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Allelic Imbalance in BRCA1 and BRCA2 Gene Expression Is Associated with an Increased
Breast Cancer Risk
Xiaowei Chen1, JoEllen Weaver1, Betsy A. Bove1, Lisa A. Vanderveer1, Susan C. Weil2,
Alexander Miron3, Mary B. Daly4, and Andrew K. Godwin1, *
1Medical Science Division, Fox Chase Cancer Center, Philadelphia, PA 19111
2Morphotek Inc, Exton, PA 19341
3Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston,
MA 02115
4Population Science Division, Fox Chase Cancer Center, Philadelphia, PA 19111
*Address for correspondence and reprints: Dr. Andrew K. Godwin, Medical Science Division,
Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111-2497. Phone: (215)
728-2205, Fax: (215) 728-2741. E-mail: Andrew.Godwin@fccc.edu.
HMG Advance Access published January 19, 2008
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ABSTRACT
The contribution of BRCA1 and BRCA2 to familial and non-familial forms of breast cancer
has been difficult to accurately estimate because of the myriad of potential genetic and epigenetic
mechanisms that can ultimately influence their expression and involvement in cellular activities.
As one of these potential mechanisms, we investigated whether allelic imbalance (AI) of BRCA1
or BRCA2 expression was associated with an increased risk of developing breast cancer. By
developing a quantitative approach utilizing allele specific real-time PCR, we first evaluated AI
caused by nonsense-mediated mRNA decay (NMD) in patients with frameshift mutations in
BRCA1 and BRCA2. We next measured AI for BRCA1 and BRCA2 in lymphocytes from three
groups: familial breast cancer patients, non-familial breast cancer patients, and age-matched
cancer-free females. The AI ratios of BRCA1, but not BRCA2, in the lymphocytes from familial
breast cancer patients were found to be significantly increased as compared to cancer-free
women (BRCA1: 0.424 vs. 0.211, p=0.00001; BRCA2: 0.206 vs. 0.172, p=0.38, respectively).
Similarly, the AI ratios were greater for BRCA1 and BRCA2 in the lymphocytes of non-familial
breast cancer cases versus controls (BRCA1: 0.353, p=0.002; BRCA2: 0.267, p=0.03).
Furthermore, the distribution of under-expressed alleles between cancer-free controls and
familial cases was significantly different for both BRCA1 and BRCA2 gene expression (p<0.02
and p<0.02, respectively). In conclusion, we have found that AI affecting BRCA1 and to a lesser
extent BRCA2 may contribute to both familial and non-familial forms of breast cancer.
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INTRODUCTION
Breast cancer is the most common cancer affecting women, with a lifetime risk among
females about 10% by the age of 80 years. In the United States, it has been reported that there
will be approximately 180,510 new cases of breast cancer, and more than 40,910 breast cancer
related deaths in 2007 (1). Current estimates suggest that family history is associated with 10-
20% of breast cancer (2, 3). BRCA1 (OMIM: 113705) and BRCA2 (OMIM: 600185) are two of
the most prominent breast cancer susceptibility genes and deleterious mutations in these two
genes are estimated to account for about 15-30% of familial breast cancer, (4-6).
Germline mutations affecting the coding region of BRCA1 and BRCA2 are thought to lead
to expression of mutant proteins, which are either inactive or function as dominant negatives.
However, these scenarios have not been supported by functional studies (7-9). In fact, Brca1 and
Brca2 knockout mouse models have demonstrated that elimination of Brca1 or Brca2 proteins is
sufficient for the development of mammary cancer (10, 11). Previously we have reported that
mutant BRCA1 mRNAs containing premature stop codons were eliminated or destabilized by
nonsense-mediated mRNA decay (NMD) (12) and lead to a state of haploinsufficiency. As a
result, the ratios between the expressions from the mutant alleles and the corresponding wild-
type alleles were significantly decreased, resulting in what was referred to as allelic imbalance
(AI). AI of BRCA1 or BRCA2 expression could decrease the level of both transcripts and
proteins and thus contribute to increased susceptibility to developing breast cancer.
