Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer.

The high-grade serous ovarian cancer (HGSOC) risk locus at chromosome 1p34.3 resides within a frequently amplified genomic region signifying the presence of an oncogene. Here, we integrate in silico variant-to-function analysis with functional studies to characterize the oncogenic potential of candidate genes in the 1p34.3 locus. Fine mapping of genome-wide association statistics identified candidate causal SNPs local to H3K27ac-demarcated enhancer regions that exhibit allele-specific binding for CTCF in HGSOC and normal fallopian tube secretory epithelium cells (FTSECs). SNP risk associations colocalized with eQTL for six genes (DNALI1, GNL2, RSPO1, SNIP1, MEAF6, and LINC01137) that are more highly expressed in carriers of the risk allele, and three (DNALI1, GNL2, and RSPO1) were upregulated in HGSOC compared to normal ovarian surface epithelium cells and/or FTSECs. Increased expression of GNL2 and MEAF6 was associated with shorter survival in HGSOC with 1p34.3 amplifications. Despite its activation of ß-catenin signaling, RSPO1 overexpression exerted no effects on proliferation or colony formation in our study of ovarian cancer and FTSECs. Instead, GNL2, MEAF6, and SNIP1 silencing impaired in vitro ovarian cancer cell growth. Additionally, GNL2 silencing diminished xenograft tumor formation, whereas overexpression stimulated proliferation and colony formation in FTSECs. GNL2 influences 60S ribosomal subunit maturation and global protein synthesis in ovarian cancer and FTSECs, providing a potential mechanism of how GNL2 upregulation might promote ovarian cancer development and mediate genetic susceptibility of HGSOC.

Morphologic and molecular correlates of EZH2 as a predictor of platinum resistance in high-grade ovarian serous carcinoma.

BACKGROUND: Enhancer of zesta homologue 2 (EZH2) is an essential component of polycomb repressive complex 2 (PRC2) that contributes to tumor progression and chemo-resistance. The aim of this study was to comprehensively assess the prognostic value of EZH2 across the morphologic and molecular spectra of high-grade serous ovarian carcinoma (HGSOC) by utilizing both immunohistochemistry (IHC) and proteogenomic technologies. METHODS: IHC of EZH2 was performed using a tissue microarray of 79 HGSOC scored (+/-) for lymphovascular invasion (LVI), tumor-infiltrating lymphocytic aggregates ≥1 mm (TIL) and architectural growth patterns. The association of EZH2 H-score with response to therapy and overall survival was evaluated by tumor features. We also evaluated EZH2 transcriptional (RNA sequencing) and protein (mass spectrometry) expression from bulk tumor samples from 336 HGSOC from The Cancer Genome Atlas (TCGA). EZH2 expression and co-expression networks were compared by clinical outcomes. RESULTS: For HGSOC without TIL (58%), EZH2 expression was almost 2-fold higher in platinum resistant tumors (P = 0.01). Conversely, EZH2 was not associated with platinum resistance among TIL+ HGSOC (P = 0.41). EZH2 expression was associated with reduced survival for tumors with LVI (P = 0.04). Analysis of TCGA found higher EZH2 expression in immunoreactive and proliferative tumors (P = 6.7 × 10- 5) although protein levels were similar across molecular subtypes (P = 0.52). Both mRNA and protein levels of EZH2 were lower in platinum resistant tumors although they were not associated with survival. Co-expression analysis revealed EZH2 networks totaling 1049 mRNA and 448 proteins that were exclusive to platinum sensitive or resistant tumors. The EZH2 network in resistant HGSOC included CARM1 which was positively correlated with EZH2 at both mRNA (r = 0.33, p = 0.003) and protein (r = 0.14, P = 0.01) levels. Further, EZH2 co-expression with CARM1 corresponded to a decreased prognostic significance of EZH2 expression in resistant tumors. CONCLUSIONS: Our findings demonstrate that EZH2 expression varies based on its interactions with immunologic pathways and tumor microenvironment, impacting the prognostic interpretation. The association between high EZH2 expression and platinum resistance in TIL- HGSOC warrants further study of the implications for therapeutic strategies.

Identification of novel epithelial ovarian cancer loci in women of African ancestry.

Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome-wide association study in African ancestry women with 755 EOC cases, including 537 high-grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC (p < 1 × 10-6 ), including rs4525119 (intronic to AKR1C3), rs7643459 (intronic to LOC101927394), rs4286604 (12 kb 3' of UGT2A2) and rs142091544 (5 kb 5' of WWC1). For HGSOC, we identified six loci with suggestive evidence of association including rs37792 (132 kb 5' of follistatin [FST]), rs57403204 (81 kb 3' of MAGEC1), rs79079890 (LOC105376360 intronic), rs66459581 (5 kb 5' of PRPSAP1), rs116046250 (GABRG3 intronic) and rs192876988 (32 kb 3' of GK2). Among the identified variants, two are near genes known to regulate hormones and diseases of the ovary (AKR1C3 and FST), and two are linked to cancer (AKR1C3 and MAGEC1). In follow-up studies of the 10 identified variants, the GK2 region SNP, rs192876988, showed an inverse association with EOC in European ancestry women (p = 0.002), increased risk of ER positive breast cancer in African ancestry women (p = 0.027) and decreased expression of GK2 in HGSOC tissue from African ancestry women (p = 0.004). A European ancestry-derived polygenic risk score showed positive associations with EOC and HGSOC in women of African ancestry suggesting shared genetic architecture. Our investigation presents evidence of variants for EOC shared among European and African ancestry women and identifies novel EOC risk loci in women of African ancestry.

Empirically-derived synthetic populations to mitigate small sample sizes.

Limited sample sizes can lead to spurious modeling findings in biomedical research. The objective of this work is to present a new method to generate synthetic populations (SPs) from limited samples using matched case-control data (n = 180 pairs), considered as two separate limited samples. SPs were generated with multivariate kernel density estimations (KDEs) with unconstrained bandwidth matrices. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization by covariance comparisons. Four synthetic samples (n = 180) were derived from their respective SPs. Similarity between observed samples with synthetic samples was compared assuming their empirical probability density functions (EPDFs) were similar. EPDFs were compared with the maximum mean discrepancy (MMD) test statistic based on the Kernel Two-Sample Test. To evaluate similarity within a modeling context, EPDFs derived from the Principal Component Analysis (PCA) scores and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons. Four SPs were generated from each sample. The probability of selecting a replicate when randomly constructing synthetic samples (n = 180) was infinitesimally small. MMD tests indicated that the observed sample EPDFs were similar to the respective synthetic EPDFs. For the samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples. The feasibility of this approach was demonstrated by producing synthetic data at the individual level, statistically similar to the observed samples. The methodology coupled KDE with DE optimization and deployed novel similarity metrics derived from PCA. This approach could be used to generate larger-sized synthetic samples. To develop this approach into a research tool for data exploration purposes, additional evaluation with increased dimensionality is required. Moreover, given a fully specified population, the degree to which individuals can be discarded while synthesizing the respective population accurately will be investigated. When these objectives are addressed, comparisons with other techniques such as bootstrapping will be required for a complete evaluation.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women.

OBJECTIVE: Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women. METHODS: Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations. RESULTS: At chromosome 6p25.2, SNP rs7748275 was associated with risk of serous EOC (odds ratio [OR] = 1.34, P = 8.7 × 10-9) and high-grade serous EOC (HGSOC) (OR = 1.34, P = 4.3 × 10-9). SNP rs6902488 at 6p25.2 (r2 = 0.97 with rs7748275) lies in an active enhancer and is predicted to impact binding of STAT3, P300 and ELF1. We identified additional risk loci with low Bayesian false discovery probability (BFDP) scores, indicating they are likely to be true risk associations (BFDP <10%). At chromosome 20q11.22, rs74272064 was associated with HGSOC risk (OR = 1.27, P = 9.0 × 10-8). Overall EOC risk was associated with rs10260419 at chromosome 7p21.3 (OR = 1.33, P = 1.2 × 10-7) and rs74917072 at chromosome 2q37.3 (OR = 1.25, P = 4.7 × 10-7). At 2q37.3, expression quantitative trait locus analysis in 404 HGSOC tissues identified ESPNL as a putative candidate susceptibility gene (P = 1.2 × 10-7). CONCLUSION: While some risk loci were shared between East Asian and European populations, others were population-specific, indicating that the landscape of EOC risk in Asian women has both shared and unique features compared to women of European ancestry.

Do preoperative radiographs help predict intraoperative challenges in revision surgery after previous shoulder hemiarthroplasty?

