Citations

Abstract Epithelial ovarian cancer (EOC) is a rare but deadly disease for which there is notably poorer survival in women of African Ancestry (AA) compared to women of European Ancestry (EA). Previous EA-based genome-wide association studies (GWAS) have identified 30 common, low penetrant EOC susceptibility alleles. Using the custom-designed 533,631 SNP Illumina OncoArray and imputation to ~12 million genetic variants in the 1000 Genomes Phase 3, we conducted a GWAS in 755 AA EOC cases, including 537 high-grade serous ovarian cancer (HGSOC) cases, and 1,235 AA controls from the Ovarian Cancer Association Consortium (OCAC). We identified novel susceptibility loci with suggestive evidence of association with EOC, based on a threshold of P<1x10-6, at three distinct loci including 10p15.1 (lead SNP rs4525119, intronic to AKR1C3, P=4.9 x 10-7, minor allele frequency [MAF]=0.33), 3p25.3 (lead SNP rs7643459, intronic to LOC101927394, P=8.4 x 10-7, MAF=0.36), and 4q13.3 (lead SNP rs4286604, 15 kb 3’ of UGT2A1, P=8.5x10-7, MAF=0.27). In analysis of HGSOC, we identified suggestive evidence of association at six distinct loci including 5q11.2 (lead SNP rs37792, 132 kb 5’ of FST, P=6.0x10-8, MAF=0.34), Xq27.2 (lead SNP rs57403204, 81 kb 3' of MAGEC1, P=1.7x10-7, MAF=0.06), 10p15.1 (lead SNP rs79079890, LOC105376360 intronic, P=3.0x10-7, MAF=0.03), 17p25.1 (lead SNP rs66459581, 5 kb 5’ of PRPSAP1, P=5.1x10-7, MAF=0.23), 15p12 (lead SNP rs116046250, GABRG3 intronic, P=8.7x10-7, MAF=0.05), and 4q21.21 (lead SNP rs192876988, 30 kb 3’ of GK2, P=9.2x10-7, MAF=0.05). These SNPs showed no evidence of association with risk in the OCAC EA GWAS including up to 23,543 EOC cases and 29,444 controls. Of the SNPs identified with suggestive evidence of association in our GWAS, two are located near genes known to regulate hormones and previously reported in relation to diseases of the ovary (AKR1C3 and FST), two have been linked to cancer (AKR1C3 and MAGEC1) and one represents testis-specific expression (GK2). Of the 30 SNPs identified previously in a EA EOC GWAS, we observed nominally significant associations (P<0.05) with consistent direction of effect for eight SNPs in AA women. Our investigation presents evidence of (1) variants for EOC shared among EA and AA women and (2) novel EOC risk loci of importance for AA women. Citation Format: Ani Manichaikul, Lauren C. Peres, Xin-Qun Wang, Kate Lawrenson, Sarah Abbott, Ann G. Schwartz, Anna H. Wu, Edward Peters, Patricia G. Moorman, Michele L. Cote, Melissa Bondy, Linda Kelemen, Ellen L. Goode, Jill Barnholtz-Sloan, Simon A. Gather, Andrew Berchuck, Jennifer A. Doherty, Paul Pharoah, Joellen Schildkraut. Identification of novel epithelial ovarian cancer loci in women of African ancestry from the Ovarian Cancer Association Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1307. doi:10.1158/1538-7445.AM2017-1307

