Abstract
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Common variants in LSP1, 2q35 and 8q24 and breast cancer risk for BRCA1 and BRCA2 mutation carriers
Abstract
Genome-wide association studies of breast cancer have identified multiple single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risks in the general population. In a previous study, we demonstrated that the minor alleles at three of these SNPs, in FGFR2, TNRC9 and MAP3K1, also confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. Three additional SNPs rs3817198 at LSP1, rs13387042 at 2q35 and rs13281615 at 8q24 have since been reported to be associated with breast cancer in the general population, and in this study we evaluated their association with breast cancer risk in 9442 BRCA1 and 5665 BRCA2 mutation carriers from 33 study centres. The minor allele of rs3817198 was associated with increased breast cancer risk only for BRCA2 mutation carriers [hazard ratio (HR) = 1.16, 95% CI: 1.07–1.25, P-trend = 2.8 × 10−4]. The best fit for the association of SNP rs13387042 at 2q35 with breast cancer risk was a dominant model for both BRCA1 and BRCA2 mutation carriers (BRCA1: HR = 1.14, 95% CI: 1.04–1.25, P = 0.0047; BRCA2: HR = 1.18 95% CI: 1.04–1.33, P = 0.0079). SNP rs13281615 at 8q24 was not associated with breast cancer for either BRCA1 or BRCA2 mutation carriers, but the estimated association for BRCA2 mutation carriers (per-allele HR = 1.06, 95% CI: 0.98–1.14) was consistent with odds ratio estimates derived from population-based case–control studies. The LSP1 and 2q35 SNPs appear to interact multiplicatively on breast cancer risk for BRCA2 mutation carriers. There was no evidence that the associations vary by mutation type depending on whether the mutated protein is predicted to be stable or not.
INTRODUCTION
Pathogenic mutations in BRCA1 and BRCA2 confer high risks of breast and ovarian cancer. The breast cancer risks for BRCA1 and BRCA2 mutation carriers have been estimated to be between 40 and 87% by age 70 (1–7), but there is substantial evidence that these risks are modified by other factors that also cluster in families. Segregation analysis models have quantified the extent of variability in the breast cancer risk for BRCA1 and BRCA2 mutation carriers by means of polygenic-modifying variances, and suggest that the relative genetic variation in disease incidence is similar to that for women from the general population (2). A plausible explanation for this finding is that genetic variants associated with breast cancer risk for women in the general population also modify the disease risk for mutation carriers.
To evaluate this hypothesis, the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) evaluated the associations between the breast cancer risk for mutation carriers and three common breast cancer susceptibility alleles identified through genome-wide association studies (GWAS) (8–11). The minor alleles of single nucleotide polymorphisms (SNPs) in FGFR2, TNRC9 and MAP3K1 were all found to be associated with breast cancer risk for BRCA2 mutation carriers, but only the TNRC9 SNP was associated with breast cancer risk for BRCA1 mutation carriers. The TNRC9 SNP was estimated to account for ~0.5% of the familial/genetic variance of breast cancer risk for BRCA1 mutation carriers estimated by segregation analyses, and the three SNPs in FGFR2, TNRC9 and MAP3K1 for 2.8% of the estimated familial/genetic variance of breast cancer risk for BRCA2 mutation carriers (8). Therefore, the large majority of the variability in breast cancer risks for BRCA1 and BRCA2 remains unexplained. The GWAS by Easton et al. (9) and Stacey et al. (11) identified three additional breast cancer susceptibility loci at 8q24, 2q35 and on 11p15 close to the LSP1 gene. To evaluate whether these loci are also associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers, we genotyped these disease-associated SNPs in a large series of female carriers assembled by the CIMBA consortium.
RESULTS
Mutation carriers were recruited by 33 study centres in 21 countries (Table 1). After quality control exclusions, we analysed data from 8136 carriers censored at a first breast cancer diagnosis, 1430 individuals censored at an ovarian cancer diagnosis, 414 censored at bilateral prophylactic mastectomy and 5127 carriers censored at the age at last follow-up (Table 2). The estimated hazard ratios (HRs) associated with each SNP are shown in Table 3.
Table 1.
Number of eligible BRCA1 and BRCA2 carriers by study group
| Study | Countrya | BRCA1, N | BRCA2, N | Genotyping platform |
|---|---|---|---|---|
| Medical University of Vienna (MUV) | Austria | 284 | 122 | iPLEXb |
| Breast Cancer Family Registry (BCFR) | USA, Canada, Australia | 499 | 359 | Taqman |
| Copenhagen Breast Cancer Study (CBCS) | Denmark | 92 | 51 | Taqman |
| Spanish National Cancer Centre (CNIO) | Spain, Greece | 170 | 199 | Taqman |
| Deutsches Krebsforschungszentrum (DKFZ) | Germany | 68 | 27 | Taqman |
| HEreditary Breast and Ovarian study Netherlands (HEBON) | The Netherlands | 769 | 294 | iPLEXb |
| EMBRACE | UK, Eire | 807 | 634 | iPLEXb |
| Fox Chase Cancer Centre (FCCC) | USA | 81 | 54 | iPLEXb |
| German Consortium of Hereditary Breast and Ovarian Cancer (GC-HBOC) | Germany | 802 | 378 | Taqman |
| Genetic Modifiers of cancer risk in BRCA1/2 mutation carriers (GEMO) | France, USA | 1123 | 567 | Taqman |
| Gynecologic Oncology Group (GOG) | USA | 398 | 282 | Taqman |
| Hospital Clinico San Carlos (HCSC) | Spain | 109 | 94 | Taqman |
| Helsinki Breast Cancer Study (HEBCS) | Finland | 102 | 104 | iPLEXb |
| Iceland Landspitali—University Hospital (ILUH) | Iceland | 0 | 87 | Sequencing |
| Interdisciplinary Health Research International Team Breast Cancer Susceptibility (INHERIT BRCAs) | Quebec, Canada | 73 | 82 | Taqman |
| kConFab | Australia | 489 | 390 | iPLEXb |
| University of California Irvine (UCI) | USA | 168 | 121 | Taqman |
| Mayo Clinic (MAYO) | USA | 213 | 118 | iPLEXb |
| Milan Breast Cancer Study Group (MBCSG) | Italy | 346 | 218 | Taqman |
| Memorial Sloane Kettering Cancer Center (MSKCC) | USA | 258 | 155 | Taqman |
| National Cancer Institute (NCI) | USA | 156 | 73 | Taqman |
| National Israeli Cancer Control Center (NICCC) | Israel | 315 | 200 | Taqman |
| Ontario Cancer Genetics Network (OCGN) | Canada | 219 | 171 | Taqman |
| Ohio State University Clinical Cancer Center (OSU CCG) | USA | 60 | 31 | Taqman |
| Odense University Hospital (OUH) | Denmark | 216 | 132 | Taqman |
| Pisa Breast Cancer Study (PBCS) | Italy | 73 | 41 | iPLEXb |
| Istituto Oncologico Veneto—Hereditary Breast Ovarian Cancer Study (IOVHBOCS) | Italy | 95 | 88 | Taqman |
| Sheba Medical Centre (SMC)—Tel Hashomer | Israel | 400 | 190 | Taqman |
| Swedish Breast Cancer Study (SWE-BRCA) | Sweden | 413 | 121 | iPLEXb |
| N.N. Petrov Institute of Oncology (NNPIO) | Russia | 67 | 0 | Taqman |
| Modifier Study of Quantitative Effects on Disease (ModSQuaD) | Czech Republic, Belgium | 272 | 130 | Taqman |
| University of Turin Breast Cancer Study (UTBCS) | Italy | 60 | 43 | Taqman |
| University of Pennsylvania (UPENN) | USA | 245 | 109 | iPLEXb |
| Total | 9442 | 5665 |
aCountry of the clinic at which carriers are recruited.
bIndicates centralized genotyping (Queensland Institute of Medical Research).
