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1.  Mammographic Breast Density Response to Aromatase Inhibition 
Purpose
Mammographic breast density (MBD) is decreased by tamoxifen, but the effect of aromatase inhibitors (AI) is less clear.
Experimental Design
We enrolled early stage postmenopausal breast cancer patients initiating adjuvant AI therapy and ascertained mammograms before and at an average 10 months of AI therapy. We matched cases to healthy postmenopausal women (controls) from a large mammography screening cohort on age, baseline body mass index, baseline MBD and interval between mammograms. We estimated change in MBD using a computer-assisted thresholding program (Cumulus) and compared differences between cases and matched controls.
Results
In predominantly white women (96%), we found 14% of the 387 eligible cases had a MBD reduction of at least 5% after an average of 10 months of AI therapy. MBD reductions were associated with higher baseline MBD, AI use for more than 12 months and prior postmenopausal hormone use. Comparing each case to her matched control, there was no evidence of an association of change in MBD with AI therapy (median case-control difference among 369 pairs was −0.1% (10th and 90th percentile: −5.9%, 5.2%) p=0.51). Case-control differences were similar by type of AI (p’s 0.41 and 0.56); prior use of postmenopausal hormones (p=0.85); baseline MBD (p=0.55); or length of AI therapy (p=0.08).
Conclusions
In postmenopausal women treated with AIs, 14% of cases had a MBD reduction of >5%, but these decreases did not differ from matched controls. These data suggest that MBD is not a clinically useful biomarker for predicting the value of AI therapy in white postmenopausal women.
doi:10.1158/1078-0432.CCR-12-2789
PMCID: PMC3630282  PMID: 23468058
aromatase inhibitors; mammographic density; breast density; biomarker
2.  Single nucleotide polymorphism rs1052501 associated with monoclonal gammopathy of undetermined significance and multiple myeloma 
Leukemia  2012;27(2):515-516.
Summary
Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant precursor to multiple myeloma (MM). Though several genetic variants have been identified for MM, none have been identified for MGUS. Recently, Broderick et al. conducted a GWAS of MM and identified three novel loci at 3p22.1 (rs1052501), 7p15.3 (rs4487645) and 2p23.3 (rs6746082) associated with MM risk. We examined the association of these variants with MGUS in a clinic-based case-control study of 391 MGUS cases and 365 controls. We also attempted to replicate the reported association with MM (243 MM cases, 365 controls). We found rs1052501 associated with increased risk of both MGUS (OR=1.32; 95% CI, 1.02 to 1.72; p=0.04) and MM (OR=1.39; 95% CI, 1.04, 1.86; p=0.03). However, there were no associations with the other two loci, rs6746082 and rs4487645, for either MGUS or MM. We identified one genetic variant that may exert its influence on MM through its association with MGUS.
doi:10.1038/leu.2012.232
PMCID: PMC3707297  PMID: 22945773
genetic variation; MGUS; MM; single nucleotide polymorphism
3.  A Novel Automated Mammographic Density Measure and Breast 
Cancer Risk 
Background Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD).
Methods Three clinic-based studies were included: a case–cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case–control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case–control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided.
Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95% CI = 4.6 to 10.4]; OR = 10.7 [95% CI = 7.5 to 15.3]; OR = 2.6 [95% CI = 1.6 to 4.2]; all P trend < .001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95% CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).
Conclusion The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.
doi:10.1093/jnci/djs254
PMCID: PMC3634551  PMID: 22761274
4.  Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk 
Vachon, Celine M. | Scott, Christopher G. | Fasching, Peter A. | Hall, Per | Tamimi, Rulla M. | Li, Jingmei | Stone, Jennifer | Apicella, Carmel | Odefrey, Fabrice | Gierach, Gretchen L. | Jud, Sebastian M. | Heusinger, Katharina | Beckmann, Matthias W. | Pollan, Marina | Fernández-Navarro, Pablo | González-Neira, Anna | Benítez, Javier | van Gils, Carla H. | Lokate, Mariëtte | Onland-Moret, N. Charlotte | Peeters, Petra H.M. | Brown, Judith | Leyland, Jean | Varghese, Jajini S. | Easton, Douglas F. | Thompson, Deborah J. | Luben, Robert N. | Warren, Ruth ML | Wareham, Nicholas J. | Loos, Ruth JF | Khaw, Kay-Tee | Ursin, Giske | Lee, Eunjung | Gayther, Simon A. | Ramus, Susan J. | Eeles, Rosalind A. | Leach, Martin O. | Kwan-Lim, Gek | Couch, Fergus J. | Giles, Graham G. | Baglietto, Laura | Krishnan, Kavitha | Southey, Melissa C. | Le Marchand, Loic | Kolonel, Laurence N. | Woolcott, Christy | Maskarinec, Gertraud | Haiman, Christopher A | Walker, Kate | Johnson, Nichola | McCormack, Valerie A. | Biong, Margarethe | Alnæs, Grethe I.G. | Gram, Inger Torhild | Kristensen, Vessela N. | Børresen-Dale, Anne-Lise | Lindström, Sara | Hankinson, Susan E. | Hunter, David J. | Andrulis, Irene L. | Knight, Julia A. | Boyd, Norman F. | Figueroa, Jonine D. | Lissowska, Jolanta | Wesolowska, Ewa | Peplonska, Beata | Bukowska, Agnieszka | Reszka, Edyta | Liu, JianJun | Eriksson, Louise | Czene, Kamila | Audley, Tina | Wu, Anna H. | Pankratz, V. Shane | Hopper, John L. | dos-Santos-Silva, Isabel
Background
Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biological mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to inter-individual differences in mammographic density measures.
