Abstract
Purpose
To identify the genetic variants associated with breast cancer survival, a genome-wide association study (GWAS) was conducted of Korean breast cancer patients.
Methods
From the Seoul Breast Cancer Study (SEBCS), 3,226 patients with breast cancer (1,732 in the discovery and 1,494 in the replication set) were included in a two-stage GWAS on disease-free survival (DFS) by tumor subtypes based on hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2). The associations of the re-classified combined prognostic markers through recursive partitioning analysis (RPA) of DFS for breast cancer were assessed with the Cox proportional hazard model. The prognostic predictive values of the clinical and genetic models were evaluated by Harrell’s C.
Results
In the two-stage GWAS stratified by tumor subtypes, rs166870 and rs10825036 were consistently associated with DFS in the HR+ HER2- and HR- HER2- breast cancer subtypes, respectively (P rs166870=2.88×10-7 and P rs10825036=3.54×10-7 in the combined set). When patients were classified by the RPA in each subtype, genetic factors contributed significantly to differentiating the high risk group associated with DFS inbreast cancer, specifically the HR+ HER2- (P discovery=1.18×10-8 and P replication=2.08×10-5) and HR- HRE2- subtypes (P discovery=2.35×10-4 and P replication=2.60×10-2). The inclusion of the SNPs tended to improve the performance of the prognostic models consisting of age, TNM stage and tumor subtypes based on ER, PR, and HER2 status.
Conclusion
Combined prognostic markers that include clinical and genetic factors by tumor subtypes could improve the prediction of survival in breast cancer.
Introduction
Breast cancer is one of the most common malignancies among women in the world. Although breast cancer patients have generally a good prognosis[1], because the 5-year survival for invasive breast cancer cases from 1999 to 2005 was about 90%, large differences exist in survival rate because of a variety of clinicopathological prognostic factors[2]. The tumor-node-metastasis (TNM) staging system approved by the American Joint Committee on Cancer (AJCC) is a well-known important prognostic factor[3]. However, there are prognostic differences within specific stages because of the biological heterogeneity of tumors; thus, additional tumor markers such as tumor grade, lymphovascular invasion, proliferation markers, estrogen and progesterone receptor (ER and PR) status, and human epidermal growth factor receptor 2 (HER2) overexpression have been suggested to provide a more precise prognosis of breast cancer[3–5].
Among those prognostic factors, ER, PR, and HER2 status have been used for breast tumor subtypes classification in terms of heterogeneous clinical behavior and systematic therapy recommendations[6]. The tumor subtype based on ER, PR, and HER2 status has been validated in independent data set with significant differences in their clinical features even in Asian and European, early and metastatic breast cancer patients suggesting the robust classification[7–10].
In addition to clinicopathological prognostic factors, there is evidence supporting that inherited genetic factors influence the prognosis of breast cancer. Several genome-wide association studies (GWAS) have identified common variants associated with the prognosis of breast cancer at multiple genetic loci including C10orf11, ARRDC3, RAD51L1, PBX1, RoR α, SYT6, NTN1, OCA2, and ZFHX3 genes[11–15]. Although genetic susceptibility markers influence differently the prognosis as well as the risk of breast cancer based on the ER, PR, and/or HER2 status[12,15–28], there are no genetic association studies on the prognosis of breast cancer which consider the heterogeneity of intrinsic tumor subtypes composed of various combinations of ER, PR, and HER2 status.
In this study, we hypothesized that the association of breast cancer prognosis with common genetic variants may vary by breast tumor subtypes. This study aims to conduct a two-stage GWAS for disease-free survival (DFS) in breast cancer stratified by tumor subtypes defined by the ER, PR, and HER2 status and evaluate the performance of prognostic models that included genetic variants with well-known clinical factors.
Materials and Methods
Study Population
The Seoul Breast Cancer Study (SEBCS) is a multicenter-based case-control study of female breast cancer in Seoul, Korea as previously reported[29,30]. This two-stage GWAS included a total of 3,226 incident breast cancer cases. A total of 4,040 histologically confirmed breast cancer patients were recruited from Seoul National University Hospital (SNUH) and ASAN Medical Center (AMC) between 2001 and 2007. For the discovery stage, 2,273 breast cancer patients who had been participated in GWAS on breast cancer risk were selected with sufficient DNA samples and successful genotyping[29]. We excluded subjects who had a previous history of breast or other cancers before the recruitment (N = 67), were diagnosed with benign breast disease (N = 17), or had no clinicopathological information (N = 73). After those exclusions which were not mutually exclusive, the subjects with a metastatic disease (N = 30) on review of their medical records were additionally excluded and 2,111 subjects remained. For survival analysis, the subjects who had a follow-up loss or follow-up time of less than 90 days (N = 113) were excluded and 1,998 subjects (95% of 2,111 eligible subjects) remained. Among those subjects, a total of 1,732 incident breast cancer patients with known tumor subtypes were included in the discovery set in this study.
For the replication set, a total of 1,837 breast cancer patients were included comprised of 508 SEBCS participants who were not included in the discovery set and 1,329 newly recruited participants who were histologically confirmed as having breast cancer at SNUH between 2000 and 2008. Of those patients, 1,735 breast cancer patients whose DNA samples were sufficient in concentration and purity were successfully genotyped. After exclusion in common with the discovery stage (Nprevious history of cancers = 13, Nbenign breast disease = 4, Nmetastatic disease = 16, Nfollow-up time of less than 90 days = 86), 1,616 subjects remained. The subjects with unknown tumor subtypes were also excluded, and a total of 1,494 subjects were included in the replication set in this study.
All participants in this study provided written informed consent. The study design was approved by the Committee on Human Research of Seoul National University Hospital (IRB No. H-0503-144-004).
