Association of Rad51 polymorphism with DNA repair in BRCA1 mutation carriers and sporadic breast cancer risk.

Abstract

Inter-individual variation in DNA repair capacity is thought to modulate breast cancer risk. The phenotypic mutagen sensitivity assay (MSA) measures DNA strand breaks in lymphocytes; women with familial and sporadic breast cancers have a higher mean number of breaks per cell (MBPC) than women without breast cancer. Here, we explore the relationships between the MSA and the Rad51 gene, which encodes a DNA repair enzyme that interacts with BRCA1 and BRCA2, in BRCA1 mutation carriers and women with sporadic breast cancer.
Peripheral blood lymphoblasts from women with known BRCA1 mutations underwent the MSA (n = 138 among 20 families). BRCA1 and Rad51 genotyping and sequencing were performed to identify SNPs and haplotypes associated with the MSA. Positive associations from the study in high-risk families were subsequently examined in a population-based case-control study of breast cancer (n = 1170 cases and 2115 controls).
Breast cancer diagnosis was significantly associated with the MSA among women from BRCA1 families (OR = 3.2 95%CI: 1.5-6.7; p = 0.004). The Rad51 5'UTR 135 C>G genotype (OR = 3.64; 95% CI: 1.38, 9.54; p = 0.02), one BRCA1 haplotype (p = 0.03) and in a polygenic model, the E1038G and Q356R BRCA1 SNPs were significantly associated with MBPC (p = 0.009 and 0.002, respectively). The Rad51 5'UTR 135C genotype was not associated with breast cancer risk in the population-based study.
Mutagen sensitivity might be a useful biomarker of penetrance among women with BRCA1 mutations because the MSA phenotype is partially explained by genetic variants in BRCA1 and Rad51.

Full text

Page 1

RESEARCH ARTICLEOpen Access
Association of Rad51 polymorphism with DNA
repair in BRCA1 mutation carriers and sporadic
breast cancer risk
Luisel J Ricks-Santi1,2*, Lara E Sucheston3, Yang Yang4, Jo L Freudenheim5, Claudine J Isaacs4, Marc D Schwartz4,
Ramona G Dumitrescu4, Catalin Marian4, Jing Nie5, Dominica Vito5, Stephen B Edge6and Peter G Shields4*
Abstract
Background: Inter-individual variation in DNA repair capacity is thought to modulate breast cancer risk. The
phenotypic mutagen sensitivity assay (MSA) measures DNA strand breaks in lymphocytes; women with familial and
sporadic breast cancers have a higher mean number of breaks per cell (MBPC) then women without breast cancer.
Here, we explore the relationships between the MSA and the Rad51 gene, which encodes a DNA repair enzyme
that interacts with BRCA1 and BRCA2, in BRCA1 mutation carriers and women with sporadic breast cancer.
Methods: Peripheral blood lymphoblasts from women with known BRCA1 mutations underwent the MSA (n = 138
among 20 families). BRCA1 and Rad51 genotyping and sequencing were performed to identify SNPs and
haplotypes associated with the MSA. Positive associations from the study in high-risk families were subsequently
examined in a population-based case-control study of breast cancer (n = 1170 cases and 2115 controls).
Results: Breast cancer diagnosis was significantly associated with the MSA among women from BRCA1 families (OR
= 3.2 95%CI: 1.5-6.7; p = 0.004). The Rad51 5’UTR 135 C>G genotype (OR = 3.64; 95% CI: 1.38, 9.54; p = 0.02), one
BRCA1 haplotype (p = 0.03) and in a polygenic model, the E1038G and Q356R BRCA1 SNPs were significantly
associated with MBPC (p = 0.009 and 0.002, respectively). The Rad51 5’UTR 135C genotype was not associated with
breast cancer risk in the population-based study.
Conclusions: Mutagen sensitivity might be a useful biomarker of penetrance among women with BRCA1
mutations because the MSA phenotype is partially explained by genetic variants in BRCA1 and Rad51.
Background
The genetic determinants of breast cancer are under
intensive study. Some women with a strong family his-
tory of breast cancer inherit BRCA1 or BRCA2 muta-
tions, which have a variable penetrance for breast
cancer, between 40 to 66% [1], suggesting that addi-
tional factors contribute to cancer risk among BRCA1
and BRCA2 carriers. For sporadic cancers, however,
many low-penetrant single-nucleotide polymorphisms
(SNPs) have been investigated in pathways ranging from
growth factor signaling to DNA repair. Yet, it has been
difficult to find consistency across study results [2-4],
due to differences in study populations, sample sizes
and study designs [5]. However, studies of high risk
populations generally help uncover the molecular
mechanisms of a disease and provide guidance and
direction for studies of sporadic disease. While BRCA1
and BRCA2 mutations are highly penetrant [1], resulting
in higher risk for breast cancer, both of these genes are
also highly polymorphic. Moreover, several of their var-
iants result in amino acid changes which could ulti-
mately change the structure and function of the genes.
It is therefore plausible that the combination of genetic
changes in these genes or in genes in their pathway may
at least contribute to the disease or the mechanisms
associated with the disease in the general population.
Another approach to enhancing the chances of
* Correspondence: lricks-santi@howard.edu; pgs2@georgetown.edu
1Howard University Cancer Center, 2041 Georgia Ave, NW Washington, DC
20060, USA
4Lombardi Comprehensive Cancer Center, Georgetown University Medical
Cancer, 3800 Reservoir Rd, NW, Washington, DC 20057, USA
Full list of author information is available at the end of the article
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
© 2011 Ricks-Santi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

Page 2

identifying true positive associations is to conduct stu-
dies based on a priori hypotheses, for example by study-
ing SNPs known to affect protein functions or levels.
Thus, genotype-phenotype associations are needed to
reduce the chances of false positive associations in
breast cancer risk studies.
The mutagen sensitivity assay (MSA) provides a phe-
notypic marker of DNA repair capacity and genomic
stress response, which has been reported as a heritable
trait that affects both familial and sporadic breast cancer
risk [6-9]. This assay measures the number of chromo-
somal breaks in cultured lymphocytes following expo-
sure to DNA damaging agents. Different mutagens have
been used, but gamma radiation has been the most
widely utilized in breast cancer studies, because it is a
direct DNA damaging agent whose effects are not
dependent on cell penetration, metabolism, or clearance
[10]. For example, using this assay, DNA from women
in high-risk families and sporadic breast cancer cases
exhibits about a 2-fold increase in the mean number of
breaks per cell compared to DNA from women without
cancer from low-risk families [6,7,11]. Thus, mutagen
sensitivity may be a biomarker for DNA repair capacity
and may specifically reflect differences in an individual’s
ability to repair DNA through the pathway of interest,
homologous repair.
BRCA1, a nuclear protein that contains 24 exons
(NC_000017.10), has a role in sensing DNA damage and
cell cycle checkpoint control. It has been shown that
BRCA1-deficient cells have DNA repair defects partially
rescued by introducing exogenous wild-type BRCA1 [12],
and that BRCA1 is a trigger of homology-directed DNA
repair [12-14]. Many BRCA1 polymorphisms with allele
frequencies >5% in Caucasians have been identified; how-
ever, only six of these (Q356R, D693N, P871L, E1038G,
K1183R, and S1613G) result in amino acid changes (BIC-
Breast Cancer Information Core; http://research.nhgri.
nih.gov/bic/). These polymorphisms, with the exception
of Q356R and D693N, are in significant linkage disequili-
brium and are inherited as part of a shared haplotype
[15]. Some studies have associated these SNPs with both
familial and sporadic breast cancer risk, although there is
a lack of consistency [16-24]. BRCA1 haplotypes have
received some attention and haplotype-risk analysis has
been done in several small and large case-control studies,
but no associations between risk and haplotypes have
been found [21,23,24]. Herein, we have chosen to study
the BRCA1 polymorphisms Q356R, D693N, and E1038G
because these SNPs could be functional variants asso-
ciated with risk [25] and are in linkage disequilibrium
with other SNPs of interest.
Rad51 was chosen for investigation because of their
interactions with BRCA1 during homologous recombi-
national (HR) DNA repair [26]. Rad51 (RecA homolog,
E. coli; NC_000015.9) has 10 exons that code for a 339
amino acid protein which forms a helical nucleoprotein
filament on DNA [27]. Several studies of Rad51 among
BRCA1 mutation carriers have found positive associa-
tions with cancer risk [28-32]. Cells deficient in BRCA1
are also defective in Rad51 irradiation-induced foci for-
mation [26,33]. Experimental studies show that the loss
of Rad51 may drive genetic instability, chromosomal
aberrations, and carcinogenesis by facilitating an accu-
mulation of genetic changes [34-36]. Rad51 is over-
expressed in a BRCA1 mutant cell line and rescues cells
from apoptosis [37]. Studies have reported that the
Rad51 5’UTR variant 135C allele (rs1801320) was asso-
ciated with a decreased risk of breast cancer in BRCA1
5382insC mutation carriers [29] and other mutation car-
riers [32], while no association was found in a case-con-
trol study of sporadic breast cancer [38,39]. Antoniou et
al. reported that the SNP modified breast cancer risk
among BRCA2 mutation carriers and BRCA1 loss-of-
function mutation carriers [40]. Although the functional
consequences of the 135G>C polymorphism is
unknown, it is speculated that because it alters a CpG
island in the promoter, it may regulate expression and
affect mRNA levels [40,41]. Additionally, there is some
evidence of an association between this variant and
decreased Rad51 protein expression in BRCA1/2 muta-
tion carriers [42]. Although, there have been some
reports of Rad51 haplotypes associated risk in high-risk
families [31,43,44] and with sporadic breast cancer risk
[31,43,44], these haplotypes are composed only of SNPs
in the Rad51 putative promoter, introns, or the 3’ un-
translated region. To date, there are no reported Rad51
haplotypes composed of SNPs in the coding region,
indicating the coding region is well conserved [45].
In order to identify SNPs and haplotypes in
BRCA1and Rad51 that might affect familial and sporadic
breast cancer risk, we conducted a study of genotype-
phenotype relationships. First, we compared and vali-
dated the mutagen sensitivity assay in Epstein Barr
Virus (EBV)-immortalized lymphocyte cell lines, com-
paring the lymphoblast results to freshly cultured lym-
phocytes from whole blood. We, then, used the MSA to
study associations between genotypes and haplotypes as
they relate to DNA-repair capacity in EBV-immortalized
lymphocytes from 138 women with known BRCA1
mutations. We then applied these results to a popula-
tion-based case-control study of breast cancer.
Methods
Subjects
Familial Cancer Registry
Participants for this study were identified through the
Lombardi Comprehensive Cancer Center (LCCC) Famil-
ial Cancer Registry (FCR). We included all FCR
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 2 of 12