Methods
We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and non-dense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, body mass index (BMI) and menopausal status.
Results
Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (p=0.00005) and adjusted percent density (p=0.001) whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (p=0.003), but not with adjusted dense area (p=0.07).
Conclusion
We identified two common breast cancer susceptibility variants associated with mammographic measures of radio-dense tissue in the breast gland.
Impact
We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association.
doi:10.1158/1055-9965.EPI-12-0066
PMCID: PMC3569092  PMID: 22454379
breast density; breast cancer; genetics; biomarkers; mammography
5.  Aromatase Immunoreactivity Is Increased in Mammographically Dense Regions of the Breast 
Mammographic breast density is one of the strongest risk factors for breast cancer. Unfortunately, the biologic basis underlying this association is unknown. This study compared aromatase expression or immunoreactivity (IR) in core biopsies from mammographically dense versus non-dense regions of the breast to examine whether estrogen synthesis in the breast is associated with mammographic breast density (MBD) and one possible mechanism through which it may influence breast cancer. Eligible participants were 40+ yrs, had a screening mammogram with visible MBD and no prior cancer or current endocrine therapy. Mammograms were used to identify dense and non-dense regions and ultrasound-guided core biopsies were performed to obtain tissue from these regions. Immunostaining for aromatase employed the streptavidin-biotin amplification method and #677 mouse monoclonal antibody. Aromatase IR was scored in terms of extent and intensity of staining for each cell type (stroma, epithelium, adipocytes) on the histologic section. A modified histological (H)-score provided quantitation of aromatase IR in each cell type and overall. Repeated measures analyses evaluated average differences (βH) in H-score in dense versus non-dense tissue within and across cell types. Forty nine women mean age 50 yrs (range: 40 to 82), participated. Aromatase IR was increased in dense (vs. non-dense) tissue in both the stroma (βH =0.58) and epithelium (βH =0.12) (p<0.01). Adipocytes from non-dense tissue, however, had a greater IR compared to those from dense tissue (βH =-0.24, p<0.01). An overall H-score which integrated results from all cell types demonstrated that aromatase IR was twice as great for dense (mean H-score=0.90, SD=0.53) vs. non-dense (mean H-score=0.45, SD=0.39) breast tissue (βH =0.45; p<0.001). Overall, aromatase IR was greater for mammographically dense vs. non-dense tissue and may partly explain how MBD influences breast cancer.
doi:10.1007/s10549-010-0944-6
PMCID: PMC2997154  PMID: 20526739
Aromatase; Breast density; Dense area
7.  Association Between Mammographic Density and Age-Related Lobular Involution of the Breast 
Journal of Clinical Oncology  2010;28(13):2207-2212.
Purpose
Mammographic density and lobular involution are both significant risk factors for breast cancer, but whether these reflect the same biology is unknown. We examined the involution and density association in a large benign breast disease (BBD) cohort.
Patients and Methods
Women in the Mayo Clinic BBD cohort who had a mammogram within 6 months of BBD diagnosis were eligible. The proportion of normal lobules that were involuted was categorized by an expert pathologist as no (0%), partial (1% to 74%), or complete involution (≥ 75%). Mammographic density was estimated as the four-category parenchymal pattern. Statistical analyses adjusted for potential confounders and evaluated modification by parity and age. We corroborated findings in a sample of women with BBD from the Mayo Mammography Health Study (MMHS) with quantitative percent density (PD) and absolute dense and nondense area estimates.
Results
Women in the Mayo BBD cohort (n = 2,667) with no (odds ratio, 1.7; 95% CI, 1.2 to 2.3) or partial (odds ratio, 1.3; 95% CI, 1.0 to 1.6) involution had greater odds of high density (DY pattern) than those with complete involution (P trend < .01). There was no evidence for effect modification by age or parity. Among 317 women with BBD in the MMHS study, there was an inverse association between involution and PD (mean PD, 22.4%, 21.6%, 17.2%, for no, partial, and complete, respectively; P trend = .04) and a strong positive association of involution with nondense area (P trend < .01). No association was seen between involution and dense area (P trend = .56).
Conclusion
We present evidence of an inverse association between involution and mammographic density.
doi:10.1200/JCO.2009.23.4120
PMCID: PMC2860438  PMID: 20351335
8.  Methods for Assessing and Representing Mammographic Density: An Analysis of 4 Case-Control Studies 
American Journal of Epidemiology  2013;179(2):236-244.
