Literature DB >> 26884359

Genetic predisposition to ductal carcinoma in situ of the breast.

Christos Petridis1,2, Mark N Brook3, Vandna Shah4, Kelly Kohut5, Patricia Gorman6, Michele Caneppele7, Dina Levi8, Efterpi Papouli9, Nick Orr10, Angela Cox11, Simon S Cross12, Isabel Dos-Santos-Silva13, Julian Peto14, Anthony Swerdlow15,16, Minouk J Schoemaker17, Manjeet K Bolla18, Qin Wang19, Joe Dennis20, Kyriaki Michailidou21, Javier Benitez22,23, Anna González-Neira24, Daniel C Tessier25, Daniel Vincent26, Jingmei Li27, Jonine Figueroa28, Vessela Kristensen29,30,31, Anne-Lise Borresen-Dale32,33, Penny Soucy34, Jacques Simard35, Roger L Milne36,37, Graham G Giles38,39, Sara Margolin40, Annika Lindblom41, Thomas Brüning42, Hiltrud Brauch43,44,45, Melissa C Southey46, John L Hopper47, Thilo Dörk48, Natalia V Bogdanova49, Maria Kabisch50, Ute Hamann51, Rita K Schmutzler52,53,54, Alfons Meindl55, Hermann Brenner56,57,58, Volker Arndt59, Robert Winqvist60,61, Katri Pylkäs62,63, Peter A Fasching64,65, Matthias W Beckmann66, Jan Lubinski67, Anna Jakubowska68, Anna Marie Mulligan69,70, Irene L Andrulis71,72, Rob A E M Tollenaar73, Peter Devilee74,75, Loic Le Marchand76, Christopher A Haiman77, Arto Mannermaa78,79,80, Veli-Matti Kosma81,82,83, Paolo Radice84, Paolo Peterlongo85, Frederik Marme86,87, Barbara Burwinkel88,89, Carolien H M van Deurzen90, Antoinette Hollestelle91, Nicola Miller92, Michael J Kerin93, Diether Lambrechts94,95, Giuseppe Floris96, Jelle Wesseling97, Henrik Flyger98, Stig E Bojesen99,100,101, Song Yao102, Christine B Ambrosone103, Georgia Chenevix-Trench104, Thérèse Truong105,106, Pascal Guénel107,108, Anja Rudolph109, Jenny Chang-Claude110, Heli Nevanlinna111, Carl Blomqvist112, Kamila Czene113, Judith S Brand114, Janet E Olson115, Fergus J Couch116, Alison M Dunning117, Per Hall118, Douglas F Easton119,120, Paul D P Pharoah121,122, Sarah E Pinder123, Marjanka K Schmidt124, Ian Tomlinson125, Rebecca Roylance126, Montserrat García-Closas127,128, Elinor J Sawyer129.   

Abstract

BACKGROUND: Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer. It is often associated with invasive ductal carcinoma (IDC), and is considered to be a non-obligate precursor of IDC. It is not clear to what extent these two forms of cancer share low-risk susceptibility loci, or whether there are differences in the strength of association for shared loci.
METHODS: To identify genetic polymorphisms that predispose to DCIS, we pooled data from 38 studies comprising 5,067 cases of DCIS, 24,584 cases of IDC and 37,467 controls, all genotyped using the iCOGS chip.
RESULTS: Most (67 %) of the 76 known breast cancer predisposition loci showed an association with DCIS in the same direction as previously reported for invasive breast cancer. Case-only analysis showed no evidence for differences between associations for IDC and DCIS after considering multiple testing. Analysis by estrogen receptor (ER) status confirmed that loci associated with ER positive IDC were also associated with ER positive DCIS. Analysis of DCIS by grade suggested that two independent SNPs at 11q13.3 near CCND1 were specific to low/intermediate grade DCIS (rs75915166, rs554219). These associations with grade remained after adjusting for ER status and were also found in IDC. We found no novel DCIS-specific loci at a genome wide significance level of P < 5.0x10(-8).
CONCLUSION: In conclusion, this study provides the strongest evidence to date of a shared genetic susceptibility for IDC and DCIS. Studies with larger numbers of DCIS are needed to determine if IDC or DCIS specific loci exist.

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Year:  2016        PMID: 26884359      PMCID: PMC4756509          DOI: 10.1186/s13058-016-0675-7

Source DB:  PubMed          Journal:  Breast Cancer Res        ISSN: 1465-5411            Impact factor:   6.466


Background

Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer including invasive ductal/no special type carcinomas (IDC). Since the introduction of screening mammography there has been a 7-fold increase in reported DCIS incidence in the USA, primarily in postmenopausal women [1], with about 20 % of screen-detected tumors being DCIS [2]. Approximately 45–78 % of all invasive breast cancers are associated with DCIS [3, 4]. It is hypothesized in the majority of these cases that the invasive component has arisen from the DCIS as they generally share the same somatic genetic changes. The proportion of IDC associated with DCIS varies depending on subtype, with luminal and human epidermal growth factor receptor 2 (HER2)-positive IDC having more frequent DCIS (53 % and 63 %, respectively) than invasive basal breast cancers (33 %) [5]. As most DCIS is treated surgically, the natural progression of untreated DCIS is not known. However, in one small study of patients with predominantly low-grade DCIS misdiagnosed as benign breast disease and who received no surgical intervention, 6 out of 13 patients developed ipsilateral invasive carcinoma with mean time to the development of invasive carcinoma being 9.0 years [6]. In two specific DCIS trials in which DCIS was treated with breast-conserving surgery alone with no radiotherapy, long-term follow up shows that up to 30 % of women develop a recurrence (half of which will be DCIS and half invasive cancer) by 10 years [7]. Methods for accurately predicting the behavior of DCIS are poor [8]. Although grade has not been shown to be a good predictor of recurrence many clinicians use this classification to determine the use of radiotherapy following breast-conserving surgery. There is a strong correlation between the grade of the in situ and co-existing invasive components in IDC, suggesting that DCIS does not progress from low through to high grade before becoming invasive [9, 10]. Most non-genetic risk factors for breast cancer have similar associations with DCIS and IDC, supporting the notion that DCIS is a precursor of invasive cancer [11, 12]. There is also evidence from epidemiological studies that there is an inherited predisposition to DCIS. Women with DCIS have been shown to be 2.4 times (95 % CI 0.8, 7.2) more likely to have an affected mother and sister with breast cancer than controls [13]. Furthermore, there is evidence from a study of almost 40,000 women that the familial relative risk of DCIS is greater than that of invasive breast cancer. For women aged 30–49 years with a family history of breast cancer the odds ratio (OR) for developing DCIS was calculated as 2.4 (95 % CI 1.1, 4.9) compared to 1.7 (95 % CI 0.9, 3.4) for invasive cancer. For women aged 50 years and above the risks were slightly reduced, but still higher for DCIS (OR = 2.2, 95 % CI 1.0, 4.2) than invasive disease (OR = 1.5, 95 % CI 1.0, 2.2) [14]. However, this was not confirmed in the Million Women Study, in which the association with family history was similar for DCIS and IDC [12]. A small part of this inherited predisposition is explained by BRCA1/2 mutations, as mutations in these genes are found in a similar proportion of DCIS and invasive breast cancer cases [15]. For low-risk common breast cancer predisposition alleles most of the initial breast cancer association studies have not been powered to identify associations with DCIS, so it is not clear whether all the low-risk susceptibility loci that have been identified are associated with DCIS and what the strength of any associations are. It is now evident that some low-risk susceptibility loci are associated with different pathological subtypes of breast cancer and support the hypothesis that breast tumor subtypes arise through distinct molecular pathways [16-18]. In order to identify further low-risk susceptibility loci, it will be necessary to look at specific morphological subtypes including DCIS and the cytonuclear grade and estrogen receptor (ER) status of the disease. In this study we analyzed 3,078 cases of pure DCIS collected through the ICICLE study (a study to Investigate the genetics of In situ Carcinoma of the ductaL subtype) and performed a meta-analysis with 2,352 in situ cases collected through the Breast Cancer Association Consortium (BCAC). Our aims were to assess whether any of the known low-risk breast susceptibility alleles have different associations for DCIS and IDC, and to identify if there are any DCIS-specific low-risk alleles.