Page 3

participants with known BRCA1 mutations and female
family members who had EBV-immortalized lympho-
blasts available for study. Notably, several of the female
family members have tested negative (true negatives) for
the familial mutation. However, because we were also
interested in subsequent breast cancer risk, FCR partici-
pants who had undergone prophylactic mastectomy and
oophorectomy were excluded from the study, although
many subjects were still contemplating these procedures.
This study received approval by the Institutional Review
Board at Georgetown University.
Case-Control Study of Sporadic Cancer Risk
Subjects were recruited for the Western New York
Exposures and Breast Cancer (WEB) study, a large,
population-based case-control study conducted between
1996 and 2001 (n = 3285). This study has been
described in detail elsewhere [46]. Cases (n = 1170)
were women with incident breast cancer between the
ages of 35 and 79 years from Erie and Niagara counties.
Controls (n = 2115) were randomly selected from the
same counties using lists of driver’s license enrollees
provided by the New York State Department of Motor
Vehicles for those less than 65 years of age, and the
Health Care Finance Administration for those 65 years
of age and older. Controls were frequency matched by
age and race to cases. The protocol was approved by the
Institutional Review Board at the University at Buffalo
and Georgetown University, as well as by the review
boards of the participating hospitals. A detailed inter-
viewer-administered questionnaire was used to assess
the use of breast cancer risk factors.
Lymphoblast preparation
FCR participants underwent phlebotomy, providing
lymphocytes for EBV-immortalization using previously
described methods [47,48]. Briefly, equal amounts of
blood and phosphate buffered solution (PBS) (Media-
tech, Inc, VA) were slowly added to a tube filled with
ficol (Amersham Biosciences, Sweden) to obtain clear
separation of blood components. The mixture was
centrifuged at 400 g. The lymphocyte layer was
removed and added to Epstein Barr virus (EBV) super-
natant (ATCC, VA), Cyclosporin A (Biomol Interna-
tional LP, PA) and RPMI1640 medium supplemented
with 10% fetal calf serum (Sigma, MO), 2% L-gluta-
mine (GIBCO, CA), 1% Sodium Pyruvate (GIBCO,
CA), 1% NEAA (non-essential amino acids-GIBCO,
CA), 0.1% 2-mercaptoethanol (GIBCO, CA), and 0.1%
gentamycin (Invitrogen, CA). After several media
changes, 2-3 days apart, and incubation at 37°C in 5%
CO2, cell pellets were transferred, kept in cell culture
freezing media (GIBCO #11101-011) and stored in
liquid nitrogen by Georgetown University’s Tissue
Culture Shared Resource.
Mutagen Sensitivity Assay
The MSA was performed on EBV-immortalized lympho-
blastoid cell lines from all 138 FCR participants and on
fresh whole blood lymphocyte cultures from a subset of
19 women, in order to validate the use of the cell lines
against cultured blood lymphocytes. Fresh whole blood,
within 24 hours of collection, was incubated in
RPMI1640 medium (GIBCO, CA) supplemented with
20% fetal calf serum (Sigma, MO) and phytohemaggluti-
nin (GIBCO, CA) at 37°C for 67 hours in 5% CO2. The
EBV-immortalized lymphoblastoid cells were cultured
similarly, except the culture media was supplemented
with fetal calf serum (10%; Sigma, MO), L-glutamine
(2%; GIBCO, CA), sodium pyruvate (1%; GIBCO, CA),
non-essential amino acids (1%; GIBCO, CA), 2-mercap-
toethanol (0.1%; GIBCO, CA), and gentamycin (0.1%;
Invitrogen, CA). After 67 hours, the cells were irradiated
with 1 Gy gamma radiation (137Cs source gamma
research irradiator), according to the method of Sanford
and Parshad [49,50]. After further incubating for 4
hours, the cultures were treated with colcemid (0.04 ug/
ml; GIBCO, CA) to arrest the cell cycle. The cells were
then treated with hypotonic solution (0.06 mol/L KCl;
Sigma, MO), fixed [3:1 methanol (Sigma, MO): glacial
acetic acid (Fisher, PA)] and then metaphase spreads
were established and Giemsa stained (Sigma-Aldrich
Corp., MO). The frequency of chromatid breaks per cell
(b/c) was calculated from metaphase spreads as a mea-
sure of an individual’s DNA repair efficiency. Fifty well-
spread, clear, and complete metaphases per culture were
scored, and then the mean number of breaks per cell
(MBPC) was determined for each subject. Readings were
blinded to subject, cancer status, replicate and paired-
sample status.
For validation purposes, MSA comparisons were
made on corresponding fresh peripheral blood and
EBV-immortalized lymphocytes for 19 participants
from the FCR. Intra-individual variation of MBPC
between concordant fresh blood and immortalized
lymphocytes was assessed using the coefficient of var-
iation (CV = SD/μ).
DNA Sequencing
BRCA1 direct sequencing was done by Myriad Genetics,
Inc. (Salt Lake City, UT). For RAD51 exon sequencing,
genomic DNA was extracted from the EBV-immorta-
lized lymphoblastoid cell culture pellets using a Qiagen
M48 Biorobot (Qiagen, # 9000708) and the MagAttract®
DNA Mini M48 Kit (Qiagen, #953336), or by phenol-
chloroform-isoamyl alcohol methods [51]. PCR was first
performed using DNA (5-10 ng), AmpliTaq Gold®PCR
Master mix (2X; Applied Biosystems #58004012-01, Fos-
ter City, CA), glycerol (50%; Sigma, MO) and Variant-
SEQr ™ RSA primer mix (Applied Biosystems, Foster
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 3 of 12