To maximize statistical power in studies of mammographic density and breast cancer, it is advantageous to combine data from several studies, but standardization of the density assessment is desirable. Using data from 4 case-control studies, we describe the process of reassessment and the resulting correlation between values, identify predictors of differences in density readings, and evaluate the strength of the association between mammographic density and breast cancer risk using different representations of density values. The pooled analysis included 1,699 cases and 2,422 controls from California (1990–1998), Hawaii (1996–2003), Minnesota (1992–2001), and Japan (1999–2003). In 2010, a single reader reassessed all images for mammographic density using Cumulus software (Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada). The mean difference between original and reassessed percent density values was −0.7% (95% confidence interval: −1.1, −0.3), with a correlation of 0.82 that varied by location (r = 0.80–0.89). Case status, weight status, age, parity, density assessment method, mammogram view, and race/ethnicity were significant determinants of the difference between original and reassessed values; in combination, these factors explained 9.2% of the variation. The associations of mammographic density with breast cancer and the model fits were similar using the original values and the reassessed values but were slightly strengthened when a calibrated value based on 100 reassessed radiographs was used.
doi:10.1093/aje/kwt238
PMCID: PMC3873107  PMID: 24124193
breast cancer; epidemiologic methods; ethnicity; mammographic density; pooling; risk
9.  An Automated Approach for Estimation of Breast Density 
Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method (ABDM) was modified and compared with a semi-automated user-assisted display method (CM) and the Breast Imaging Reporting and Data System (BI-RADS) four-category tissue composition measure for their ability to predict future breast cancer risk. The three estimation methods were evaluated in a matched breast cancer case (n=372) control (n=713) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal (CC) view of the noncancerous breast were acquired on average seven years before diagnosis. Two controls with no prior history of breast cancer from the screening practice were matched to each case on age, number of prior screening mammograms, final screening exam date, menopausal status at this date, interval between earliest and latest available mammograms, and residence. Both Pearson linear correlation (R) and Spearman rank correlation ( r ) coefficients were used for comparing the three methods where appropriate. Conditional logistic regression was used to estimate the risk of breast cancer (odds ratios [ORs] and 95% confidence intervals [CIs]) associated with the quartiles of percent density (ABDM, CM) or BI-RADS category. The area under the receiver operator characteristic curve (AUC) was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures ABDM and CM were highly correlated with each other (R=0.70) but less with BI-RADS (r=0.49 for ABDM and r=0.57 for CM). Risk estimates associated with the lowest to highest quartiles of ABDM were greater in magnitude (ORs: 1.0[ref], 2.3, 3.0, 5.2, p-trend<0.001) than the corresponding quartiles for CM (ORs: 1.0[ref], 1.7, 2.1 and 3.8; p-trend<0.001) and BI-RADS (ORs: 1.0[ref], 1.6, 1.5, 2.6; p-trend<0.001) methods. However, all methods similarly discriminated between case and control status: AUCs were 0.64, 0.63 and 0.61 for ABDM, CM and BI-RADS, respectively. The ABDM is a viable option for quantitatively assessing breast density from digitized film mammograms.
doi:10.1158/1055-9965.EPI-08-0170
PMCID: PMC2705972  PMID: 18990749
Automated density; breast density; methodology
10.  Association of Mammographic Density with Pathology of Subsequent Breast Cancer among Postmenopausal Women 
Background
Limited studies have examined the associations between mammographic density and subsequent breast tumor characteristics.
Methods
Eligible women were part of a case-control study of postmenopausal breast cancer, 40 years and older, who had a routine mammogram four years or more before their diagnosis. Mammographic density (percent density [PD], dense area and nondense area) was estimated using a computer-assisted thresholding program. At the time of cancer diagnosis cases were classified as asymptomatic or symptomatic based on medical record review and breast imaging workup. Pathologic review was performed blinded to the density status. Linear regression models and tests for trend examined the association between pathologic characteristics of the breast tumor (except histology) and the components of density for all participants, and stratified by symptom status at diagnosis.
Results
Of the 286 eligible cases, 77% were 60 years or older and mean PD was 29.5% (SD=14.6%). Density was not significantly associated with tumor size (p=0.22), histologic type (p=0.77), estrogen receptor (ER) (p=0.11) or progesterone receptor (PR) (p=0.37) status, mitotic activity (p=0.12) or nuclear pleomorphism (p=0.09) [p-values for PD]. An inverse association was suggested between tumor grade and PD (31.95%, 30.29%, 26.73% for grade I-III; p for trend= 0.06). The inverse association with tumor grade and its components (nuclear pleomorphism and tubular differentiation) was only evident among the 97 symptomatic women; positive associations of ER (p=0.009) and PR (p=0.04) were also seen with PD only in this subgroup.
Conclusions
The inverse association between tumor grade and PD in the symptomatic population could inform the biology of the association between mammographic density and breast cancer risk.
doi:10.1158/1055-9965.EPI-07-0559
PMCID: PMC2705947  PMID: 18398028
mammographic density; pathology; breast cancer
11.  Genetic modifiers of menopausal hormone replacement therapy and breast cancer risk: A genome-wide interaction study 
Endocrine-related cancer  2013;20(6):875-887.