Methods

Ethics statement

All studies were performed with ethical committee approval (listed in acknowledgements) and subjects participated in the studies after providing informed consent.

Study populations

Cases came from ICICLE (MREC 08/H0502/4), a UK study of DCIS, and from 37 studies forming part of the BCAC included in the Collaborative Oncological Gene-Environment Study (COGS) [19] (Additional file 1). The ICICLE study recruited patients from participating centers throughout the UK with the aim of identifying predisposition genes for DCIS. Patients aged 60 years or less at the time of diagnosis, with a current or past history of DCIS (without invasive disease of any histological subtype) were eligible. A total of 3,078 subjects were recruited following identification from local pathology reports in 97 UK hospitals. All cases were genotyped with the iCOGS chip and compared to 5,000 UK controls selected from four UK studies (BBCS 1,231 controls, SBCS 704 controls, UKBGS 370 controls, SEARCH 2,695 controls) participating in BCAC (Additional file 2) and already typed on the iCOGS chip. Controls were randomly selected prior to analysis, and were excluded from case–control comparisons with BCAC cases from the originating study. After excluding individuals based on genotyping quality (see subsection “Genotyping and analysis”) and non-European ancestry, data for the ICICLE study available for analysis included 2,715 subjects with DCIS (cases) and 4,813 controls. Women with all types of breast cancer were recruited into the BCAC studies. Pathological information in BCAC was collected in the individual studies but was also combined and checked through standardized data control in a central database. A total of 2,352 subjects with DCIS were identified in the central BCAC pathology database (see Additional file 3 for number of cases by study). Controls came from the 37 BCAC studies (37,654 in total).

Genotyping and analysis

After DNA extraction from peripheral blood, ICICLE samples were genotyped on the iCOGS custom Illumina iSelect array (Illumina, San Diego, CA), which contains 211,155 single nucleotide polymorphisms (SNPs), at King’s College London. The remaining cases and controls were genotyped as part of the COGS project described in detail elsewhere [19]. The ICICLE cases were analyzed using the same quality control (QC) criteria as the COGS project. Briefly, genotypes were called using Illumina’s proprietary GenCall algorithm and 10,000 SNPs were manually inspected to verify the algorithm calls. Individuals were excluded if genotypically non-European or not female, or had an overall call rate <95 %. SNPs were excluded with a Gen-Train score <0.4, call rate <95 % (call rate <99 % if minor allele frequency (MAF) was <0.1) and Hardy Weinberg equilibrium (HWE) value of P <10-7 or evidence of poor clustering on inspection of cluster plots. All SNPs with MAF <0.01 were excluded. A cryptic relatedness analysis of the whole dataset was performed using 46,789 uncorrelated SNPs and led to the exclusion of 28 cases and 18 controls due to relatedness between the ICICLE and BCAC samples (PIHAT >0.1875). For ICICLE cases and controls, principal component analysis (PCA) was carried out on a subset of 46,789 uncorrelated SNPs and individuals or groups distinct from the main cluster (327 cases and 164 controls) were excluded using the first five principal components (PCs) (Additional file 4). Following removal of outliers, the PCA was repeated and the first five PCs were included as covariates in the analysis. The adequacy of the case–control matching was evaluated using quantile-quantile plots of test statistics and the inflation factor (λ) calculated using 37,289 uncorrelated SNPs that were not selected by BCAC and were not within one of the four common fine-mapping regions, to minimize selection for SNPs associated with breast cancer (Additional file 5). As the majority of the SNPs on the iCOGS array are associated with breast, ovarian or prostate cancer, the SNPs selected for this analysis were taken from the set of prostate cancer SNPs, with the assumption that these SNPs were more likely to be representative of common SNPs in terms of population structure in our study. For each SNP, we estimated a per-allele OR and reported corresponding 95 % CI using logistic regression analysis, including the five PCs as covariates, using PLINK v1.07 (http://pngu.mgh.harvard.edu/~purcell/plink/). Genotyping and analysis of BCAC studies have been described in detail elsewhere [19]. In brief, data were analyzed using the Genotype Library and Utilities (GLU) package to estimate per-allele ORs for each SNP using unconditional logistic regression. All analyses were performed in subjects of European ancestry (determined by PC analyses) and adjusted for study and seven principal components. Case–control ORs for DCIS cases vs controls from BCAC and ICICLE were combined using inverse variance-weighted fixed-effects meta-analysis, as implemented in METAL [20]. Case-only analyses were also carried out to compare genotype frequencies for (1) ER-positive (ER+) vs ER-negative (ER–) DCIS, (2) high grade DCIS vs low and intermediate grade DCIS, and (3) DCIS vs IDC (see Additional file 3 for number of cases by study), (4) DCIS diagnosis in patients <50 years of age vs DCIS diagnosis in patients ≥50 years, and were used as a test for heterogeneity of ORs by tumor subtype/age (see Additional file 6 for number of cases by group). Only studies with data on both subtypes contributed to case-only analysis comparing these subtypes. Similar case-only analyses were performed for the IDC cases in these studies to assess whether any heterogeneity evident in DCIS also occurred in IDC. Novel SNPs showing the strongest evidence of association with DCIS (P <6 × 10-6) in the meta-analysis (after excluding previously reported loci) were genotyped in a phase II analysis at LGC Genomics (LGC, Teddington, UK). The phase II samples consisted of 653 DCIS cases from the ICICLE and Breakthrough Generation Studies and 1,882 controls from the ICICLE study not previously genotyped on the iCOGS chip. All individuals included in the analysis were of European ancestry (self-reported). For the known breast cancer predisposition loci P <0.00066 was considered statistically significant (with Bonferroni correction for multiple testing on 76 known loci). All of the known breast cancer susceptibility loci were included in the iCOGS chip with the exception of rs2284378 (20q11), which was identified as an ER– breast cancer predisposition SNP after the iCOGS chip was developed [21].

Assessment of grade and ER status

For the ICICLE study, information on cytonuclear grade of DCIS was available for 2,578 cases, mostly from the local histopathology reports. In 200 cases where the grade data were missing from the report but the tumor block was available, an H&E section was cut and the DCIS was graded by the study histopathologist (SEP) according to UK and College of American Pathologists guidelines [22]. Data on grade of DCIS were available from histopathology reports for 828 BCAC cases. A subset of 81 ICICLE cases, graded in the pathology report and with a tumor block available, were examined to assess the reliability of the cytonuclear grade provided by the pathology reports. In the majority of cases (86.5 %) grade was concordant with the pathology report. Nine cases were re-graded as low/intermediate grade and two cases as high grade. As the study pathologist re-graded the samples on a single H&E section, rather than all the blocks from an individual case, and in some cases on re-excision specimens with residual disease rather than the original excision specimen, the grade reported in the pathology report, if available, was used for the purposes of this study. ER status from local histopathology reports was available for 1,086 ICICLE cases. For the remaining 781 ICICLE cases where the tumor block was available, immunohistochemistry was performed on 3-μM sections, which were incubated at 60 °C for 1 h prior to automated staining using the VENTANA®. Estrogen receptor staining was carried out using CONFIRM™ anti-estrogen receptor (SP1) rabbit monoclonal primary antibody (Catalog number 790-4324) with no variation to the recommended protocol. ER staining was scored by three independent reviewers (CP, VS, DLe) using the Allred method, and any discrepancies were reviewed by the study histopathologist (SEP). DCIS with an Allred score ≥3 was considered ER+ and DCIS with scores of 0–2 (approximately equivalent to <1 % of nuclei) was regarded as ER–. ER status was available on 965 cases from BCAC (Additional file 6).