Page 4

City, and CA). The reaction was run on the Gen-
eAmp®PCR system 9700 and the thermal cycler pro-
gram was as follows: 96°C for 5 minutes; 40 cycles of
94°C for 30 seconds, 60°C for 45 seconds and 72°C for
45 seconds and extension at 72°C for 10 minutes. The
PCR product (5 ul) was treated with ExoSAP-IT (Exo-
nuclease I and Shrimp Alkaline Phosphatase-USB
7820) to degrade unused reagents. For the sequencing
reaction, PCR product (10-20 ug; 1-2 ul final PCR
volume), M13 Universal Forward Primer (5 pmole/μl;
5’TGTAAAACGACGGCCAGT-3’) and DYEnamic ET
terminator reagent premix (Amersham Biosciences,
Piscataway, NJ) were subjected to PCR (30 cycles of
95°C, 20 s; 50°C, 15 s; 60°C, 1 min). For the post reac-
tion clean-up, an AutoSeq96 plate (Amersham Bios-
ciences, Piscataway, NJ) was used. Sequencing was
performed with a capillary sequencer (MegaBACE
1000, GE Healthcare Bio-Sciences Corp., Piscataway,
NJ) and the data were analyzed with the Sequencher
software (Sequencher 4.7, Gene Codes Corporation,
Ann Arbor, MI). Twenty percent of the sequences
were repeated for quality control and mutations were
confirmed by running PCR products in reverse sense
with M13 Universal reverse primer (5’CAGGAAA-
CAGCTATGACC-3’). Subjects in the highest and low-
est quartiles of MBPC for the MSA were chosen for
Rad51 sequencing of the coding regions (n = 92).
Genotyping and Haplotyping
For the FCR subjects, we genotyped three SNPs in
BRCA1, namelyD693N
(rs1799950), and E1038G (rs16941). One SNP in Rad51
was genotyped, namely 5’UTR 135G>C (rs1801320).
Genotyping was carried by allelic discrimination Real
Time PCR with TaqMan probes using primers and
probes from Applied Biosystems (Applied Biosystems,
Foster City, CA) as previously described [52]. Briefly,
TaqMan®Universal PCR Master Mix (Applied Biosys-
tems, Foster City, CA) and TaqMan®SNP Genotyping
Assay Mix were combined with 5-10 ng of genomic
DNA. PCR was conducted using the ABI Prism 7900HT
Real Time PCR instrument (Applied Biosystems, Foster
City, CA) with the following amplification protocol: 50°
C, 2 minutes; 95°C, 10 min and 49 cycles of 92°C, 15 s
and 60°C, 1 min. HapMap genotype data for Caucasians
[15] and Haploview©(Haploview 3.32, Broad Institute of
MIT and Harvard, Boston, MA) [53,54] were used to
identify BRCA1 tag SNPs. Input files of LCCC-FCR
BRCA1 genotyping results were also used to identify tag
SNPs. The genotyping techniques and methods men-
tioned above where also used for tagging SNPs with
probes and primers from Applied Biosystems using the
same PCR conditions.
(rs4986850),Q356R
Statistical Analysis
MSA Assay
To compare MSA MBPCs in whole blood culture and
EBV cell lines, the Spearman rank correlation statistics
(rho) was calculated. The Wilcoxon signed rank test was
also performed to compare the MBPC from whole
blood to that of EBV-transformed cell lines. These ana-
lyses were done with SPSS (version 12.0 for windows).
Genotype-Phenotype Association Analysis in FCR cohort
Because the FCR cohort includes related individuals, two
association analyses were performed; we analyzed the
entire cohort, taking into account familial relationships,
and we also analyzed only unrelated probands or the
first affected family member who sought medical atten-
tion (n = 110).
For the family-based analysis, SAGE (Statistical Analy-
sis for Genetic Epidemiology, Release 6.0.1) [55] was
used to calculate familial correlations (FCOR) (e.g., par-
ent-offspring and sibling), which were then compared
for MBPC. The relationships analyzed were sister: sister
(n = 15), aunt-through-mother: niece (n = 6), female-
cousin-through-father: female-cousin-through-father (n
= 3), and female-cousin-through-mother: female-cousin-
through-mother (n = 7). A variance-component model
developed for family-based association was used to
assess single SNP association with the continuous mea-
sure of MBPC [56-58]. The model used for analysis of
MBPC is as follows:
h (y) = h (α + γ1c1+ γ2c2+ ... + γncni+ δzi) + ρI+ εi
where i is the individual or a pair (i.e. sister pair, aunt
through mom: niece, etc.), ziis a genotype indicator
with effect coding, h is the generalized modulus power
transformation [59], piis a random polygenic effect, the
ciare covariates, and εiis a random residual individual
effect.
Analysis of unrelated individuals was done by dichoto-
mizing subjects as having high or low MBPC, based on
the median value in unaffected subjects (median = 0.22
MBPC). Chi-square tests for independence were per-
formed to assess the relationship between MBPC and
genotypes. Fisher’s exact test was used for 2 × 2 tables
when cells had a frequency lower than 5. The associa-
tion between genotypes/haplotypes and MBPC was
examined with logistic regression using genetic model-
ing (co-dominant, dominant, and log-additive models),
and odds ratios with 95% confidence intervals were cal-
culated, adjusting for age and stratifying by BRCA1
mutation.
Haplotype Analysis
SNP allele frequencies in unrelated affected and unaf-
fected subjects were tested for Hardy-Weinberg equili-
brium (Graphpad Software) [60]. Haplotypes were
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 4 of 12

Page 5

constructed using the PHASE software program
(PHASE 2.1, Department of Statistics, University of
Washington, Seattle, WA) [61,62]. To assess the sensi-
tivity of the regression results to the uncertainty in the
estimated haplotypes, we simulated 10 datasets utilizing
the haplotype probabilities generated by PHASE.
Case-control analysis
Statistical analyses of the case-control study were done
with the SAS/STAT®software (version 9.1, SAS Insti-
tute Inc., Cary, NC). Allele frequencies in cases and con-
trols were tested for Hardy-Weinberg equilibrium. The
associations for disease status and polymorphisms were
analyzed using logistic regression. Odds ratios and 95%
confidence intervals were adjusted only for age and
first-degree relative with breast cancer. Two-sided p-
values of ≤ 0.05 were considered as statistically signifi-
cant. Using the Bonferroni test, p-values were adjusted
for multiple testing (p = 0.013). For Rad51 5’UTR
135G>C genotyping, the GC and CC genotypes were
combined for regression analysis due to the low propor-
tion of subjects with the CC genotype (<1%).
Results
Demographics
Table 1 provides the demographic information for sub-
jects selected from the FCR. In total 138 eligible women
were identified, 73 women with known BRCA1 muta-
tions and a history of breast cancer (affected), 3 affected
BRCA1 negative, 6 affected Jewish panel negative
(185delAG, 5382insC), 48 women with known BRCA1
mutations and no history of breast cancer (unaffected)
and 8 women negative for the mutation found in the
family member (true negative), for a total of 56 unaf-
fected subjects. Sixty two of the participants were
related (from a total of 20 families). The mean age of
the unaffected participants was 47.44 (SD = 13.63; range
= 25-78) and the mean age of the affected participants
was 44.73 (SD = 10.65; range = 27-79).
Mutagen Sensitivity Assay Validation
Comparisons were made on corresponding fresh periph-
eral blood and EBV-immortalized lymphocytes using the
MSA assay for 19 participants from the FCR. The
MBPC (mean number of breaks per cell) was 0.27 (SD =
0.14) in fresh blood and 0.29 (SD = 0.13) in EBV-
immortalized cell lines (difference = -0.021 ± 0.073; p =
0.17). The MBPC in EBV-immortalized lymphoblastoid
cell lines was highly correlated with the MBPC in freshly
cultured PHA lymphocyte-stimulated whole blood from
the same subjects (Figure 1a.; rho = 0.92, 95%CI = 0.79-
0.97). Intra-individual variation of MBPC between con-
cordant fresh blood and immortalized lymphocytes was
assessed using the CV and ranged from 0-28.9% (mean
CV = 13.0%). An analysis was done separately for
affected and unaffected individuals, and although the
sample sizes were small, results were similar to the
pooled analysis (Figures 1b and 1c).
MBPC correlations between relative pairs
Analyses revealed that MBPC among the 15 sister pairs
were poorly correlated (r = 0.27) and not statistically
significant (p = 0.33). None of the other relationships,
aunt-through-mother: niece, female-cousin-through-
father: female-cousin-through-father, and female-cousin-
through-mother: female-cousin-through-mother showed
correlations significantly different from 0 at the 0.05
level either (r = 0.16 ± 0.19 [p = 0.41], -0.98 ± -0.28 [p
= 0.06], -0.44 ± -0.22 [p = 0.08], respectively).
MBPC in affected and unaffected BRCA1 carriers
In the family-based analysis using all 138 participants
and adjusting for familial correlation and dichotomizing
subjects as low or high MBPC, we found an association
between MBPC and breast cancer status (OR = 3.2 95%
CI: 1.5-6.7; p = 0.004). The variance component-model
also showed a correlation between MBPC and breast
cancer diagnosis (p = 0.004). Analysis of unrelated indi-
viduals, also after dichotomizing MBPC, showed an
increased risk that was not statistically significant (n =
110, OR = 1.6, 95% CI: 0.7-3.7; p = 0.34). Additionally,
Table 1 Demographics of high-risk breast cancer subjects
UnaffectedAffectedp-
value
N = 56N = 82
Age at Diagnosis Range
(yrs)
25-7827-79
Mean Age at Diagnosis
(yrs)
47.4444.730.19*
Median Age at Diagnosis
(yrs)
5045
Standard Deviation
13.6310.65
Mutation
185delAG (BRCA1)
19 (31.6%)21
(24.7%)
5382insC4 (7.0%)12
(14.8%)
0.15**
Other35 (61.4%)49
(60.5%)
0.57**
Relationship
Information
Num.
Pairs
Sister-Sister 15
Other relationship
i.e. Aunt-Niece and Cousins 16
*Unpaired t-test, t = 1.13, df = 136
**Determined by Fisher’s exact test.
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 5 of 12