Table 2.
Summary characteristics for the 15 107 eligible BRCA1 and BRCA2 carriers used in the analysis
| Characteristic | BRCA1 | BRCA2 | ||
|---|---|---|---|---|
| Unaffected | Breast cancer | Unaffected | Breast cancer | |
| Number | 4462 | 4980 | 2509 | 3156 |
| Person-years follow-up | 190 973 | 203 416 | 111 555 | 139 078 |
| Median age at censure (IQRa) | 41 (34–50) | 40 (34–46) | 43 (35–53) | 43 (37–50) |
| Age at censure, N (%) | ||||
| 630 (14.1) | 444 (8.9) | 322 (12.8) | 149 (4.7) | |
| 1284 (28.8) | 2027 (40.7) | 677 (27.0) | 991 (31.4) | |
| 1338 (30.0) | 1702 (34.2) | 694 (27.7) | 1184 (37.5) | |
| 776 (17.4) | 603 (12.1) | 467 (18.6) | 570 (18.1) | |
| 287 (6.4) | 155 (3.1) | 225 (9.0) | 206 (6.5) | |
| 147 (3.3) | 49 (1.0) | 124 (4.9) | 56 (1.8) | |
| Year of birth, N (%) | ||||
| 32 (0.7) | 43 (0.9) | 20 (0.8) | 37 (1.2) | |
| 124 (2.8) | 193 (3.9) | 89 (3.6) | 157 (5.0) | |
| 326 (7.3) | 459 (9.2) | 216 (8.6) | 396 (12.6) | |
| 685 (15.4) | 1163 (23.4) | 356 (14.2) | 776 (24.6) | |
| 1062 (23.8) | 1568 (31.5) | 594 (23.7) | 984 (31.2) | |
| 2233 (50.0) | 1554 (31.2) | 1234 (49.2) | 806 (25.5) | |
| Mutation class, N (%) | ||||
| 3388 (75.9) | 3387 (68.0) | 2367 (94.3) | 2926 (92.7) | |
| 838 (18.8) | 1222 (24.5) | 52 (2.1) | 80 (2.5) | |
| 236 (5.3) | 371 (7.5) | 90 (3.6) | 150 (4.8) | |
| Mutation description, N (%) | ||||
| 1210 (27.1) | 1322 (26.6) | 440 (17.5) | 394 (12.5) | |
| 3252 (72.9) | 3658 (73.5) | 2069 (82.5) | 2762 (87.5) | |
aIQR, interquartile range.
bSee methods for definitions.
cAshkenazi Jewish includes 185delAG and 5382insC for BRCA1 and 6174 delT for BRCA2.
Table 3.
Genotype frequencies by disease status and HR estimates
| Mutation/gene | Genotype | Unaffected (%) | Affected (%) | HR | 95% CI | P-value |
|---|---|---|---|---|---|---|
| LSP1 rs3817198 | ||||||
| BRCA1 | TT | 1940 (46.2) | 2114 (44.2) | 1.00 | ||
| TC | 1810 (43.0) | 2112 (44.2) | 1.05 | 0.96–1.13 | ||
| CC | 453 (10.8) | 555 (11.6) | 1.11 | 0.97–1.27 | ||
| 2df test | 0.24 | |||||
| Per allele | 1.05 | 0.99–1.11 | 0.090 | |||
| BRCA2 | TT | 1090 (45.3) | 1283 (42.3) | 1.00 | ||
| TC | 1057 (44.0) | 1375 (45.4) | 1.11 | 1.00–1.24 | ||
| CC | 257 (10.7) | 372 (12.3) | 1.39 | 1.16–1.67 | ||
| 2df test | 0.00098 | |||||
| Per allele | 1.16 | 1.07–1.25 | 0.00028 | |||
| 2q35 rs13387042 | ||||||
| BRCA1 | GG | 1029 (24.1) | 1007 (21.1) | 1.00 | ||
| GA | 2034 (47.7) | 2423 (50.9) | 1.18 | 1.07–1.30 | ||
| AA | 1205 (28.2) | 1333 (28.0) | 1.08 | 0.97–1.21 | ||
| 2df test | 0.003 | |||||
| Per allele | 1.03 | 0.98–1.09 | 0.24 | |||
| Dominant | 1.14 | 1.04–1.25 | 0.0047 | |||
| BRCA2 | GG | 604 (25.1) | 672 (22.1) | 1.00 | ||
| GA | 1143 (47.5) | 1526 (50.2) | 1.21 | 1.06–1.37 | ||
| AA | 660 (27.4) | 844 (27.7) | 1.12 | 0.97–1.31 | ||
| 2df test | 0.015 | |||||
| Per allele | 1.06 | 0.98–1.14 | 0.14 | |||
| Dominant | 1.18 | 1.04–1.33 | 0.0079 | |||
| 8q24 rs13281615 | ||||||
| BRCA1 | AA | 1366 (32.1) | 1581 (33.2) | 1.00 | ||
| AG | 2135 (50.2) | 2325 (48.8) | 0.96 | 0.88–1.04 | ||
| GG | 753 (17.7) | 856 (18.0) | 1.01 | 0.90–1.13 | ||
| 2df test | 0.54 | |||||
| Per allele | 1.00 | 0.94–1.05 | 0.88 | |||
| BRCA2 | AA | 821 (34.1) | 938 (31.0) | 1.00 | ||
| AG | 1182 (49.1) | 1547 (51.1) | 1.07 | 0.95–1.20 | ||
| GG | 405 (16.8) | 540 (17.9) | 1.11 | 0.96–1.29 | ||
| 2df test | 0.35 | |||||
| Per allele | 1.06 | 0.98–1.14 | 0.15 | |||
The LSP1 SNP rs3817198 was not associated with an increased risk of breast cancer for BRCA1 mutation carriers (P-trend = 0.09), but there was significant evidence of association with breast cancer risk for BRCA2 mutation carriers (P-trend = 0.00028). The estimated association in BRCA2 mutation carriers was consistent with a multiplicative model in which each copy of the minor allele was estimated to confer a HR of 1.16 (95% CI: 1.07–1.25). There was some evidence of heterogeneity in the study HRs for both BRCA1 and BRCA2 mutation carriers (P = 0.002 and 0.013, respectively, Fig. 1). To investigate whether the observed heterogeneity was due to the inclusion of several small studies and whether this influenced our results, we repeated the analysis by excluding any studies with fewer than 100 BRCA1 or 100 BRCA2 mutation carriers. The results were similar to those from the overall analysis. The estimated per allele HR for BRCA1 mutation carriers was 1.05 (95% CI: 0.99–1.12, P-trend = 0.12) and for BRCA2 it was 1.17 (95% CI: 1.07–1.27, P-trend = 0.00027). However, there was no longer evidence of heterogeneity in the HRs across studies for either BRCA1 or BRCA2 (P = 0.06 and 0.18, respectively). There was no significant evidence that the HRs varied by age for either BRCA1 or BRCA2 mutation carriers (P = 0.99 and 0.17, respectively).
