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. Author manuscript; available in PMC: 2016 Dec 15.
Published in final edited form as: Int J Cancer. 2015 Aug 14;137(12):2837–2845. doi: 10.1002/ijc.29446

Association of Breast Cancer Risk loci with Breast Cancer Survival

Myrto Barrdahl 1, Federico Canzian 2, Sara Lindström 3, Irene Shui 4, Amanda Black 5, Robert N Hoover 5, Regina G Ziegler 5, Julie E Buring 6,7, Stephen J Chanock 5,8, W Ryan Diver 9, Susan M Gapstur 9, Mia M Gaudet 9, Graham G Giles 10,11, Christopher Haiman 12, Brian E Henderson 12, Susan Hankinson 4,13,14, David J Hunter 3, Amit D Joshi 4, Peter Kraft 4, I-Min Lee 4,15, Loic Le Marchand 16, Roger L Milne 10,11, Melissa C Southey 17, Walter Willett 18, Marc Gunter 19, Salvatore Panico 20, Malin Sund 21, Elisabete Weiderpass 22,23,24,25, María-José Sánchez 26,27, Kim Overvad 28, Laure Dossus 29,30,31, Petra H Peeters 32,33, Kay-Tee Khaw 34, Dimitrios Trichopoulos 4,35,36, Rudolf Kaaks 1, Daniele Campa 1
PMCID: PMC4615576  NIHMSID: NIHMS711946  PMID: 25611573

Abstract

The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele=0.70; 95% CI: 0.58–0.85; Ptrend=2.84×10−4; HRheterozygotes=0.71; 95% CI: 0.55–0.92; HRhomozygotes=0.48; 95% CI: 0.31–0.76; P2DF=1.45×10−3). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04–1.15; Ptrend=6.6×10−4; HRheterozygotes=0.96 95% CI: 0.90–1.03; HRhomozygotes= 1.21; 95% CI: 1.09–1.35; P2DF=1.25×10−4). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662.

Keywords: breast cancer, SNP, survival, BPC3, meta-analysis

Introduction

Traditional prognostic factors, such as tumor size, lymph node metastasis [1], tumor grade and hormone receptor status are the most important markers of breast cancer survival [2,3]. There is, however, growing evidence that inherited genetic variation might influence the disease prognosis and the response to treatment [4]. For example, several genetic variants in xenobiotic metabolism, which might influence the effects of cancer treatments and oxidative stress related genes recently were found to be associated with survival of breast cancer patients [5–7].

Furthermore, there are reports suggesting that single nucleotide polymorphisms (SNPs) affecting breast cancer risk might also be associated with breast cancer mortality. For example, SNPs on chromosome 20q13 were reported to be related to both breast cancer risk and breast cancer clinical outcome [8] and the G870A (rs603965) polymorphic variant in the CCND1 gene was proposed to contribute to breast cancer risk and prognosis, especially in young women [9]. In a recent study by the Breast Cancer Association Consortium (BCAC), the T-allele of TNRC9-rs3803662, which has been reported to increase breast cancer risk, was shown to be associated with poor survival independent of traditional prognostic factors [10].

With the aim of further exploring the possibility that there exist genetic components common to breast cancer risk and prognosis, we selected 35 SNPs that had been associated with breast cancer risk at a significance level of p<5×10−7 and tested their possible association with overall survival (OS) in breast cancer patients. The study was conducted in the context of the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) and consisted of 10,255 patients, of which 1,379 died (625 deaths by any cause; 754 breast cancer-specific deaths). Since breast cancer prognosis differs strongly depending on the estrogen receptor (ER) status of the tumor, and because ER status is also associated with molecular breast tumor sub-types that may have different etiologies, we investigated the association between SNPs and survival in subgroups by ER status. Finally, we combined our results with those reported by BCAC [10] in a meta-analysis including almost 35,000 breast cancer patients and 5,000 deaths by any cause. Finally, we performed in silico analyses to determine the possible functional effects of the SNPs that showed statistically significant associations with overall or breast cancer-specific survival.

