Citations

Abstract Background: Radiologically dense area (DA) on a mammogram represents the stromal and epithelial tissue in the breast, while non-dense area (NDA) represents adipose tissue. Quantitative measurements of DA, NDA as well as the proportion of dense area in the breast (percent density, PD) adjusted for age and body mass index (BMI) have all been associated with breast cancer (BC) risk. Previous genome-wide association studies (GWAS) have identified genetic loci that show association with both mammographic density (MD) phenotypes and BC, but little is known about the genome-wide and local genetic correlation between MD phenotypes and BC. Methods: We estimated the genome-wide and local genetic correlation between MD phenotypes adjusted for age and BMI and BC, leveraging GWAS summary statistics for MD phenotypes based on 12,866 women (MODE consortium) and for BC based on 122,977 cases and 105,974 controls (BCAC consortium). We also conducted partitioned heritability analysis to identify specific functional annotations that contribute to MD heritability. Results: We observed a negative genetic correlation between DA and NDA (-0.62, p=0.002). Both DA and PD showed positive genetic correlations with overall BC (DA: rg=0.24, p=0.004; PD: rg=0.27, p=0.0001), estrogen receptor-positive (ER+) BC (DA: rg=0.23, p=0.005; PD: rg=0.23, p=0.001), and ER- BC (DA: rg=0.26, p=0.01; PD: rg=0.32, p=0.0006). In contrast, NDA showed a nonsignificant negative genetic correlation with BC (rg=-0.11, p=0.21). Local genetic correlation analyses between MD phenotypes and BC reflected known findings including positive correlation at 10q21 region between both PD and DA and BC (both p<5x10-7), negative correlation at 22q13.1 between DA and BC (p=4.6x10-6), and positive correlation at 8p11.23 between NDA and BC (p=2.8x10-6). Two known BC regions that have not previously been associated with MD showed suggestive genetic correlations between BC and DA: 1q21.2 (negative genetic correlation, p=1.3x10-4) and 12q21.31 (positive genetic correlation, p=6.7x10-4). In exploratory analyses estimating the genome-wide genetic correlation between MD and hormone-related phenotypes in UK Biobank (n=337,199), we observed nominally significant positive genetic correlations between PD and benign mammary dysplasia (rg=0.45, p=0.01), NDA and endometriosis (rg=0.45, p=0.02), and negative genetic correlation between NDA and menstrual cycle length (rg=-0.30, p=0.05). We identified multiple functional annotations that contribute to MD heritability including histone mark H3K27ac that marks active enhancers (~2,5-fold enrichment in heritability for both DA and NDA) and DNAsel Hypersensitive Sites (DHS, 9.1-fold enrichment in heritability for DA). Conclusion: Our results provide evidence of shared genetic basis between mammographic density and breast cancer. Citation Format: Hongjie Chen, Julie Douglas, William Gordon, Deborah Thompson, Fergus Couch, Peter Fasching, Per Hall, John Hopper, Gertraud Maskarenic, Marina Pollan, Jennifer Stone, Rulla Tamimi, Sara Lindstroem. Evidence for a shared genetic basis between breast cancer and mammographic density phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 224.

Abstract Introduction: Genome-wide association studies over the past 10 years have identified over 100 common variants associated with breast cancer risk. The association of these polymorphisms varies according to estrogen receptor status of the tumor, with some associated primarily with ER+ or ER- disease, or both subtypes. However, ER expression in breast cancer cells is continuous. The aim of this study was to establish whether the association between polygenic risk scores and ER expression in breast cancer is continuous in nature. Methods: Data were taken from the international Breast Cancer Association Consortium (BCAC). Germline genotype data for 75 SNPs previously reported to be associated with breast cancer were available for 4,999 breast cancer cases for which ER expression data were also available. Automated ER expression on a continuous scale was measured using two image analysis algorithms: Astrogrid and Ariol. A third form of ER expression score, the manual Allred ordinal, was used. Genotype data were used to calculate polygenic risk scores using the equation: PRS = ∑ βx where β is the log-odds of the odds ratio (OR) of breast cancer associated with that single nucleotide polymorphism (SNP), and x is the number of variant alleles at a SNP locus. Five different PRSs were calculated: i) SNPs associated with both ER+ and ER- disease (PRS-A, n= 74) ii) SNPs associated with ER+ disease at P<0.01 (PRS-B, n= 65) iii) SNPs associated with ER- disease at P<0.01 (PRS-C, n= 43) iv) SNPs associated with ER+ but not ER- disease (PRS-D, n= 30) v) SNPs associated with ER- but not ER+ disease (PRS-E, n = 8) The ER-specific weights for the PRS were taken from previous OR estimates. Linear regression models adjusted for study were used to test for a relationship between the five PRSs and ER expression measured on the Ariol, Astrogrid and Allred scales. Results: There was a strong positive correlation found between PRS-B against Astrogrid (β=0.0176, P=4.06x10-4), a weak negative correlation found with PRS-C (β=-0.012, P=0.019) and a weak positive correlation for PRS-A (β=0.012, P=0.0144). PRS-D and E showed far stronger association with Astrogrid than PRS-B and C, respectively (β=0.0186 and -0.017, P=1.97x10-4 and 8.1x10-4). There was a qualitatively similar association when the PRSs were correlated with Ariol and Allred, apart from the stronger correlations with PRS-B (β=91.2 and 0.0853, P=5.52x10-4 and 1.33x10-3). Conclusion: We have shown a robust association between PRS and ER expression in the directions we predicted. This suggests that the continuous variation in ER expression is biologically plausible and linked to genetic polymorphism. The weakening of association with PRS-A and the stronger associations with PRS-D and E show that having SNPs that are associated with both ER subtypes in the same PRS weakens significance, and suggests that subtype-specific PRSs may be improved on by using SNPs exclusive to the respective subtype. Citation Format: Anamay H. Shetty, Raza Ali, Fiona Blows, Carlos Caldas, Nicholas Inard, Javier Benítez, Jenny Chang-Claude, Georgia Chenevix-Trench, Fergus Couch, Angela Cox, Peter Devilee, Douglas Easton, Montserrat Garcia-Closas, Arto Mannermaa, Heli Nevanlinna, Marjanka Schmidt, Paul Pharoah. Patient level polygenic risk scores and continuous estrogen receptor expression in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2211.