There is growing evidence to support this concept. Epigenetic studies have shown that loss
of BRCA1 expression due to promoter hypermethylation is associated with ~10% of sporadic
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cases of breast and ovarian cancer (13-18). However, screens to evaluate AI have not been
applied in depth to study its potential role in the genesis of familial forms of these diseases. A
previous study reported that 6 out of 13 human genes, including BRCA1 and p53, were expressed
with significant difference between the two alleles, and this difference was transmitted by
Mendelian inheritance (19). Furthermore, Yan and his colleagues observed that decreased
expression of one of the adenomatous polyposis coli tumor suppressor gene (APC) alleles was
associated with the development of familial adenomatous polyposis (20). Their studies also
found that even more modest decreases in the expression of one APC allele could contribute to
attenuated forms of polyposis (20). Based on these findings, we hypothesize that a subset of
non-BRCA1/2 mutation carriers with a strong family history of breast cancer are at increased risk
of developing this disease as a result of AI in BRCA1 and BRCA2 gene expression.
In the present study, we have developed a quantitative approach to measure the allele-
specific expression of BRCA1 and BRCA2. We compared BRCA1/2 allelic variation in a cohort
of BRCA1/2 mutation-negative familial breast cancer patients, non-familial breast cancer patients,
and age-matched cancer-free volunteers. Since susceptibility to breast cancer is far from being
fully understood, our study may help to further identify genetic factors which contribute to breast
cancer susceptibility.
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RESULTS
Development of a Quantitative Allelic Imbalance Assay
In order to determine if allele specific real-time PCR is able to quantitatively measure the
AI in BRCA1 and BRCA2 gene expression from the individual allele, RNAs were isolated from
the blood lymphocytes of two individuals determined by genotype and sequence analysis to be
homozygous for either BRCA1-c.4308T/T or BRCA1-c.4308C/C (Figure 1A). This
polymorphism was chosen since it is relatively common, based on NCBI dbSNP data. The
samples were then reverse transcribed and the cDNAs were mixed at various ratios (8:1, 4:1, 2:1,
1:1, 1:2, 1:4, and 1:8) as described in the method section. BRCA1-c.4308T/T was detected by
the VIC fluorescence signal and BRCA1-c.4308C/C was detected by the FAM fluorescence
signal. As shown in Figure 1B, with decreasing cDNA ratios of c.4308T to c.4308C, the VIC
curve (detecting the c.4308T allele) shifted to the right with the increasing value of CT-c.4308T (VIC),
while the curve of FAM (detecting c.4308C allele) shifted to the left with the decreasing value of
CT-c.4308C (VIC). At the same time, the value of ΔCT (CT-c.4308T (VIC) – CT-c.4308C (FAM)) changed from
the negative to the positive. By regression analysis, a linear relationship between Log2 ratio of
cDNAs c.4308T to c.4308C and ΔCT was identified: Log2 (c.4308T/C) = – 0.0877 + 1.57897 *
ΔCT (P < 0.001) (Figure 1C). The Pearson correlation coefficient (r) between Log2
(c.4308T/c.4308C) and ΔCT was 0.9798. To establish a similar standard curve for BRCA2 allelic
expression, cDNAs from two individuals, who were either homozygous for BRCA2-c.3396A/A
or BRCA2-c.3396G/G, were mixed at the following ratios: 8:1, 4:1, 2:1, 1:1, 1:2, 1:4, and 1:8
(c.3396A/A allele:c.3396G/G allele). BRCA2-c.3396A was detected by the VIC fluorescence
signal and BRCA2-c.3396G was detected by the FAM fluorescence signal. As shown in Figure
1D, with decreasing ratios of c.3396A to c.3396G, the VIC curve (detecting c.3396A allele)
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shifted to the right while the FAM curve (detecting c.3396G allele) shifted to the left. After
regression analysis, a linear relationship between Log2 (c.3396A/c.3396G) and ΔCT was
identified: Log2 (c.3396A/G) = 0.11726 + 1.26458 * ΔCt (P < 0.001) (Figure 1E). The Pearson
correlation coefficient (r) between Log2 (c.3396A/G) and ΔCT was 0.9868.