Material and methods

Study subjects

The BPC3 has been described extensively elsewhere [11]. Briefly, it consists of large, well-established cohorts assembled in Europe, Australia and the United States that have both DNA samples and extensive questionnaire information collected before breast cancer (BC) diagnosis. The cohorts are: the European Prospective Investigation into Cancer and Nutrition (EPIC) [12], the Melbourne Collaborative Cohort Study (MCCS) [13], the Nurses’ Health Study (NHS) [14], the Women’s Health Study (WHS) [15], the American Cancer Society Cancer Prevention Study II (CPS-II) [16], the Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial (PLCO) [17], and the Multiethnic Cohort (MEC) [18].

Study participants were Caucasian women who had been diagnosed with invasive BC after enrolment in one of the BPC3 cohorts. The diagnosis was confirmed by medical records or tumor registries (the method varied between cohorts). Vital status was collected in different ways in the various studies. In EPIC, vital status was collected using cancer registries, boards of health, death registries (Denmark, Italy, The Netherlands, Spain, the UK), or by active follow-up (France, Germany, Greece). In MCCS, vital status was ascertained using the Victorian Cancer Registry and the National Death Index. In NHS, WHS and CPS-II, it was confirmed using the US National Death Index. In PLCO, deaths were ascertained primarily by means of an annual study update form, supplemented by periodic linkage to the National Death Index (NDI), and verified by retrieval of death certificates. MEC used death certificate files in Hawaii and California and the US National Death Index to gather information on vital status.

Relevant institutional review boards from each cohort approved the project and informed consent was obtained from all study participants. Clinical and epidemiological characteristics of the study population are shown in table 1.

Table 1.

Characteristics of the study subjects.

CPS-II EPIC MCCS MEC NHS PLCO WHS ALL
No. Of breast cancer cases 2468 3823 501 493 1661 715 594 10255
Age at diagnosis, mean (sd) 69.3 (7.0) 58.7 (8.0) 61.8 (8.8) 65.3 (8.5) 62.6 (9.7) 66.3 (5.6) 60.3 (7.5) 63.0 (9.0)
Months from diagnosis to death or end of follow-up, median (sd) 99.0 (48.5) 57.0 (32.4) 112.0 (46.7) 108.0 (41.5) 120.0 (45.9) 86 (27.9) 51.8 (27.1) 81.0 (46.8)
No. of overall Deaths 337 (14%) 420 (11%) 107 (21%) 114 (23%) 292 (18%) 83 (12%) 26 (4%) 1379 (13%)
No. of breast cancer specific Deaths 127 (5%) 323 (8%) 63 (13%) 43 (9%) 134 (8%) 44 (6%) 20 (3%) 754 (7%)
TNM stage
TNM stage 1 1262 (51%) 1553 (41%) 247 (49%) 289 (59%) 0 (0%) 398 (56%) 0 (0%) 2487 (24%)
TNM stage 2 435 (18%) 549 (14%) 169 (34%) 70 (14%) 0 (0%) 197 (28%) 0 (0%) 1418 (14%)
TNM stage 3 and 4 42 (2%) 102 (3%) 15 (3%) 14 (3%) 0 (0%) 19 (3%) 0 (0%) 192 (2%)
TNM stage unkn. 729 (30%) 1619 (42%) 70 (14%) 120 (24%) 1661 (100%) 101 (14%) 594 (100%) 6158 (60%)
Tumor size
tumor<= 2cm 1624 (66%) 0 (0%) 14 (3%) 350 (71%) 957 (58%) 463 (65%) 426 (72%) 2553 (25%)
tumor >2–<=5cm 404 (16%) 0 (0%) 22 (4%) 112 (23%) 237 (14%) 124 (17%) 126 (21%) 704 (7%)
tumor >5cm 41 (2%) 0 (0%) 449 (90%) 31 (6%) 0 (0%) 12 (2%) 18 (3%) 545 (5%)
tumor size unkn. 399 (16%) 3823 (100%) 16 (3%) 0 (0%) 467 (28%) 116 (16%) 24 (4%) 6453 (63%)
Tumor grade
well diff. 544 (22%) 283 (7%) 111 (22%) 129 (26%) 323 (19%) 186 (26%) 125 (21%) 1701 (17%)
moderately diff. 937 (38%) 744 (20%) 201 (40%) 192 (39%) 569 (34%) 251 (35%) 256 (43%) 3150 (31%)
poorly diff. 484 (20%) 598 (16%) 143 (29%) 107 (22%) 395 (24%) 125 (18%) 130 (22%) 1982 (19%)
diff. not det. 503 (20%) 2198 (57%) 46 (9%) 65 (13%) 374 (23%) 153 (21%) 83 (14%) 3422 (33%)
ER status
ER negative 70 (3%) 624 (16%) 103 (20%) 67 (14%) 296 (18%) 69 (10%) 87 (15%) 1316 (13%)
ER positive 1798 (73%) 2172 (57%) 339 (68%) 353 (72%) 1239 (75%) 492 (69%) 474 (80%) 6867 (67%)
ER not classified 600 (24%) 1027 (27%) 59 (12%) 73 (14%) 126 (7%) 154 (21%) 33 (5%) 2072 (20%)
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SNP selection and genotyping