Results

Assessment of known breast cancer susceptibility loci for association with DCIS

For the majority of known loci (n = 46) the risk allele for invasive breast cancer is the minor allele. For the ORs presented here the reference allele was set as the non-risk allele to make it clear whether the association with DCIS was in the same direction as previously published for invasive breast cancer. Thus, ORs for DCIS will be >1 if in the same direction as invasive disease and <1 if in the opposite direction. Of the 76 known common breast cancer susceptibility loci genotyped on the iCOGS array, 51 were associated with DCIS (P <0.05), with the effect in the same direction as previously reported in IDC (Fig. 1 and Additional file 7). Sixteen SNPs were significantly associated with DCIS (P <0.00066) with three being genome-wide significant (P <5 × 10-8, Table 1). The strongest associations were with for loci in FGFR2 (rs2981579: OR 1.29, 95 % CI 1.24, 1.35; P = 9.0 × 10-30) and TOX3 (rs3803662: OR 1.15, 95 % CI 1.1, 1.21; P = 1.7 × 10-8).
Fig. 1

Known breast cancer predisposition loci for ductal carcinoma in situ plotted according to the risk allele for invasive disease. Odds ratios >1 indicate that the association is in the same direction as previously published for invasive breast cancer

Table 1

Loci showing a significant association with ductal carcinoma in situ (DCIS) at P <0.00066

ChromosomeSNPLocusRAFDCIS vs controls (meta-analysis)IDC vs controlsCase-only DCIS vs IDC
ControlsOR(95 % CI) P OR(95 % CI) P P-Het
10rs2981579FGFR20.401.29(1.24, 1.35)9.0 × 10-30 1.24(1.21, 1.28)6.1 × 10-66 0.14
10rs2981582FGFR20.381.28(1.23, 1.34)1.8 × 10-27 1.23(1.20, 1.26)2.1 × 10-59 0.21
16rs3803662TOX30.261.15(1.10, 1.21)1.7 × 10-8 1.23(1.20, 1.27)1.5 × 10-50 0.69
5rs889312MAP3K10.281.14(1.09, 1.20)6.9 × 10-8 1.11(1.08, 1.14)2.2 × 10-14 0.13
3rs4973768SLC4A70.471.13(1.08, 1.18)9.1 × 10-8 1.09(1.07, 1.12)8.2 × 10-13 0.58
5rs109416795p120.251.14(1.09, 1.20)1.3 × 10-7 1.14(1.11, 1.18)1.2 × 10-20 0.90
3rs3821902ATXN70.131.16(1.09, 1.23)3.0 × 10-6 1.06(1.02, 1.09)0.00300.33
19rs4808801SSBP40.651.12(1.06, 1.18)3.1 × 10-6 1.09(1.05, 1.11)3.5 × 10-9 0.16
10rs10995190ZNF3650.851.16(1.09, 1.23)4.1 × 10-6 1.15(1.11, 1.19)7.5 × 10-16 0.61
2rs133870422q350.511.10(1.05, 1.15)1.1 × 10-5 1.14(1.11, 1.16)8.3 × 10-25 0.34
6rs3757318ESR10.071.20(1.10, 1.30)1.4 × 10-5 1.16(1.10, 1.21)1.2 × 10-9 0.85
11rs554219CCND10.121.15(1.08, 1.22)2.8 × 10-5 1.27(1.22, 1.32)6.4 × 10-38 0.88
6rs2046210ESR10.341.10(1.05, 1.15)8.6 × 10-5 1.09(1.06, 1.12)4.0 × 10-10 0.32
12rs10771399PTHLH0.881.15(1.06, 1.23)0.000211.18(1.12, 1.22)1.2 × 10-14 0.53
8rs117801568q24.210.161.11(1.05, 1.18)0.000271.10(1.06, 1.14)2.3 × 10-8 0.88
16rs17817449FTO0.601.09(1.03, 1.14)0.000521.06(1.04, 1.10)5.9 × 10-7 0.32

SNP single nucleotide polymorphism, IDC invasive ductal carcinoma, OR odds ratio; P-Het P value for heterogeneity; RAF risk allele frequency

Known breast cancer predisposition loci for ductal carcinoma in situ plotted according to the risk allele for invasive disease. Odds ratios >1 indicate that the association is in the same direction as previously published for invasive breast cancer Loci showing a significant association with ductal carcinoma in situ (DCIS) at P <0.00066 SNP single nucleotide polymorphism, IDC invasive ductal carcinoma, OR odds ratio; P-Het P value for heterogeneity; RAF risk allele frequency The case-only analysis (DCIS vs IDC) confirmed the shared genetic susceptibility between DCIS and IDC as none of the heterogeneity P values (P-Het) were significant after Bonferroni adjustment for 76 SNPs (Additional file 7). The case-only analysis (DCIS diagnosed at <50 years vs ≥50 years of age) revealed one SNP (rs527616, 18q11.2) that was significantly associated with DCIS in younger women (P-Het<50/≥50 = 0.0003) even though the overall P value for DCIS was not statistically significant after Bonferroni correction (OR 1.05, 95 % CI 1.01, 1.11; P = 0.020) (Additional file 8).

Assessment of known breast cancer susceptibility loci for association with DCIS by ER status

Following immunohistochemistry for ER in the ICICLE study samples, 1,484 cases (54 %) were classified as ER+ and 383 (14 %) as ER–. The ER data on BCAC DCIS were less complete with 664 (28 %) ER+, 301 (13 %) ER– and 1,387 cases (59 %) of unknown ER status (Additional file 6). Analysis by ER status confirmed that loci associated with ER+ IDC were also associated with ER+ DCIS (Fig. 2 and Additional file 9). These similarities were less clear for ER– DCIS and ER– IDC but this may be due to small numbers of ER– DCIS cases. A case-only analysis of ER+ vs ER– DCIS was not performed due to the small numbers of ER– cases.
Fig. 2

Known breast cancer predisposition loci for estrogen receptor-positive (ER+) (black lines) and ER– ductal carcinoma in situ (gray lines). Due to the large number of single nucleotide polymorphisms (SNPs), for better visual representation the plot is split into two different sections (a and b) with a descending order of effect size for the ER+ group. OR odds ratio

Known breast cancer predisposition loci for estrogen receptor-positive (ER+) (black lines) and ER– ductal carcinoma in situ (gray lines). Due to the large number of single nucleotide polymorphisms (SNPs), for better visual representation the plot is split into two different sections (a and b) with a descending order of effect size for the ER+ group. OR odds ratio

Assessment of known breast cancer susceptibility loci for association with DCIS by grade

Grade data were available for 95 % of ICICLE DCIS cases; 1,635 (60 %) were of high cytonuclear grade and 943 (35 %) of low/intermediate grade. The grade data on the BCAC DCIS were less complete with data only available for 35 % of cases: 306 (13 %) high grade and 522 (22 %) low/intermediate grade cases (Additional file 6). Case–control analysis was performed separately on the low/intermediate and high grade subsets and a case-only analysis of low/intermediate grade vs high grade DCIS was performed to assess whether any of these loci were grade-specific. Analysis of DCIS by grade revealed that although the majority of SNPs predispose to all grades of DCIS, some are grade-specific (Additional files 10 and 11). The two SNPs close to CCND1 were strongly associated with low/intermediate grade DCIS (rs75915166, OR 1.36, 95 % CI 1.17, 1.59; P = 7.2 × 10-5; rs554219, OR 1.32, 95 % CI 1.18, 1.48; P = 8.2 × 10-7) and there was no association with high grade DCIS (Table 2). Case-only analysis confirmed that these loci were low/intermediate grade-specific (rs75915166, P-Hetlow/highgrade = 0.00014; rs554219, P-Hetlow/highgrade = 0.00013) and this was independent of ER status (adjusted for ER status rs75915166, P = 0.0050; rs554219, P = 0.019).
Table 2