Detection of Allelic Imbalance Caused by Nonsense-mediated mRNA Decay
To examine whether the allele specific real-time PCR assay is able to detect AI of BRCA1
and BRCA2 gene expression in cell lines, we evaluated RNAs isolated from lymphoblastoid cell
lines (LCLs) which were derived from deleterious mutation carriers heterozygous for BRCA1-
c.3671ins4 or BRCA2-c.796delT. These frame-shift mutations create the premature stop codons,
which are predicted to activate the nonsense-mediated mRNA decay pathway and thus lead to
decreased levels of mRNAs from the mutant alleles (12). As shown in Figure 2A and 2B, the
ratios of BRCA1-c.4308T to -c.4308C between wild type and BRCA1-c.3671ins4 heterozygous
samples were 0.93 ± 0.04 and 2.07 ± 0.06, respectively (p<0.01). By subcloning and sequencing
the individual transcripts, we found that the under-expressed allele contained both the BRCA1-
c.3671ins4 mutation and the BRCA1-c.4308C polymorphism (detected by the FAM signal) (data
not shown). To further examine if the loss of BRCA1-c.3671ins4 was associated with NMD, we
treated the BRCA1-c.3671ins4 LCLs with puromycin, a translational inhibitor, 14 hours prior to
RNA isolation. The ratio of BRCA1-c.4308T to -c.4308C in BRCA1-c.3671ins4 heterozygous
cells decreased about 30%, in comparison to the non treatment group (1.50 ± 0.05 vs. 2.07 ± 0.06,
p<0.01) (Figure 2B). Our data indicated that treatment with puromycin was able to partially
recover the AI caused by NMD. Significant AI was also observed for the BRCA2-c.796delT
mutant allele. The ratios of BRCA2-c.3396G to -c.3396A between wild-type and BRCA2-
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c.796delT heterozygous samples were 0.98 ± 0.06 and 6.59 ± 1.31, respectively (p<0.01). After
treating the BRCA2-c.796delT LCLs with puromycin, the ratio of BRCA2-c.3396G to -c.3396A
in BRCA2-c.796delT heterozygous cells decreased about 31%, in comparison to the non-
treatment group (4.90 ± 0.87 vs. 6.25 ± 1.17) (Figure 2C and 2D). Our results suggested that
the loss of expression of BRCA1 or BRCA2 mutant alleles via NMD significantly contributed to
the observed AI.
BRCA1 and BRCA2 Allelic Imbalance Is Associated with Breast Cancer Risk
To evaluate AI of BRCA1 and BRCA2 gene expression, genotype analysis of the two
common polymorphisms, BRCA1-c.4308T/C and BRCA2-c.3396A/G, was performed on DNA
samples isolated from fresh-frozen peripheral blood lymphocytes from 85 unrelated BRCA1/2
mutation-negative familial breast cancer carriers (median age at sample collection: 47), 112 non-
familial breast cancer carriers (mdian age at sample collection:: 52), and 102 age-matched
cancer-free females (median age at sample collection: 51) (Table 1). From these analyses, 37
(43.5%), 48 (42.9%), and 41 (40.2%) of the samples evaluated were determined to be
heterozygote for the BRCA1-c.4308T/C polymorphism for familial breast cancer patients, non-
familial cancer patients, and cancer-free controls, respectively (Table 1). Furthermore, 39
(45.9%), 44 (39.3%), and 36 (35.3%) of the samples above were found to be heterozygous for
the BRCA2-c.3396A/G polymorphism (Table 1). Since our initial validation studies were
preformed using immortalized lymphoblastoid cell lines, we first compared AI in RNA isolated
from 20 fresh-frozen lymphocytes versus 20 established Epstein-Barr Virus (EBV)-lines. No
significant differences were detected between these two sample sets [BRCA1: 0.424 ± 0.129 vs.