We selected SNPs for analysis that had statistically significant associations with breast cancer risk (P<5×10−7) in at least one report (table 2). In particular, for the following SNPs we have genotyped either the original SNP or a surrogate: rs4415084 (surrogate rs920329, r2=1 in HapMap CEU), rs9344191 (surrogate rs9449341, r2=1), rs1250003 (surrogate rs704010, r2=1 in HapMap CEU), rs999737 (surrogate rs10483813, r2=1 in HapMap CEU), rs2284378 (surrogate rs8119937 r2=1 in HapMap CEU and rs6059651 r2=1 in HapMap CEU), and rs9344208 (surrogate rs1917063, r2=1 in HapMap CEU).

Table 2.

Information on the selected SNPs.

SNP Gene Chr Location bp (hg19)a Major allele/minor allele Allele associated with increased breast cancer risk Coding annotationh Reference
rs11249433 NOTCH2 1p11 121280363 T/C C intronic variant [37,38]
rs10931936 CASP8 2q33 202143678 C/T T intronic variant [39]
rs1045485 CASP8 2q33 202149339 G/C G missense, non-coding transcript variant, 3′-UTR [40]
rs13387042 Intergenic 2q35 219905582 A/G A intergenic [41]
rs4973768 SLC4A7 3p24 27415763 C/T T 3′-UTR variant [42]
rs4415084b Intergenic 5p12 27415763 C/T T intergenic [43]
rs10941679 Intergenic 5p12 44706248 A/G G intergenic [43]
rs10069690 TERT 5p15 1279540 C/T T intronic variant [44]
rs889312 MAP3K1 5q11 56031634 A/C C intergenic [45]
rs17530068 Intergenic 6q14 82192859 T/C C intergenic [38]
rs13437553 Intergenic 6q14 82303985 T/C C intergenic [38]
rs1917063c Intergenic 6q14 82322957 C/T T intergenic [38]
rs9344191d Intergenic 6q14 82197935 T/G G intergenic [38]
rs3757318 Intergenic 6q25 151913863 G/A A intronic variant [46]
rs9383938 Intergenic 6q25 151987107 G/T T intergenic [38]
rs2046210 Intergenic 6q25 151948116 C/T T intergenic [47]
rs13281615 Intergenic 8q24 128355368 A/G G intronic variant [42]
rs1562430 Intergenic 8q24 128387602 T/C T intronic variant [46]
rs1011970 CDKN2BAS 9p21 22061884 G/T T intronic variant [48]
rs865686 Intergenic 9q31 110888228 T/G T intergenic [48]
rs2380205 Intergenic 10p15 5886484 C/T C intergenic [48]
rs10995190 ZNF365 10q21 68278432 G/A G intronic variant, upstream variant 2KB [48]
rs1250003e ZMIZ1 10q22 80846564 T/C C intronic variant [48]
rs3750817 FGFR2 10q26 123332327 C/T C intronic variant [49]
rs2981582 FGFR2 10q26 123352067 C/T T intronic variant [42]
rs3817198 LSP1 11p15 1908756 T/C C intronic variant, 3′-UTR [45]
rs909116 LSP1 11p15 1941696 T/C T intronic variant [46]
rs614367 Intergenic 11q13 69328514 C/T T intergenic [50]
rs999737f RAD51L1 14q24 69034432 C/T C intronic variant [37]
rs3803662 TNRC9 16q12 52586091 C/T T non-coding transcript [45,41]
rs6504950 COX11 17q22 53056221 G/A G intronic variant [42]
rs12982178 USHBP1 19p13 17371318 T/C T intronic variant [44]
rs8170 C19Orf62 19p13 17389454 G/A T (ER−) synonymous codon [38]
rs2284378g RALY 20q11 32587845 C/T T (ER−) intronic variant [38]
rs4911414 Intergenic 20q11 32729194 G/T T (ER−) intergenic [38]
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a