Page 6

when unrelated affecteds (n = 76) were compared to
unrelated true negatives (n = 8), true negatives had
lower MBPC, and the results just failed to reach statisti-
cal significance (p = 0.07). A summary of MBPC statis-
tics in affected and unaffected can also be found in
additional file 1.
Rad51 Sequencing Results
Exons 2-9 were sequenced (n = 92) and analyzed in sub-
jects in the upper and lower quartiles of the distribution
for MBPC (n = 79). Although several genetic variants
were discovered and confirmed among subjects, none
had a frequency >5% and were thus not pursued for
genotyping and haplotyping.
Genetic Associations
The BRCA1 E1038G, D693N, Q356R, and Rad51 5’UTR
135G>C genotypes followed Hardy-Weinberg equili-
brium in the unaffected subjects (p = 0.70, 0.61, 0.29,
and 0.39, respectively). There was no statistically signifi-
cant association using any of the genetic models for the
BRCA1 E1038G, D693N, and Q356R genotypes and
MBPC either among unrelated individuals (n = 110)
(Table 2) or among all subjects as assessed by the var-
iance component model. However, in an analysis of
family members only, the polygenic model revealed that
the E1038G and Q356R BRCA1 SNPs were significantly
associated with MBPC (p = 0.009 and 0.002, respec-
tively). BRCA1 mutation-SNP interactions for associa-
tions with MBPC were also examined via stratification
by status for BRCA1 mutation. For 185delAG and
5382insC mutation carriers, there were no associations
between BRCA1 genotypes and MBPC in unrelated sub-
jects after adjusting for age (Table 3).
An association with MBPC was found with the Rad51
5’UTR 135 CC/CG genotypes; the combined homozy-
gous CC genotype and the heterozygote had an age-
adjusted OR of 3.64 (95% CI: 1.38-9.54; p = 0.03) for
unrelated women (Table 2). However, after adjusting for
multiple testing using the Bonferroni test, this SNP did
not remain significantly associated with MBPC. Never-
theless, the variance-component model also revealed a
significant association between MBPC and the Rad51
5’UTR 135 SNP (p = 0.02). In unrelated individuals,
subjects carrying both the 185delAG mutation and the
Rad51 5’UTR C allele, also, tended to have higher
MBPC (p = 0.06) (Table 3).
Haplotype results in high-risk subjects
Myriad Genetics, Inc. (Salt Lake City, UT) sequencing
data for 35 individuals were used to construct BRCA1
haplotypes. The tagging SNPs identified by the Haplo-
view software revealed that BRCA1 E1038G, Q356R, and
D693N tagging SNPs could identify 5 haplotypes in
Caucasians, totaling 95% of possible haplotypes. After
genotyping the aforementioned tag SNPs in all of the
probands (n = 110), BRCA1 haplotypes were recon-
structed. Linear regression models indicated that
BRCA1 haplotype, CTC, yields a lower risk of high
MBPC after adjusting for age (p = 0.03) (Table 4). How-
ever, this haplotype was only present in approximately
0.10
0.20
0.30
0.40
0.50
0.60
0.70

Cell
Mean Breaks per C
phocytes
EBV-immortalized Lymp
0.4
0.5
0.6
a.
b.
Mean Breaks per Cell
EBV-immortalized Lymphocytes
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Mean Breaks per Cell
Peripheral Blood Lymphocytes
0.00.1 0.20.30.40.50.6 0.7
Mean Breaks per Cell
Peripheral Blood Lymphocytes
0.00.1 0.20.30.4 0.5 0.60.7
0.0
0.1
0.2
0.3
Unaffected
Affected
Regression

PBL EBV
M
B
k
C
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Unaffected
Affected
c.

Mean Breaks per Cell
EBV-immortalized Lymphocytes
Figure 1 Correlation of MBPC in peripheral blood lymphocytes
and EBV-immortalized cell lines. a) Scatter plot of paired
peripheral blood lymphocytes and EBV-immortalized cell lines
MBPC. b) Peripheral blood lymphocytes and EBV-immortalized cell
line MBPC stratified by cancer status. c) Bar graph representing
stratification by cancer status and type of cell line.
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 6 of 12

Page 7

3.8% of subjects. In 4 out of 10 simulated datasets, this
haplotype had p ≤ 0.03 with non-significant results
being associated with a haplotype frequency ≤3. Haplo-
type analysis of Rad51 was not performed because
sequencing analysis did not reveal SNPs with >5% fre-
quency in the coding/exonic region.
Analysis of Rad51 in the case-control study of sporadic
breast cancer
In the control population, the Rad51 5’UTR 135G>C
genotypes were in Hardy-Weinberg equilibrium propor-
tions in self-reported white women (p = 0.30), but not
in women who self-reported as non-white (p = 0.07).
Genotype distributions in whites (n = 2741) and non-
whites (n = 253) were significantly different (c2= 87.32,
p < 0.001), and because of the violation of Hardy-Wein-
berg equilibrium proportions and significant allele fre-
quency difference, all subsequent analyses were
performed using the self-reported white population only.
There was no association of the Rad51 5’UTR
135G>C genotype with breast cancer risk in either pre-
or postmenopausal women. Adjusted and unadjusted
models were similar and adjusting did not substantially
change the estimation of the OR. Specifically, adjusted
ORs were 0.87 (95% CI 0.57-1.31) and 1.11 (95% CI
0.86-1.44) for pre- and postmenopausal women, respec-
tively (Table 5). Characteristics of WEB participants by
case-control status and Rad51 SNP can be found in
additional file 2.
Discussion
In this study, we found an association between the
MSA and breast cancer, and some genotype-phenotype
relationships, in subjects from high risk breast cancer
families. While there was no overall association for the
MSA with BRCA1 Q356R, D693N, and E1038G geno-
types in unrelated individuals, associations were found
among family members using the polygenic model
where the E1038G and Q356R BRCA1 SNPs were sig-
nificantly associated with MBPC. Furthermore, the rare
CTC (356R, 693N, and 1038G) haplotype also was
found to be associated with the MSA. As for Rad51,
those with the 5’UTR 135C SNP had statistically
Table 2 Relationship between mutagen sensitivity and polymorphisms in unrelated subjects (n = 110)
Genetic Model Genotype LowHighOR* 95% CIp
BRCA1n = 44% n = 66%
E1038R Co-Dominant TT20 18.2%23 20.9%1.00
CT 1816.4% 32 29.1%1.08 0.45 - 2.64
CC6 5.5%11 10.0%1.07 0.32 - 3.60 0.46
DominantCC/CT 2421.8%43 39.1%1.60 0.47 - 2.470.34
Log-Additive0.64 - 1.940.70
D693N DominantCC41 37.3% 5751.8% 1.00
CT10.9%98.2%6.03 0.69 - 52.020.10
Q356RDominant TT3229.1%5650.9%1.00
CT 1110.0%9 8.2%0.57 0.20 - 1.600.28
Rad515’ UTR 135G>C
Dominant GG 36 35.0%3534.0%1.00
CC/CG76.8%25 24.3%3.64 1.38 - 9.540.03
*Adjusted for age
Table 3 Relationship between mutagen sensitivity and
polymorphisms in BRCA1 185delAG and 5382insC
mutations carriers who are unrelated
Genotype LowHighFisher’s
p
185delAG n =
8
%n =
17
%
E1038GTT0000
CT320.0%853.3%
CC320.0%213.3%0.25
D693NCC4 28.6%857.1%
CT00.0%214.3%>0.99
Q356R TT517.9%7 25.0%
CT13.6%13.6%0.45
Rad51 GG832.0% 1040.0%
CG/CC00.0%728.0%0.06
LowHigh Fisher’s p
5382insC n =
3
%n = 8%
E1038G TT228.6%342.9%
CT/CC00.0%228.6%>0.99
D693N CC228.6%457.1%
CT0 0.0%1 14.3%0.73
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 7 of 12