Association between rs75915166 or rs554219 and grade in ductal carcinoma in situ

Meta-analysis
OR (95 % CI) P Low/intermediate grade, numberHigh grade, numberControls, number
rs75915166
Low/intermediate grade vs controls1.36 (1.17, 1.59)7.2 × 10-5 1,46535,521
High grade vs controls0.92 (0.79, 1.08)0.311,94132,202
Case-only high vs low/intermediate grade
Unadjusted0.68 (0.55, 0.83)1.4 × 10-4 1,3071,941
unadjusted (only cases with ER status)0.65 (0.51, 0.84)1.1 × 10-3 7911,360
adjusted for ER status0.68 (0.52, 0.89)0.00507911,360
ER+ only0.68 (0.55, 0.84)5 × 10-4 709985
rs554219
Low/intermediate grade vs controls1.32 (1.18, 1.48)8.2 × 10-7 1,46535,521
High grade vs controls1.02 (0.91, 1.14)0.751,94132,202
Case-only high vs low/intermediate grade
Unadjusted0.75 (0.65, 0.87)1.3 × 10-4 1,3071,941
unadjusted (only cases with ER status)0.75 (0.63, 0.88)2.1 × 10-4 7911,360
adjusted for ER status0.80 (0.67, 0.96)0.0197921,360
ER+ only0.76 (0.65, 0.89)6.7 × 10-4 709985

OR odds ratio, ER estrogen receptor

Association between rs75915166 or rs554219 and grade in ductal carcinoma in situ OR odds ratio, ER estrogen receptor A similar-case-only analysis of IDC by grade confirmed that the two SNPs on 11q13.3 close to CCND1 were also invasive grade 1/2-specific in IDC (rs75915166, OR 1.42, P = 1.7 × 10-30, P-Het = 2.8 × 10-10; rs554219, OR 1.39, P = 4.7 × 10-49, P-Het = 1.3 × 10-17) and again were independent of ER status (P = 1.3 × 10-6, P = 1.6 × 10-6, respectively) (Additional file 12). In addition, other grade-specific loci were identified including three (rs2363956, rs8170 and rs10069690) specific to grade 3 invasive disease (Additional file 13). rs10941679, 5p12 were borderline associated with low/intermediate grade DCIS (OR 1.26, P = 2.1 × 10-7, P-Hetlow/highgrade = 0.0033). This locus has previously been shown to be associated with low grade progesterone receptor (PR) + IDC [23]. There was no evidence of any high grade DCIS specific loci (Additional file 11).

Search for new DCIS predisposition loci

All SNPs that were genome-wide significant (P <5 × 10-8) in the meta-analysis were correlated with one of the known breast cancer predisposition loci. There were three SNPs that were not correlated with known loci at P <6 × 10-6 (Table 3), all with very little evidence of an association with IDC.
Table 3

Potential new ductal carcinoma in situ susceptibility loci

Single nucleotide polymorphismrs12631593rs13236351rs73179023
Chromosome3722
Position607018849777251343424477
LocusFHITLMTK2PACSIN2:TTLL1
Minor allele frequency0.110.0320.13
ICICLE DCIS phase I
Odds ratio (95 % CI)1.15 (1.04, 1.28)1.31 (1.10, 1.56)0.83 (0.75, 0.91)
P 0.00880.00290.00020
BCAC DCIS
Odds ratio (95 % CI)1.25 (1.14, 1.36)1.3 (1.12, 1.51)0.86 (0.79, 0.94)
P 1.0 × 10-6 0.000600.0012
Meta-analysis phase I
Odds ratio (95 % CI)1.21 (1.13, 1.29)1.3 (1.16, 1.46)0.85 (0.79, 0.90)
P 5.5 × 10-8 5.7 × 10-6 1.1 × 10-6
Phase II DCIS
Odds ratio (95 % CI)0.93 (0.76, 1.14)0.91 (0.63, 1.31)0.95 (0.78, 1.15)
P 0.490.610.57
Meta-analysis phase II
Odds ratio (95 % CI)1.18 (1.10, 1.25)1.26 (1.13, 1.41)0.86 (0.80, 0.91)
P 7.8 × 10-7 2.9 × 10-5 1.7 × 10-6
BCAC IDC
Odds ratio (95 % CI)1.01 (0.97, 1.05)1.05 (0.99, 1.13)0.97 (0.93, 1.00)
P 0.540.130.060
Case-only
DCIS vs IDC P-Het0.00480.170.0099

DCIS ductal carcinoma in situ, IDC invasive ductal carcinoma, BCAC Breast Cancer Association Consortium, ICICLE Study to investigate the genetics of in situ carcinoma of the ductal subtype, P-Het P value for heterogeneity

Potential new ductal carcinoma in situ susceptibility loci DCIS ductal carcinoma in situ, IDC invasive ductal carcinoma, BCAC Breast Cancer Association Consortium, ICICLE Study to investigate the genetics of in situ carcinoma of the ductal subtype, P-Het P value for heterogeneity Of these novel SNPs, rs12631593, 3p14.2, (an intronic variant in FHIT, chr3: 60726844) was the most strongly associated with DCIS (OR 1.21, 95 % CI 1.13, 1.29; P = 5.5 × 10-8). This SNP showed little association with IDC (OR 1.01, 95 % CI 0.97, 1.05; P = 0.54) and this was supported by the case-only analysis (P-HetDCIS/IDC = 0.0048). The other loci were on 22q13.2, rs73179023 (DCIS only: OR 0.85, 95 % CI 0.79, 0.90; P = 1.1 × 10-6; IDC only: OR 0.97, 95 % CI 0.93, 1.00; P = 0.060, P-HetDCIS/IDC = 0.0099) and 7q21.3, rs13236351 (DCIS only: OR 1.30, 95 % CI 1.16, 1.46; P = 5.7 × 10-6; IDC only: OR 1.05, 95 % CI 0.99, 1.13; P = 0.13, P-HetDCIS/IDC = 0.17). These SNPs were genotyped in a validation study including a further 653 DCIS cases and 1,882 controls, however, for all three loci there was no evidence of an association (for rs12631593, rs13236351, and rs73179023, P = 0.49, 0.61, and 0.57, respectively) and none were genome wide significant following a meta-analysis of all data (P = 7.8 × 10-7, 2.9 × 10-5, and 1.7 × 10-6 respectively) (Table 3).

Discussion

This study provides the strongest evidence to date for a shared genetic susceptibility between DCIS and IDC, based on 5,067 cases with pure DCIS (no invasive disease) and 24,670 cases with IDC. It differs from previous BCAC analyses of DCIS, as it has included an additional 3,078 DCIS cases, excluded all cases of pure LCIS and has also compared DCIS to IDC rather than all invasive disease. An important finding of this study is the lack of DCIS/IDC-specific loci among the known breast cancer predisposition loci. Of the five breast cancer predisposition alleles originally reported by Easton et al. [24], three were shown to be associated with in situ (998 cases of DCIS and LCIS) disease (rs2981582-FGFR2, rs3803662-TOX3, rs889312-MAP3K1) with rs889312 showing a stronger association with DCIS (P-trend 0.007, per allele OR 1.30 for DCIS, per allele OR 1.13 for invasive disease). However, this finding of potential DCIS-specific loci was not confirmed in the Million women study which found no differential association with DCIS vs IDC for twelve breast cancer susceptibility loci, including rs889312, although their sample size was smaller (873 DCIS and 4,959 IDC) [12]. In the recent BCAC COGS analysis all 41 novel SNPs identified on the iCOGS chip had comparable ORs for invasive and in situ disease (based on data from 2,335 in situ, and 42,118 invasive cases), with the exceptions of rs12493607 (TGFBR2), and rs3903072 (11q13.1), for which associations seemed to be restricted to invasive disease [19]; however, we found no evidence of an IDC-specific association with these loci after correcting for multiple testing. A recent study investigating the association between 39 of the known breast cancer predisposition loci and breast cancer in situ (BCIS) suggested that rs1011970 (9p21.3, CDKN2BAS) had a stronger association with BCIS than invasive breast cancer (BC), P-HetBCIS/BC = 0.0065. This trend remained in a DCIS vs BC analysis (P-HetDCIS/BC = 0.021) [25]. Our data, however, do not support this finding (DCIS OR 1.08, 95 % CI 1.02, 1.14; P = 0.011; IDC OR 1.05, 95 % CI 1.0, 1.09; P = 0.0025, P-HetDCIS/IDC = 0.33). We have also shown for the first time that seven of the known invasive breast cancer predisposition loci not previously shown to be associated with DCIS have comparable ORs for IDC and DCIS: rs4973768 (SLC4A7), rs3821902 (ATXN7) [26], rs10995190 (ZNF365), rs554219 (CCND1), rs3757318 and rs2046210 (ESR1). This lack of DCIS/IDC-specific loci is in contrast to our previous study of lobular cancer in which we showed that there are loci that are specific to invasive lobular cancer (ILC), showing no association with lobular carcinoma in situ (LCIS) and there was also a suggestion of LCIS-specific loci [16]. When we compare the DCIS data presented here to our previous LCIS analyses it reveals that there is some overlap between loci that are associated with ER+ DCIS and LCIS (Fig. 3 and Additional file 14). However, there are also some differences: rs6678914, LGR6 and rs865686, 9q31.2 are strongly associated with LCIS but there is little evidence of association with ER+ DCIS (P-HetDCIS/LCIS = 7.4 × 10-5 and 6.6 × 10-4, respectively). We have also previously shown that rs11249433, 1p11.2 and rs11977670, 7q34 have a stronger association with invasive lobular cancer than IDC [16]. These loci were only weakly associated with LCIS and were not associated with ER+ DCIS in this analysis.
Fig. 3