Genome Reference Consortium Human, build 37 (http://genome.ucsc.edu/cgi-bin/hgGateway).

b

5p12-rs4415084 or surrogate 5p12-rs920329

c

6q14-rs1917063 or surrogate 6q14-rs9344208

d

6q14-rs9344191 or surrogate 6q14-rs9449341

e

ZMIZ1-rs1250003 or surrogate ZMIZ1-rs704010

f

RAD51L1-rs999737 or surrogate RAD51L1-rs10483813

g

RALY-rs2284378 or surrogate RALY-rs6059651, RALY-rs8119937

h

“The coding annotation for these SNPs is referred to multiple, alternative transcripts of each gene, therefore each SNP can be attributed to more than one functional class (e.g. missense in one transcript and non-coding in a different transcript of the same gene). Coding annotations were retrieved from the 1000 genomes (http://browser.1000genomes.org/index.html) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP/).”

Genotyping was performed in the context of other projects and the laboratory procedures have been described in detail elsewhere [19]. Within each study, blinded duplicate samples (~8%) were also included and concordance of these samples was greater than 99.99%.

Statistical analysis

Only subjects with a call rate of at least 90% were included in the study and prevalent case subjects were excluded, resulting in a final number of 10,255 study subjects. Each SNP was tested for Hardy-Weinberg equilibrium using unaffected individuals in the BPC3 cohorts (the information was collected by previous studies [19]). OS as well as breast cancer specific survival were investigated, but our power to detect significant associations between the risk alleles and the latter was limited due to the relative small number of disease specific deaths (N=754).

The association between genetic variants and survival was investigated using Cox proportional hazards regression and hazard ratios (HRs) and confidence intervals (CIs) were computed. Patients were right-censored after 15 years, hence any deaths occurring more than 15 years after diagnosis were considered to be unrelated to breast cancer. To assess the validity of the proportional hazards assumption for the non-genetic factors, we used Schoenfeld residuals [20]. We then created an adjusted, per-allele SNP model, accounting for cohort, age at diagnosis (in 6-year categories) and ER status. Using a backward selection on the remaining prognostic factors (progesterone receptor status, TNM stage, tumor grade, tumor size), we found that TNM stage and tumor grade were associated with survival and thus added them as covariates to the final model. We also carried out subgroup analyses with respect to ER status.

Finally, we carried out tests for heterogeneity between our results from the BPC3 and those reported from the Breast Cancer Association Consortium (BCAC) [10], before combining the HRs in a fixed-effects meta-analysis. The BCAC analysis consisted of up to 25,853 patients (depending on the SNP) of whom 4,076 died of any cause and of whom 2,282 died of breast cancer.

The number of independent tests carried out in this analysis was computed taking into account that some of the SNPs map to the same regions and might be in linkage disequilibrium. Hence, for each locus we calculated the effective number of independent SNPs (Meff), using the SNP Spectral Decomposition approach (simple M method) [21]. The study-wise Meff obtained was 27 and since we modelled survival in the over-all study population as well as in subgroups determined by ER status, the threshold of statistical significance was 9.3×10−4 (0.05/(27×2)). All statistical tests were two sided and all statistical analyses were performed with SAS version 9.2.