Tumor Subtypes
Information on ER, PR, and HER2 status was obtained from the medical records of patients’ based on laboratory results and the interpretation of pathologists. The ER and PR status was determined with immunohistochemistry (IHC) test. When ER and/or PR tumor cells showed 10% or more expression by IHC, the hormone receptor (HR) status was considered positive. Otherwise, HR was considered negative when both ER and PR tumor cells showed less than 10% expression by IHC. The HER2 status was defined by IHC and fluorescence in situ hybridization (FISH) tests according to HercepTest criteria[31]. When using the IHC staining score of HER2, 0 or 1+ was regarded as negative, while 3+ was considered as positive. When the IHC staining score of HER2 was 2+, the HER2 status was estimated with the FISH test. Tumor subtypes were classified as ER and/or PR positive and HER2 negative (HR+ HER2-), ER and/or PR positive and HER2 positive (HR+ HER2+), ER and PR negative and HER2 positive (HR- HER2+), and ER and PR negative and HER2 negative (HR- HER2-) subtypes.
Genotyping and Quality Control
Genotyping was conducted using Affymetrix Genome-Wide Human SNP array 6.0 chip (Affymetrix, Inc.) and quality control steps ((a) a p-value<1.0×10-6 for deviation from Hardy-Weinberg equilibrium (HWE), (b) a call rate<95%, (c) a minor allele frequency (MAF)<1%, (d) a p-value<1.0×10-4 for differential missingness between cases and controls, and (e) multiple positioning and/or mitochondrial SNPs) were considered, as previously described[29]. Finally, a total of 555,525 genotyped SNPs remained in the discovery set. Moreover, an imputation of the SNPs based on the Han Chinese from Beijing and Japanese from Tokyo (CHB+JPT) data from the HapMap Phase II database (release 22) as a reference panel was done with the hidden Markov model using MaCH 1.0[32]. Among the 2,416,663 inferred SNPs, 2,210,580 remained after excluding SNPs that had an imputation quality score (r2) of <0.3 in the discovery set. When SNPs were genotyped as well as imputed, the information from the genotyped SNPs was used.
For the replication set, SNPs with a p-value less than 5.0×10-6 and a MAF equal to or more than 10% for the per allele hazard ratio (HR) were selected from each tumor subtype in the discovery stage. A total of 10 lead SNPs that included other SNPs in linkage disequilibrium (LD, r2>0.4) at loci with multiple SNPs were selected for genotyping in the replication stage as follows: rs161041, rs2835688, rs9935088, and rs166870 in HR+ HER2-; rs1896346 and rs12940572 in HR+ HER2+; rs34073156 and rs10906761 in HR- HER2+, and rs10825036 and rs10862597 in HR- HER2-. Proxy SNPs, rs1081228 (r2 = 0.98) and rs4750561 (r2 = 1.00), were genotyped for rs166870 at 15q25 and rs10906761 at 10p31, respectively, because of the genotyping failure of the original ones. The LD metrics (r2) of the selected SNP pairs were calculated using the SNP Annotation and Proxy Search (SNAP) based on HapMap release 22 in the CHB+JPT population panel. When the selected SNP pairs showed LD (r2>0.4), SNPs with the lowest p-value were selected for the per-allele HR, which were genotyped with the Fluidigm 192.24 Dynamic Array. Integrated Fluidic Circuit (IFC) (Fluidigm Corp. South San Francisco, CA, USA) was used according to the manufacturer’s instructions. When the selected SNPs failed genotyping, proxy SNPs were selected based on the LD metrics (r2) and genotyped. The success rates for genotyping were greater than 99% for all replication SNPs.
Outcomes
The information on follow-up time, and recurrence status was obtained through retrospectively reviewing the patients’ medical records. The DFS time was defined as the time from the initial breast cancer surgery to the time of recurrence which includes loco-regional recurrence, first distant metastasis, contralateral breast cancer, and second primary cancer. The breast cancer patients who did not have evidence on recurrence were censored at last follow-up until 2011.
Statistical Analysis
The associations between each SNP and DFS stratified by tumor subtypes were estimated with Cox proportional hazard models adjusted for age, recruiting center, and TNM stage. The hazard ratios (HRs) and 95% confidence intervals (CIs) per allele for each SNP were assessed in the additive model which was based on the number of rare alleles carried. The statistical significance of the associations was estimated with the p-value for the trend test with 1 degree of freedom. The analyses were done with the PLINK program version 1.07 and R 2.15.1 package (GenABEL and ProbABEL) and confirmed with SAS 9.3. To validate previously reported association, the SNPs identified from previous GWAS also analyzed. Using web-based Locus Zoom, regional association plots of the selected gene regions were generated. To estimate combined associations of the discovery and replication sets between SNPs and DFS, random-effects meta-analyses were done with STATA version 12.
A recursive partitioning analysis (RPA) of the prognostic factors was performed to classify breast cancer patients by distinguished groups based on the survival time[33]. The prognostic factors assessed by RPA were age, recruiting center, TNM stage, tumor subtype, and selected SNPs (rs166870 and rs10825036) were included. RPA was also done within specific tumor subtypes for those SNPs from the GWAS that were considered predictive factors. Kaplan-Meier graphs and HRs and 95% CIs of the Cox model are presented for the combined prognostic groups. Within each group, the probabilities of DFS and the percentage of breast cancer patients were measured. The predictive powers of survival models which included age, recruiting centers, TNM stage, and tumor subtypes with or without selected SNPs were calculated with Harrell’s C statistics, and the differences between the predictive powers were estimated with the p-value expressed by the lincom command in STATA. All statistical analyses were done again among patients with TNM stage I-III as a sensitivity analysis, and a statistically significant level was a two-sided p-value of 0.05.
Results
Characteristics of the Study Population
The characteristics of the 3,226 study participants and the associations with DFS are summarized in Table 1. The median follow-up time was 3.8 years (range, 0.3–8.0 years) in the discovery and 4.6 years (range, 0.3–8.5 years) in the replication sets. During the follow-up period, 214 (12.4%) patients in the discovery set and 164 (11.0%) patients in the replication set had events. Tumor size, nodal status, TNM stage, and tumor subtypes were statistically significant in associations with DFS in both the discovery and replication sets. The participants had a similar distribution for age, nodal status and ER and PR status, but a different distribution for tumor size, TNM stage, HER2 status, and breast tumor subtypes between discovery and replication sets (p-value<0.05 by Chi-square test). The characteristics including age, TNM stage, and tumor subtypes were not significantly different between remained and excluded subjects due to follow-up loss (data not shown). The characteristics of the study participants by tumor subtypes are presented in S1 Table.