0.409 ± 0.127, (n=11); BRCA2: 0.212 ± 0.180 vs. 0.225 ± 0.209, (n=10)]. However, to limit any
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AI variation potentially introduced by EBV transformation, all subsequent AI assays were
performed using RNAs isolated from peripheral blood lymphocytes. Next, RNA isolated from
BRCA1-c.4308T/C (n=126) and BRCA2-c.3396A/G (n=119) heterozygotes, including single
heterozygotes and double heterozygotes, were evaluated for integrity and quantity. Those
samples demonstrating high quality and the necessary quantities were used in the AI assay, as
described in the Methods section.
To evaluate the AI, we used the absolute values of Log2 (BRCA1-c.4308T/C) or Log2
(BRCA2-c.3396A/c.3396G). The mean value of Log2 (c.4308T/C) of BRCA1 in the lymphocytes
from familial breast cancer carriers was found to be ~104% higher than that that in the
lymphocytes from cancer-free controls [0.424 ± 0.157 (n=32) vs. 0.211 ± 0.169 (n=40),
p=0.00001; t-test] (Table 2 and Figure 3A and 3B). Log2 of BRCA1-c.4308T/C in the
lymphocytes from non-familial breast cancer carriers was 73% higher than that in cancer-free
controls [0.353±0.209 (n=32), p=0.002 vs. control] (Table 2 and Figure 3A and 3C). In
comparison, the mean value of Log2 of BRCA2-c.3396A/G in the lymphocytes from familial
breast cancer patients was moderately higher (10%) than that in cancer-free controls [0.206 ±
0.180 (n=37) vs. 0.172 ± 0.123 (n=31), p=0.38; t-test] (Table 2 and Figure 4A and 4B). A
similar result (38% higher) was observed for Log2 (c.3396A/G) of BRCA2 in the lymphocytes of
non-familial breast cancer carriers [0.267±0.171 (n=26), p=0.03 vs. control] (Table 2 and
Figure 4A and 4C).
Interestingly, the distribution of under-expressed alleles of BRCA1 and BRCA2 was found
to be significantly different between cancer-free control and familial breast carriers, but not
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between cancer-free control and non-familial breast carriers. As shown in the Table 3 and
Figure 3, under-expressed BRCA1-c.4308T (i.e., Log2 [4308T/C]<0) and BRCA1-c.4308C (i.e.,
Log2 [4308T/C]>0) alleles were found in ~53% (21 of 40) and ~47% (19 of 40) of cancer-free
controls as compared to ~28% (9 of 32) and ~72% (23 of 32) of familial breast cancer carriers,
respectively (p<0.02). In addition, under-expressed BRCA2-c.3396A (i.e., Log2 [3396A/G]<0)
and BRCA2-c.3396G (i.e., Log2 [3396A/G]>0) alleles were found in ~45% (14 of 31) and ~55%
(17 of 31) of cancer-free controls as compared to ~70% (26 of 37) and ~30% (11 of 37) of
familial breast cancer carriers, respectively (p<0.02), respectively (Table 3 and Figure 4).
Inheritance Effects of AI in BRCA1
A previous study has indicated that AI for several tumor suppressor genes could be
transmitted by Mendelian inheritance (19). To test if the AI observed in our study may be
inherited, we identified three affected women (i.e., probands) reporting a significant family
history of breast and/or ovarian cancer for which we had blood from at least one of their sisters.
Furthermore, each sister had to be heterozygous for the BRCA1-c.4308T/C polymorphism. As
shown in family A (Table 4), sister Sis-02 displayed a similar AI pattern as compared to the
proband, while sister Sis-01 displayed no AI. In the two other families both the affected
probands and their corresponding sisters showed AI (Table 4). We further performed a
haplotype analysis to determine whether the alleles showing AI were shared between siblings.