Abstract BACKGROUND: Glioma accounts for ~27% of all primary brain tumors and is responsible for ~13,000 cancer-related deaths in the US each year. Glioma tumors can be broadly classified into glioblastoma (GBM) and lower-grade non-GBM. Typically gliomas have a poor prognosis irrespective of medical care, with the most common form, GBM, having a five-year survival rate of only 5%. While genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, individual studies have had limited power to identify risk loci. METHODS: We performed the largest glioma GWAS to date, comprising a meta-analysis of six existing GWAS (6,405 cases, 14,100 controls) as well as new GWAS from the Glioma International Case Control Consortium (GICC; 4,572 cases and 3,286 controls) and University of California, San Francisco (UCSF)-Mayo (1,519 cases, 804 controls), totaling 12,496 cases (6,191 classified as GBM, 5,819 as non-GBM) and 18,190 controls. RESULTS: We identified five new risk loci for GBM at 1p31.3 (rs12752552; near JAK1, P=2.04×10-9, odds ratio (OR)=1.22), 11q14.1 (rs11233250; P=9.95×10-10, OR=1.24), 16p13.3 (rs2562152; near MPG, P=1.93x10-8, OR=1.21), 16q12.1 (rs10852606; HEATR3, P=1.29×10-11, OR=1.18), 22q13.1 (rs2235573; P=1.76×10-10, OR=1.15) and eight for non-GBM at 1q32.1 (rs4252707; MDM4, P=3.34×10-9, OR=1.19), 1q44 (rs12076373; AKT3, P=2.63×10-10, OR=1.23), 2q33.3 (rs7572263; near IDH1, P=2.18×10-10, OR=1.20), 3p14.1 (rs11706832; LRIG1, P=7.66×10-9, OR=1.15), 10q24.33 (rs11598018; OBFC1, P=3.39×10-8, OR=1.14), 11q21 (rs7107785; P=3.87×10-10, OR=1.16), 14q12 (rs10131032; P=5.07x10-11, OR=1.33) and 16p13.3 (rs3751667; P=2.61×10-9, OR=1.18). Case-only analyses confirmed the specificity of 11q14.1, 16p13.3 and 22q13.1 associations for GBM and 1q44, 2q33.3, 3p14.1, 11q21 and 14q12 for non-GBM tumors. In the combined meta-analysis, among previously published glioma risk SNPs, those for all glioma at 17p13.1 (TP53), GBM at 5p15.33 (TERT), 7p11.2 (EGFR), 9p21.3 (CDKN2B-AS1) and 20q13.33 (RTEL1) and for non-GBM at 8q24.21 (CCDC26), 11q23.2, 11q23.3 (PHLDB1) and 15q24.2 (ETFA) showed even greater evidence for association. SNPs at 10q25.2 and 12q12.1 for non-GBM tumors retained genome-wide significance (i.e. P<5.0x10-8). Associations at the previously reported loci for GBM at 3q26.2 (near TERC) and 12q23.33 (POLR3B) did not retain statistical significance. CONCLUSIONS: Our findings substantiate genetic susceptibility to GBM and non-GBM glioma being highly distinct, consistent with their distinctive molecular profiles presumably resulting from different etiological pathways. Functional analyses should lead to further insights into the biological basis of the different glioma histologies. Such information can inform gene discovery initiatives and therefore have a measurable impact on the successful development of new therapeutic agents. Citation Format: Ben Kinnersley, Beatrice S. Melin, Jill S. Barnholtz-Sloan, Margaret R. Wrensch, Christoffer Johansen, Dora Il’yasova, Quinn Ostrom, and members of GICC, Karim Labreche, Jeanette E. Eckel-Passow, Paul A. Decker, Marianne Labussière, Ahmed Idbaih, Khe Hoang-Xuan, Anna-Luisa Di Stefano, Karima Mokhtari, Jean-Yves Delattre, Pilar Galan, Konstantinos Gousias, Johannes Schramm, Minouk J. Schoemaker, Sarah J. Fleming, Stefan Herms, Stefanie Heilmann, Marcus M. Nöthen, Heinz-Erich Wichmann, Stefan Schreiber, Anthony Swerdlow, Mark Lathrop, Matthias Simon, Marc Sanson, Preetha Rajaraman, Stephen Chanock, Martha Linet, Zhaoming Wang, Meredith Yeager, Rose K. Lai, Elizabeth B. Claus, Sara H. Olson, Robert B. Jenkins, Richard S. Houlston, Melissa L. Bondy. Genome-wide association study of glioma reveals specific differences in genetic susceptibility to glioblastoma and non-glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1302. doi:10.1158/1538-7445.AM2017-1302

AbstractVisual identification is the most common identification method used by medical examiners but is not always possible. Alternative methods include X‐ray, fingerprint, or DNA comparison, but these methods require additional resources. Comparison of serial numbers on implanted medical devices is a rapid and definitive method of identification. To assess the practicality of using this method, we reviewed 608 consecutive forensic autopsies performed at a regional medical examiner office. Of these, 56 cases required an alternative method of identification due to decomposition (n = 35), gunshot wound (n = 9), blunt trauma (n = 6), or charring (n = 6). Of these 56 cases, eight (14.3%) were known to have an implanted medical device. Of these eight cases, five (63%) could be positively identified by comparing serial numbers. If an implanted medical device is known to be present, and medical records are available, identification by medical device serial number should be a first‐line method.

Investigating the most likely causal variants identified by fine‐mapping analyses may improve the power to detect gene–environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine‐scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER–) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene–environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR‐rs7558475 and current smoking (ORint = 0.77, 95% CI: 0.67–0.88, pint = 1.8 × 10−4). The interaction with the strongest statistical evidence was found between 5q14‐rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16–1.59, pint = 1.9 × 10−5) in relation to ER– disease risk. The remaining two gene–environment interactions were also identified in relation to ER– breast cancer risk and were found between 3p21‐rs6796502 and age at menarche (ORint = 1.26, 95% CI: 1.12–1.43, pint =1.8 × 10−4) and between 8q23‐rs13267382 and age at first full‐term pregnancy (ORint = 0.89, 95% CI: 0.83–0.95, pint = 5.2 × 10−4). While these results do not suggest any strong gene–environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.

Purpose To characterize reporting of P values, confidence intervals (CIs), and statistical power in health professions education research (HPER) through manual and computerized analysis of published research reports. Method The authors searched PubMed, Embase, and CINAHL in May 2016, for comparative research studies. For manual analysis of abstracts and main texts, they randomly sampled 250 HPER reports published in 1985, 1995, 2005, and 2015, and 100 biomedical research reports published in 1985 and 2015. Automated computerized analysis of abstracts included all HPER reports published 1970–2015. Results In the 2015 HPER sample, P values were reported in 69/100 abstracts and 94 main texts. CIs were reported in 6 abstracts and 22 main texts. Most P values (≥77%) were ≤.05. Across all years, 60/164 two-group HPER studies had ≥80% power to detect a between-group difference of 0.5 standard deviations. From 1985 to 2015, the proportion of HPER abstracts reporting a CI did not change significantly (odds ratio [OR] 2.87; 95% CI 1.04, 7.88) whereas that of main texts reporting a CI increased (OR 1.96; 95% CI 1.39, 2.78). Comparison with biomedical studies revealed similar reporting of P values, but more frequent use of CIs in biomedicine. Automated analysis of 56,440 HPER abstracts found 14,867 (26.3%) reporting a P value, 3,024 (5.4%) reporting a CI, and increased reporting of P values and CIs from 1970 to 2015. Conclusions P values are ubiquitous in HPER, CIs are rarely reported, and most studies are underpowered. Most reported P values would be considered statistically significant.