Known breast cancer predisposition loci for estrogen receptor-positive (ER+) (black) ductal carcinoma in situ and lobular carcinoma in situ (gray). Due to the large number of single nucleotide polymorphisms (SNPs), for better visual representation, the plot is split into two different sections (a and b) with a descending order of effect size for the ER+ group. OR odds ratio

Known breast cancer predisposition loci for estrogen receptor-positive (ER+) (black) ductal carcinoma in situ and lobular carcinoma in situ (gray). Due to the large number of single nucleotide polymorphisms (SNPs), for better visual representation, the plot is split into two different sections (a and b) with a descending order of effect size for the ER+ group. OR odds ratio Most association studies of invasive breast cancer involve subgroup analyses based on ER status. In contrast to invasive breast cancer, ER status in DCIS is not routinely assessed in all centers despite evidence from the NSABP B-24 trial of benefit from endocrine therapy in ER+ DCIS [7]. A national audit of DCIS in the UK revealed that ER status was assessed in only 50 % of DCIS cases and ER positivity in low and intermediate grade DCIS was significantly more common than in high grade DCIS (P <0.001) (ER+ high grade 69 %, intermediate grade 94 %, low grade 99 %) [27]. In order to overcome this issue we performed ER immunohistochemistry on the samples from ICICLE for which ER status was unknown. However, there was still a large amount of missing data on ER status in the BCAC cases, resulting in only 684 ER– DCIS cases being available for analysis, making it difficult to draw definitive conclusions about ER– DCIS. In essence the findings are similar to invasive breast cancer, with ER– and ER+ DCIS having different genetic susceptibility profiles and ER+ DCIS having a very similar profile to ER+ IDC. Cytonuclear grade of DCIS is used by many clinicians to select those cases most likely to benefit from radiotherapy despite the fact that grade has not been shown to be a good predictor of recurrence. In the UK audit of DCIS, grade data were available for 99 % of DCIS cases, with 59 % classified as high grade, 29 % as intermediate and 11 % as low grade [27]. Similarly, in our study data on grade were available for 95 % of cases in ICICLE. In invasive disease only a minority of predisposition loci have been shown to be grade specific; rs2981582 (FGFR2) and rs13281615 (8q24) [28, 29] and rs10941679 (5p12) [23]. We have shown that analysis of DCIS by grade reveals other known loci that are grade specific. The loci with the strongest association with grade were SNPs on 11q13, which had a stronger association with low/intermediate grade DCIS and IDC than high grade lesions. The finding of a strong association with low and intermediate grade ductal carcinomas that is independent of ER status in both DCIS and IDC for these loci is novel. rs614367 was the first locus on 11q13 shown to be associated with invasive breast cancer [30]. Fine mapping of the region subsequently identified two independent signals (rs554219 and rs78540526, r2 = 0.38), which are the loci reported in this analysis. Functional analyses demonstrated that the risk variants modify enhancer and silencer elements, with the likely target gene being CCND1 [31]. A study of 150 cases of subsequent breast cancer (invasive and in situ) after DCIS observed significant association for both grade and ER status between the index DCIS and the subsequent breast cancer (whether ipsilateral or contralateral), suggesting that women with DCIS are at risk of developing subsequent breast cancers of a similar phenotype [32]. This finding supports the genetic predisposition data presented here, with ER and grade-specific loci in DCIS having similar specificity in IDC. Although we did not identify any novel loci that reached genome wide significance, we did identify three potential novel DCIS predisposition loci, two of which were DCIS-specific (rs12631593, rs73179023), and therefore need further investigation in other cohorts of DCIS. As at least 45 % of patients with IDC have associated DCIS present at diagnosis consistent with direct precursor behavior, it may seem biologically implausible that an SNP predisposes to DCIS but is not associated with IDC. However, it is possible that there is a subset of patients with DCIS with very low probability of progression. If the finding of DCIS-specific predisposition loci were confirmed in other studies, identifying such a subset of patients with low-risk DCIS would be clinically valuable.

Conclusion

In conclusion this is the largest study to assess genetic predisposition in DCIS and shows that the majority of invasive breast cancer predisposition loci also predispose to DCIS. It highlights that, as for invasive disease, different SNPs predispose to ER+ and ER– DCIS. In addition it shows the importance of grade in both DCIS and IDC.
ABCSLeiden University Medical Center (LUMC) Commissie Medische Ethiek and Protocol Toetsingscommissie van het Nederlands Kanker Instituut/Antoni van Leeuwenhoek Ziekenhuis
BBCCFriedrich-Alexander-Universitat Erlangen-Nurnberg Medizinische Fakultat Ethik-Commission
BBCSSouth East Multi-Centre Research Ethics Committee
BIGGSGalway University College Hospital Clinical Research Ethical Committee
BSUCHMedizinische Fakultat Heidelberg Ethikkommission
CECILEComite Consultatif de Protection des Personnes dans la Recherche Biomedicale de Bicetre
CGPSKobenhavns Amt den Videnskabsetiske Komite
CNIO-BCSHospital Universitario La Paz Comite Etico de Investigacion Clinica
ESTHERRuprecht-Karls-Universitat Medizinische Fakultat Heidelberg Ethikkommission
GC-HBOCEthik-Kommission der Medizinischen Fakultat der Universitat zu Koln
HEBCSHelsingin ja uudenmaan sairaanhoitopiiri (Helsinki University Central Hospital Ethics Committee)
HMBCSMedizinische Hochschule Hannover Ethik-Kommission
ICICLESouthampton and South West Hampshire Research Ethics Committee A (MREC 08/H0502/4)
KBCPPohjois-Savon Sairraanhoitopiirin Kuntayhtyma Tutkimuseettinen Toimikunta
kConFab/AOCSkConFab: The Queenland Institute of Medical Research Human Research Ethics Committee (QIMR-HREC)
AOCS: Peter MacCallum Cancer Centre Ethics Committee
MARIERuprecht-Karls-Universitat Medizinische Fakultat Heidelberg Ethikkommission
MBCSGComitato Etico Indipendente della Fondazione IRCCS “Istituto Nazionale dei Tumori”
MCBCSMayo Clinic IRB
MECUniversity of Southern California Health Sciences Campus IRB
OBCSEthical Committee of the Medical Faculty of University of Oulu and Northern Ostrobothnia Hospital District Ethical Committee
OFBCRMount Sinai Hospital Research Ethics Board
ORIGOMedical Ethical Committee and Board of Directors of the Leiden University Medical Center (LUMC)
pKARMARegionala Etikprovningsnamnden i Stockholm (Regional Ethical Review Board in Stockholm)
RBCSMedische Ethische Toetsings Commissie Erasmus Medisch Centrum
SBCSSouth Sheffield Research Ethics Committee
SEARCHMulti Centre Research Ethics Committee (MREC)
SZBCSKomisji Bioetycznej Pomorskiej Akademii Medycznej
UKBGSSouth East Multi-Centre Research Ethics Committee
  32 in total