In-silico analyses

In order to identify any possible effects of alleles on expression levels of closely situated genes (eQTL) we used Genevar (http://www.sanger.ac.uk/resources/software/genevar/) [22] and Gtex (http://commonfund.nih.gov/GTEx) [23]. To investigate any regulatory effects in the proximity of the SNPs we used RegulomeDB (http://regulome.stanford.edu/) [24] and HaploReg v2B [25].

Results

Using a stringent threshold for statistical significance, we observed one statistically significant association with improved OS for the minor allele (C) of LSP1-rs3817198 (HRallele=0.70; 95% CI: 0.58–0.85; Ptrend=2.84×10−4; HRTC=0.71; 95% CI: 0.55–0.92; HRCC=0.48; 95% CI: 0.31–0.76; P2DF= 1.45×10−3). The HR estimates and the P value were lower among ER− cases (HRallele=0.51; 95% CI: 0.33–0.81; Ptrend=4.16×10−3; HRTC=0.48; 95% CI:0.27–0.85; HRCC=0.30; 95% CI:0.10–0.97; P2DF=0.014) than among ER+ cases (HRallele=0.77; 95% CI: 0.62–0.95; Ptrend=0.015; HRTC=0.82; 95% CI:0.61–1.10; HRCC=0.55; 95% CI: 0.34–0.90; P2DF=0.05), but the test of heterogeneity was not statistically significant (P=0.11).

There were additional associations at the conventional 0.05 level of significance (Table 3). In particular the T allele of CDKN2BAS-rs1011970 was associated with worse OS (HRallele=1.28; 95% CI: 1.03–1.59; Ptrend=0.03; HRGT=1.25; 95% CI:0.96–1.64; HRTT=0.74; 95% CI:0.90–3.36; P2DF=0.09), as well as the G allele of 8q24-rs13281615 (HRallele=1.19; 95% CI: 1.0–1.42; Ptrend=0.04; HRAG=1.23; 95% CI:0.92–1.65; HRGG=1.42; 95% CI:1.00–2.01; P2DF=0.13), the T allele of 11q13-rs614367 (HRallele=1.26; 95% CI: 1.01–1.56; Ptrend=0.04; HRCT=1.14; 95% CI:0.87–1.50; HRTT=2.01; 95% CI:1.15–3.52; P2DF=0.04), and the A allele of ZNF365-rs10995190 (HRallele=1.26; 95% CI: 1.00–1.60; Ptrend=0.05; HRGA=1.15; 95% CI:0.87–1.51; HRAA=2.47; 95% CI:1.19–5.14; P2DF=0.09). A complete list of results is shown in supplementary table 1. Supplementary Figure 1 shows the overall breast cancer survival curves for LSP1-rs3817198. No significant associations were found between the risk alleles and breast cancer specific survival (data not shown).

Table 3.

Results of survival analyses with P<0.05 in BPC3.