Table 1. Characteristics of breast cancer patients and associations with disease-free survival (DFS).
| Discovery set | Replication set | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ntotal | (%) | Nevent | (%) | HR a | (95% CI) | Ntotal | (%) | Nevent | (%) | HR a | (95% CI) | |
| No. of patients | 1,732 | (100.0) | 214 | (100.0) | 1,494 | (100.0) | 164 | (100.0) | ||||
| Median follow-up time, years (range) | 3.8 | (0.3–8.0) | 2.1 | (0.3–8.1) | 4.6 | (0.3–8.5) | 2.2 | (0.3–8.0) | ||||
| Total time at risk, person-years | 6978.4 | 562.0 | 6677.8 | 429.4 | ||||||||
| Median age at surgery, years (range) | 47 | (26–82) | 46 | (30–82) | 1.01 | (0.99–1.02) | 47 | (21–82) | 44 | (21–74) | 0.98 | (0.97–1.00) |
| Tumor size | ||||||||||||
| ≤2 cm | 942 | (54.4) | 69 | (32.2) | 1.00 | ref. | 798 | (53.4) | 54 | (32.9) | 1.00 | ref. |
| 2–5 cm | 648 | (37.4) | 109 | (50.9) | 1.58 | (1.15–2.18) | 638 | (42.7) | 94 | (57.3) | 1.63 | (1.15–2.32) |
| >5 cm | 98 | (5.7) | 33 | (15.4) | 2.95 | (1.87–4.64) | 51 | (3.4) | 16 | (9.8) | 3.25 | (1.79–5.87) |
| Nodal status | ||||||||||||
| negative | 1,059 | (61.1) | 80 | (37.4) | 1.00 | ref. | 933 | (62.5) | 68 | (41.5) | 1.00 | ref. |
| positive | 673 | (38.9) | 134 | (62.6) | 2.15 | (1.59–2.91) | 561 | (37.6) | 96 | (58.5) | 2.19 | (1.57–3.06) |
| TNM stage | ||||||||||||
| 0 | 167 | (9.6) | 5 | (2.3) | 0.44 | (0.18–1.12) | 29 | (1.9) | 0 | (0.0) | - | |
| I | 596 | (34.4) | 43 | (20.1) | 1.00 | ref. | 566 | (37.9) | 35 | (21.3) | 1.00 | ref. |
| II | 694 | (40.1) | 87 | (40.7) | 1.51 | (1.04–2.18) | 705 | (47.2) | 82 | (50.0) | 1.85 | (1.24–2.75) |
| III | 275 | (15.9) | 79 | (36.9) | 4.15 | (2.84–6.06) | 194 | (13.0) | 47 | (28.7) | 4.63 | (2.97–7.22) |
| ER status | ||||||||||||
| positive | 1,069 | (61.7) | 100 | (46.7) | 1.00 | ref. | 921 | (61.7) | 81 | (49.4) | 1.00 | ref. |
| negative | 663 | (38.3) | 114 | (53.3) | 1.53 | (0.13–2.08) | 573 | (38.4) | 83 | (50.6) | 1.35 | (0.96–1.89) |
| PR status | ||||||||||||
| positive | 936 | (54.0) | 76 | (35.5) | 1.00 | ref. | 778 | (52.1) | 63 | (38.4) | 1.00 | ref. |
| negative | 793 | (45.8) | 138 | (64.5) | 1.75 | (1.28–2.39) | 714 | (47.8) | 101 | (61.6) | 1.33 | (0.94–1.89) |
| HER2 status | ||||||||||||
| negative | 1,270 | (73.3) | 152 | (71.0) | 1.00 | ref. | 1,186 | (79.4) | 107 | (65.2) | 1.00 | ref. |
| positive | 462 | (26.7) | 62 | (29.0) | 0.90 | (0.66–1.23) | 308 | (20.6) | 57 | (34.8) | 1.60 | (1.15–2.24) |
| Tumor subtypes | ||||||||||||
| HR+ HER2- | 995 | (57.5) | 91 | (42.5) | 1.00 | ref. | 907 | (60.7) | 68 | (41.5) | 1.00 | ref. |
| HR+ HER2+ | 241 | (13.9) | 26 | (12.2) | 1.11 | (0.72–1.73) | 162 | (10.8) | 28 | (17.1) | 1.87 | (1.20–2.93) |
| HR- HER2+ | 221 | (12.8) | 36 | (16.8) | 1.93 | (1.30–2.86) | 146 | (9.8) | 29 | (17.7) | 2.51 | (1.61–3.90) |
| HR- HER2- | 275 | (15.9) | 61 | (28.5) | 2.41 | (1.73–3.35) | 279 | (18.7) | 39 | (23.8) | 1.76 | (1.18–2.62) |
Abbreviations: DFS, disease-free survival; HR, hazard ratio; CI, confidence interval; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; HR, hormone receptor; ref. reference.
aCox proportional hazard model adjusted for age, recruiting center, TNM stage, and tumor subtypes.
Genome-Wide Association Study on Prognosis
The associations between previously identified SNPs through the GWAS of prognosis and DFS in the SEBCS by tumor subtypes are listed in S2 Table. Although none of those SNPs reported in the previous GWAS were further replicated in the overall breast cancer, 4 SNPs showed significant associations with DFS in the specific tumor subtypes.
Although there were no SNPs that reached a nominal genome-wide statistical significance (p-value<5.0×10-8), a total of 10 SNP for DFS achieved p-values of 5.0×10-5f in each subtype in the discovery set (Table 2). Among these SNPs, rs166870 in HR+ HER2- (p trend = 0.03) and rs10825036 in HR- HER2- (p trend = 0.06) had statistically marginal significance in the replication set (Table 2). The regional plots for those two SNPs in associations with DFS in breast cancer for each subtype are shown in Fig 1. In combined analyses of the discovery and replication sets, those two SNPs had strong associations among breast cancer patients for each subtype (HRrs166870 = 2.30, 95% CI = 1.67–3.15, p trend = 2.88×10-7 in HR+ HER2- and HRrs10825036 = 2.26, 95% CI = 1.34–3.81, p trend = 3.54×10-7 in HR- HER2-, Table 2). The results were similar when breast cancer patients with TNM stage 0 were excluded (S3 Table). To identify the heterogeneity of the prognosis for those SNPs according to tumor subtypes, the associations with DFS for the other tumor subtypes of breast cancer were estimated, and they were not statistically associated with the other subtypes (Fig 2) and p-values for heterogeneity by tumor subtypes were statistically significant (p rs166870<0.01 and p rs10825036 = 0.02 in combined set).