Women using menopausal hormone therapy (MHT) are at increased risk to develop breast cancer (BC). To detect genetic modifiers of the association between current use of MHT and BC risk, we conducted a meta-analysis of four genome-wide case-only studies followed by replication in eleven case-control studies. We used a case-only design to assess interactions between single nucleotide polymorphisms (SNPs) and current MHT use on risk of overall and lobular BC. The discovery stage included 2,920 cases (541 lobular) from four genome-wide association studies. The top 1,391 SNPs showing P-values for interaction (Pint) <3.0×10−03 were selected for replication using pooled case-control data from eleven studies of the Breast Cancer Association Consortium, including 7,689 cases (676 lobular) and 9,266 controls. Fixed effects meta-analysis was used to derive combined Pint. No SNP reached genome-wide significance in either the discovery or combined stage. We observed effect modification of current MHT use on overall BC risk by two SNPs on chr13 near POMP (combined Pint≤8.9×10−06), two SNPs in SLC25A21 (combined Pint≤4.8×10−05), and three SNPs in PLCG2 (combined Pint≤4.5×10−05). The association between lobular BC risk was potentially modified by one SNP in TMEFF2 (combined Pint≤2.7×10−05), one SNP in CD80 (combined Pint≤8.2×10−06), three SNPs on chr17 near TMEM132E (combined Pint≤2.2×10−06), and two SNPs on chr18 near SLC25A52 (combined Pint≤4.6×10−05). In conclusion, polymorphisms in genes related to solute transportation in mitochondria, transmembrane signaling and immune cell activation are potentially modifying BC risk associated with current use of MHT. These findings warrant replication in independent studies.
doi:10.1530/ERC-13-0349
PMCID: PMC3863710  PMID: 24080446
breast cancer; genetic variation; menopausal hormone therapy; genome-wide
12.  A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis 
Bioinformatics  2013;29(22):2877-2883.
Motivation: Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data.
Results: We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies.
Conclusion: We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well.
Availability and implementation: The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article.
Contact: reesese@vcu.edu or eckel@mayo.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt480
PMCID: PMC3810845  PMID: 23958724
13.  Mammographic texture resemblance generalizes as an independent risk factor for breast cancer 
Introduction
Breast density has been established as a major risk factor for breast cancer. We have previously demonstrated that mammographic texture resemblance (MTR), recognizing the local texture patterns of the mammogram, is also a risk factor for breast cancer, independent of percent breast density. We examine if these findings generalize to another population.
Methods
Texture patterns were recorded in digitalized pre-diagnosis (3.7 years) film mammograms of a nested case–control study within the Dutch screening program (S1) comprising of 245 breast cancers and 250 matched controls. The patterns were recognized in the same study using cross-validation to form resemblance scores associated with breast cancer. Texture patterns from S1 were examined in an independent nested case–control study within the Mayo Mammography Health Study cohort (S2) of 226 cases and 442 matched controls: mammograms on average 8.5 years prior to diagnosis, risk factor information and percent mammographic density (PD) estimated using Cumulus were available. MTR scores estimated from S1, S2 and S1 + S2 (the latter two as cross-validations) were evaluated in S2. MTR scores were analyzed as both quartiles and continuously for association with breast cancer using odds ratios (OR) and adjusting for known risk factors including age, body mass index (BMI), and hormone usage.
Results
The mean ages of S1 and S2 were 58.0 ± 5.7 years and 55.2 ± 10.5 years, respectively. The MTR scores on S1 showed significant capability to discriminate cancers from controls (area under the operator characteristics curve (AUC) = 0.63 ± 0.02, P <0.001), which persisted after adjustment for PD. S2 showed an AUC of 0.63, 0.61, and 0.60 based on PD, MTR scores trained on S2, and MTR scores trained on S1, respectively. When adjusted for PD, MTR scores of S2 trained on S1 showed an association with breast cancer for the highest quartile alone: OR in quartiles of controls as reference; 1.04 (0.59 to 1.81); 0.95 (0.52 to 1.74); 1.84 (1.10 to 3.07) respectively. The combined continuous model with both PD and MTR scores based on S1 had an AUC of 0.66 ± 0.03.
Conclusions
The local texture patterns associated with breast cancer risk in S1 were also an independent risk factor in S2. Additional textures identified in S2 did not significantly improve risk segregation. Hence, the textural patterns that indicated elevated risk persisted under differences in X-ray technology, population demographics, follow-up time and geography.
doi:10.1186/bcr3641
PMCID: PMC4053089  PMID: 24713478
14.  Genetic Susceptibility to Triple Negative Breast Cancer 
Cancer research  2013;73(7):2025-2030.