SNP Gene Chr Number of Subjects/Events
HR (95% CI) Ptrend HRhet (95% CI) HRhom (95% CI) P2DF
All cases hom. hetero hom. minor
rs1011970 CDKN2BAS 9 6427/858 2666/398 264/35 1.28 (1.03–1.59) 0.03 1.25 (0.96–1.64) 1.74 (0.90–3.36) 0.09
rs13281615 Intergenic 8 2783/404 4125/597 1598/233 1.19 (1.00–1.42) 0.04 1.23 (0.92–1.65) 1.42 (1.00–2.01) 0.13
rs614367 Intergenic 11 6590/919 2483/334 302/44 1.26 (1.01–1.56) 0.04 1.14 (0.87–1.50) 2.01 (1.15–3.52) 0.04
rs3817198 LSP1 11 3961/596 3725/536 904/119 0.70 (0.58–0.85) 2.84×10−4 0.71 (0.55–0.92) 0.48 (0.31–0.76) 1.45×10−3
rs10995190 ZNF365 10 6929/956 2220/302 172/36 1.26 (1.00–1.60) 0.05 1.15 (0.87–1.51) 2.47 (1.19–5.14) 0.09
ER− cases only
rs3817198 LSP1 11 568/132 543/95 111/17 0.51 (0.33–0.81) 4.16×10−3 0.48 (0.27–0.85) 0.30 (0.10–0.97) 0.014
rs614367 Intergenic 11 779/145 278/52 22/8 1.53 (0.93–2.52) 0.09 0.79 (0.44–1.41) 1.48 (0.64–3.43) 0.36
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SNP=single nucleotide polymorphism; HR= per allele hazard ratio; CI=95% confidence interval; Ptrend= P-values for trend (two-sided) were derived from the Wald test (df=1); HRhet=hazard ratio comparing heterozygotes to homozygotes major; HRhom=hazard ratio comparing homozygotes minor to homozygotes major; P2DF = 2 degree of freedom P-value for co-dominant model; Estimates were derived from the Cox proportional hazards regression adjusted for age at diagnosis, cohort, ER status, tumor grade and TNM stage.

Meta-analysis

In the meta-analysis consisting of approximately 35,000 patients and 5,000 deaths, we found significant associations between the minor allele (T) of TNRC9-rs3803662 and worse OS (HRMETA =1.09; 95% CI: 1.04–1.15; Ptrend=6.6×10−4; HRCT=0.96; 95% CI: 0.90–1.03; HRTT=1.21; 95% CI: 1.09–1.35; P2DF=1.25×10−4). This association was particularly strong among ER+ cases (HR =1.14; 95% CI: 1.07–1.21; Ptrend=1.04×10−4; HRCT=0.87; 95% CI: 0.64–1.17; HRTT=1.32; 95% CI: 0.86–2.01; P2DF=0.16) as shown in table 4. This association was originally reported by Fasching [10]. Another relevant result, albeit not significant, was noted for LSP1-rs3817198 among ER− cases (HRMETA=0.85; 95% CI: 0.77–0.94; Ptrend=1.01×10−3; HRTC=0.97; 95% CI: 0.90–1.05; HRCC=0.89; 95% CI: 0.79–1.00; P2DF=0.07). Tests for heterogeneity between the studies were not significant. The complete results from the meta-analysis are shown in supplementary table 2.

Table 4.

Results from meta-analysis (BPC3 and BCAC) on breast cancer overall survival, displaying combined results with P<0.05.