As shown in Table 4, sisters with the same AI phenotype shared the same haplotype with their
affected sister. Importantly, sister Sis-01 in Family A did not share the same haplotype. Her
blood sample displayed no AI (0.007 ± 0.147) for BRCA1 gene expressions whereas the AI was
detected in her unaffected and affected sisters (Sis-02 and Proband, 0.382 ± 0.176 and 0.375 ±
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DISCUSSION
In this study we developed a quantitative AI assay to examine the expression difference
between the alleles of BRCA1 and BRCA2 (Figure 1). By performing this AI assay with specific
primers and probes that target common single nucleotide polymorphisms in BRCA1 and in
BRCA2, we were able to detect allelic imbalance associated with nonsense-mediated mRNA
decay in patients carrying frameshift mutations in BRCA1 and BRCA2 (Figure 2). We next
compared AI of BRCA1 and BRCA2 expression among three groups, familial breast cancer
patients, non-familial breast cancer patients, and age-matched cancer-free females. AI ratios of
BRCA1 in familial breast cancer cases were significantly higher than those from cancer-free
controls (p=0.00001) (Table 2 and Figure 3). Similar results were observed for AI ratios of
BRCA1 in the lymphocytes from non-familial breast cancer patients (p=0.002). AI ratios of
BRCA2 in familial or non-familial breast cancer cases were also higher than those from cancer-
free controls (p=0.38 or p=0.03, respectively). However, the difference was not statistically
significant in the ratios of mRNA expressed from the BRCA2 alleles found in familial breast
cancer cases when compared to cancer-free controls (Table 2 and Figure 4). In addition, the
distribution of under-expressed alleles between cancer-free controls and familial cases was
significantly different for both BRCA1 and BRCA2 gene expression (p<0.02 and p<0.02,
respectively) (Table 3). Furthermore, we have demonstrated that the AI patterns for BRCA1
expression, albeit in a small number of families, can be transmitted by Mendelian inheritance
(Table 4). Although these findings are consistent with a previous study (19), future evaluations
will benefit from evaluating AI in large families for evidence of disease segregation.
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Several methods have been developed to evaluate allele specific expression. The first
method combines primer extension and capillary electrophoresis (19, 21). The second approach
utilizes microarray technology to measure allele-specific mRNA expression (22). Compared to
the AI assay presented here, the method of primer extension plus capillary electrophoresis is also
accurate but relatively time consuming and expensive. The microarray approach provides a
high-throughput and a powerful platform for the simultaneous analysis of large numbers of genes
to analyze allele-specific gene expression, but it has less power to define the AI. Like the
majority of allelic expression methods (23), our AI assay also requires a transcribed
heterozygous variant in the individuals to be evaluated. In the present study, we targeted two
common polymorphisms, BRCA1-c.4308T>C and BRCA2-c.3396A>G in the general population.
Therefore, a substantial number of subjects homozygous for the polymorphisms had to be
excluded. To overcome this limitation of population selection based on genotypes, other primers
and probes will need to be developed to target other common polymorphisms in BRCA1 and/or
BRCA2. In addition, our approach could easily be applied for studying AI in other cancer
susceptibility genes, such as p53, APC and PTEN, etc.
In this study, we have demonstrated AI for both BRCA1 and BRCA2 in breast cancer
populations. Interestingly, the increase of AI ratios in familial and non-familial breast cancer
patients was more significant for BRCA1 than BRCA2. Loss of BRCA1 expression in breast
cancer has been reported to be related to the pathogenesis of breast cancer (13-17). Loss of
BRCA2 expression in cancers, in contrast, is still controversial (24, 25). These findings indicate
that AI in BRCA1 appears to be a more common event in breast cancer development than AI
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involving BRCA2. However, the mechanism(s) leading to the observed AI is for the most part
unknown.
We have demonstrated that both BRCA1 and BRCA2 deleterious mutations can activate the
NMD pathway and result in AI [Figure 2, and (12)]. However, all the familial breast cancer
patients evaluated in the current study were determined to lack a mutation in BRCA1 and BRCA2
that would trigger NMD. Furthermore, we evaluated the BRCA1 and BRCA2 genes in the
sporadic breast cancer patients and cancer-free controls demonstrating AI [i.e., allele expression
ratio > 0.25 or < – 0.25 (Figure 3 and 4)]. Again, no deleterious germline mutations were
detected (data not shown). This is not entirely surprising given that germline mutations in
BRCA1 and BRCA2 are rare in women affected with breast cancer without a strong family
history of the disease (26-29).