Table 2. Associations between SNPs with the level of p-value<5.0×10-6 and disease-free survival (DFS) in breast cancer patients by tumor subtypes.
| SNP | Loci | Nearby gene | Alleles a | Discovery set | Replication set | Combined set | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MAF | HR b | (95% CI) | P | MAF c | HR b | (95% CI) | P | HR c | (95% CI) | P | |||||
| HR+ HER2- | |||||||||||||||
| rs161041 | 5q32 | PPP2R2B | C | T | 0.14 | 2.35 | (1.68–3.30) | 7.45×10-7 | 0.13 | 0.84 | (0.50–1.43) | 0.53 | 1.44 | (0.53–3.93) | 0.48 |
| rs2835688 | 21q22 | intergenic/DSCR3 | A | G | 0.26 | 2.65 | (1.71–4.11) | 1.40×10-6 | 0.27 | 1.07 | (0.73–1.56) | 0.74 | 1.67 | (0.68–4.07) | 0.26 |
| rs9935088 | 16q23 | WWOX | A | G | 0.41 | 2.00 | (1.50–2.66) | 1.98×10-6 | 0.41 | 0.91 | (0.64–1.29) | 0.58 | 1.35 | (0.62–2.94) | 0.44 |
| rs166870 d | 15q25 | intergenic/MTHFS | C | T | 0.13 | 2.34 | (1.64–3.35) | 3.16×10-6 | 0.87 | 2.13 | (1.07–4.23) | 0.03 | 2.30 | (1.67–3.15) | 2.88×10-7 |
| HR+ HER2+ | |||||||||||||||
| rs1896346 | 12q24 | intergenic/TBX3 | G | A | 0.38 | 0.12 | (0.05–0.28) | 1.09×10-6 | 0.44 | 1.08 | (0.62–1.88) | 0.80 | 0.37 | (0.04–3.13) | 0.36 |
| rs12940572 | 17q21 | LASP1 | T | C | 0.29 | 0.18 | (0.09–0.36) | 2.77×10-6 | 0.35 | 1.11 | (0.63–1.96) | 0.72 | 0.45 | (0.07–2.72) | 0.38 |
| HR- HER2+ | |||||||||||||||
| rs34073156 | 8q24 | intergenic/LOC100129367 | G | A | 0.10 | 4.14 | (2.26–7.60) | 4.58×10-6 | 0.07 | 1.27 | (0.48–3.35) | 0.64 | 2.44 | (0.77–7.74) | 0.13 |
| rs10906761 e | 10p13 | intergenic/CDNF | C | T | 0.49 | 0.26 | (0.14–0.47) | 4.94×10-6 | 0.48 | 0.95 | (0.54–1.68) | 0.87 | 0.50 | (0.14–1.80) | 0.29 |
| HR- HER2- | |||||||||||||||
| rs10825036 | 10q21 | intergenic/PCDH15 | T | G | 0.32 | 2.87 | (1.90–4.34) | 6.22×10-7 | 0.29 | 1.68 | (0.97–2.89) | 0.06 | 2.26 | (1.34–3.81) | 3.54×10-7 |
| rs10862597 | 12q21 | intergenic/RPL6P25 | C | G | 0.32 | 2.90 | (1.76–4.77) | 3.04×10-6 | 0.30 | 1.07 | (0.67–1.70) | 0.79 | 1.75 | (0.66–4.66) | 0.26 |
Abbreviations: SNP, single-nucleotide polymorphism; DFS, disease-free survival; RAF, risk-allele frequency; HR, hazard ratio; CI, confidence interval; MAF, minor allele frequency; HR, hormone receptor; HER2, human epidermal growth factor receptor 2.
aMajor and minor alleles.
bCox proportional hazard model adjusted for age, recruiting center, and TNM stage.
cRandom-effect meta-analysis of discovery and replication set.
dA proxy SNP, rs1081228, was genotyped for rs166870 in the replication set (r2 = 0.96 and D' = 1.00 in CHB+JPT).
eA proxy SNP,rs4750561, was genotyped for rs10906761 in the replication set (r2 = 1.00 and D' = 1.00 in CHB+JPT).
Fig 1. Regional plots for SNPs, (A) rs166870 and (B) rs10825036, in associations with DFS in the HR+ HER2- and HR- HER2- breast cancer subtype, respectively.
Fig 2. Associations between selected SNPs and disease-free survival (DFS) of breast cancer patients by tumor subtypes.
(A) rs166870. (B) rs10825036.
Prognostic Value of the Combined Markers of Clinical and Genetic Factors
RPA classified patients into distinct prognostic groups in each subtype shown in Table 3, which were significantly associated with the DFS of breast cancer in both the discovery and replication sets. The rs166870 (CC+CT or TT) was the second node among the HR+ HER2- patients after the TNM stage (0-II or III) (p discovery = 1.18×10-8 and p replication = 2.08×10–5, Table 2), and rs10825036 (TT+TG or GG) was the only node among the HR- HER2- patients showing significant differences between the groups (p discovery = 2.35×10-4 and p relication = 2.60×10-2, Table 3). The similar results were presented when breast cancer patients with TNM stage 0 were excluded (S4 Table).