Study-specific per-allele HR estimates for SNP rs3817198 in LSP1. The area of the square is proportional to the inverse of the variance of the estimate. Horizontal lines represent the 95% confidence intervals.
There was significant evidence that rs13387042 at 2q35 was associated with breast cancer risk for both BRCA1 and BRCA2 mutation carriers (2df P = 0.003 and 0.015, respectively). However, for both BRCA1 and BRCA2 mutation carriers, the estimated risk for carriers homozygous for the risk allele was lower than the estimated risk for heterozygotes. Therefore, the effects were not consistent with a multiplicative model (P = 0.001 for BRCA1, 0.013 for BRCA2). The most parsimonious model for the association of the risk allele was a dominant model for both BRCA1 and BRCA2 (P = 0.07 and 0.27, respectively, compared with the general model). Under the dominant model, the HR for carriers who had a copy of the A allele at this SNP was estimated to be 1.14 (95% CI: 1.04–1.25, P = 0.0047) for BRCA1 mutation carriers and 1.18 (95% CI: 1.04–1.33, P = 0.0079) for BRCA2 mutation carriers. There was no evidence of heterogeneity in the HRs across studies for BRCA1 mutation carriers (P = 0.13), but there was significant evidence of heterogeneity for BRCA2 mutation carriers (P = 0.9 × 10−5; Fig. 2). The heterogeneity for BRCA2 mutation carriers remained after the exclusion of studies with fewer than 100 BRCA2 mutation carriers (Phet = 7 × 10−5); this finding was largely driven by the UCI study (Phet = 0.44 after excluding UCI). Exclusion of this study, however, made little difference to the overall HR estimates (P-dominant = 0.014, HR = 1.18, 95% CI: 1.03–1.34). There was no evidence that the HRs varied by age (P = 0.86 and 0.34 for BRCA1 and BRCA2, respectively).
Study-specific HR estimates for SNP rs13387042 at 2q35 under the dominant model. The area of the square is proportional to the inverse of the variance of the estimate. Horizontal lines represent the 95% confidence intervals.
SNP rs13281615 at 8q24 was not associated with breast cancer risk for either BRCA1 or BRCA2 mutation carriers (P-trend = 0.88 and 0.15, respectively). The estimated per allele HR for BRCA1 mutation carriers was 1.00 (95% CI: 0.94–1.05) and for BRCA2 mutation carriers it was 1.06 (95% CI: 0.98–1.14). There was evidence of heterogeneity in the HRs across studies for BRCA1, but not for BRCA2 mutation carriers (P-heterogeneity=0.0002 and 0.59, respectively; Fig. 3). There was also evidence that the per-allele HR in BRCA1 mutation carriers decreased with age (P-trend = 0.002). However, this was mainly driven by a significant association for women aged 60–69 years (HR = 0.68, 95% CI: 0.52–0.90; Supplementary Material, Table S1).
Study-specific per-allele HR estimates for SNP rs13281615 at 8q24. The area of the square is proportional to the inverse of the variance of the estimate. Horizontal lines represent the 95% confidence intervals.
To investigate whether the inclusion of prevalent cancer cases influenced our HR estimates, we repeated the analysis after excluding carriers diagnosed with breast or ovarian cancer more than 5 years prior to recruitment, leaving 6507 BRCA1 carriers (4086 unaffected, 2421 affected) and 3997 BRCA2 carriers (2386 unaffected, 1611 affected). The HR estimates were similar to those from the analysis which included all cancer cases (Supplementary Material, Table S2).
Approximately 72% of the BRCA1 mutation carriers harboured class 1 mutations and 22% class 2 mutations, and the remainder of mutations were unclassifiable. To investigate whether there were differences in the strength of the associations by mutation type, we carried out separate analyses for BRCA1 class 1 and class 2 mutations (Table 4). rs13281615 (8q24) and rs3817198 (LSP1) were not associated with breast cancer risk for either class1 or class 2 mutation carriers, consistent with the analysis in which all BRCA1 mutation carriers were combined. The 2q35 SNP (rs13387042) appeared to be more strongly associated with breast cancer risk for BRCA1 class 2 mutation carriers (under dominant model: HR = 1.30, 95% CI: 1.06–1.60, P = 0.013) than class 1 mutation carriers (HR = 1.10, 95% CI: 0.99–1.23, P = 0.069), but the difference in HR was not statistically significant (P = 0.17). The number of BRCA2 mutation carriers harbouring class 2 mutations was too small to warrant separate analyses.
Table 4.
BRCA1 analysis by mutation class
| Mutation/gene | Genotype | Unaffected (%) | Affected (%) | HR | 95% CI | P-value |
|---|---|---|---|---|---|---|
| LSP1 rs3817198 | ||||||
| BRCA1—Class 1 | TT | 1476 (46.4) | 1421 (43.7) | 1.00 | ||
| TC | 1353 (42.5) | 1445 (44.5) | 1.05 | 0.96–1.16 | ||
| CC | 353 (11.1) | 382 (11.8) | 1.12 | 0.96–1.29 | ||
| 2df test | 0.27 | |||||
| Per allele | 1.06 | 0.99–1.13 | 0.11 | |||
| BRCA1—Class 2 | TT | 347 (44.1) | 531 (45.7) | 1.00 | ||
| TC | 362 (46.1) | 504 (43.3) | 0.95 | 0.79–1.15 | ||
| CC | 77 (9.8) | 128 (11.0) | 1.11 | 0.81–1.53 | ||
| 2df test | 0.63 | |||||
| Per allele | 1.02 | 0.88–1.17 | 0.82 | |||
| 2q35 rs13387042 | ||||||
| BRCA1—Class 1 | GG | 791 (24.4) | 717 (22.1) | 1.00 | ||
| GA | 1550 (47.8) | 1620 (50.0) | 1.13 | 1.01–1.26 | ||
| AA | 899 (27.8) | 904 (27.9) | 1.07 | 0.94–1.21 | ||
| 2df test | 0.11 | |||||
| Per Allele | 1.03 | 0.97–1.10 | 0.37 | |||
| Dominant | 1.10 | 0.99–1.23 | 0.069 | |||
| BRCA1—Class 2 | GG | 186 (23.3) | 218 (18.8) | 1.00 | ||
| GA | 379 (47.4) | 608 (52.3) | 1.35 | 1.09–1.67 | ||
| AA | 234 (29.3) | 336 (28.9) | 1.21 | 0.95–1.56 | ||
| 2df test | 0.021 | |||||
| Per allele | 1.08 | 0.95–1.22 | 0.22 | |||
| Dominant | 1.30 | 1.06–1.60 | 0.013 | |||
| 8q24 rs13281615 | ||||||
| BRCA1—Class 1 | AA | 1044 (32.2) | 1083 (33.4) | 1.00 | ||
| AG | 1635 (50.4) | 1582 (48.7) | 0.95 | 0.87–1.05 | ||
| GG | 564 (17.4) | 582 (17.9) | 1.01 | 0.88–1.15 | ||
| 2df test | 0.54 | |||||
| Per allele | 1.00 | 0.93–1.06 | 0.91 | |||
| BRCA1—Class 2 | AA | 246 (31.2) | 376 (32.4) | 1.00 | ||
| AG | 397 (50.3) | 566 (48.8) | 0.98 | 0.82–1.17 | ||
| GG | 146 (18.5) | 218 (18.8) | 1.01 | 0.79–1.30 | ||
| 2df test | 0.95 | |||||
| Per allele | 1.00 | 0.89–1.13 | 0.98 | |||
Genotype frequencies and HR estimates.