BACKGROUND: This study compares preoperative radiographic evaluation with intraoperative video and explant data in patients undergoing revision of a hemiarthroplasty. METHODS: From 2004 to 2017, 182 shoulder hemiarthroplasties underwent revision to reverse shoulder arthroplasty for symptomatic failure. Preoperative radiographs were evaluated for stem fixation, stability, and glenohumeral registry. Intraoperative videos (n = 48) were evaluated for humeral component stability and bone loss after humeral stem extraction. All explants (n = 83) were reviewed for humeral head wear patterns and extraction artifacts (EAs). RESULTS: A well-fixed stem was reliably identified on radiographs as well fixed (true-negative rate, 95%). Of cemented implants, 94% (97 of 103) were radiographically stable and 90% (18 of 20) were stable on intraoperative video. Significant proximal humeral bone loss was identified after cemented stem extraction in 83% of cases, and severe EAs were noted in 28% (14 of 50). Of uncemented implants, 95% (75 of 79) were radiographically stable and 96% (24 of 25) were operatively stable. Significant proximal humeral bone loss was identified after extraction in 36% of cases (9 of 25) (P = .001). Severe EAs were seen in 13% of explanted stems (3 of 23). Eccentrically worn humeral head explants were associated with eccentric glenohumeral registry in 84% of cases (P = .0075). CONCLUSION: Preoperative radiographs for revision of a failed hemiarthroplasty help identify well-fixed stems and predict humeral bone loss during extraction. Cemented stems will have more EAs and result in greater bone loss than uncemented stems. Glenohumeral registry can help to predict humeral head wear. Eccentric registry leads to eccentric humeral head wear in 84% of cases.

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium.

This corrects the article DOI: 10.1038/bjc.2017.231.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Motivation: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . Contact: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Total shoulder arthroplasty with minimum 5-year follow-up: does the presence of subchondral cysts in the glenoid increase risk of failure?

BACKGROUND: This study evaluated the effect of cystic changes in the glenoid on postoperative outcomes and implant survival after total shoulder arthroplasty (TSA). MATERIALS AND METHODS: From 2004 to 2012, 75 patients underwent TSA for primary osteoarthritis with minimum 5-year follow-up. Preoperative 3-dimensional models based on computed tomography imaging were created for all patients. A qualitative evaluation of cystic osteoarthritis was performed through survey grading by 3 fellowship-trained shoulder surgeons. The extent of cyst formation in the glenoid (no cysts, small, medium, or large) was assigned for every patient. In addition, quantitative evaluation was performed on 3-dimensional glenoid models. Functional outcomes, radiographic findings, and the need for revision were compared between group 1 (large and medium cysts) and group 2 (small and no cysts). RESULTS: Qualitative evaluation of cyst formation resulted in the following distribution: no cysts in 8 patients (11%), small cyst formation in 27 (36%), medium cysts in 19 (25%), and large cysts in 21 patients (28%; κ = 0.605). The difference in total cyst volume between group 1 and group 2 was significant (P = .004). The overall revision rate was 7% (5 of 75). All revised patients were in the groups with medium or large cysts. There were no statistical differences in American Shoulder and Elbow Surgeons (ASES) Standardized Shoulder Assessment scores or presence of radiographic loosening among the study groups. CONCLUSION: Qualitative computed tomography evaluation of cystic osteoarthritis correlates with quantitative analysis of cyst volume. Severe cyst formation portends a higher risk of failure at midterm follow-up. Cystic disease did not affect functional outcome or the presence of radiographic glenoid loosening.

Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study.

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10(-15)), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10(-6)). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium.

BACKGROUND: Evidence on height and prostate cancer risk is mixed, however, recent studies with large data sets support a possible role for its association with the risk of aggressive prostate cancer. METHODS: We analysed data from the PRACTICAL consortium consisting of 6207 prostate cancer cases and 6016 controls and a subset of high grade cases (2480 cases). We explored height, polymorphisms in genes related to growth processes as main effects and their possible interactions. RESULTS: The results suggest that height is associated with high-grade prostate cancer risk. Men with height >180 cm are at a 22% increased risk as compared to men with height <173 cm (OR 1.22, 95% CI 1.01-1.48). Genetic variants in the growth pathway gene showed an association with prostate cancer risk. The aggregate scores of the selected variants identified a significantly increased risk of overall prostate cancer and high-grade prostate cancer by 13% and 15%, respectively, in the highest score group as compared to lowest score group. CONCLUSIONS: There was no evidence of gene-environment interaction between height and the selected candidate SNPs.Our findings suggest a role of height in high-grade prostate cancer. The effect of genetic variants in the genes related to growth is seen in all cases and high-grade prostate cancer. There is no interaction between these two exposures.

Long non-coding RNAs (LncRNA) regulated by transforming growth factor (TGF) ß: LncRNA-hit-mediated TGFß-induced epithelial to mesenchymal transition in mammary epithelia.