Table 3. Associations between different combined groups of clinical and genetic factors and disease-free survival (DFS) among breast cancer patients.
| Discovery set | Replication set | |||||
|---|---|---|---|---|---|---|
| HR a | (95% CI) | P | HR a | (95% CI) | P | |
| Group by RPA among HR+ HER2- breast cancer patients | ||||||
| Group 1: TNM stage 0-II and rs166870CC+CT | 1.00 | ref. | 1.18×10-8 | 1.00 | ref. | 2.08×10-5 |
| Group 2: TNM stage 0-II and rs166870TT | 5.52 | (2.00–15.28) | 2.01 | (0.90–4.47) | ||
| Group 3: TNM stage III and rs166870CC+CT | 3.61 | (2.29–5.68) | 3.07 | (0.64–14.83) | ||
| Group 4: TNM stage III and rs166870TT | 10.50 | (1.43–77.06) | 7.26 | (2.95–17.88) | ||
| Group by RPA among HR- HER2- breast cancer patients | ||||||
| Group1: rs10825036TT+TG | 1.00 | ref. | 2.35×10-4 | 1.00 | ref. | 2.60×10-2 |
| Group2: rs10825036GG | 3.45 | (1.78–6.67) | 2.17 | (1.10–4.28) | ||
Abbreviations: DFS, disease-free survival; HR, hazard ratio; CI, confidence interval; RPA, recursive partitioning analysis; HR, hormone receptor; HER2, human epidermal growth factor receptor 2.
aCox proportional hazard model adjusted for age and recruiting center, additional TNM stage for group by tumor subtypes and selected SNPs.
The predictive powers of DFS for breast cancer were compared between the model with clinical variables alone and the model with combined clinical and genetic variables, and the latter tended to have better predictive powers in overall (Harrell’s Cclinical model = 70.92% and Harrell’s Ccombined model = 71.37%, p = 0.03), HR+ HER2- (Harrell’s Cclinical model = 65.08% and Harrell’s Ccombined model = 66.69%, p<0.01), and HR- HER2- breast cancer (Harrell’s Cclinical model = 63.26% and Harrell’s Ccombined model = 65.88%, p<0.01).
Discussion
From the two-stage GWAS, genetic factors that were associated with DFS in breast cancer were identified by tumor subtypes, and the prognostic values for the combined clinical and genetic factors were evaluated. The SNPs, rs166870 and rs10825036, showed a statistically significant association with DFS in the HR+ HER2- and HR- HER2-breast tumor subtypes, respectively, and these associations were not seen in the other tumor subtypes. They contributed to the prognostic models by improving the prediction of DFS within specific subtypes.
We conducted a subtype-specific GWAS, unlike other previous studies that had conducted a GWAS for overall breast cancer before stratifying by ER, PR, and HER2 status because breast cancer is considered as a heterogeneous disease for which the prognosis varies across subtypes[34]. This intertumor heterogeneity is plausible in that breast cancer could originate from different cell types according to the tumor subtype[35] and is supported by previous studies showing the heterogeneous associations between SNPs and the prognosis of breast cancer by ER, PR and HER2 status[15,18–27], in agreement with the current study (Fig 2). Another reason for the subtype-specific analyses was that breast cancer subtypes are considered as a predictor factor that distinguishes different responses to particular therapies among patients[36]. Because those differences in responses to particular therapies could have been a result of subtype-specific biological differences, the stratification of breast tumors by subtypes is necessary[37].
Among previously identified SNPs by GWAS for the prognosis of breast cancer, none of the SNPs were associated with DFS overall in this study (S2 Table). Of those SNPs that showed an association in the subtypes, rs3784099 and rs9934948 had been associated with the total mortality, for overall and ER+ breast cancer in Chinese women[15]. Although the association of SNP rs9934948 was not in the same direction as in this study, the reason for this might be because the tumor subtypes, specifically HR+ and HR-, had a different tumor biology from that of ErbB2, and the luminal subtypes showed entirely different up-regulated gene patterns even in the same organ relapse patients[38]. The other identified SNPs, rs1387389, rs2774307, and rs4778137 (especially in ER-), are associated with survival in European women, and the same directions for the estimates are shown in our patients[12,14]. The SNP rs4778137 is also significantly associated with the overall survival (OS) of breast cancer in Chinese women, even though it has not been replicated in the ER- subtype[15].
In the region surrounding the SNP rs166870, an acetylation of lysine 27 as an activation mark in the H3 histone protein (H3K27Ac) was observed by in silico analysis (S1A Fig). Rs166870 is close to the methenyltetrahydrofolate synthetase (MTHFS) gene, which is involved in folate mediated one-carbon metabolism. Although associations between SNPs in the MTHFS gene and the risk and prognosis of breast cancer have not been reported, an association has been reported between MTHFS variants and the prognosis of lung cancer[39], and also other one-carbon metabolism pathway genes are associated with the prognosis of breast cancer[22,40–42]. One-carbon metabolism influences DNA methylation and synthesis[43], regulating Bcl-2/adenovirus E1B 19 kDa-interacting protein 3 (BNIP3). The loss of BNIP3 expression has been correlated with poor prognostic features such as lymph node metastasis, a higher mitotic activity index (MAI), and tubule formation in breast cancer[44]. Moreover, the MTHFS protein is known as a potential mediator of insulin-like growth factor-1 receptor (IGF-1R) dependent transformation[45]. Breast cancer patients, especially HR+, HER2-, and tumor patients with a Ki-67≥14%, who had a better score for IGF-1R expression had a higher survival[46].
SNP rs10825036 was also represented as a H3K27Ac mark by in silico analysis (S1B Fig). Although there are no studies on rs10825036, the SNP was weakly correlated with rs583012, which was associated with the c-reactive protein[47], and rs12256830 was associated with antibody levels[48]. Rs10825036 is close to the PCDH15 gene which encodes integral membrane proteins that mediate calcium-dependent cell-cell adhesion. Previously, the SNPs of the PCDH15 gene are known for associations with adverse events caused by chemotherapy in breast cancer[49] as well as with lipid abnormalities[50]. The lipid profiles have been associated with the risk, stage, and recurrence of breast cancer[51–53]. Moreover, lipids profiles have been distinguished between triple-negative and other breast tumor subtypes[54].
The predictive power of the combined model including rs166870 and rs10825036 identified from the two-stage GWAS, was more improved than that of the clinical model which did not include the SNPs. In previous multivariate survival models, Harrell’s C statistics were estimated ranging from 0.69 to 0.82 according to the number and type of clinicopathological factors and the characteristics of the study population included in the models[55–57]. There were no SNPs whose c-indices were estimated, but the gene expression signatures improved the predictive powers when additionally included in multivariate clinicopathological models[58].