We also conducted analyses restricted to carriers of the most frequent mutations, 185delAG and 5382insC in BRCA1 (2532 carriers) and 6174delT in BRCA2 (834 carriers). The estimated HRs did not differ from those for the overall study (results not shown).
To evaluate the combined associations of rs3817198 in LSP1 and rs13387042 at 2q35 on breast cancer risk for BRCA2 mutation carriers, we fitted a multiplicative model that included a HR parameter for the effect of each SNP and compared this against a fully saturated model which included a separate HR parameter for each combined genotype (Table 5). The genotype-specific HR ratio estimates for the combined associations of these SNPs were similar with the multiplicative and saturated models, and there was no evidence that the fully saturated model gave a better fit than the multiplicative model (P = 0.16, 6df).
Table 5.
HR estimates for the combined effects of LSP1 and 2q35 on breast cancer risk for BRCA2 mutation carriers
| LSP1/2q35 genotype | Hazard ratio | |
|---|---|---|
| Multiplicative modela | Saturated model | |
| TT/GG | 1.00 | 1.00 |
| TT/GA | 1.14 | 1.09 |
| TT/AA | 1.14 | 1.24 |
| TC/GG | 1.16 | 1.20 |
| TC/GA | 1.32 | 1.35 |
| TC/AA | 1.32 | 1.18 |
| CC/GG | 1.34 | 1.14 |
| CC/GA | 1.53 | 1.64 |
| CC/AA | 1.53 | 1.60 |
aHR estimates under multiplicative mode; LSP1: per-allele HR = 1.16 (1.07–1.26); 2q35 dominant HR = 1.14 (1.02–1.27).
DISCUSSION
Three GWAS in 2007 identified six loci with common SNPs that are associated with increased breast cancer risk (9–11). We previously evaluated SNPs at three of these loci (FGFR2, TNRC9, MAP3K1) in BRCA1 and BRCA2 carriers (8). In the present analysis we evaluated the SNPs in the three additional loci for association with breast cancer risk for carriers. Our results provide evidence that rs3817198 in LSP1 is associated with breast cancer risk for BRCA2 mutation carriers, but we found no evidence of an association for BRCA1 mutation carriers. The 95% CI for the HR estimates, however, included the estimated odds ratio (OR) from population-based studies (1.07 (9)). For the SNP at 2q35, we found evidence of association for both BRCA1 and BRCA2 mutation carriers. The estimated HR for homozygotes was lower than that for heterozygotes, such that a log-additive model was rejected. This was in contrast to the population-based studies which clearly showed a higher risk for homozygotes (11,12). The effect did not appear to be driven by any particular study: the heterozygote HR estimate was greater than, or equal to, the HR in the homozygotes for 19 of the studies, including 4 of the largest studies (BCFR, EMBRACE, GEMO and GC-HBOC). This effect was also observed in analyses subdivided by mutation class, and when prevalent cases were excluded. It is possible that this departure from log-additivity is due to chance, although it might also reflect some distinct mechanism that operates only in mutation carriers. It may be possible to clarify this once the causal variant(s) are identified, and/or by studying larger numbers of carriers. The two SNPs in LSP1 and 2q35 appeared to interact multiplicatively on the breast cancer risk for BRCA2 mutation carriers. Finally, the 8q24 SNP was not significantly associated with risk for either BRCA1 or BRCA2 carriers. Although the CI for BRCA1 carriers excludes the estimated OR from population-based studies (1.08), the 95% CI HR estimate for BRCA2 carriers (0.98–1.14) includes this estimate.
There was significant evidence of heterogeneity in the HR estimates across studies for the associations of rs3817198 (LSP1) and rs13387042 (2q35) for BRCA2 mutation carriers. This heterogeneity is unlikely to be due to genotyping quality issues as all genotyping centres were required to adhere to strict genotyping quality control criteria, and studies which failed these criteria were excluded from the analyses. Our analyses indicate that much of the heterogeneity was driven by studies with small numbers of mutation carriers (Table 1). When studies with fewer than 100 mutation carriers were excluded from the analysis there was no longer evidence of heterogeneity in the HRs across studies for the LSP1 SNP, and yet the results remained virtually unchanged. The heterogeneity in the HR estimates for rs13387042 (2q35) and breast cancer risk for BRCA2 mutation carriers was largely due to one study (UCI) with 121 BRCA2 carriers; removal of this study, however, did not materially affect the overall association. We conclude that the associations we observed are likely to apply broadly to mutation carriers of European origin.
In our analysis of the FGFR2, TNRC9 and MAP3K1 loci, we noted that the differences in the associations between BRCA1 and BRCA2 mutation carriers may reflect the differences in the distribution of tumour subtype. Approximately 90% of BRCA1 breast cancer tumours have been reported to be estrogen receptor (ER)-negative whereas tumours in BRCA2 mutation carriers have an ER distribution similar to those arising in women from the general population, the majority of which are ER-positive (13). All three SNPs were associated with risk in BRCA2 carriers, whereas only the TNRC9 SNP was associated with risk in BRCA1 carriers, consistent with the differences in association between ER-positive and ER-negative disease reported by the Breast Cancer Association Consortium (BCAC) (8,14). BCAC has also investigated the associations of the LSP1 and 8q24 SNPs by the ER status of cancer cases (14). For the 8q24 SNP, the association was stronger for ER-positive disease, with no evidence of an association for ER-negative disease. Although we did not detect a significant difference in the HR estimates between BRCA1 and BRCA2 carriers, the absence of an association for BRCA1 carriers (HR 1.0) and the proximity of the HR estimate derived from 5665 BRCA2 carriers (1.06) to that reported from the population-based studies (1.08) is consistent with the differential effect of this SNP between ER-positive and ER-negative disease. BCAC did not observe a significant difference between ER-positive and ER-negative disease for the effect of the LSP1 SNP rs3817198 (14), although the estimated OR was higher for ER-positive than ER-negative disease. Therefore, our observation that this SNP is only associated with breast cancer risk for BRCA2 mutation carriers is also consistent with the BCAC results. Stacey et al. (11) reported that SNP rs13387042 at 2q35 was associated only with risk of ER-positive breast cancer. Recent results from the much larger BCAC study, however, indicated that this SNP is associated with the risk of both ER-positive and ER-negative disease (12), a result that is more consistent with our observation of an association with disease for both BRCA1 and BRCA2 carriers. Overall, the results for these three SNPs are consistent with the hypothesis that the breast cancer relative risks conferred by these SNPs are broadly similar for BRCA1 and BRCA2 carriers to those conferred on women from the general population, considering ER-positive and ER-negative disease separately.