Triple negative breast cancers (TNBC), defined by the absence of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 expression, account for 12-24% of all breast cancers. TNBC is associated with early recurrence of disease and poor outcome. Germline mutations in the BRCA1 and BRCA2 breast cancer susceptibility genes have been associated with up to 15% of TNBC, and TNBC accounts for 70% of breast tumors arising in BRCA1 mutation carriers and 16-23% of breast tumors in BRCA2 carriers. Whether germline mutations in other breast cancer susceptibility genes also predispose to TNBC remains to be determined. Common variation in a subset of the 72 known breast cancer susceptibility loci identified through genome wide association studies and other large-scale genotyping efforts have also been associated with risk of TNBC (TOX3, ESR1, RAD51L1, TERT, 19p13.1, 20q11, MDM4, 2p24.1, and FTO). Furthermore, variation in the 19p13.1 locus and the MDM4 locus has been associated with TNBC but not other forms of breast cancer suggesting that these are TNBC-specific loci. Thus, TNBC can be distinguished from other breast cancer subtypes by a unique pattern of common and rare germline predisposition alleles. Additional efforts to combine genetic and epidemiological data are needed to better understand the etiology of this aggressive form of breast cancer, to identify prevention and therapeutic targets, and to impact clinical practice through development of risk prediction models.
doi:10.1158/0008-5472.CAN-12-1699
PMCID: PMC3654815  PMID: 23536562
15.  Mapping of the IRF8 gene identifies a 3’ UTR variant associated with risk of chronic lymphocytic leukemia but not other common non-Hodgkin lymphoma subtypes 
Background
Our genome-wide association study (GWAS) of chronic lymphocytic leukemia (CLL) identified 4 highly-correlated intronic variants within the IRF8 gene that were associated with CLL. These results were further supported by a recent meta-analysis of our GWAS with two other GWAS of CLL, supporting the IRF8 gene as a strong candidate for CLL risk.
Methods
To refine the genetic association of CLL risk, we performed Sanger sequencing of IRF8 in 94 CLL cases and 96 controls. We then performed fine-mapping by genotyping 39 variants (of which 10 were identified from sequencing) in 745 CLL cases and 1521 controls. We also assessed these associations with risk of other non-Hodgkin lymphoma (NHL) subtypes.
Results
The strongest association with CLL risk was observed with a common SNP located within the 3’ UTR of IRF8 (rs1044873, log additive odds ratio = 0.7, P=1.81×10−6). This SNP was not associated with the other NHL subtypes (all P>0.05).
Conclusions
We provide evidence that rs1044873 in the IRF8 gene accounts for the initial GWAS signal for CLL risk. This association appears to be unique to CLL with little support for association with other common NHL subtypes. Future work is needed to assess functional role of IRF8 in CLL etiology.
Impact
These data provide support that a functional variant within the 3’ UTR of IRF8 may be driving the GWAS signal seen on 16q24.1 for CLL risk.
doi:10.1158/1055-9965.EPI-12-1217
PMCID: PMC3596428  PMID: 23307532
CLL; NHL; SNPs; IRF8; risk locus
16.  Differences in the distribution of cytogenetic subtypes between multiple myeloma patients with and without a family history of monoclonal gammopathy and multiple myeloma 
European journal of haematology  2013;91(3):193-195.
We previously reported an increased risk of monoclonal gammopathy of undetermined significance (MGUS) in first-degree relatives of MGUS and multiple myeloma patients. Here, we examine whether primary cytogenetic categories of myeloma differ between patients with and without a family history of MGUS or myeloma. We studied 201 myeloma patients with available data on family history and molecular cytogenetic classification. Myeloma with trisomies was more common in probands who had an affected first-degree relative with MGUS or myeloma compared to those without a family history (46.9% vs. 33.5%, p=0.125); however, the difference was not statistically significant. Additional studies on the cytogenetic types of myeloma associated with familial tendency are needed.
doi:10.1111/ejh.12133
PMCID: PMC3762589  PMID: 23647020
Multiple myeloma; MGUS; family history; cytogenetics
17.  Genome-wide Association Study Identifies Multiple Risk Loci for Chronic Lymphocytic Leukemia 