To assign the risk group according to the prognosis of breast cancer, clinical and genetic factors were combined and re-classified with RPA. From the results of the RPA, the genetic factors selected from the two-stage GWAS were more valuable when the analyses were stratified by tumor subtypes, and only one node of the genetic factors was statistically significant regardless of the clinical factors in HR- HER2- breast cancer. Therefore, prognostic markers that include the SNPs identified from the GWAS could be valuable in predicting the prognosis of breast cancer, particularly in specific tumor subtypes.
This is the first study that conducted a two-stage GWAS by tumor subtypes based on HR and HER2 status. Furthermore, combined survival models that include genetic factors identified by the two-stage GWAS as well as other well-known clinical factors were evaluated for predicting the prognosis of breast cancer. The first limitation of this study was that the statistical significances of the associations from the two-stage GWAS did not reach a p-value<5.0×10-8 as the nominal significance for the GWAS[59]. However, there have been a few GWAS on the prognosis of breast cancer, and none of the SNPs associated with the prognosis of breast cancer have had a nominal significance from the GWAS so far[11–15]. Second, the treatment information for breast cancer was not controlled in the analyses because of substantial missing data. Although the adjuvant chemotherapy and radiation did not affect associations of survival, the hormone therapy was associated with survival in the discovery set but not in the replication set (data not shown), which tended to depend on the tumor subtypes. All the analyses were adjusted or stratified by tumor subtypes instead of controlling for treatments.
It has been inconclusive whether genetic factors influence survival by intrinsic subtypes. In this analysis, the novel genetic markers including rs166870 and rs10825036 were associated with survival in HR+ HER2- and HR- HER2- tumors showing heterogeneity between tumor subtypes. The novel genetic markers identified in this study would be helpful to understand biological insights in heterogeneous breast cancer patients. Furthermore, RPA showed those genetic markers played a role in distinguishing between high and low risk groups of breast cancer patients. The combined prognostic markers that include the genetic markers and well-known clinical factors could be useful to predict the clinical outcome for breast cancer patients.
In conclusion, our two-stage GWAS identified two novel SNPs (rs166870 and rs10825036) associated with DFS in the HR+ HER2- and HR- HER2- subtypes, respectively. When these genetic factors were added to well-known clinical survival models that included age, TNM stage, and tumor subtype, improved predictive powers of the models were observed. Furthermore, our RPA showed that genetic factors had a role in distinguishing between high and low risk groups when using combined prognostic markers. To validate these results, further studies are needed to evaluate the predictive power of the survival models which include genetic factors as well as clinical factors.
Supporting Information
(A) rs166870 and (B) rs10825036.
(TIF)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
Acknowledgments
This research was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012–0000347) and by the Seoul National University Hospital (2014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability
All relevant data are within the paper and its Supporting Information files.
Funding Statement
This research was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347) and by the Seoul National University Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
References
- 1. Horner MJ, Ries LAG, Krapcho M, Neyman N, Aminou R, Howlader N, et al. (2009) SEER Cancer Statistics Review, 1975–2006, National Cancer Institute; Bethesda, MD, http://seer.cancer.gov/csr/1975_2006/, based on November 2008 SEER data submission, posted to the SEER web site. 10.1016/j.healun.2014.01.862 [DOI] [Google Scholar]
- 2. Rosenberg J, Chia YL, Plevritis S (2005) The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the U.S. SEER database. Breast Cancer Res Treat 89: 47–54. [DOI] [PubMed] [Google Scholar]
- 3. Singletary SE, Allred C, Ashley P, Bassett LW, Berry D, Bland KI, et al. (2002) Revision of the American Joint Committee on Cancer staging system for breast cancer. J Clin Oncol 20: 3628–3636. [DOI] [PubMed] [Google Scholar]
- 4. Cianfrocca M, Goldstein LJ (2004) Prognostic and predictive factors in early-stage breast cancer. Oncologist 9: 606–616. [DOI] [PubMed] [Google Scholar]
- 5. Ludwig JA, Weinstein JN (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5: 845–856. [DOI] [PubMed] [Google Scholar]
- 6. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ, et al. (2011) Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22: 1736–1747. 10.1093/annonc/mdr304 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Kurebayashi J, Moriya T, Ishida T, Hirakawa H, Kurosumi M, Akiyama F, et al. (2007) The prevalence of intrinsic subtypes and prognosis in breast cancer patients of different races. Breast 16 Suppl 2: S72–77. [DOI] [PubMed] [Google Scholar]
- 8. Puig-Vives M, Sanchez MJ, Sanchez-Cantalejo J, Torrella-Ramos A, Martos C, Ardanaz E, et al. (2013) Distribution and prognosis of molecular breast cancer subtypes defined by immunohistochemical biomarkers in a Spanish population-based study. Gynecol Oncol 130: 609–614. 10.1016/j.ygyno.2013.05.039 [DOI] [PubMed] [Google Scholar]
- 9. Falck AK, Ferno M, Bendahl PO, Ryden L (2013) St Gallen molecular subtypes in primary breast cancer and matched lymph node metastases—aspects on distribution and prognosis for patients with luminal A tumours: results from a prospective randomised trial. BMC Cancer 13: 558 10.1186/1471-2407-13-558 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Zhao J, Liu H, Wang M, Gu L, Guo X, Gu F, et al. (2009) Characteristics and prognosis for molecular breast cancer subtypes in Chinese women. J Surg Oncol 100: 89–94. 10.1002/jso.21307 [DOI] [PubMed] [Google Scholar]