In summary, of the common breast cancer susceptibility variants identified by GWAS evaluated to date, the present and previous (8) results indicate that SNPs in FGFR2, TNRC9, MAP3K1, LSP1 and 2q35 are all associated with breast cancer risk for BRCA2 mutation carriers, but only the TNRC9 and 2q35 SNPs show evidence of association with breast cancer risk for BRCA1 mutation carriers. Based on the estimated HRs, and estimates of the genetic variance of breast cancer risk for BRCA1 and BRCA2 mutation carriers, it is predicted that the five SNPs would account for 3.7% of the BRCA2 genetic-modifying variance. The TNRC9 and 2q35 SNPs are estimated to account for 0.7% of the BRCA1 genetic modifying variance. These findings suggest that the majority of the genetic variability in breast cancer risk for BRCA1 and BRCA2 mutation carriers still remains unexplained and future studies to identify further genetic modifiers of risk will be worthwhile.
MATERIALS AND METHODS
Subjects
Subjects were mutation carriers recruited by 33 study centres in 21 countries through the CIMBA initiative (Table 1). The large majority of carriers were recruited through cancer genetics clinics offering genetic testing, and enrolled into national (Australia, Austria, France, Netherlands, UK/Eire) or regional studies. Some carriers were identified by population-based sampling of cases, and some by community recruitment (e.g. in Ashkenazi Jewish populations). Eligibility to participate in CIMBA was restricted to carriers of pathogenic BRCA1 or BRCA2 mutations who were 18 years old or older at recruitment. Information collected included the year of birth; mutation description, including nucleotide position and base change; age at last follow-up; ages at breast and ovarian cancer diagnosis; and age or date at bilateral prophylactic mastectomy. Information was also available on the country of residence, which was defined to be the country of the clinic at which the carrier family was recruited to the study. Related individuals were identified through a unique family identifier. Women were included in the analysis if they carried mutations that were pathogenic according to generally recognized criteria (15) (Breast Cancer Information Core). All carriers participated in clinical or research studies at the host institutions under ethically approved protocols. Further details of the CIMBA initiative can be found elsewhere (16).
Women who self-reported as ‘non-white’ and those who carried pathogenic mutations in both BRCA1 and BRCA2 were excluded from the current analysis. We investigated possible overlap between studies by comparing the year of birth, exact mutation description and the reported ages, to identify potential duplicate individuals. Where possible, we also used SNP genotype data available within the CIMBA database. When a potential duplicate was identified, we contacted the relevant centres for further information about these individuals, in a manner that protected the identity of the individuals in question, in order to determine precisely the extent of true overlap in subjects and families appearing more than once in the data set. Duplicated mutation carriers were included only once in the analysis. To avoid inclusion of families extending over several studies, we included the individual in the study with the most complete version of the family. 106 mutation carriers were excluded for this reason.
Genotyping
The genotyping platforms used by each study are shown in Table 1. The DNA samples from 10 studies were genotyped using the iPLEX Mass Array platform at a single genotyping centre. One study genotyped by direct sequencing. All remaining studies used the 5′ endonuclease assay (Taqman), with reagents supplied by Applied Biosystems and tested centrally. All centres included at least 2% of the samples in duplicate, no template controls in every plate and a random mixture of affected and unaffected carriers. Samples that failed for two or more of the SNPs genotyped (or ≥20% of the SNPs typed if more than three SNPs were analysed using multiPLEX genotyping platforms) were excluded from the analysis. A study was included in the analysis only if the call rate was over 95% after samples that failed at multiple SNPs had been excluded. The concordance between duplicates had to be at least 98%. To assess the accuracy of genotyping across genotyping centres, all centres genotyped 95 DNA samples from a standard test plate (Coriell Institute) for all three SNPs. If the genotyping was inconsistent for more than one sample in the test plate, the study was excluded from the analysis of that SNP. Three studies failed these criteria for one or more of the SNPs. One study was excluded from all three SNP analyses, another from the analysis of rs13281615 and rs13387042 and one from the analysis of rs13387042. As an additional genotyping quality-control check, we also evaluated the deviation from Hardy–Weinberg equilibrium (HWE) for unrelated subjects separately for each SNP and study. For one study, the HWE P-value for rs3817198 was P = 2 × 10−13 and this study excluded from the analysis of that SNP. If HWE P-values were in the range 0.01–0.05 (six studies for the 8q24 SNP and four studies for the LSP1 SNP) we examined the cluster plots; none revealed any unusual patterns and these studies were included in all the analyses. After the above exclusions a total of 15 107 unique mutation carriers (9442 BRCA1 and 5665 BRCA2) from 33 studies had an observed genotype for at least of one of the SNPs and were therefore included in the analysis (Table 1).