SNP Study Stratum HR (95% CI) Ptrend HRhet (95% CI) HRhom (95% CI) P2DF
rs3803662 BPC3 all 1.04 (0.89–1.23) 0.60 0.89 (0.70–1.12) 1.26 (0.90–1.77) 0.13
BCAC 1.10 (1.04–1.16) 6.44×10−4 0.97 (0.91–1.04) 1.21 (1.09–1.35) 2.00×10−4
META 1.09 (1.04–1.15) 6.60×10−4 0.96 (0.90–1.03) 1.21 (1.09–1.35) 1.25×10−4
BPC3 ER+ 1.06 (0.86–1.30) 0.61 0.87 (0.64–1.17) 1.32 (0.86–2.01) 0.16
BCAC 1.14 (1.07–1.22) 9.32×10−5 0.98 (0.89–1.08) 1.31 (1.13–1.50) 2.00×10−4
META 1.14 (1.07–1.21) 1.04×10−4 0.97 (0.88–1.06) 1.31 (1.15–1.49) 1.37×10−4
BPC3 ER− 1.06 (0.70–1.59) 0.80 0.79 (0.44–1.41) 1.48 (0.64–3.43) 0.36
BCAC 1.05 (0.97–1.14) 0.20 0.97 (0.85–1.10) 1.11 (0.90–1.30) 0.45
META 1.05 (0.97–1.14) 0.19 0.96 (0.85–1.09) 1.12 (0.96–1.31) 0.46
rs3817198 BPC3 all 0.76 (0.64–0.90) 1.26×10−3 0.83 (0.66,1.04) 0.51 (0.34,0.77) 4.40×10−3
BCAC 0.96 (0.90–1.02) 0.18 0.99 (0.93,1.07) 0.92 (0.81,1.04) 0.41
META 0.94 (0.89–1.00) 0.05 0.97 (0.90,1.05) 0.89 (0.79,1.00) 0.07
BPC3 ER+ 0.77 (0.62–0.95) 0.01 0.82 (0.61,1.10) 0.55 (0.34,0.90) 0.05
BCAC 1.01 (0.93–1.10) 0.82 1.04 (0.94,1.15) 1.02 (0.86,1.21) 0.72
META 0.98 (0.91–1.07) 0.69 1.02 (0.92,1.12) 0.97 (0.82,1.14) 0.33
BPC3 ER− 0.51 (0.33–0.81) 4.16×10−3 0.48 (0.27–0.85) 0.30 (0.10–0.97) 0.014
BCAC 0.86 (0.78–0.95) 2.57×10−3 0.92 (0.81–1.00) 0.74 (0.59–0.90) 0.030
META 0.85 (0.77–0.94) 1.01×10−3 0.91 (0.84–0.99) 0.73 (0.60–0.88) 8.59×10−3
rs10941679 BPC3 ER+ 1.27 (1.02,1.57) 0.03 1.29 (0.96–1.72) 1.56 (0.94–2.59) 0.10
BCAC 1.07 (0.98,1.17) 0.15 1.04 (0.94–1.15) 1.14 (0.95–1.36) 0.33
META 1.09 (1.00,1.18) 0.04 1.07 (0.97–1.17) 1.19 (1.00–1.40) 0.21
rs13387042 BPC3 ER− 0.85 (0.59,1.25) 0.41 1.10 (0.54–2.24) 0.75 (0.34–1.66) 0.52
BCAC 0.94 (0.89,1.00) 0.050 1.03 (0.88–1.20) 0.89 (0.75–1.00) 0.17
META 0.94 (0.89,0.997) 0.04 1.03 (0.89–1.20) 0.89 (0.79–0.995) 0.21
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SNP=single nucleotide polymorphism; HR= per-allele hazard ratio; CI=95% confidence interval; Ptrend=P-values for trend (two-sided) were derived from the Wald test (df=1); HRhet=hazard ratio comparing heterozygotes to homozygotes major; HRhom=hazard ratio comparing homozygotes minor to homozygotes major; P2DF = 2 degree of freedom P-value for co-dominant model; Estimates are derived from the Cox proportional hazards regression adjusted for age at diagnosis, cohort, ER status, tumor grade and TNM stage

Possible functional effects

The potential functional relevance of the observed significant associations was investigated using different bioinformatics tools. Using Genevar, we found that the C-allele of LSP1-rs3817198 was associated with increased expression of the cyclin-dependent kinase inhibitor 1C (CDKN1C) gene. This association had a significance (P=1.4×10−3) just above the threshold suggested by Genevar (P<1×10−3). Utilizing HaploReg, we found three SNPs (LSP1-rs72843959, rs11041665, rs112907808) in LD (r2 > 0.97) with LSP1-rs3817198, but we could not find any information on the latter ones using Genevar. The RegulomeDB showed a score of 5, 3a and 6 respectively, which indicate no strong functional importance.

Discussion

The identification of genetic variants that could affect breast cancer survival may further our understanding of the biological mechanisms of disease progression and might be a useful tool for the clinical assessment with the long-term goal of optimizing personalized treatments. Since there are some indications that breast cancer risk-associated variants might also be involved in the disease prognosis [10,8,9] we selected 35 SNPs that have previously been related to breast cancer risk and tested their possible association with survival in 10,255 breast cancer patients enrolled from a consortium of large prospective studies, as well as in a meta-analysis of these data and those from the BCAC for a total of up to 35,000 patients.

The most significant and novel result of this study was the association between the C allele of LSP1-rs3817198 and improved survival (Ptrend=2.84×10−4; P2DF=1.45×10−3) observed in the BPC3 study, and the association between the T allele of TNRC9-rs3803662 and worse survival (P=6.6×10−4; P2DF=1.25×10−4) observed through the meta-analysis.