Berndt, Sonja I. | Skibola, Christine F. | Joseph, Vijai | Camp, Nicola J. | Nieters, Alexandra | Wang, Zhaoming | Cozen, Wendy | Monnereau, Alain | Wang, Sophia S. | Kelly, Rachel S. | Lan, Qing | Teras, Lauren R. | Chatterjee, Nilanjan | Chung, Charles C. | Yeager, Meredith | Brooks-Wilson, Angela R. | Hartge, Patricia | Purdue, Mark P. | Birmann, Brenda M. | Armstrong, Bruce K. | Cocco, Pierluigi | Zhang, Yawei | Severi, Gianluca | Zeleniuch-Jacquotte, Anne | Lawrence, Charles | Burdette, Laurie | Yuenger, Jeffrey | Hutchinson, Amy | Jacobs, Kevin B. | Call, Timothy G. | Shanafelt, Tait D. | Novak, Anne J. | Kay, Neil E. | Liebow, Mark | Wang, Alice H. | Smedby, Karin E | Adami, Hans-Olov | Melbye, Mads | Glimelius, Bengt | Chang, Ellen T. | Glenn, Martha | Curtin, Karen | Cannon-Albright, Lisa A. | Jones, Brandt | Diver, W. Ryan | Link, Brian K. | Weiner, George J. | Conde, Lucia | Bracci, Paige M. | Riby, Jacques | Holly, Elizabeth A. | Smith, Martyn T. | Jackson, Rebecca D. | Tinker, Lesley F. | Benavente, Yolanda | Becker, Nikolaus | Boffetta, Paolo | Brennan, Paul | Foretova, Lenka | Maynadie, Marc | McKay, James | Staines, Anthony | Rabe, Kari G. | Achenbach, Sara J. | Vachon, Celine M. | Goldin, Lynn R | Strom, Sara S. | Lanasa, Mark C. | Spector, Logan G. | Leis, Jose F. | Cunningham, Julie M. | Weinberg, J. Brice | Morrison, Vicki A. | Caporaso, Neil E. | Norman, Aaron D. | Linet, Martha S. | De Roos, Anneclaire J. | Morton, Lindsay M. | Severson, Richard K. | Riboli, Elio | Vineis, Paolo | Kaaks, Rudolph | Trichopoulos, Dimitrios | Masala, Giovanna | Weiderpass, Elisabete | Chirlaque, María-Dolores | Vermeulen, Roel C H | Travis, Ruth C. | Giles, Graham G. | Albanes, Demetrius | Virtamo, Jarmo | Weinstein, Stephanie | Clavel, Jacqueline | Zheng, Tongzhang | Holford, Theodore R | Offit, Kenneth | Zelenetz, Andrew | Klein, Robert J. | Spinelli, John J. | Bertrand, Kimberly A. | Laden, Francine | Giovannucci, Edward | Kraft, Peter | Kricker, Anne | Turner, Jenny | Vajdic, Claire M. | Ennas, Maria Grazia | Ferri, Giovanni M. | Miligi, Lucia | Liang, Liming | Sampson, Joshua | Crouch, Simon | Park, Ju-hyun | North, Kari E. | Cox, Angela | Snowden, John A. | Wright, Josh | Carracedo, Angel | Lopez-Otin, Carlos | Bea, Silvia | Salaverria, Itziar | Martin, David | Campo, Elias | Fraumeni, Joseph F. | de Sanjose, Silvia | Hjalgrim, Henrik | Cerhan, James R. | Chanock, Stephen J. | Rothman, Nathaniel | Slager, Susan L.
Nature genetics  2013;45(8):868-876.
doi:10.1038/ng.2652
PMCID: PMC3729927  PMID: 23770605
18.  A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11 
Siddiq, Afshan | Couch, Fergus J. | Chen, Gary K. | Lindström, Sara | Eccles, Diana | Millikan, Robert C. | Michailidou, Kyriaki | Stram, Daniel O. | Beckmann, Lars | Rhie, Suhn Kyong | Ambrosone, Christine B. | Aittomäki, Kristiina | Amiano, Pilar | Apicella, Carmel | Baglietto, Laura | Bandera, Elisa V. | Beckmann, Matthias W. | Berg, Christine D. | Bernstein, Leslie | Blomqvist, Carl | Brauch, Hiltrud | Brinton, Louise | Bui, Quang M. | Buring, Julie E. | Buys, Saundra S. | Campa, Daniele | Carpenter, Jane E. | Chasman, Daniel I. | Chang-Claude, Jenny | Chen, Constance | Clavel-Chapelon, Françoise | Cox, Angela | Cross, Simon S. | Czene, Kamila | Deming, Sandra L. | Diasio, Robert B. | Diver, W. Ryan | Dunning, Alison M. | Durcan, Lorraine | Ekici, Arif B. | Fasching, Peter A. | Feigelson, Heather Spencer | Fejerman, Laura | Figueroa, Jonine D. | Fletcher, Olivia | Flesch-Janys, Dieter | Gaudet, Mia M. | Gerty, Susan M. | Rodriguez-Gil, Jorge L. | Giles, Graham G. | van Gils, Carla H. | Godwin, Andrew K. | Graham, Nikki | Greco, Dario | Hall, Per | Hankinson, Susan E. | Hartmann, Arndt | Hein, Rebecca | Heinz, Judith | Hoover, Robert N. | Hopper, John L. | Hu, Jennifer J. | Huntsman, Scott | Ingles, Sue A. | Irwanto, Astrid | Isaacs, Claudine | Jacobs, Kevin B. | John, Esther M. | Justenhoven, Christina | Kaaks, Rudolf | Kolonel, Laurence N. | Coetzee, Gerhard A. | Lathrop, Mark | Le Marchand, Loic | Lee, Adam M. | Lee, I-Min | Lesnick, Timothy | Lichtner, Peter | Liu, Jianjun | Lund, Eiliv | Makalic, Enes | Martin, Nicholas G. | McLean, Catriona A. | Meijers-Heijboer, Hanne | Meindl, Alfons | Miron, Penelope | Monroe, Kristine R. | Montgomery, Grant W. | Müller-Myhsok, Bertram | Nickels, Stefan | Nyante, Sarah J. | Olswold, Curtis | Overvad, Kim | Palli, Domenico | Park, Daniel J. | Palmer, Julie R. | Pathak, Harsh | Peto, Julian | Pharoah, Paul | Rahman, Nazneen | Rivadeneira, Fernando | Schmidt, Daniel F. | Schmutzler, Rita K. | Slager, Susan | Southey, Melissa C. | Stevens, Kristen N. | Sinn, Hans-Peter | Press, Michael F. | Ross, Eric | Riboli, Elio | Ridker, Paul M. | Schumacher, Fredrick R. | Severi, Gianluca | dos Santos Silva, Isabel | Stone, Jennifer | Sund, Malin | Tapper, William J. | Thun, Michael J. | Travis, Ruth C. | Turnbull, Clare | Uitterlinden, Andre G. | Waisfisz, Quinten | Wang, Xianshu | Wang, Zhaoming | Weaver, JoEllen | Schulz-Wendtland, Rüdiger | Wilkens, Lynne R. | Van Den Berg, David | Zheng, Wei | Ziegler, Regina G. | Ziv, Elad | Nevanlinna, Heli | Easton, Douglas F. | Hunter, David J. | Henderson, Brian E. | Chanock, Stephen J. | Garcia-Closas, Montserrat | Kraft, Peter | Haiman, Christopher A. | Vachon, Celine M.