1.  Comparison of the effects of genetic and environmental risk factors on in situ and invasive ductal breast cancer.

Authors:  Gillian K Reeves; Kirstin Pirie; Jane Green; Diana Bull; Valerie Beral
Journal:  Int J Cancer       Date:  2011-11-28       Impact factor: 7.396

2.  Breast carcinoma in situ: risk factors and screening patterns.

Authors:  E B Claus; M Stowe; D Carter
Journal:  J Natl Cancer Inst       Date:  2001-12-05       Impact factor: 13.506

3.  Pathologic characteristics of second breast cancers after breast conservation for ductal carcinoma in situ.

Authors:  Nils D Arvold; Rinaa S Punglia; Melissa E Hughes; Wei Jiang; Stephen B Edge; Sara H Javid; Christine Laronga; Joyce C Niland; Richard L Theriault; Jane C Weeks; Yu-Ning Wong; Sandra J Lee; Michael J Hassett
Journal:  Cancer       Date:  2012-06-06       Impact factor: 6.860

4.  Long-term outcomes of invasive ipsilateral breast tumor recurrences after lumpectomy in NSABP B-17 and B-24 randomized clinical trials for DCIS.

Authors:  Irene L Wapnir; James J Dignam; Bernard Fisher; Eleftherios P Mamounas; Stewart J Anderson; Thomas B Julian; Stephanie R Land; Richard G Margolese; Sandra M Swain; Joseph P Costantino; Norman Wolmark
Journal:  J Natl Cancer Inst       Date:  2011-03-11       Impact factor: 13.506

5.  Presence of an in situ component is associated with reduced biological aggressiveness of size-matched invasive breast cancer.

Authors:  H Wong; S Lau; T Yau; P Cheung; R J Epstein
Journal:  Br J Cancer       Date:  2010-04-27       Impact factor: 7.640

6.  11q13 is a susceptibility locus for hormone receptor positive breast cancer.

Authors:  Diether Lambrechts; Therese Truong; Christina Justenhoven; Manjeet K Humphreys; Jean Wang; John L Hopper; Gillian S Dite; Carmel Apicella; Melissa C Southey; Marjanka K Schmidt; Annegien Broeks; Sten Cornelissen; Richard van Hien; Elinor Sawyer; Ian Tomlinson; Michael Kerin; Nicola Miller; Roger L Milne; M Pilar Zamora; José Ignacio Arias Pérez; Javier Benítez; Ute Hamann; Yon-Dschun Ko; Thomas Brüning; Jenny Chang-Claude; Ursel Eilber; Rebecca Hein; Stefan Nickels; Dieter Flesch-Janys; Shan Wang-Gohrke; Esther M John; Alexander Miron; Robert Winqvist; Katri Pylkäs; Arja Jukkola-Vuorinen; Mervi Grip; Georgia Chenevix-Trench; Jonathan Beesley; Xiaoqing Chen; Florence Menegaux; Emilie Cordina-Duverger; Chen-Yang Shen; Jyh-Cherng Yu; Pei-Ei Wu; Ming-Feng Hou; Irene L Andrulis; Teresa Selander; Gord Glendon; Anna Marie Mulligan; Hoda Anton-Culver; Argyrios Ziogas; Kenneth R Muir; Artitaya Lophatananon; Suthee Rattanamongkongul; Puttisak Puttawibul; Michael Jones; Nicholas Orr; Alan Ashworth; Anthony Swerdlow; Gianluca Severi; Laura Baglietto; Graham Giles; Melissa Southey; Federik Marmé; Andreas Schneeweiss; Christof Sohn; Barbara Burwinkel; Betul T Yesilyurt; Patrick Neven; Robert Paridaens; Hans Wildiers; Hermann Brenner; Heiko Müller; Volker Arndt; Christa Stegmaier; Alfons Meindl; Sarah Schott; Claus R Bartram; Rita K Schmutzler; Angela Cox; Ian W Brock; Graeme Elliott; Simon S Cross; Peter A Fasching; Ruediger Schulz-Wendtland; Arif B Ekici; Matthias W Beckmann; Olivia Fletcher; Nichola Johnson; Isabel Dos Santos Silva; Julian Peto; Heli Nevanlinna; Taru A Muranen; Kristiina Aittomäki; Carl Blomqvist; Thilo Dörk; Peter Schürmann; Michael Bremer; Peter Hillemanns; Natalia V Bogdanova; Natalia N Antonenkova; Yuri I Rogov; Johann H Karstens; Elza Khusnutdinova; Marina Bermisheva; Darya Prokofieva; Shamil Gancev; Anna Jakubowska; Jan Lubinski; Katarzyna Jaworska; Katarzyna Durda; Børge G Nordestgaard; Stig E Bojesen; Charlotte Lanng; Arto Mannermaa; Vesa Kataja; Veli-Matti Kosma; Jaana M Hartikainen; Paolo Radice; Paolo Peterlongo; Siranoush Manoukian; Loris Bernard; Fergus J Couch; Janet E Olson; Xianshu Wang; Zachary Fredericksen; Grethe Grenaker Alnaes; Vessela Kristensen; Anne-Lise Børresen-Dale; Peter Devilee; Robert A E M Tollenaar; Caroline M Seynaeve; Maartje J Hooning; Montserrat García-Closas; Stephen J Chanock; Jolanta Lissowska; Mark E Sherman; Per Hall; Jianjun Liu; Kamila Czene; Daehee Kang; Keun-Young Yoo; Dong-Young Noh; Annika Lindblom; Sara Margolin; Alison M Dunning; Paul D P Pharoah; Douglas F Easton; Pascal Guénel; Hiltrud Brauch
Journal:  Hum Mutat       Date:  2012-04-30       Impact factor: 4.878