Page 8

significantly higher MBPC than those who had the
wild-type allele. When stratified by 185delAG or
5382insC mutation carriers, only 185delAG carriers
with the Rad51 5’UTR 135C allele were marginally
associated with higher MBPC. On the other hand, the
Rad51 SNP was not associated with risk in a popula-
tion-based study of sporadic breast cancer. These data
indicate that mutagen sensitivity, and therefore, DNA
repair capacity, might be a useful biomarker for deter-
mining penetrance among women with BRCA1 muta-
tions, and that the mutagen sensitivity phenotype is
partially explained by genetic variants in BRCA1 and
Rad51.
The MSA, as a phenotypic assay, has been generally
applied to freshly collected peripheral blood lympho-
cytes. Because we aimed to identify SNPs in genotype-
phenotype relationships from EBV-immortalized studies
and relate them to breast cancer risk, we first assessed
the correlation of MSA results between EBV-immorta-
lized and fresh peripheral blood lymphocytes. Since
EBV-immortalized lymphoblasts are primarily derived
from B-lymphocytes, while PHA-stimulated whole blood
cultures yield primarily T-lymphocytes, it was possible
that these lymphocyte subpopulations would yield quan-
titative differences in MBPC, and that the classification
of women by high and low MBPC could be different. In
this report, we demonstrate that the results for both
assays were statistically related and quantitatively
similar.
Mutagen sensitivity is regarded as a heritable trait as
reviewed by Wu et al [63]. Given that our sample
included several families with women of differing rela-
tionships, the correlation of MBPC between family
members was calculated and the sibling (sister:sister)
correlation was found to be consistent with previous
reports of mutagen sensitivity in dizygotic twins; the
correlation coefficient for sisters in this study (r = 0.33),
albeit not statistically significant due to a small sample
size, was very similar to the study by Wu et al. (r =
0.27) [64].
In this study, MBPC was significantly higher in the
group of affected cases compared to women without
cancer when the variance-component model developed
for family-based association was applied [56-58]. How-
ever, when related cases were removed from the analy-
sis, the OR remained elevated but was not statistically
significant. It may be that including related cases biased
the results because the MSA is a heritable trait
(although only with a correlation of 0.33), or that
removing the related subjects resulted in lower statistical
power due to a smaller sample size. Although the study
has a small number of families and the pedigrees are
sparse, the variance-component model is considered
more informative than the unrelated family member
analysis because of its ability to simultaneously estimate
residual and multi-factorial (polygenic, familial, marital
or sibling) variance components. In addition this
approach uses the quantitative trait as is without dichot-
omizing, thus possibly increasing the power to correctly
detect allelic association. Also, the method combines the
original association method by George and Elston, with
the pedigree TDT-type analysis in such a way as to
maximize power [58,65]. Although familial components
can be incorporated into the equation, the random poly-
genic effect was the only variance component included
when analyzing the two populations. Moreover, when
unrelated affecteds were compared to true negatives
(unrelated unaffected women without the BRCA1 muta-
tion), the difference was borderline statistically signifi-
cant (p = 0.07), further supporting a true relationship.
Our results are consistent with other studies showing
Table 4 Linear Regression of MBPC on BRCA1 Haplotypes in Unrelated Individuals
HalpotypeFrequency% PARAMETERP-value PARAMETER*P-value*
C C T9 6.8% 0.10 0.740.090.75
C T C5 3.8%-0.80
0.03
-0.810.03
T C C4131.1%0.08 0.68 0.08 0.68
T C T6750.8% 0.28 0.20 0.280.20
T T C96.8%-0.020.94-0.02 0.95
T T T10.8%-1.470.07-1.480.07
*Adjusted for Age.
† First Position = Q356R; second position = D693N; third position = E1038G
Table 5 Risk of Breast Cancer by Rad51 5’UTR 135G>C
among whites, WEB study
CasesControlsCrude OR (CI) Adjusted OR* (CI)
Pre-menopausal
GG236 439 1.001.00
CG + CC43 860.93 (0.62-1.39) 0.87 (0.57-1.31)
Post-menopausal
GG 59410361.00 1.00
CG + CC122 1851.15 (0.90-1.48)1.11 (0.86-1.44)
*Odds ratios and 95% confidence intervals adjusted for age and first-degree
relative with breast cancer.
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 8 of 12

Page 9

decreased DNA repair capacity in both comparisons of
breast cancer cases to controls from population studies
and from studies of high risk families [6-9]. However,
differences in findings could also reflect how DNA
repair capacity was measured.
BRCA1 genotypes as a predictor of mutagen sensitivity
have not been previously studied. Although we found no
overall association for the BRCA1 E1038G, D693N, and
Q356R genotypes with MBPC, in a polygenic model, the
1038G and 356Q BRCA1 SNPs predicted higher MBPC.
These SNPs, however, have had only limited study for
breast cancer risk, and null results were reported for
women from high risk families [16-20] and sporadic
breast cancer [21-23] possibly because of their low
minor allele frequencies in the general population. The
Q356R SNP has been studied, but both positive associa-
tion and null results have been reported in sporadic
breast cancer [21-23], and a positive association
reported in one study of familial breast cancer [17], but
not in another [16]. For the Q356R and E1038G SNPs,
in silico analysis indicated that these could have adverse
effects due to their location in the BRCA1 gene [25].
Recently, the 1038G polymorphism, which was in LD
with 1183R, 871L, and 1613G in our study set, as well
as in the HapMap CEU data (Utah residents with ances-
try from northern and western Europe), was associated
with increased BRCA1 protein expression in a small
case-control study of breast cancer risk [66]. However,
the same study did not find an association with K1183R,
P871L, and S1613S, indicating that population admix-
ture may have contributed to differences in haplotype
frequencies.
The BRCA1 haplotypes examined herein were con-
structed from the 35 sequenced subjects and HapMap
data, using the E1038G SNP, Q356R and D693N SNPs.
Given the presumed detrimental effects of these SNPs
on BRCA1 based on the in silico analysis [25], it would
seem that the 3 SNP haplotype, CTC, would be asso-
ciated with increased MBPC in our study. To the con-
trary, we found that the CTC haplotype was
significantly associated with decreased MBPC. This asso-
ciation, though, is based on simulated data that only
found significant associations in 4 of 10 simulations,
using a frequency >3. Given that this haplotype is made
up of the minor alleles of our SNPs, it is rare. Other
studies evaluating BRCA1 haplotypes have used a com-
bination of the SNPs used in our study to evaluate risk
in sporadic breast cancer [22] and interactions with hor-
monal therapy [21], but we are the first to have used
the 3-SNP haplotype found to be associated with low
MBPC in our population. For example, Freedman and
coworkers genotyped 28 BRCA1 SNPs, including the
E1038G and Q356R SNPs, and observed 13 common
haplotypes, but, none were associated with risk [22].
The Marie-Genica group studied a haplotype consisting
of the Q356R (rs1799950), P871L (rs799917), and
K1183R (rs16942) SNPs and found that carriers of one
haplotype were at a higher risk of developing breast can-
cer after estrogen therapy use compared to those with
the common haplotype [21]. Although the results are
mixed for these genotypes and haplotypes for breast
cancer risk, the genotype-phenotype associations indi-
cate that further study is warranted.
For Rad51, sequencing was completed for 92 women,
namely those with the highest and lowest MBPC. In this
study, the Rad51 5’UTR 135G®C was found to be asso-
ciated with decreased DNA repair capacity among unre-
lated subjects, in agreement with other findings [42,67].
Several Rad51 variants were identified and confirmed by
reverse sequencing. However, consistent with the NCBI
databases (http://www.ncbi.nlm.nih.gov/sites/entrez),
these were found in very low frequency (<1%), with the
exception of the Rad51 5’UTR 135G®C. According to
HapMap, Rad51 has only a single haplotype block and
SNP frequencies are low (<10%). Thus, haplotypes for
Rad51 were not studied. For this DNA repair gene, the
lack of observed genetic variation in functional compo-
nents of the gene and homology across species [45]
indicate that genetic variation in this gene might have
detrimental effects. The 5’UTR 135G>C SNP, studied
herein, can affect mRNA stability and/or translation effi-
ciency, leading to altered product levels [41,42]. One
study examined the effects of a Rad51 genotypes in
BRCA1/2 carriers and reported that Rad51 135G>C
genotype association with breast cancer risk was greater
in BRCA1 carriers with truncating mutations (i.e.
185delAG) [40].
The Rad51 5’UTR variant C allele was then tested in
the WEB case-control study because of the a priori
hypotheses developed from the MSA assays. For the
case-control analysis, we used the most parsimonious
model because none of the other covariates, such as
education, body mass index, age at first birth, age at
menarche, age at menopause (for post-menopausal
women only), number of births, and previous benign
breast disease, changed the impact the genotype has on
the odds of disease by greater than 5%. Including non-
genetic variables that do not affect the impact of the
genotype on odds of disease may actually mute the
effect. Our results, however, did not indicate that the
SNP was associated with breast cancer risk as a main
effect, which is consistent with other studies [39,68].
The strength of this study lies using MBPC, a vali-
dated intermediate phenotype for breast cancer, as the
dependent variable in EBV-immortalized cell lines from
a large number of BRCA1 mutation carriers in order to
assess genotype-phenotype associations. The advantage
of evaluating an intermediate phenotype rather than the
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 9 of 12