Human Molecular Genetics  2012;21(24):5373-5384.
Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of ER-negative disease to date, comprising 4754 ER-negative cases and 31 663 controls from three GWAS: NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (2188 ER-negative cases; 25 519 controls of European ancestry), Triple Negative Breast Cancer Consortium (TNBCC) (1562 triple negative cases; 3399 controls of European ancestry) and African American Breast Cancer Consortium (AABC) (1004 ER-negative cases; 2745 controls). We performed in silico replication of 86 SNPs at P ≤ 1 × 10-5 in an additional 11 209 breast cancer cases (946 with ER-negative disease) and 16 057 controls of Japanese, Latino and European ancestry. We identified two novel loci for breast cancer at 20q11 and 6q14. SNP rs2284378 at 20q11 was associated with ER-negative breast cancer (combined two-stage OR = 1.16; P = 1.1 × 10−8) but showed a weaker association with overall breast cancer (OR = 1.08, P = 1.3 × 10–6) based on 17 869 cases and 43 745 controls and no association with ER-positive disease (OR = 1.01, P = 0.67) based on 9965 cases and 22 902 controls. Similarly, rs17530068 at 6q14 was associated with breast cancer (OR = 1.12; P = 1.1 × 10−9), and with both ER-positive (OR = 1.09; P = 1.5 × 10−5) and ER-negative (OR = 1.16, P = 2.5 × 10−7) disease. We also confirmed three known loci associated with ER-negative (19p13) and both ER-negative and ER-positive breast cancer (6q25 and 12p11). Our results highlight the value of large-scale collaborative studies to identify novel breast cancer risk loci.
doi:10.1093/hmg/dds381
PMCID: PMC3510753  PMID: 22976474
19.  Common variants within 6p21.31 locus are associated with chronic lymphocytic leukaemia and potentially other non-Hodgkin lymphoma subtypes 
British journal of haematology  2012;159(5):572-576.
Summary
A recent meta-analysis of three genome-wide association studies of chronic lymphocytic leukaemia (CLL) identified two common variants at the 6p21.31 locus that are associated with CLL risk. To verify and further explore the association of these variants with other non-Hodgkin lymphoma (NHL) subtypes, we genotyped 1196 CLL cases, 1699 NHL cases, and 2410 controls. We found significant associations between the 6p21.31 variants and CLL risk (rs210134: P=0.01; rs210142: P=6.8×10−3). These variants also showed a trend towards association with some of the other NHL subtypes. Our results validate the prior work and support specific genetic pathways for risk among NHL subtypes.
doi:10.1111/bjh.12070
PMCID: PMC3614403  PMID: 23025533
CLL; NHL; SNPs; BAK1; risk locus
20.  Mammographic density and risk of breast cancer by age and tumor characteristics 
Breast Cancer Research : BCR  2013;15(6):R104.
Introduction
Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.
Methods
Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55–64, and ≥65 years).
Results
MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55–64 and ≥65 years (Page-interaction = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (Page-interaction = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.
Conclusion
MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.
doi:10.1186/bcr3570
PMCID: PMC3978749  PMID: 24188089
21.  Higher alcohol intake may modify the association between mammographic density and breast cancer: An analysis of three case-control studies 
Cancer epidemiology  2012;36(5):458-460.
Alcohol consumption and mammographic density are established risk factors for breast cancer. This study examined whether the association of mammographic density with breast cancer varies by alcohol intake. Mammographic density was assessed in digitized images for 1,207 cases and 1,663 controls from three populations (Japan, Hawaii, California) using a computer-assisted method. Associations were estimated by logistic regression. When comparing ever to never drinking, mean density was similar and consumption was not associated with breast cancer risk. However, within the Hawaii/Japan subset, women consuming >1 drink/day had a non-significantly elevated relative risk compared to never drinkers. Also in the Hawaii/Japan population, alcohol intake only modified the association between mammographic density and breast cancer in women consuming >1 drink/day (pinteraction=0.05) with significant risk estimates of 3.65 and 6.58 for the 2nd and 3rd density tertiles as compared to 1.57 and 1.61 for never drinkers in Hawaii/Japan. Although these findings suggest a stronger association between mammographic density and breast cancer risk for alcohol consumers, the small number of cases requires caution in interpreting the results.
doi:10.1016/j.canep.2012.06.007
PMCID: PMC3438372  PMID: 22785031
breast cancer; alcohol; mammographic density; case-control study; pooling
22.  Identification of a novel percent mammographic density locus at 12q24 
Human Molecular Genetics  2012;21(14):3299-3305.