Statistical analysis
The aim of the analysis was to evaluate the association between each genotype and breast cancer risk. The phenotype of each individual was therefore defined by their age at diagnosis of breast cancer or their age at last follow-up. For this purpose, individuals were censored at the age of the first breast cancer diagnosis, ovarian cancer diagnosis, or bilateral prophylactic mastectomy or the age at last observation. Mutation carriers censored at ovarian cancer diagnosis were considered unaffected. Since mutation carriers were not sampled randomly with respect to their disease status, standard methods of survival analysis (such as Cox regression) may lead to biased estimates of the HRs (17). This can be illustrated by considering an individual affected at age t. In a standard analysis of a cohort study, the SNP genotype for the individual will be compared with those of all individuals at risk at age t. This analysis leads to consistent estimates of the HR. However, in the present design, mutation carriers are already selected on the basis of disease status (where affected individuals are over-sampled). If standard cohort analysis were applied to these data, it would lead to affected individuals at age t being compared with unaffected carriers selected on the basis of their future disease status. If the genotype is associated with the disease, the risk estimate will be biased to zero because too many affected individuals (in whom the at-risk genotype is overrepresented) are included in the comparison group. Simulation studies have shown that this effect can be quite marked (17). We therefore conducted the analysis by modelling the retrospective likelihood of the observed genotypes conditional on the disease phenotypes. The derivation of the retrospective likelihood is described in detail elsewhere (15). In this model, the breast cancer incidence was assumed to depend on the underlying SNP genotype through a Cox proportional hazards model: λi (t) = λ0 (t) exp (βi), where exp(βi) is the HR for genotype i and λ0(t) is the breast cancer incidence rate in the baseline category. Under this approach, the baseline age-specific incidence rates in the Cox proportional-hazards model were chosen such that the overall breast cancer incidence rates, averaged over all genotypic categories, agree with external estimates of incidence for BRCA1 and BRCA2 mutation carriers (2). The effect of each SNP was modelled either as a per-allele HR (multiplicative model) or as separate HRs for heterozygotes and homozygotes, and these were estimated on the log scale (i.e. βi). Where there was evidence of deviation from the multiplicative model, dominant and recessive models were also fitted. The HRs were assumed to be independent of age (i.e. we used a Cox proportional-hazards model). The assumption of proportional hazards was tested by adding a ‘genotype x age’ interaction term to the model in order to fit models in which the HR changed with age. Analyses were carried out with the pedigree-analysis software MENDEL (18). We examined between-study heterogeneity by comparing the models that allowed for study-specific log-HRs against models in which the same log-HR was assumed to apply to all studies. All analyses were stratified by study group and country of residence and used calendar-year- and cohort-specific breast cancer incidence rates for BRCA1 and BRCA2 (2). We used a robust variance-estimation approach to allow for the non-independence among related carriers (19). The median family size in our sample was 1 (inter-quartile range: 1–2). To evaluate the combined effects of the significant SNPs on breast cancer risk, we fitted a multiplicative (log-additive) model that included a parameter for the log-HR for each of the SNPs (depending on the locus specific genetic model) and compared this with a fully saturated model in which a separate parameter was fitted for each multi-locus genotype.
To investigate whether our results were influenced by any of our assumptions we performed additional sensitivity analyses. If any of the SNPs were associated with disease survival, the inclusion of prevalent cases may influence the HR estimates. We therefore repeated our analysis by excluding mutation carriers diagnosed more than 5 years prior to the age at recruitment into the study. To examine whether SNP associations differed by type of mutations, we classified mutations according to their potential functional effect. Class 1 mutations comprised loss-of-function mutations, expected to result in a reduced transcript or protein level due to mRNA nonsense-mediated decay (NMD) and/or degradation or instability of truncated proteins (20–23), translation re-initiation but no production of stable protein (24), or the absence of expression because of the deletion of transcription regulatory regions. Class 2 mutations were those likely to generate potentially stable mutant proteins that might have dominant negative action, partially preserved normal function, or loss of function. Class 2 mutations include missense substitutions, in-frame deletions and insertion, as well as truncating mutations with premature stop codons occurring in the last exon (22). Mutations, whose consequences at transcript or protein level could not be inferred, were not considered for this classification. These were mainly mutations located in splice sites but not characterised for their effect at transcript level, or large deletions or insertions with undetermined boundaries. Additional analyses were performed by restricting to carriers of the mutations 185delAG and 5382insC in BRCA1 and 6174delT in BRCA2, which were the most frequent mutations observed.
The proportions of the modifying variance explained by the set of associated SNPs were estimated by ln(c)/σ2, where c is the estimated coefficient of variation in incidences associated with SNP (25,26) and σ2 is the estimated modifying variance [1.32 and 1.73 for BRCA1 and BRCA2 mutation carriers, respectively (2)]. We estimated the total proportion of the modifying variance due to all SNPs by adding the individual proportions, i.e. by assuming that the loci combined multiplicatively.
ACKNOWLEDGEMENTS
The CIMBA genotyping, data management and statistical analysis are supported by Cancer Research UK. A.C.A. is a Cancer Research UK Senior Cancer Research Fellow. We thank Ellen Goode for organizing the distribution of the standard DNA plates.
Conflict of Interest statement:The authors declare no conflicts of interest.
APPENDIX
CIMBA COLLABORATING CENTRES
Breast Cancer Family Registry (BCFR)
This work was supported by the National Cancer Institute, National Institutes of Health under RFA-CA-06-503 and through cooperative agreements with members of the Breast Cancer Family Registry and Principal Investigators, including Cancer Care Ontario (U01 CA69467), Columbia University (U01 CA69398), Fox Chase Cancer Center (U01 CA69631), Huntsman Cancer Institute (U01 CA69446), Northern California Cancer Center (U01 CA69417), University of Melbourne (U01 CA69638) and Research Triangle Institute Informatics Support Center (RFP No. N02PC45022-46). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centres in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR.
Copenhagen Breast Cancer Study (CBCS)
This work is supported by the Neye Foundation.
Spanish National Cancer Centre (CNIO)
Thanks to Rosario Alonso, Alicia Barroso, Guillermo Pita and Roger Milne for their support. The samples studied at CNIO were recruited through centres participating in the Spanish Consortium for the Study of Genetic Modifiers of BRCA1 and BRCA2: Spanish National Cancer Centre (Madrid), Catalan Institute of Oncology (Barcelona), Sant Pau Hospital (Barcelona), Dexeus Institute (Barcelona) and Demokritos Institute (Athens, Greece). The work carried out at CNIO was partly funded by FIS 081120 and Asociación Española Contra el Cáncer (AECC) grants.
Deutsches Krebsforschungszentrum (DKFZ)
We thank Antje Seidel-Renkert for expert technical assistance.
HEreditary Breast and Ovarian study Netherlands (HEBON)
Coordinating Center: Netherlands Cancer Institute, Amsterdam: F.B.L.H., Senno Verhoef, Anouk Pijpe, Laura van ‘t Veer, Flora van Leeuwen, M.A.R.; Erasmus Medical Center, Rotterdam: J.M.C., Ans van den Ouweland, Mieke Kriege, Mieke Schutte, Maartje Hooning, Caroline Seynaeve; Leiden University Medical Center, Leiden: Rob Tollenaar, Christi van Asperen, J.W., Maaike Vreeswijk, P.D.; Radboud University Nijmegen Medical Center, Nijmegen: Nicoline Hoogerbrugge, M.J.L.; University Medical Center Utrecht, Utrecht: Margreet Ausems, R.v.d.L.; Amsterdam Medical Center: C.M.A., Theo van Os; VU University Medical Center, Amsterdam: Hanne Meijers-Heijboer, Hans Gille; University Hospital Maastricht, Maastricht: Encarna Gomez-Garcia, Rien Blok. The HEBON study is supported by the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI 2007-3756.
