activity of the above pathways in cranial mesoderm and cranial neural crest tissue compartments.