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.
doi:10.1093/hmg/dds158
PMCID: PMC3384385  PMID: 22532574
23.  Tissue composition of mammographically dense and non-dense breast tissue 
Mammographic density is a strong risk factor for breast cancer but its underlying biology in healthy women is not well-defined. Using a novel collection of core biopsies from mammographically dense versus non-dense regions of the breasts of healthy women, we examined histologic and molecular differences between these two tissue types. Eligible participants were 40+ years, had a screening mammogram and no prior breast cancer or current endocrine therapy. Mammograms were used to identify dense and non-dense regions and ultrasound-guided core biopsies were performed to obtain tissue from these regions. Quantitative assessment of epithelium, stroma, and fat was performed on dense and non-dense cores. Molecular markers including Ki-67, estrogen receptor (ER) and progesterone receptor (PR) were also assessed for participants who had >0% epithelial area in both dense and non-dense tissue. Signed rank test was used to assess within woman differences in epithelium, stroma and fat between dense and non-dense tissue. Differences in molecular markers (Ki-67, ER, and PR) were analyzed using generalized linear models, adjusting for total epithelial area. Fifty-nine women, mean age 51 years (range: 40–82), were eligible for analyses. Dense tissue was comprised of greater mean areas of epithelium and stroma (1.1 and 9.2 mm2 more, respectively) but less fat (6.0 mm2 less) than non-dense tissue. There were no statistically significant differences in relative expression of Ki-67 (P = 0.82), ER (P = 0.09), or PR (P = 0.96) between dense and non-dense tissue. Consistent with prior reports, we found that mammographically dense areas of the breast differ histologically from non-dense areas, reflected in greater proportions of epithelium and stroma and lesser proportions of fat in the dense compared to non-dense breast tissue. Studies of both epithelial and stromal components are important in understanding the association between mammographic density and breast cancer risk.
doi:10.1007/s10549-011-1727-4
PMCID: PMC3707294  PMID: 21877142
Mammographic density; Histology; Stroma; Breast cancer
24.  Healthy Women’s Motivators and Barriers to Participation in a Breast Cancer Cohort Study: A Qualitative Study 
Annals of epidemiology  2009;19(7):484-493.
Background
This focus group study describes motivators and barriers to participation in the Mayo Mammography Health Study (MMHS), a large-scale longitudinal study examining the causal association of breast density with breast cancer, involving completion of a survey, providing access to a residual blood sample for genetic analyses, and sharing their results from a screening mammogram. These women would then be followed long-term for breast cancer incidence and mortality.
Methods
48 Women participated in six focus groups, four with MMHS non-respondents (N=27), and two with MMHS respondents (N=21). Major themes were summarized using content analysis. Social Cognitive Theory (SCT) was used as a framework for interpretation of the findings.
Results
Barriers to participation among MMHS non-respondents were: 1) lack of confidence in their ability to fill out the survey accurately (self-efficacy); 2) lack of perceived personal connection to the study or value of participation (expectancies); and 3) fear related to some questions about perceived cancer risk and worry/concern (emotional coping responses). Among MMHS respondents, personal experience with cancer was reported as a primary motivator for participation (expectancies).
Conclusions
Application of a theoretical model such as SCT to the development of a study recruitment plan could be used to improve rates of study participation and provide a reproducible and evolvable strategy.
doi:10.1016/j.annepidem.2009.01.002
PMCID: PMC3682676  PMID: 19269854
Focus groups; participation; epidemiology; recruitment; social cognitive theory; breast cancer; mammography; qualitative
25.  Increased prevalence of light chain monoclonal gammopathy of undetermined significance (LC-MGUS) in first-degree relatives of individuals with multiple myeloma 
British Journal of Haematology  2012;157(4):472-475.
Previously, we reported increased risk of heavy-chain (HC) monoclonal gammopathy of undetermined significance (MGUS) among first-degree (1°) relatives of multiple myeloma (MM) or HC-MGUS probands. This study investigated whether there was comparable risk for light-chain (LC) MGUS among 911 relatives of the same HC-MGUS/MM probands versus a reference population of 21,463. Seventeen 1° relatives had LC-MGUS (adjusted prevalence =1.7%, 95% CI=0.9%–2.6%). There was increased risk of LC-MGUS in relatives of MM probands (RR=3.4, 95% CI=2.0–5.5). We saw no increased risk in relatives of HC-MGUS probands. We conclude that the prevalence of LC-MGUS is significantly higher among 1° relatives of MM probands.
PMCID: PMC3375594  PMID: 22629552
MGUS; myeloma; epidemiology

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