Based on these observations, we conclude that NMD is not likely to be responsible for the
observed AI in our case-control comparisons. Therefore, other mechanisms are likely to exist to
account for the observed increased AI of BRCA1 and BRCA2 gene expression in female breast
cancer patients. For example, the 5’ and 3’ non-coding regions of BRCA1 and BRCA2 are rarely
evaluated through genetic testing, even though genetic alterations in these non-coding regions
could be important in regulating BRCA1 and BRCA2 expression. For instance, genetic
alterations within 5’ DNA or the putative promoter regions are able to disrupt the binding of
transcription factors to DNA regulatory elements and hence lead to the loss of allelic gene
expression. Several studies have shown that large genomic deletions involving the BRCA1
promoter were associated with hereditary breast cancer (30-32). This concept is further
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supported by studies of Cowden syndrome (CS) showing that ~10% of CS-related PTEN
mutations occur in the PTEN promoter and lead to a 50% reduction in PTEN expression (33, 34).
Also, allele-specific hypermethylation of the BRCA1 promoter region and decreased BRCA1
expression is associated with ~10% of sporadic breast cancer cases (18, 30, 35). Recent
advances have identified a new pathway for gene regulation, i.e., via microRNAs (miRNAs) (36,
37). These 21-22 nt RNA molecules are complementary to the 3’ UTR sequence of transcripts
and mediate negative post-transcriptional regulation through RNA duplex formation (36, 38).
By performing in silico analyses in 4 BRCA1 SNPs and 2 BRCA2 SNPs (39), we have identified
three rare BRCA1 alleles (c.5628G, c.6273T, c.6924A) that could potentially create target sites
for selected microRNAs (Supplemental Table S2). Therefore, it is possible that altered mRNA
targeting could contribute to AI of BRCA1 gene expression in the absence of frameshift
mutations. It will be important in future studies to determine the mechanisms that either disrupt
transcription factors binding or alter miRNA binding, leading to constitutively decreased levels
of BRCA1 and BRCA2 and an increased risk of developing breast cancer.
In summary, we have developed a quantitative approach to evaluate expression of BRCA1
and BRCA2 from individual alleles, and we have found that AI in BRCA1 and to a lesser extent
BRCA2 is associated with increased breast cancer risk. Furthermore, we have demonstrated that
the AI patterns for BRCA1 expression could be transmitted by Mendelian inheritance. Since
susceptibility to breast cancer is far from being fully understood, our study suggests that alternate
mechanisms, other than deleterious coding mutations, may contribute to breast cancer.
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MATERIALS AND METHODS
Databases:
RefSeqs (GenBank Accession No: NM_007295.2 and NM_000059.1) were used for
BRCA1 and BRCA2 mRNA numbering, respectively. The A of ATG translation initiation codon
is defined as position +1.
Subjects and Genotype Analysis:
Three populations were used in this study, i) BRCA1/2 mutation-negative women reporting
a personal and family history of breast cancer, i.e., familial; ii) female breast cancer patients
without a significant family history of disease, i.e., non-familial; and iii) age-matched cancer-free
female controls (Table 1). All participants were Caucasian women with European-American
ancestry and were from the Delaware Valley, including the greater Philadelphia Metropolitan
area in Pennsylvania. For family studies, eligible subjects were women with a personal and
family history of cancer (at least two first and/or second-degree relatives affected with either
breast and/or ovarian cancer) and were ascertained from the Family Risk Assessment Program
(FRAP) at the Fox Chase Cancer Center (FCCC). All relevant institutional review boards
approved the study protocol and written informed consent was obtained from all participants.
Genotype analyses of the two common polymorphisms, BRCA1-c.4308T/C and BRCA2-
c.3396A/G were carried out using ABI PRISM 7900HT Sequence Detection System and Assays-
on-Demand SNP Genotyping products for fluorogenic polymerase chain reaction allelic
discrimination (Applied Biosystems, Foster City, CA).