Page 10

actual clinical phenotype, such as disease, is that the
number of genetic and environmental factors influen-
cing the intermediate is probably smaller than the num-
ber of factors affecting the disease resulting in a better
powered study. Because the clinical outcome is taken
out of the equation, the risks of spurious associations
are limited and in fact, we can better explore the
mechanisms of the disease. And, while this study
assessed the effect of BRCA1 and Rad51 genetic varia-
tion on MBPC and risk of sporadic breast cancer, other
genes in the HR pathway, such as BRCA2, PALB2,
MERIT40, and others, could potentially be assessed for
effects on the breast cancer intermediate phenotype,
DNA repair capacity, as well as risk.
The use of a priori hypotheses also helped identify the
most plausible SNPs to be examined in our family- and
population-based epidemiological studies. Furthermore,
the present study used data from a large case-control
study of environmental exposures in the etiology of
sporadic breast cancer. These types of studies can pro-
vide corroborative evidence to epidemiological studies of
breast cancer.
This study does have some limiting factors. The FCR
study subjects were small in number, limiting statistical
power for detecting genotype-phenotype relationships.
Additionally, while removing BRCA1/2 mutation carriers
that had received prophylactic mastectomy and oophor-
ectomy was reasonable, it is also possible that this may
have introduced selection bias and that the reason that
some women chose prophylactic surgery may have been
because their perceived risk was greater, perhaps due to
higher family penetrance. If Rad51 variants were an
underlying factor for the increased penetrance and the
subjects were excluded from the present study, it is pos-
sible that these exclusion criteria would decrease power.
The MBPCs in this study were lower than in other
studies [6-8,69-71]. However, this difference may be due
to the lower radiation dose and lower post-radiation
incubation time used in this study. Although lower, the
dose of 1 Gy for g-irradiation and post-irradiation incu-
bation time (4 hours vs. 0.5-1.5 hours) [6-8,69-71] con-
ditions were chosen herein based on experiments
identifying the optimal cell survival at the highest dose
for these cell lines; dose-response evaluations showed
100% cell death at 2 Gy (data not shown). Radio-sensi-
tivity due to germ-line BRCA1 mutations could also
result in a lower dose response. Another explanation for
lower MBPCs could be that only frank chromatid breaks
were counted and all gaps were excluded, whereas other
studies counted gaps as well. Ultimately, several associa-
tions were found making it 1) important to explore
these associations among women without BRCA1 muta-
tions and 2) essential to replicate in our larger case-con-
trol study.
Conclusions
In conclusion, this study found an association for the
MSA and breast cancer risk using subjects from a famil-
ial breast cancer registry. The MSA was then used as a
phenotype to identify associated genetic variants, and
several were found. These SNPs, namely the Rad51
5’UTR 135 C>G, BRCA1 E1038G and BRCA1 Q356R
BRCA1 genotypes, and one BRCA1 haplotypes might be
risk modifiers for BRCA1 mutation carriers. Our results
provide evidence that deficient DNA repair may be a
biomarker to identify higher-risk individuals in BRCA1
families, and provides an a priori hypothesis for further
studies of BRCA1 and RAD51 SNPs in familial breast
cancer risk.
Additional material
Additional file 1: Summary table of MBPC in unaffected compared
to affected subjects in all subjects and unrelated subjects. This is a
description of the MBPC in Unaffecteds and Affecteds in related and
unrelated individuals.
Additional file 2: Characteristics of Study Sample by Case-Control
Status and Rad51 Genotypes. This table describes the characteristics of
the WEB study participants by case-control status and genotypes.
Acknowledgements
This project was supported by Department of Defense grants (LSR pre-
doctoral training grant BC030134), a Department of Defense Breast Cancer
Center of Excellence (BC022346), and the Lombardi Cancer Center Familial
Cancer Registry (NCI P30 CA51008-12). Some of the results of this paper
were obtained by using the program package S.A.G.E., which is supported
by a U.S. Public Health Service Resource Grant (RR03655) from the National
Center for Research Resources. The authors would also like to thank Shiva
Krishnan, David Goerlitz, Leoni Leondaridis, Bin Yi, and Camille Jasper for
their technical assistance.
Author details
1Howard University Cancer Center, 2041 Georgia Ave, NW Washington, DC
20060, USA.2National Human Genome Center at Howard University, 2041
Georgia Ave, NW #615, Washington, DC 20059, USA.3Department of
Biostatistics, University at Buffalo, State University of New York, Buffalo, NY
14214, USA.4Lombardi Comprehensive Cancer Center, Georgetown
University Medical Cancer, 3800 Reservoir Rd, NW, Washington, DC 20057,
USA.5Department of Social and Preventive Medicine, University at Buffalo,
State University of New York, Buffalo, NY 14214, USA.6Department of
Surgery, University at Buffalo, State University of New York, Buffalo, NY
14214, USA.
Authors’ contributions
LJR-S conceived the study, carried out the molecular genetic studies, and
drafted the manuscript. LES participated in the statistical design of the family
study and carried out the statistical analysis of the family data. YY
participated in the statistical design of the study and carried out the
statistical analysis of the family data. JLF participated in the design of the
study and coordinated the WEB study. CJI participated in the design of the
family study, provided subject epidemiologic data, and coordinated the
statistical analysis of the family data. MDS participated in the design of the
family study, provided subject epidemiologic data, and helped carry out
mutation analyses of BRCA1 and BRCA2 in the subjects. RGD carried out the
mutagen sensitivity assay. CM carried out the haplotype analysis. JN carried
out the final statistical analysis of the WEB study. DV participated in the
design of the study and coordinated the WEB study. SBE participated in the
design of the study and coordinated the WEB study. PGS conceived the
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 10 of 12