7.  A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11.

Authors:  Afshan Siddiq; Fergus J Couch; Gary K Chen; Sara Lindström; Diana Eccles; Robert C Millikan; Kyriaki Michailidou; Daniel O Stram; Lars Beckmann; Suhn Kyong Rhie; Christine B Ambrosone; Kristiina Aittomäki; Pilar Amiano; Carmel Apicella; Laura Baglietto; Elisa V Bandera; Matthias W Beckmann; Christine D Berg; Leslie Bernstein; Carl Blomqvist; Hiltrud Brauch; Louise Brinton; Quang M Bui; Julie E Buring; Saundra S Buys; Daniele Campa; Jane E Carpenter; Daniel I Chasman; Jenny Chang-Claude; Constance Chen; Françoise Clavel-Chapelon; Angela Cox; Simon S Cross; Kamila Czene; Sandra L Deming; Robert B Diasio; W Ryan Diver; Alison M Dunning; Lorraine Durcan; Arif B Ekici; Peter A Fasching; Heather Spencer Feigelson; Laura Fejerman; Jonine D Figueroa; Olivia Fletcher; Dieter Flesch-Janys; Mia M Gaudet; Susan M Gerty; Jorge L Rodriguez-Gil; Graham G Giles; Carla H van Gils; Andrew K Godwin; Nikki Graham; Dario Greco; Per Hall; Susan E Hankinson; Arndt Hartmann; Rebecca Hein; Judith Heinz; Robert N Hoover; John L Hopper; Jennifer J Hu; Scott Huntsman; Sue A Ingles; Astrid Irwanto; Claudine Isaacs; Kevin B Jacobs; Esther M John; Christina Justenhoven; Rudolf Kaaks; Laurence N Kolonel; Gerhard A Coetzee; Mark Lathrop; Loic Le Marchand; Adam M Lee; I-Min Lee; Timothy Lesnick; Peter Lichtner; Jianjun Liu; Eiliv Lund; Enes Makalic; Nicholas G Martin; Catriona A McLean; Hanne Meijers-Heijboer; Alfons Meindl; Penelope Miron; Kristine R Monroe; Grant W Montgomery; Bertram Müller-Myhsok; Stefan Nickels; Sarah J Nyante; Curtis Olswold; Kim Overvad; Domenico Palli; Daniel J Park; Julie R Palmer; Harsh Pathak; Julian Peto; Paul Pharoah; Nazneen Rahman; Fernando Rivadeneira; Daniel F Schmidt; Rita K Schmutzler; Susan Slager; Melissa C Southey; Kristen N Stevens; Hans-Peter Sinn; Michael F Press; Eric Ross; Elio Riboli; Paul M Ridker; Fredrick R Schumacher; Gianluca Severi; Isabel Dos Santos Silva; Jennifer Stone; Malin Sund; William J Tapper; Michael J Thun; Ruth C Travis; Clare Turnbull; Andre G Uitterlinden; Quinten Waisfisz; Xianshu Wang; Zhaoming Wang; Joellen Weaver; Rüdiger Schulz-Wendtland; Lynne R Wilkens; David Van Den Berg; Wei Zheng; Regina G Ziegler; Elad Ziv; Heli Nevanlinna; Douglas F Easton; David J Hunter; Brian E Henderson; Stephen J Chanock; Montserrat Garcia-Closas; Peter Kraft; Christopher A Haiman; Celine M Vachon
Journal:  Hum Mol Genet       Date:  2012-09-13       Impact factor: 6.150

8.  Confirmation of 5p12 as a susceptibility locus for progesterone-receptor-positive, lower grade breast cancer.

Authors:  Roger L Milne; Ellen L Goode; Montserrat García-Closas; Fergus J Couch; Gianluca Severi; Rebecca Hein; Zachary Fredericksen; Núria Malats; M Pilar Zamora; Jose Ignacio Arias Pérez; Javier Benítez; Thilo Dörk; Peter Schürmann; Johann H Karstens; Peter Hillemanns; Angela Cox; Ian W Brock; Graeme Elliot; Simon S Cross; Sheila Seal; Clare Turnbull; Anthony Renwick; Nazneen Rahman; Chen-Yang Shen; Jyh-Cherng Yu; Chiun-Sheng Huang; Ming-Feng Hou; Børge G Nordestgaard; Stig E Bojesen; Charlotte Lanng; Grethe Grenaker Alnæs; Vessela Kristensen; Anne-Lise Børrensen-Dale; John L Hopper; Gillian S Dite; Carmel Apicella; Melissa C Southey; Diether Lambrechts; Betül T Yesilyurt; Giuseppe Floris; Karin Leunen; Suleeporn Sangrajrang; Valerie Gaborieau; Paul Brennan; James McKay; Jenny Chang-Claude; Shan Wang-Gohrke; Paolo Radice; Paolo Peterlongo; Siranoush Manoukian; Monica Barile; Graham G Giles; Laura Baglietto; Esther M John; Alexander Miron; Stephen J Chanock; Jolanta Lissowska; Mark E Sherman; Jonine D Figueroa; Natalia V Bogdanova; Natalia N Antonenkova; Iosif V Zalutsky; Yuri I Rogov; Peter A Fasching; Christian M Bayer; Arif B Ekici; Matthias W Beckmann; Hermann Brenner; Heiko Müller; Volker Arndt; Christa Stegmaier; Irene L Andrulis; Julia A Knight; Gord Glendon; Anna Marie Mulligan; Arto Mannermaa; Vesa Kataja; Veli-Matti Kosma; Jaana M Hartikainen; Alfons Meindl; Joerg Heil; Claus R Bartram; Rita K Schmutzler; Gilles D Thomas; Robert N Hoover; Olivia Fletcher; Lorna J Gibson; Isabel dos Santos Silva; Julian Peto; Stefan Nickels; Dieter Flesch-Janys; Hoda Anton-Culver; Argyrios Ziogas; Elinor Sawyer; Ian Tomlinson; Michael Kerin; Nicola Miller; Marjanka K Schmidt; Annegien Broeks; Laura J Van 't Veer; Rob A E M Tollenaar; Paul D P Pharoah; Alison M Dunning; Karen A Pooley; Frederik Marme; Andreas Schneeweiss; Christof Sohn; Barbara Burwinkel; Anna Jakubowska; Jan Lubinski; Katarzyna Jaworska; Katarzyna Durda; Daehee Kang; Keun-Young Yoo; Dong-Young Noh; Sei-Hyun Ahn; David J Hunter; Susan E Hankinson; Peter Kraft; Sara Lindstrom; Xiaoqing Chen; Jonathan Beesley; Ute Hamann; Volker Harth; Christina Justenhoven; Robert Winqvist; Katri Pylkäs; Arja Jukkola-Vuorinen; Mervi Grip; Maartje Hooning; Antoinette Hollestelle; Rogier A Oldenburg; Madeleine Tilanus-Linthorst; Elza Khusnutdinova; Marina Bermisheva; Darya Prokofieva; Albina Farahtdinova; Janet E Olson; Xianshu Wang; Manjeet K Humphreys; Qin Wang; Georgia Chenevix-Trench; Douglas F Easton
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-07-27       Impact factor: 4.254

9.  METAL: fast and efficient meta-analysis of genomewide association scans.

Authors:  Cristen J Willer; Yun Li; Gonçalo R Abecasis
Journal:  Bioinformatics       Date:  2010-07-08       Impact factor: 6.937