- 11. Azzato EM, Pharoah PD, Harrington P, Easton DF, Greenberg D, Caporaso NE, et al. (2010) A genome-wide association study of prognosis in breast cancer. Cancer Epidemiol Biomarkers Prev 19: 1140–1143. 10.1158/1055-9965.EPI-10-0085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Azzato EM, Tyrer J, Fasching PA, Beckmann MW, Ekici AB, Schulz-Wendtland R, et al. (2010) Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival. J Natl Cancer Inst 102: 650–662. 10.1093/jnci/djq057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kiyotani K, Mushiroda T, Tsunoda T, Morizono T, Hosono N, Kubo M, et al. (2012) A genome-wide association study identifies locus at 10q22 associated with clinical outcomes of adjuvant tamoxifen therapy for breast cancer patients in Japanese. Hum Mol Genet 21: 1665–1672. 10.1093/hmg/ddr597 [DOI] [PubMed] [Google Scholar]
- 14. Rafiq S, Tapper W, Collins A, Khan S, Politopoulos I, Gerty S, et al. (2013) Identification of inherited genetic variations influencing prognosis in early-onset breast cancer. Cancer Res 73: 1883–1891. 10.1158/0008-5472.CAN-12-3377 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Shu XO, Long J, Lu W, Li C, Chen WY, Delahanty R, et al. (2012) Novel genetic markers of breast cancer survival identified by a genome-wide association study. Cancer Res 72: 1182–1189. 10.1158/0008-5472.CAN-11-2561 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Fasching PA, Pharoah PD, Cox A, Nevanlinna H, Bojesen SE, Karn T, et al. (2012) The role of genetic breast cancer susceptibility variants as prognostic factors. Hum Mol Genet 21: 3926–3939. 10.1093/hmg/dds159 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Roberts MR, Hong CC, Edge SB, Yao S, Bshara W, Higgins MJ, et al. (2013) Case-only analyses of the associations between polymorphisms in the metastasis-modifying genes BRMS1 and SIPA1 and breast tumor characteristics, lymph node metastasis, and survival. Breast Cancer Res Treat 139: 873–885. 10.1007/s10549-013-2601-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Fu F, Wang C, Chen LM, Huang M, Huang HG (2013) The influence of functional polymorphisms in matrix metalloproteinase 9 on survival of breast cancer patients in a Chinese population. DNA Cell Biol 32: 274–282. 10.1089/dna.2012.1928 [DOI] [PubMed] [Google Scholar]
- 19. Muendlein A, Lang AH, Geller-Rhomberg S, Winder T, Gasser K, Drexel H, et al. (2013) Association of a common genetic variant of the IGF-1 gene with event-free survival in patients with HER2-positive breast cancer. J Cancer Res Clin Oncol 139: 491–498. 10.1007/s00432-012-1355-3 [DOI] [PubMed] [Google Scholar]
- 20. Eroglu A, Karabiyik A, Akar N (2012) The association of protease activated receptor 1 gene -506 I/D polymorphism with disease-free survival in breast cancer patients. Ann Surg Oncol 19: 1365–1369. 10.1245/s10434-011-1969-8 [DOI] [PubMed] [Google Scholar]
- 21. Hsieh SM, Look MP, Sieuwerts AM, Foekens JA, Hunter KW (2009) Distinct inherited metastasis susceptibility exists for different breast cancer subtypes: a prognosis study. Breast Cancer Res 11: R75 10.1186/bcr2412 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Martin DN, Boersma BJ, Howe TM, Goodman JE, Mechanic LE, Chanock SJ, et al. (2006) Association of MTHFR gene polymorphisms with breast cancer survival. BMC Cancer 6: 257 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Boyapati SM, Shu XO, Ruan ZX, Cai Q, Smith JR, Wen W, et al. (2005) Polymorphisms in ER-alpha gene interact with estrogen receptor status in breast cancer survival. Clin Cancer Res 11: 1093–1098. [PubMed] [Google Scholar]
- 24. Ishitobi M, Miyoshi Y, Ando A, Hasegawa S, Egawa C, Tamaki Y, et al. (2003) Association of BRCA2 polymorphism at codon 784 (Met/Val) with breast cancer risk and prognosis. Clin Cancer Res 9: 1376–1380. [PubMed] [Google Scholar]
- 25. Figueroa JD, Garcia-Closas M, Humphreys M, Platte R, Hopper JL, Southey MC, et al. (2011) Associations of common variants at 1p11.2 and 14q24.1 (RAD51L1) with breast cancer risk and heterogeneity by tumor subtype: findings from the Breast Cancer Association Consortium. Hum Mol Genet 20: 4693–4706. 10.1093/hmg/ddr368 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Broeks A, Schmidt MK, Sherman ME, Couch FJ, Hopper JL, Dite GS, et al. (2011) Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum Mol Genet 20: 3289–3303. 10.1093/hmg/ddr228 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Garcia-Closas M, Hall P, Nevanlinna H, Pooley K, Morrison J, Richesson DA, et al. (2008) Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet 4: e1000054 10.1371/journal.pgen.1000054 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Stevens KN, Vachon CM, Couch FJ (2013) Genetic susceptibility to triple-negative breast cancer. Cancer Res 73: 2025–2030. 10.1158/0008-5472.CAN-12-1699 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Kim HC, Lee JY, Sung H, Choi JY, Park SK, Lee KM, et al. (2012) A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study. Breast Cancer Res 14: R56 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Song N, Choi JY, Sung H, Chung S, Song M, Park SK, et al. (2014) Heterogeneity of epidemiological factors by breast tumor subtypes in Korean women: A case-case study. Int J Cancer 135: 669–681. 10.1002/ijc.28685 [DOI] [PubMed] [Google Scholar]
- 31. Jacobs TW, Gown AM, Yaziji H, Barnes MJ, Schnitt SJ (1999) Specificity of HercepTest in determining HER-2/neu status of breast cancers using the United States Food and Drug Administration-approved scoring system. J Clin Oncol 17: 1983–1987. [DOI] [PubMed] [Google Scholar]
- 32. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816–834. 10.1002/gepi.20533 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Curran WJ Jr, Scott CB, Horton J, Nelson JS, Weinstein AS, Fischbach AJ, et al. (1993) Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials. J Natl Cancer Inst 85: 704–710. [DOI] [PubMed] [Google Scholar]
- 34. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. (2000) Molecular portraits of human breast tumours. Nature 406: 747–752. [DOI] [PubMed] [Google Scholar]