Page 11

study and participated in the design of the study. All authors read and
approved the manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 6 January 2011 Accepted: 27 June 2011
Published: 27 June 2011
References
1.Chen S, Parmigiani G: Meta-analysis of BRCA1 and BRCA2 penetrance. J
Clin Oncol 2007, 25:1329-1333.
2. Kadouri L, Easton DF, Edwards S, Hubert A, Kote-Jarai Z, Glaser B, et al: CAG
and GGC repeat polymorphisms in the androgen receptor gene and
breast cancer susceptibility in BRCA1/2 carriers and non-carriers. Br J
Cancer 2001, 85:36-40.
3. Rebbeck TR, Kantoff PW, Krithivas K, Neuhausen S, Blackwood MA,
Godwin AK, et al: Modification of BRCA1-associated breast cancer risk by
the polymorphic androgen-receptor CAG repeat. Am J Hum Genet 1999,
64:1371-1377.
4.Kadouri L, Kote-Jarai Z, Easton DF, Hubert A, Hamoudi R, Glaser B, et al:
Polyglutamine repeat length in the AIB1 gene modifies breast cancer
susceptibility in BRCA1 carriers. Int J Cancer 2004, 108:399-403.
5. Wacholder S, Chanock S, Garcia-Closas M, El GL, Rothman N: Assessing the
probability that a positive report is false: an approach for molecular
epidemiology studies. J Natl Cancer Inst 2004, 96:434-442.
6. Helzlsouer KJ, Harris EL, Parshad R, Perry HR, Price FM, Sanford KK: DNA
repair proficiency: potential susceptiblity factor for breast cancer. J Natl
Cancer Inst 1996, 88:754-755.
7.Parshad R, Price FM, Bohr VA, Cowans KH, Zujewski JA, Sanford KK:
Deficient DNA repair capacity, a predisposing factor in breast cancer. Br
J Cancer 1996, 74:1-5.
8.Patel RK, Trivedi AH, Arora DC, Bhatavdekar JM, Patel DD: DNA repair
proficiency in breast cancer patients and their first-degree relatives. Int J
Cancer 1997, 73:20-24.
9. Scott D, Spreadborough AR, Jones LA, Roberts SA, Moore CJ: Chromosomal
radiosensitivity in G2-phase lymphocytes as an indicator of cancer
predisposition. Radiat Res 1996, 145:3-16.
10.Natarajan TG, Ganesan N, Carter-Nolan P, Tucker CA, Shields PG, ms-
Campbell LL: gamma-Radiation-induced chromosomal mutagen
sensitivity is associated with breast cancer risk in African-American
women: caffeine modulates the outcome of mutagen sensitivity assay.
Cancer Epidemiol Biomarkers Prev 2006, 15:437-442.
11. Parshad R, Sanford KK: Radiation-induced chromatid breaks and deficient
DNA repair in cancer predisposition. Crit Rev Oncol Hematol 2001,
37:87-96.
12.Moynahan ME, Cui TY, Jasin M: Homology-directed dna repair,
mitomycin-c resistance, and chromosome stability is restored with
correction of a Brca1 mutation. Cancer Res 2001, 61:4842-4850.
13.Scully R, Xie A, Nagaraju G: Molecular functions of BRCA1 in the DNA
damage response. Cancer Biol Ther 2004, 3:521-527.
14.
Tibbetts RS, Cortez D, Brumbaugh KM, Scully R, Livingston D, Elledge SJ,
et al: Functional interactions between BRCA1 and the checkpoint kinase
ATR during genotoxic stress. Genes Dev 2000, 14:2989-3002.
15.
The international HapMap Project. [http://hapmap.ncbi.nlm.nih.gov/].
16.Durocher F, Shattuck-Eidens D, McClure M, Labrie F, Skolnick MH,
Goldgar DE, et al: Comparison of BRCA1 polymorphisms, rare sequence
variants and/or missense mutations in unaffected and breast/ovarian
cancer populations. Hum Mol Genet 1996, 5:835-842.
17. Dunning AM, Chiano M, Smith NR, Dearden J, Gore M, Oakes S, et al:
Common BRCA1 variants and susceptibility to breast and ovarian cancer
in the general population. Hum Mol Genet 1997, 6:285-289.
18. Tommasi S, Crapolicchio A, Lacalamita R, Bruno M, Monaco A, Petroni S,
et al: BRCA1 mutations and polymorphisms in a hospital-based
consecutive series of breast cancer patients from Apulia, Italy. Mutat Res
2005, 578:395-405.
19.Dombernowsky SL, Weischer M, Freiberg JJ, Bojesen SE, Tybjaerg-Hansen A,
Nordestgaard BG: Missense polymorphisms in BRCA1 and BRCA2 and risk
of breast and ovarian cancer. Cancer Epidemiol Biomarkers Prev 2009,
18:2339-2342.
20.Sehl ME, Langer LR, Papp JC, Kwan L, Seldon JL, Arellano G, et al:
Associations between single nucleotide polymorphisms in double-
stranded DNA repair pathway genes and familial breast cancer. Clin
Cancer Res 2009, 15:2192-2203.
The MARIE-GENICA Consortium on Genetic Susceptibility for Menopausal
Hormone Therapy Related Breast Cancer Risk: Polymorphisms in the
BRCA1 and ABCB1 genes modulate menopausal hormone therapy
associated breast cancer risk in postmenopausal women. Breast Cancer
Res Treat 2009.
Freedman ML, Penney KL, Stram DO, Riley S, Kean-Cowdin R, Le ML, et al: A
haplotype-based case-control study of BRCA1 and sporadic breast
cancer risk. Cancer Res 2005, 65:7516-7522.
Soucek P, Borovanova T, Pohlreich P, Kleibl Z, Novotny J: Role of single
nucleotide polymorphisms and haplotypes in BRCA1 in breast cancer:
Czech case-control study. Breast Cancer Res Treat 2007, 103:219-224.
Cox DG, Kraft P, Hankinson SE, Hunter DJ: Haplotype analysis of common
variants in the BRCA1 gene and risk of sporadic breast cancer. Breast
Cancer Res 2005, 7:R171-R175.
Tommasi S, Pilato B, Pinto R, Monaco A, Bruno M, Campana M, et al:
Molecular and in silico analysis of BRCA1 and BRCA2 variants. Mutat Res
2008, 644:64-70.
Davies AA, Masson JY, McIlwraith MJ, Stasiak AZ, Stasiak A,
Venkitaraman AR, et al: Role of BRCA2 in control of the RAD51
recombination and DNA repair protein. Mol Cell 2001, 7:273-282.
Galkin VE, Wu Y, Zhang XP, Qian X, He Y, Yu X, et al: The Rad51/RadA N-
terminal domain activates nucleoprotein filament ATPase activity.
Structure 2006, 14:983-992.
Wang WW, Spurdle AB, Kolachana P, Bove B, Modan B, Ebbers SM, et al: A
single nucleotide polymorphism in the 5’ untranslated region of RAD51
and risk of cancer among BRCA1/2 mutation carriers. Cancer Epidemiol
Biomarkers Prev 2001, 10:955-960.
Jakubowska A, Narod SA, Goldgar DE, Mierzejewski M, Masojc B, Nej K, et al:
Breast cancer risk reduction associated with the RAD51 polymorphism
among carriers of the BRCA1 5382insC mutation in Poland. Cancer
Epidemiol Biomarkers Prev 2003, 12:457-459.
Jakubowska A, Gronwald J, Menkiszak J, Gorski B, Huzarski T, Byrski T, et al:
The RAD51 135 G>C polymorphism modifies breast cancer and ovarian
cancer risk in Polish BRCA1 mutation carriers. Cancer Epidemiol Biomarkers
Prev 2007, 16:270-275.
Gal I, Kimmel G, Gershoni-Baruch R, Papa MZ, Dagan E, Shamir R, et al: A
specific RAD51 haplotype increases breast cancer risk in Jewish non-
Ashkenazi high-risk women. Eur J Cancer 2006, 42:1129-1134.
Sehl ME, Langer LR, Papp JC, Kwan L, Seldon JL, Arellano G, et al:
Associations between single nucleotide polymorphisms in double-
stranded DNA repair pathway genes and familial breast cancer. Clin
Cancer Res 2009, 15:2192-2203.
Xia F, Taghian DG, DeFrank JS, Zeng ZC, Willers H, Iliakis G, et al: Deficiency
of human BRCA2 leads to impaired homologous recombination but
maintains normal nonhomologous end joining. Proc Natl Acad Sci USA
2001, 98:8644-8649.
Richardson C: RAD51, genomic stability, and tumorigenesis. Cancer Lett
2005, 218:127-139.
Sonoda E, Sasaki MS, Buerstedde JM, Bezzubova O, Shinohara A, Ogawa H,
et al: Rad51-deficient vertebrate cells accumulate chromosomal breaks
prior to cell death. EMBO J 1998, 17:598-608.
Thacker J: The RAD51 gene family, genetic instability and cancer. Cancer
Lett 2005, 219:125-135.
Martin RW, Orelli BJ, Yamazoe M, Minn AJ, Takeda S, Bishop DK: RAD51 Up-
regulation Bypasses BRCA1 Function and Is a Common Feature of
BRCA1-Deficient Breast Tumors. Cancer Res 2007, 67:9658-9665.
Blasiak J, Przybylowska K, Czechowska A, Zadrozny M, Pertynski T, Rykala J,
et al: Analysis of the G/C polymorphism in the 5’-untranslated region of
the RAD51 gene in breast cancer. Acta Biochim Pol 2003, 50:249-253.
Loizidou MA, Michael T, Neuhausen SL, Newbold RF, Marcou Y, Kakouri E,
et al: DNA-repair genetic polymorphisms and risk of breast cancer in
Cyprus. Breast Cancer Res Treat 2009, 115:623-627.
Antoniou AC, Sinilnikova OM, Simard J, Leone M, Dumont M,
Neuhausen SL, et al: RAD51 135G>C modifies breast cancer risk among
BRCA2 mutation carriers: results from a combined analysis of 19 studies.
Am J Hum Genet 2007, 81:1186-1200.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 11 of 12