Abstract Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

Purpose The relevance of inherited pathogenic mutations in cancer predisposition genes in pancreatic cancer is not well understood. We aimed to assess the characteristics of patients with pancreatic cancer referred for hereditary cancer genetic testing and to estimate the risk of pancreatic cancer associated with mutations in panel-based cancer predisposition genes in this high-risk population. Methods Patients with pancreatic cancer (N = 1,652) were identified from a 140,000-patient cohort undergoing multigene panel testing of predisposition genes between March 2012 and June 2016. Gene-level mutation frequencies relative to Exome Aggregation Consortium and Genome Aggregation Database reference controls were assessed. Results The frequency of germline cancer predisposition gene mutations among patients with pancreatic cancer was 20.73%. Mutations in ATM, BRCA2, CDKN2A, MSH2, MSH6, PALB2, and TP53 were associated with high pancreatic cancer risk (odds ratio, > 5), and mutations in BRCA1 were associated with moderate risk (odds ratio, > 2). In a logistic regression model adjusted for age at diagnosis and family history of cancer, ATM and BRCA2 mutations were associated with personal history of breast or pancreatic cancer, whereas PALB2 mutations were associated with family history of breast or pancreatic cancer. Conclusion These findings provide insight into the spectrum of mutations expected in patients with pancreatic cancer referred for cancer predisposition testing. Mutations in eight genes confer high or moderate risk of pancreatic cancer and may prove useful for risk assessment for pancreatic and other cancers. Family and personal histories of breast cancer are strong predictors of germline mutations.

AbstractEpidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.

Limited epidemiological evidence suggests that the etiology of hormone receptor positive (HR+) breast cancer may differ by levels of histologic grade and proliferation. We pooled risk factor and pathology data on 5,905 HR+ breast cancer cases and 26,281 controls from 11 epidemiological studies. Proliferation was determined by centralized automated measures of KI67 in tissue microarrays. Odds ratios (OR), 95% confidence intervals (CI) and p‐values for case–case and case–control comparisons for risk factors in relation to levels of grade and quartiles (Q1–Q4) of KI67 were estimated using polytomous logistic regression models. Case–case comparisons showed associations between nulliparity and high KI67 [OR (95% CI) for Q4 vs. Q1 = 1.54 (1.22, 1.95)]; obesity and high grade [grade 3 vs. 1 = 1.68 (1.31, 2.16)] and current use of combined hormone therapy (HT) and low grade [grade 3 vs. 1 = 0.27 (0.16, 0.44)] tumors. In case–control comparisons, nulliparity was associated with elevated risk of tumors with high but not low levels of proliferation [1.43 (1.14, 1.81) for KI67 Q4 vs. 0.83 (0.60, 1.14) for KI67 Q1]; obesity among women ≥50 years with high but not low grade tumors [1.55 (1.17, 2.06) for grade 3 vs. 0.88 (0.66, 1.16) for grade 1] and HT with low but not high grade tumors [3.07 (2.22, 4.23) for grade 1 vs. 0.85 (0.55, 1.30) for grade 3]. Menarcheal age and family history were similarly associated with HR+ tumors of different grade or KI67 levels. These findings provide insights into the etiologic heterogeneity of HR+ tumors.