Epidemiological study of BRCA1 & BRCA2 mutation carriers (EMBRACE)
DFE is the PI of the study. EMBRACE Collaborating Centers are: Coordinating Centre, Cambridge: S.P., M.C., C.O., D.F. North of Scotland Regional Genetics Service, Aberdeen: Zosia Miedzybrodzka, Helen Gregory. Northern Ireland Regional Genetics Service, Belfast: Patrick Morrison. West Midlands Regional Clinical Genetics Service, Birmingham: T.C., Carole McKeown, Lucy Burgess. South West Regional Genetics Service, Bristol: Alan Donaldson. East Anglian Regional Genetics Service, Cambridge: Joan Paterson. Medical Genetics Services for Wales, Cardiff: Alexandra Murray, Mark Rogers, Emma McCann. St James's Hospital, Dublin & National Centre for Medical Genetics, Dublin: John Kennedy, David Barton. South East of Scotland Regional Genetics Service, Edinburgh: Mary Porteous. Peninsula Clinical Genetics Service, Exeter: Carole Brewer, Emma Kivuva, Anne Searle, Selina Goodman. West of Scotland Regional Genetics Service, Glasgow: R.D., Victoria Murday, Nicola Bradshaw, Lesley Snadden, Mark Longmuir, Catherine Watt. South East Thames Regional Genetics Service, Guys Hospital London: L.I., Gabriella Pichert, Caroline Langman. North West Thames Regional Genetics Service, Harrow: Huw Dorkins. Leicestershire Clinical Genetics Service, Leicester: Julian Barwell. Yorkshire Regional Genetics Service, Leeds: C.C., Tim Bishop, Julie Miller. Merseyside & Cheshire Clinical Genetics Service, Liverpool: Ian Ellis. Manchester Regional Genetics Service, Manchester: D.G.E., F.L., Felicity Holt. D.G.E. and F.L. are supported the NIHR Biomedical research centre, Manchester. North East Thames Regional Genetics Service, NE Thames: Alison Male, Anne Robinson. Nottingham Centre for Medical Genetics, Nottingham: Carol Gardiner. Northern Clinical Genetics Service, Newcastle: Fiona Douglas. Oxford Regional Genetics Service, Oxford: Lisa Walker, Sarah Durell. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust: Ros Eeles, Susan Shanley, Nazneen Rahman, Richard Houlston, Elizabeth Bancroft, Lucia D'Mello, Elizabeth Page, Audrey Ardern-Jones, Anita Mitra. North Trent Clinical Genetics Service, Sheffield: Jackie Cook, Oliver Quarrell, Cathryn Bardsley. South West Thames Regional Genetics Service, London: S.H., Sheila Goff, Glen Brice, Lizzie Winchester. Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton: Gillian Crawford, Emma Tyler, Donna McBride, Anneke Lucassen. S.P., M.C., D.F., C.O. and R.P. are funded by Cancer Research-UK Grants C1287/A10118 and C1287/A8874. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. RE/EB/L D'M are also supported by Cancer Research UK Grant C5047/A8385.
Fox Chase Cancer Center (FCCC)
A.K.G. was funded by SPORE P-50CA83638, U01CA69631, 5U01CA113916 and the Eileen Stein Jacoby Fund.
German Consortium of Hereditary Breast and Ovarian Cancer (GC-HBOC)
GC-HBOC is supported by a grant of the German Cancer Aid (grant107054) to R.K.S. We thank Juliane Köhler for her excellent technical assistance and the 14 centres of the GC-HBOC for providing samples and clinical data.
Genetic Modifiers of cancer risk in BRCA1/2 mutation carriers (GEMO)
We wish to thank all the GEMO collaborating members (Cancer Genetics Network ‘Groupe Génétique et Cancer’, Fédération Nationale des Centres de Lutte Contre le Cancer, France) for their contribution to this study. We acknowledge the contribution of the Biobank of Institut de Cancérologie Gustave Roussy for providing their samples. The GEMO study was supported by the Ligue National Contre le Cancer and the Association ‘Le cancer du sein, parlons-en!’ Award. The GEMO study (Genetic Modifiers of cancer risk in BRCA1/2 mutation carriers) Collaborating Centers: Coordinating Centres, Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon/Centre Léon Bérard and UMR5201 CNRS, Université de Lyon, Lyon: O.M.S., Laure Barjhoux, Sophie Giraud, Mélanie Léone, Sylvie Mazoyer; and INSERM U509, Service de Génétique Oncologique, Institut Curie, Paris: D.S.-L., Marion Gauthier-Villars, Claude Houdayer, Virginie Moncoutier, Muriel Belotti, Antoine de Pauw. Institut Gustave Roussy, Villejuif: B.B.-d.-P., A.R., Véronique Byrde, Corinne Capoulade, G.M.L. Centre Jean Perrin, Clermont–Ferrand: Yves-Jean Bignon, Nancy Uhrhammer. Centre Léon Bérard, Lyon: Christine Lasset, Valérie Bonadona. Centre François Baclesse, Caen: Agnès Hardouin, Pascaline Berthet. Institut Paoli Calmettes, Marseille: Hagay Sobol, Violaine Bourdon, François Eisinger. Groupe Hospitalier Pitié-Salpétrière, Paris: Florence Coulet, Chrystelle Colas, Florent Soubrier. CHU de Arnaud-de-Villeneuve, Montpellier: Isabelle Coupier. Centre Oscar Lambret, Lille: Jean-Philippe Peyrat, Joëlle Fournier, Philippe Vennin, Claude Adenis. Centre René Huguenin, St Cloud: Etienne Rouleau, Rosette Lidereau, Liliane Demange, Catherine Nogues. Centre Paul Strauss, Strasbourg: Danièle Muller, Jean-Pierre Fricker. Institut Bergonié, Bordeaux: M.L., N.S. Institut Claudius Regaud, Toulouse: Christine Toulas, Rosine Guimbaud, Laurence Gladieff, Viviane Feillel. CHU de Grenoble: Dominique Leroux, Hélène Dreyfus, Christine Rebischung. CHU de Dijon: Laurence Olivier-Faivre. CHU de St-Etienne: F.P. Centre Antoine Lacassagne, Nice: Marc Frénay. Creighton University, Omaha, USA: Henry T. Lynch.
Gynecologic Oncology Group (GOG)
This study was supported by National Cancer Institute grants to the Gynecologic Oncology Group Administrative Office (CA 27469) and the Gynecologic Oncology Group Statistical and Data Center (CA 37517). Genotyping of GOG DNA samples was performed by NCI's Core Genotyping Facility. The technical expertise of Tim Sheehy, and Amy Hutchinson is gratefully acknowledged.
Hospital Clinico San Carlos (HCSC)
This work was supported by grant RD06/0020/0021 from ISCIII, Spanish Ministry of Science and Innovation.
Helsinki Breast Cancer Study (HEBCS)
The HEBCS thanks Drs Kristiina Aittomäki, Kirsimari Aaltonen and Carl Blomqvist and RN Hanna Jäntti for their help with the patient data and gratefully acknowledges the Finnish Cancer Registry for the cancer data. The HEBCS study has been financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland [110663], the Finnish Cancer Society and the Sigrid Juselius Foundation.
Iceland Landspitali—University Hospital (ILUH)
The Research Fund of Landspitali-University Hospital, The Icelandic Avon Group, Gongum saman.