10.  Large-scale genotyping identifies 41 new loci associated with breast cancer risk.

Authors:  Kyriaki Michailidou; Per Hall; Anna Gonzalez-Neira; Maya Ghoussaini; Joe Dennis; Roger L Milne; Marjanka K Schmidt; Jenny Chang-Claude; Stig E Bojesen; Manjeet K Bolla; Qin Wang; Ed Dicks; Andrew Lee; Clare Turnbull; Nazneen Rahman; Olivia Fletcher; Julian Peto; Lorna Gibson; Isabel Dos Santos Silva; Heli Nevanlinna; Taru A Muranen; Kristiina Aittomäki; Carl Blomqvist; Kamila Czene; Astrid Irwanto; Jianjun Liu; Quinten Waisfisz; Hanne Meijers-Heijboer; Muriel Adank; Rob B van der Luijt; Rebecca Hein; Norbert Dahmen; Lars Beckman; Alfons Meindl; Rita K Schmutzler; Bertram Müller-Myhsok; Peter Lichtner; John L Hopper; Melissa C Southey; Enes Makalic; Daniel F Schmidt; Andre G Uitterlinden; Albert Hofman; David J Hunter; Stephen J Chanock; Daniel Vincent; François Bacot; Daniel C Tessier; Sander Canisius; Lodewyk F A Wessels; Christopher A Haiman; Mitul Shah; Robert Luben; Judith Brown; Craig Luccarini; Nils Schoof; Keith Humphreys; Jingmei Li; Børge G Nordestgaard; Sune F Nielsen; Henrik Flyger; Fergus J Couch; Xianshu Wang; Celine Vachon; Kristen N Stevens; Diether Lambrechts; Matthieu Moisse; Robert Paridaens; Marie-Rose Christiaens; Anja Rudolph; Stefan Nickels; Dieter Flesch-Janys; Nichola Johnson; Zoe Aitken; Kirsimari Aaltonen; Tuomas Heikkinen; Annegien Broeks; Laura J Van't Veer; C Ellen van der Schoot; Pascal Guénel; Thérèse Truong; Pierre Laurent-Puig; Florence Menegaux; Frederik Marme; Andreas Schneeweiss; Christof Sohn; Barbara Burwinkel; M Pilar Zamora; Jose Ignacio Arias Perez; Guillermo Pita; M Rosario Alonso; Angela Cox; Ian W Brock; Simon S Cross; Malcolm W R Reed; Elinor J Sawyer; Ian Tomlinson; Michael J Kerin; Nicola Miller; Brian E Henderson; Fredrick Schumacher; Loic Le Marchand; Irene L Andrulis; Julia A Knight; Gord Glendon; Anna Marie Mulligan; Annika Lindblom; Sara Margolin; Maartje J Hooning; Antoinette Hollestelle; Ans M W van den Ouweland; Agnes Jager; Quang M Bui; Jennifer Stone; Gillian S Dite; Carmel Apicella; Helen Tsimiklis; Graham G Giles; Gianluca Severi; Laura Baglietto; Peter A Fasching; Lothar Haeberle; Arif B Ekici; Matthias W Beckmann; Hermann Brenner; Heiko Müller; Volker Arndt; Christa Stegmaier; Anthony Swerdlow; Alan Ashworth; Nick Orr; Michael Jones; Jonine Figueroa; Jolanta Lissowska; Louise Brinton; Mark S Goldberg; France Labrèche; Martine Dumont; Robert Winqvist; Katri Pylkäs; Arja Jukkola-Vuorinen; Mervi Grip; Hiltrud Brauch; Ute Hamann; Thomas Brüning; Paolo Radice; Paolo Peterlongo; Siranoush Manoukian; Bernardo Bonanni; Peter Devilee; Rob A E M Tollenaar; Caroline Seynaeve; Christi J van Asperen; Anna Jakubowska; Jan Lubinski; Katarzyna Jaworska; Katarzyna Durda; Arto Mannermaa; Vesa Kataja; Veli-Matti Kosma; Jaana M Hartikainen; Natalia V Bogdanova; Natalia N Antonenkova; Thilo Dörk; Vessela N Kristensen; Hoda Anton-Culver; Susan Slager; Amanda E Toland; Stephen Edge; Florentia Fostira; Daehee Kang; Keun-Young Yoo; Dong-Young Noh; Keitaro Matsuo; Hidemi Ito; Hiroji Iwata; Aiko Sueta; Anna H Wu; Chiu-Chen Tseng; David Van Den Berg; Daniel O Stram; Xiao-Ou Shu; Wei Lu; Yu-Tang Gao; Hui Cai; Soo Hwang Teo; Cheng Har Yip; Sze Yee Phuah; Belinda K Cornes; Mikael Hartman; Hui Miao; Wei Yen Lim; Jen-Hwei Sng; Kenneth Muir; Artitaya Lophatananon; Sarah Stewart-Brown; Pornthep Siriwanarangsan; Chen-Yang Shen; Chia-Ni Hsiung; Pei-Ei Wu; Shian-Ling Ding; Suleeporn Sangrajrang; Valerie Gaborieau; Paul Brennan; James McKay; William J Blot; Lisa B Signorello; Qiuyin Cai; Wei Zheng; Sandra Deming-Halverson; Martha Shrubsole; Jirong Long; Jacques Simard; Montse Garcia-Closas; Paul D P Pharoah; Georgia Chenevix-Trench; Alison M Dunning; Javier Benitez; Douglas F Easton
Journal:  Nat Genet       Date:  2013-04       Impact factor: 38.330

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  22 in total

1.  Leveraging premalignant biology for immune-based cancer prevention.

Authors:  Avrum Spira; Mary L Disis; John T Schiller; Eduardo Vilar; Timothy R Rebbeck; Rafael Bejar; Trey Ideker; Janine Arts; Matthew B Yurgelun; Jill P Mesirov; Anjana Rao; Judy Garber; Elizabeth M Jaffee; Scott M Lippman
Journal:  Proc Natl Acad Sci U S A       Date:  2016-09-16       Impact factor: 11.205

Review 2.  Genomic approaches to accelerate cancer interception.

Authors:  Jennifer Beane; Joshua D Campbell; Julian Lel; Jessica Vick; Avrum Spira
Journal:  Lancet Oncol       Date:  2017-07-26       Impact factor: 41.316

3.  Discoidin domain receptor 1: New star in cancer-targeted therapy and its complex role in breast carcinoma.

Authors:  Hui Jing; Jingyuan Song; Junnian Zheng
Journal:  Oncol Lett       Date:  2018-01-15       Impact factor: 2.967

Review 4.  In Vitro Models for Studying Invasive Transitions of Ductal Carcinoma In Situ.

Authors:  Ethan J Brock; Kyungmin Ji; Seema Shah; Raymond R Mattingly; Bonnie F Sloane
Journal:  J Mammary Gland Biol Neoplasia       Date:  2018-07-28       Impact factor: 2.673

5.  Disease evolution and heterogeneity in bilateral breast cancer.

Authors:  Elena Fountzilas; Vassiliki Kotoula; Flora Zagouri; Eleni Giannoulatou; George Kouvatseas; George Pentheroudakis; Triantafyllia Koletsa; Mattheos Bobos; Kyriaki Papadopoulou; Epaminontas Samantas; Efterpi Demiri; Spyros Miliaras; Christos Christodoulou; Sofia Chrisafi; Evangelia Razis; Florentia Fostira; Dimitrios Pectasides; George Zografos; George Fountzilas
Journal:  Am J Cancer Res       Date:  2016-11-01       Impact factor: 6.166

Review 6.  Genome evolution in ductal carcinoma in situ: invasion of the clones.

Authors:  Anna K Casasent; Mary Edgerton; Nicholas E Navin
Journal:  J Pathol       Date:  2016-11-27       Impact factor: 7.996

7.  Can dedicated breast PET help to reduce overdiagnosis and overtreatment by differentiating between indolent and potentially aggressive ductal carcinoma in situ?

Authors:  Lucía Graña-López; Michel Herranz; Inés Domínguez-Prado; Sonia Argibay; Ángeles Villares; Manuel Vázquez-Caruncho
Journal:  Eur Radiol       Date:  2019-08-02       Impact factor: 5.315

8.  A comprehensive analysis of polymorphic variants in steroid hormone and insulin-like growth factor-1 metabolism and risk of in situ breast cancer: Results from the Breast and Prostate Cancer Cohort Consortium.

Authors:  Myrto Barrdahl; Federico Canzian; Mia M Gaudet; Susan M Gapstur; Antonia Trichopoulou; Kostas Tsilidis; Carla H van Gils; Signe Borgquist; Elisabete Weiderpass; Kay-Tee Khaw; Graham G Giles; Roger L Milne; Loic Le Marchand; Christopher Haiman; Sara Lindström; Peter Kraft; David J Hunter; Regina Ziegler; Stephen J Chanock; Xiaohong R Yang; Julie E Buring; I-Min Lee; Rudolf Kaaks; Daniele Campa
Journal:  Int J Cancer       Date:  2017-11-17       Impact factor: 7.396

9.  Family History and Risk of Second Primary Breast Cancer after In Situ Breast Carcinoma.

Authors:  Michelle L Baglia; Mei-Tzu C Tang; Kathleen E Malone; Peggy Porter; Christopher I Li
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2018-01-16       Impact factor: 4.254

10.  Precancer Atlas to Drive Precision Prevention Trials.

Authors:  Avrum Spira; Matthew B Yurgelun; Ludmil Alexandrov; Anjana Rao; Rafael Bejar; Kornelia Polyak; Marios Giannakis; Ali Shilatifard; Olivera J Finn; Madhav Dhodapkar; Neil E Kay; Esteban Braggio; Eduardo Vilar; Sarah A Mazzilli; Timothy R Rebbeck; Judy E Garber; Victor E Velculescu; Mary L Disis; Douglas C Wallace; Scott M Lippman
Journal:  Cancer Res       Date:  2017-04-01       Impact factor: 13.312

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