Page 12

41.Hasselbach L, Haase S, Fischer D, Kolberg HC, Sturzbecher HW:
Characterisation of the promoter region of the human DNA-repair gene
Rad51. Eur J Gynaecol Oncol 2005, 26:589-598.
Antoniou CAntonis, Sinilnikova MOlga, Simard Jacques, Léoné Mélanie:
RAD51 135G>C modifies breast cancer risk among BRCA2 mutation
carriers: results from a combined analysis of 19 studies. The American
Journal of Human Genetics 2007.
Ding SL, Yu JC, Chen ST, Hsu GC, Kuo SJ, Lin YH, et al: Genetic variants of
BLM interact with RAD51 to increase breast cancer susceptibility.
Carcinogenesis 2009, 30:43-49.
Rebbeck TR, Mitra N, Domchek SM, Wan F, Chuai S, Friebel TM, et al:
Modification of ovarian cancer risk by BRCA1/2-interacting genes in a
multicenter cohort of BRCA1/2 mutation carriers. Cancer Res 2009,
69:5801-5810.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al: The
human genome browser at UCSC. Genome Res 2002, 12:996-1006.
McCann SE, Ip C, Ip MM, McGuire MK, Muti P, Edge SB, et al: Dietary intake
of conjugated linoleic acids and risk of premenopausal and
postmenopausal breast cancer, Western New York Exposures and Breast
Cancer Study (WEB Study). Cancer Epidemiol Biomarkers Prev 2004,
13:1480-1484.
Boyum A: Separation of White Blood Cells. Nature 1964, 204:793-794.
walls EV, Crawford DH: Generation of Human B lymphoblatoid cell lines
using Epstein-Barr virus. Klaus GGB. Lymphocytes - A practical Approach
IRL Press, Oxford; 1987, 149-162.
Parshad R, Sanford KK, Jones GM: Chromatid damage after G2 phase x-
irradiation of cells from cancer-prone individuals implicates deficiency in
DNA repair. Proc Natl Acad Sci USA 1983, 80:5612-5616.
Sanford KK, Parshad R, Price FM, Jones GM, Tarone RE, Eierman L, et al:
Enhanced chromatid damage in blood lymphocytes after G2 phase x
irradiation, a marker of the ataxia-telangiectasia gene. J Natl Cancer Inst
1990, 82:1050-1054.
Tennis M, Krishnan S, Bonner M, Ambrosone CB, Vena JE, Moysich K, et al:
p53 Mutation analysis in breast tumors by a DNA microarray method.
Cancer Epidemiol Biomarkers Prev 2006, 15:80-85.
Kadouri L, Kote-Jarai Z, Hubert A, Durocher F, Abeliovich D, Glaser B, et al: A
single-nucleotide polymorphism in the RAD51 gene modifies breast
cancer risk in BRCA2 carriers, but not in BRCA1 carriers or noncarriers. Br
J Cancer 2004, 90:2002-2005.
Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of
LD and haplotype maps. Bioinformatics 2005, 21:263-265.
Haploview. [http://www.broadinstitute.org/scientific-community/science/
programs/medical-and-population-genetics/haploview/haploview].
S.A.G.E. Statistical Analysis for Genetic Epidemiology. 2009, Release 6.0.1.
George VT, Elston RC: Generalized modulus power transformations.
Commun Statist - TheoryMeth 1988, 17:2933-2952.
Elston RC, George VT, Severtson F: The Elston-Stewart algorithm for
continuous genotypes and environmental factors. Hum Hered 1992,
42:16-27.
George VT, Elston RC: Testing the association between polymorphic
markers and quantitative traits in pedigrees. Genet Epidemiol 1987,
4:193-201.
Gray-McGuire C, Sinha R, Iyengar S, Millard C, Rybicki BA, Elston RC, et al:
Genetic characterization and fine mapping of susceptibility loci for
sarcoidosis in African Americans on chromosome 5. Hum Genet 2006,
120:420-430.
GraphPad Software. [http://www.graphpad.com/quickcalcs/index.cfm].
Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype
reconstruction from population data. Am J Hum Genet 2001, 68:978-989.
Stephens M, Donnelly P: A comparison of bayesian methods for
haplotype reconstruction from population genotype data. Am J Hum
Genet 2003, 73:1162-1169.
Wu X, Gu J, Spitz MR: Mutagen sensitivity: a genetic predisposition factor
for cancer. Cancer Res 2007, 67:3493-3495.
Wu X, Spitz MR, Amos CI, Lin J, Shao L, Gu J, et al: Mutagen sensitivity has
high heritability: evidence from a twin study. Cancer Res 2006,
66:5993-5996.
Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage
disequilibrium: the insulin gene region and insulin-dependent diabetes
mellitus (IDDM). Am J Hum Genet 1993, 52:506-516.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.Mangia A, Chiriatti A, Tommasi S, Menolascina F, Petroni S, Zito FA, et al:
BRCA1 expression and molecular alterations in familial breast cancer.
Histol Histopathol 2009, 24:69-76.
Lee SE, Pellicioli A, Vaze MB, Sugawara N, Malkova A, Foiani M, et al: Yeast
Rad52 and Rad51 recombination proteins define a second pathway of
DNA damage assessment in response to a single double-strand break.
Mol Cell Biol 2003, 23:8913-8923.
Palanca SS, Esteban CE, Barragan GE, de JJI, Chirivella GI, Segura HA, et al:
CASP8 D302H polymorphism delays the age of onset of breast cancer in
BRCA1 and BRCA2 carriers. Breast Cancer Res Treat 2009.
Jyothish B, Ankathil R, Chandini R, Vinodkumar B, Nayar GS, Roy DD, et al:
DNA repair proficiency: a potential marker for identification of high risk
members in breast cancer families. Cancer Lett 1998, 124:9-13.
Knight RD, Parshad R, Price FM, Tarone RE, Sanford KK: X-ray-induced
chromatid damage in relation to DNA repair and cancer incidence in
family members. Int J Cancer 1993, 54:589-593.
Riches AC, Bryant PE, Steel CM, Gleig A, Robertson AJ, Preece PE, et al:
Chromosomal radiosensitivity in G2-phase lymphocytes identifies breast
cancer patients with distinctive tumour characteristics. Br J Cancer 2001,
85:1157-1161.
67.
68.
69.
70.
71.
Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2407/11/278/prepub
doi:10.1186/1471-2407-11-278
Cite this article as: Ricks-Santi et al.: Association of Rad51 polymorphism
with DNA repair in BRCA1 mutation carriers and sporadic breast cancer
risk. BMC Cancer 2011 11:278.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Ricks-Santi et al. BMC Cancer 2011, 11:278
http://www.biomedcentral.com/1471-2407/11/278
Page 12 of 12