- 35. Stingl J, Caldas C (2007) Opinion—Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis. Nature Reviews Cancer 7: 791–799. [DOI] [PubMed] [Google Scholar]
- 36. Clark GM (1995) Prognostic and Predictive Factors for Breast Cancer. Breast Cancer 2: 79–89. [DOI] [PubMed] [Google Scholar]
- 37. Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, et al. (2010) Subtyping of Breast Cancer by Immunohistochemistry to Investigate a Relationship between Subtype and Short and Long Term Survival: A Collaborative Analysis of Data for 10,159 Cases from 12 Studies. Plos Medicine 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Smid M, Wang Y, Zhang Y, Sieuwerts AM, Yu J, Klijn JG, et al. (2008) Subtypes of breast cancer show preferential site of relapse. Cancer Res 68: 3108–3114. 10.1158/0008-5472.CAN-07-5644 [DOI] [PubMed] [Google Scholar]
- 39. Matakidou A, El Galta R, Rudd MF, Webb EL, Bridle H, Eisen T, et al. (2007) Prognostic significance of folate metabolism polymorphisms for lung cancer. Br J Cancer 97: 247–252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Lee Y, Lee SA, Choi JY, Song M, Sung H, Jeon S, et al. (2012) Prognosis of breast cancer is associated with one-carbon metabolism related nutrients among Korean women. Nutr J 11: 59 10.1186/1475-2891-11-59 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Xu X, Gammon MD, Wetmur JG, Bradshaw PT, Teitelbaum SL, Neugut AI, et al. (2008) B-vitamin intake, one-carbon metabolism, and survival in a population-based study of women with breast cancer. Cancer Epidemiol Biomarkers Prev 17: 2109–2116. 10.1158/1055-9965.EPI-07-2900 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Shrubsole MJ, Shu XO, Ruan ZX, Cai Q, Cai H, Niu Q, et al. (2005) MTHFR genotypes and breast cancer survival after surgery and chemotherapy: a report from the Shanghai Breast Cancer Study. Breast Cancer Res Treat 91: 73–79. [DOI] [PubMed] [Google Scholar]
- 43. Xu X, Chen J (2009) One-carbon metabolism and breast cancer: an epidemiological perspective. J Genet Genomics 36: 203–214. 10.1016/S1673-8527(08)60108-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Naushad SM, Prayaga A, Digumarti RR, Gottumukkala SR, Kutala VK (2012) Bcl-2/adenovirus E1B 19 kDa-interacting protein 3 (BNIP3) expression is epigenetically regulated by one-carbon metabolism in invasive duct cell carcinoma of breast. Mol Cell Biochem 361: 189–195. 10.1007/s11010-011-1103-z [DOI] [PubMed] [Google Scholar]
- 45. Dumenil G, Rubini M, Dubois G, Baserga R, Fellous M, Pellegrini S (1997) Identification of signalling components in tyrosine kinase cascades using phosphopeptide affinity chromatography. Biochem Biophys Res Commun 234: 748–753. [DOI] [PubMed] [Google Scholar]
- 46. Yerushalmi R, Gelmon KA, Leung S, Gao D, Cheang M, Pollak M, et al. (2012) Insulin-like growth factor receptor (IGF-1R) in breast cancer subtypes. Breast Cancer Res Treat 132: 131–142. 10.1007/s10549-011-1529-8 [DOI] [PubMed] [Google Scholar]
- 47. Benjamin EJ, Dupuis J, Larson MG, Lunetta KL, Booth SL, Govindaraju DR, et al. (2007) Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med Genet 8 Suppl 1: S11 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Ovsyannikova IG, Kennedy RB, O'Byrne M, Jacobson RM, Pankratz VS, Poland GA (2012) Genome-wide association study of antibody response to smallpox vaccine. Vaccine 30: 4182–4189. 10.1016/j.vaccine.2012.04.055 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Chung S, Low SK, Zembutsu H, Takahashi A, Kubo M, Sasa M, et al. (2013) A genome-wide association study of chemotherapy-induced alopecia in breast cancer patients. Breast Cancer Res 15: R81 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Huertas-Vazquez A, Plaisier CL, Geng R, Haas BE, Lee J, Greevenbroek MM, et al. (2010) A nonsynonymous SNP within PCDH15 is associated with lipid traits in familial combined hyperlipidemia. Hum Genet 127: 83–89. 10.1007/s00439-009-0749-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Lane DM, Boatman KK, McConathy WJ (1995) Serum lipids and apolipoproteins in women with breast masses. Breast Cancer Res Treat 34: 161–169. [DOI] [PubMed] [Google Scholar]
- 52. Ray G, Husain SA (2001) Role of lipids, lipoproteins and vitamins in women with breast cancer. Clin Biochem 34: 71–76. [DOI] [PubMed] [Google Scholar]
- 53. Marnett LJ, Tuttle MA (1980) Comparison of the mutagenicities of malondialdehyde and the side products formed during its chemical synthesis. Cancer Res 40: 276–282. [PubMed] [Google Scholar]
- 54. Kang HS, Lee SC, Park YS, Jeon YE, Lee JH, Jung SY, et al. (2011) Protein and lipid MALDI profiles classify breast cancers according to the intrinsic subtype. BMC Cancer 11: 465 10.1186/1471-2407-11-465 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al. (2007) Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 25: 4414–4422. [DOI] [PubMed] [Google Scholar]
- 56. Ladoire S, Mignot G, Dabakuyo S, Arnould L, Apetoh L, Rebe C, et al. (2011) In situ immune response after neoadjuvant chemotherapy for breast cancer predicts survival. J Pathol 224: 389–400. 10.1002/path.2866 [DOI] [PubMed] [Google Scholar]
- 57. Mook S, Schmidt MK, Rutgers EJ, van de Velde AO, Visser O, Rutgers SM, et al. (2009) Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol 10: 1070–1076. 10.1016/S1470-2045(09)70254-2 [DOI] [PubMed] [Google Scholar]
- 58. Ladoire S, Mignot G, Dalban C, Chevriaux A, Arnould L, Rebe C, et al. (2012) FOXP3 expression in cancer cells and anthracyclines efficacy in patients with primary breast cancer treated with adjuvant chemotherapy in the phase III UNICANCER-PACS 01 trial. Ann Oncol 23: 2552–2561. [DOI] [PubMed] [Google Scholar]
- 59. Huang Z, Wang J, Wu CC, Houlston RS, Bondy ML, Shete S (2011) False-Negative-Rate Based Approach for Selecting Top Single-Nucleotide Polymorphisms in the First Stage of a Two-Stage Genome-Wide Association Study. Stat Interface 4: 359–371. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
(A) rs166870 and (B) rs10825036.
(TIF)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
Data Availability Statement
All relevant data are within the paper and its Supporting Information files.