Long noncoding RNAs (lncRNAs) are emerging as key regulators in various biological processes. Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by tumor cells to depart from the primary tumor site, invade surrounding tissue, and establish distant metastases. Transforming growth factor ß (TGFß) signaling has been shown to be a major inducer of EMT and to facilitate breast cancer metastasis. However, the role of lncRNAs in this process remains largely unknown. Here we report a genome-wide lncRNA profile in mouse mammary epithelial NMuMG cells upon TGFß induction of EMT. Among 10,802 lncRNAs profiled, over 600 were up-regulated and down-regulated during the EMT, respectively. Furthermore, we identify that lncRNA-HIT (HOXA transcript induced by TGFß) mediates TGFß function, i.e. depletion of lncRNA-HIT inhibits TGFß-induced migration, invasion, and EMT in NMuMG. LncRNA-HIT is also significantly elevated in the highly metastatic 4T1 cells. Knockdown of lncRNA-HIT in 4T1 results in decrease of cell migration, invasion, tumor growth, and metastasis. E-cadherin was identified as a major target of lncRNA-HIT. Moreover, lncRNA-HIT is conserved in humans and elevated expression associates with more invasive human primary breast carcinoma. Collectively, these data suggest that a subset of lncRNAs such as lncRNA-HIT play a significant role in regulation of EMT and breast cancer invasion and metastasis, and could be potential therapeutic targets in breast cancers.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

Prediction of individual genetic risk to prostate cancer using a polygenic score.

BACKGROUND: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

Differences in BRCA counseling and testing practices based on ordering provider type.

PURPOSE: The purpose of this study was to assess potential differences in genetic counseling services delivered by board-certified genetic health-care providers versus non-genetic health-care providers. We evaluated (i) patient recall and content of pretest genetic counseling for hereditary breast and ovarian cancer and (ii) whether full BRCA1 and 2 gene sequencing was performed when less expensive single-site or Ashkenazi Jewish founder mutation testing may have been sufficient. METHODS: Participants completed a questionnaire and provided BRCA test reports that included testing provider and type of test. Chi-square tests and logistic regression were used for analysis. RESULTS: Of 473 participants, >90% were white, female, and BRCA mutation carriers. Of the 276 (58%) with genetic health-care provider involvement, 97% recalled a pretest discussion as compared with 59% of those without genetic health-care provider involvement (P < 0.001). Among the subgroup who recalled a pretest discussion (n = 385), those with genetic health-care provider involvement indicated higher adherence to eight recognized genetic counseling elements, four of which were statistically significant. Furthermore, involvement of a genetic health-care provider halved the likelihood that comprehensive BRCA testing was ordered among the 266 for whom single-site or multisite-3 testing may have been sufficient (P = 0.02). CONCLUSION: Our results suggest that genetic health-care provider involvement is associated with adherence to nationally recommended genetic counseling practices and could potentially reduce costs of BRCA genetic testing.

Epidemiology - identifying the causes and preventability of cancer?

It has been almost 25 years since Doll and Peto performed their landmark analysis of epidemiological data to identify the causes of cancers and possible modes of cancer prevention. Since then, there have been many additional studies of cancer incidence using various epidemiological techniques. These studies revealed expanded opportunities for cancer prevention through approaches that include vaccination, increased physical activity, weight control and avoidance of post-menopausal hormone therapy.

Identification of a novel percent mammographic density locus at 12q24.

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Risk factors for ovarian cancers with and without microsatellite instability.

OBJECTIVE: In a population-based sample of epithelial ovarian cancers, the objective of this study was to evaluate the association between microsatellite instability (MSI) status and the following factors: (1) ovarian cancer risk factors and (2) the distribution of the specific histologic subtypes. PATIENTS AND METHODS: Participants were drawn from 3 population-based studies of primary epithelial ovarian cancer; tumor DNA was analyzed using 5 standardized microsatellite markers to assess the MSI status. Patients were divided into 3 groups (MSI-high, MSI-low, and MSI-stable) according to the National Cancer Institute criteria. We compared the prevalence of specific known risk and protective factors among the 3 subgroups, including body mass index, smoking history, parity, BRCA1 and BRCA2 mutation status, past oral contraceptive use, and tubal ligation. Similarly, we compared the distribution of the histologic subtypes among the 3 subgroups. RESULTS: A total of 917 ovarian cancer patients were included. One hundred twenty-seven cases of cancer (13.8%) were MSI-high. Subgroup analyses according to smoking, body mass index, parity, past oral contraceptive use, and past tubal ligation did not reveal any statistically significance differences among the groups. Among the 29 patients with BRCA1 mutations, 20.7% had MSI-high cancers compared with 5.9% among 17 patients with BRCA2 mutations. The proportions of different ovarian cancer histologies among the various MSI subgroups were similar. CONCLUSIONS: The prevalence of risk and protective factors among ovarian cancer patients is similar for cancers with and without MSI. The distributions of MSI do not differ significantly among ovarian cancers with different histologies. Ovarian cancer patients with BRCA1 mutations had a 21% rate of MSI-high tumors compared with 6% among patients with BRCA2 mutations, but this difference was not statistically significant.