In order to understand the biological plausibility of our findings we performed in silico analyses using HaploReg, Regulome DB and Genevar. Genevar analyses indicated that the C allele of LSP1-rs3817198 increases the expression of the cyclin-dependent kinase inhibitor 1C (CDKN1C). Cyclin-dependent kinases (CDKs) control the cell cycle and their activity depends on cyclin levels. It is known that up-regulation of cyclins and down-regulation of CDKs promote cell proliferation in malignant tumor cells [26]. In particular CDKN1C is a tumor suppressor gene [27,28] that is often down-regulated in breast cancer cells [29,30]. It is therefore plausible that the observed improvement in survival for carriers of the C allele of LSP1-rs3817198 is potentially due to up-regulation of the CDKN1C gene. However, the p-value for the association between the C-allele of LSP1-rs3817198 and increased CDKN1C expression (P=1.4×10−3) was not significant considering the threshold used in the Genevar software (P<1×10−3), which is rather permissive. Therefore in vitro functional studies are needed to validate the in-silico prediction.

It is worth noting that the C allele of LSP1-rs3817198 has also been consistently found to be associated with increased mammographic density (MD) [31–33]. However, the association between MD and breast cancer prognosis is controversial [34–36], therefore the hypothesis that the C-allele of LSP1-rs3817198 affects survival through MD needs further elucidation. Since data on MD are not available in the BPC3 we could not investigate this further.

The meta-analysis of BPC3 and BCAC identified a significant association between TNRC9-rs3803662 and increased death hazard, both in the overall study population (P=6.6×10−4; P2DF=1.25×10−4) and among ER+ cases (P=1.04×10−4; P2DF=1.37×10−4). The association was not statistically significant, still it is interesting to note that this minor allele has also been associated with increased MD [32]. The association of LSP1-rs3817198 with OS was substantially weaker in the meta-analysis than in the BPC3 study. Although BCAC reported no significant association for this SNP, the HR indicated improved survival, which is consistent with the results from the BPC3. A possible explanation for the lack of complete concordance between the consortia regarding TNRC9-rs3803662 and LSP1-rs3817198 might be that the SNP model used by Fasching and colleagues [10] was not adjusted for TNM stage but for nodal status and tumor size and ER status was only used for stratification and not as adjustment variable.

Our study had several limitations. The majority of the cases were post-menopausal women and all of them were of Caucasian origin, it is therefore impossible to generalize our findings to all breast cancer patients and especially to those of other ethnicities. Furthermore, it should be pointed out that the prognosis of breast cancer is influenced by other factors, such as chemotherapy, radiotherapy, and targeted treatments that we could not account for in our study. Still, this is one of the largest studies to investigate the association between genetic breast cancer risk loci and survival of breast cancer patients.

In conclusion, our study shows that breast cancer risk alleles are not associated with disease prognosis in general; however, we found a significant association between the C allele of LSP1-rs3817198 and improved survival that could be explained by its potential association with an up-regulating effect on the tumor suppressor gene CDKN1C.

Supplementary Material

Supp TableS1-S2 & FigureS1

Novelty and impact.

Our results show that the investigated risk alleles are not associated with over-all survival of breast cancer cases.

Acknowledgments

This work was supported by US National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233-07 to D.J.H., U01-CA98710-06 to M.J.T., U01-CA98216-06 to E.R. and R.K., and U01-CA98758-07 to B.E.H.) and Intramural Research Program of National Institutes of Health and National Cancer Institute, Division of Cancer Epidemiology and Genetics. EPIC Greece was supported the Hellenic Health Foundation and the Stavros Niarchos Foundation. IMS was supported by a Department of Defense Prostate Cancer Research Program Fellowship. The Founding Sources had no role in the study design; in the collection, analysis, and interpretation of data and in the decision to submit the paper for publication.

Footnotes

Conflict of interest: The authors declare that they have no conflict of interest.

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Supplementary Materials

Supp TableS1-S2 & FigureS1
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