Interdisciplinary Health Research International Team Breast Cancer Susceptibility (INHERIT BRCAs)
J.S., Francine Durocher, Rachel Laframboise, Marie Plante, Centre Hospitalier Universitaire de Quebec & Laval University, Quebec, Canada; Peter Bridge, Jilian Parboosingh, Molecular Diagnostic Laboratory, Alberta Children's Hospital, Calgary, Canada; Jocelyne Chiquette, Hôpital du Saint-Sacrement, Quebec, Canada; Bernard Lesperance, Hôpital du Sacré-Cœur de Montréal, Montréal, Canada. J.S. is Chairholder of the Canada Research Chair in Oncogenetics. This work was supported by the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program.
kConFab
We wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics and the Clinical Follow Up Study (funded by NHMRC grants 145684, 288704 and 454508) for their contributions to this resource, and the many families who contribute to kConFab. kConFab is supported by grants from the National Breast Cancer Foundation, the National Health and Medical Research Council (NHMRC) and by the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia.
University of California Irvine (UCI)
This work was supported by NIH grant R01-CA74415 (to S.L.N.).
Mayo Clinic (MAYO)
This work was supported in part by grants from the Breast Cancer Research Foundation (BCRF), the Komen Foundation and the National Cancer Institute, NIH (CA128978 and CA122340). The authors thank Mary Karaus for co-ordination or recruitment of carriers to the MAYO study.
Milan Breast Cancer Study Group (MBCSG)
MBCSG is supported by Fondazione Italiana per la Ricerca sul Cancro (FIRC, Special Project ‘Hereditary tumors’) and Associazione Italiana per la Ricerca sul Cancro (AIRC). MBCSG acknowledges Marco Pierotti, Siranoush Manoukian, Daniela Zaffaroni and Carla B. Ripamonti of the Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, Monica Barile of the Istituto Europeo di Oncologia, Milan, Italy and Laura Tizzoni of the Cogentech, Consortium for Genomic Technologies, Milan, Italy.
Memorial Sloane Kettering Cancer Center (MSKCC)
We acknowledge support from the Breast Cancer Research Foundation, the Starr Foundation and the Niehaus Cancer Research Initiative.
Modifier Study of Quantitative Effects on Disease (MODSQUAD)
C.I.S. is supported by Susan G. Komen Foundation Basic, Clinical and Translational Research grant (BCTR0402923) and the Mayo Rochester Early Career Development Award for Non-Clinician Scientists; We acknowledge the contributions of Petr Pohlreich and Zdenek Kleibl (Department of Biochemistry and Experimental Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic) and the support of the Research Project of the Ministry of Education, Youth, and Sports of the Czech Republic grant MSM0021620808 (to M.Z., Zdenek Kleibl and Petr Pohlreich). L.F., Machakova Eva and Lukesova Miroslava's are supported through the Ministry of Health grant CR-MZ0 MOU 2005. We acknowledge the contribution of Kim De Leeneer, Bruce Poppe, and Anne De Paepe. This research was supported by grant 1.5.150.07 from the Fund for Scientific Research Flanders (FWO) to Kathleen Claes and by grant 12051203 from the Ghent university to Anne De Paepe. Bruce Poppe is Senior Clinical Investigator of the Fund for Scientific Research of Flanders (FWO – Vlaanderen).
National Cancer Institute (NCI)
The research of M.H.G. and P.L.M. is supported by the Intramural Research Program of the US National Cancer Institute, and by support services contracts NO2-CP-11019-50 and N02-CP-65504 with Westat, Inc, Rockville, MD. Genoptyping of NCI DNA samples was performed by NCI's Core Genotyping Facility, Gaithersburg, MD.
Ontario Cancer Genetics Network (OCGN)
We wish to thank Mona Gill, Lucine Collins, Nalan Gokgoz, Teresa Selander, Nayana Weerasooriya and members of the Ontario Cancer Genetics Network for their contributions to the study.
Ohio State University Clinical Cancer Genetics (OSU CCG)
This work was supported by the OSU Comprehensive Cancer Center. We thank Kevin Sweet and Caroline Craven for patient accrual and data management, the Human Genetics Sample Bank for sample preparation and the OSU Nucleic Acids Shared Resource for genotyping plate reads.
Odense University Hospital (OUH)
Dorthe Cruger, Vejle Hospital, is acknowledged for sending patient material to this study.
Istituto Oncologico Veneto—Hereditary Breast Ovarian Cancer Study (IOVHBOCS)
The study was supported by the Ministero dell'Università e della Ricerca (MIUR), Ministero della Sanità and Alleanza Contro il Cancro.
Sheba Medical Centre (SMC)—Tel Hashomer
This work was partially supported by research grants to EF from the Tel Aviv University and the Israel cancer association.
The Swedish BRCA1 and BRCA2 Study (SWE-BRCA)
SWE-BRCA collaborators: P.K., Margareta Nordling, Annika Bergman and Zakaria Einbeigi, Gothenburg, Sahlgrenska University Hospital; Marie Stenmark-Askmalm and Sigrun Liedgren, Linkoping University Hospital; Ake Borg, Niklas Loman, Hakan Olsson, Ulf Kristoffersson, Helena Jernstrom, K.H. and Karin Henrisson, Lund University Hospital; Annika Lindblom, Brita Arver, Anna von Wachenfeldt, Annelie Liljegren, G.B.-B. and J.R., Stockholm, Karolinska.
University Hospital; Beatrice Malmer, Eva-Lena Stattin and Monica Emanuelsson, Umea University Hospital; H.E., Richard Rosenquist Brandell and Niklas Dahl, Uppsala University Hospital.
N.N. Petrov Institute of Oncology (NNPIO)
Supported by RFBR grants 08-04-00369 and 09-04-90402.
University of Turin Breast Cancer Study (UTBCS)
The work of A.A. has been supported by Compagnia di San Paolo (Progetto Oncologia).
University of Pennsylvania (UPENN)
We acknowledge support from the Breast Cancer Research Foundation.
REFERENCES
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Funding
Funders who supported this work.
CIHR
Cancer Research UK (5)
Grant ID: C5047/A8385
Grant ID: C1287/A8874
Strangeways Infrastructure - Easton Programme
Professor Douglas Easton, University of Cambridge
Grant ID: 11022
Grant ID: C1287/A10118
Genetic Epidemiology of Cancer
Professor Douglas Easton, University of Cambridge
Grant ID: 10118
Intramural NIH HHS
NCI NIH HHS (17)
Grant ID: N02-CP-11019-50
Grant ID: U01 CA69638
Grant ID: CA 37517
Grant ID: RFA-CA-06-503
Grant ID: U01 CA69631
Grant ID: CA128978
Grant ID: N02-CP-65504
Grant ID: U01 CA69446
Grant ID: U01 CA69467
Grant ID: P-50CA83638
Grant ID: U01 CA69398
Grant ID: U01 CA69417
Grant ID: U01CA69631
Grant ID: 5U01CA113916
Grant ID: CA 27469
Grant ID: CA122340
Grant ID: R01-CA74415