Allelic Imbalance Assay
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1.25 μL reaction of the cDNA synthesized in the RT reaction was used in a real time PCR
reaction (25 μL total volume), performed with ABI PRISM 7900HT Sequence Detection System
following methods recommended by the manufacturer. Optimal conditions were as follows:
Step 1, 95ºC for 10 min; Step 2, 92ºC for 15 sec, 60ºC for 60 sec with Optics; repeated for 40
cycles. The primer and probe sets used in real-time PCR reaction to detected BRCA1-c.4308T/C
(rs1060915) and BRCA2-c.3396A/G (rs1801406) allelic expression were obtained from Applied
Biosystem TaqMan® SNP Assay program (Assay ID: C.3178676 and C.7605673.1 for BRCA1
and BRCA2, respectively). Sequence information for primers and probes is available upon
request. Each 96-well PCR plate included negative controls, positive controls, and unknown
samples. Real-time PCR data were analyzed with ABI SDS 2.2.2 software. In order to produce
the BRCA1 allelic expression standard curve, cDNAs from the two samples with homozygous
genotypes, BRCA1-c.4308T/T and BRCA2-c.4308C/C, were mixed as the following ratios: 8:1,
4:1, 2:1, 1:1, 1:2, 1:4, and 1:8 (c.4308T/T allele:c.4308C/T allele). For the same purpose,
cDNAs from the two samples with homozygous genotypes, BRCA2-c.3396A/A and BRCA2-
c.3396G/G, were mixed as the following ratios: 8:1, 4:1, 2:1, 1:1, 1:2, 1:4, and 1:8 (c.3396A/A
allele:c.3396G/G allele).
The principles of quantitative real time PCR provide the basis of this linear relation
between Log2 ratio and ΔCT established in our approach to detect AI (40, 41). Previous data
have shown that AmpliTaq DNA polymerase cleaves the matched and well-hybridized probe and
target sequences and produces a fluorescent signal (42). In contrast, mismatches between a
probe and target are expected to reduce the efficiency of probe hybridization, and AmpliTaq
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DNA polymerase is more likely to displace a mismatched probe without cleaving it, which does
not produce a fluorescent signal.
Theoretically,
Allele 1 gene copy number (detected by FAM):
Log2 [Allele–1] = -A1 * CT1 + B1 (i)
Allele 2 gene copy number (detected by VIC):
Log2 [Allele–2] = -A2 * CT2 + B2 (ii)
If the fluorescence probes have the same efficiency to hybridize with matched target sequence,
that is, A1 = A2 = A, therefore,
Log2 [Allele–1 / 2] = A * (CT2 - CT1) + (B1 – B2) (iii)
The function (iii) was confirmed by two standard curves, Log2 (c.4308T/C) = -0.0877 + 1.57897
* ΔCt and Log2 (c.3396A/G) = 0.11726 + 1.26458 * ΔCt, set up by our experimental data
(Figure 1). Besides using function (iii) to calculate of the ratio of mRNA expression between
the two alleles, function (i) and function (ii) are able to be applied for examining the absolute
value of each allele mRNA expression. However, the direct analysis of single allele expression
is often complicated by the potential variations between individuals with different environmental
or physiological background rather than genetic factors. Comparing the relative expression
levels of two alleles of the same gene within the same biologic sample will help to minimize
these variations.
Peripheral Blood Lymphocytes and Lymphoblastoid Cell Lines
Lymphocytes were isolated from peripheral blood and stored at -150˚C until needed. None
of the blood samples from breast cancer patients were collected at the time of chemo- or
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radiation therapy. In addition, a subset of cyropreserved lymphocytes from BRCA1 or BRCA2
mutation carriers (e.g., BRCA1-c.3671ins4 and BRCA2-c.796delT) or disease-free individuals
were infected with EBV to establish immortal lymphoblastoid cell lines (LCLs). LCLs were
maintained in RPMI (GIBCO BRL) media supplemented with 20% fetal calf serum (FCS) and
antibiotics at 37˚C, 5% CO2 atmospheric condition, and 95% humidity. The immortalized LCLs
from cancer-free individuals that had been tested negative for mutations in BRCA1 and BRCA2
served as wild-type controls. To prevent potential degradation of unstable transcripts by
nonsense-mediated mRNA decay a translation inhibitor, puromycin (Sigma, St. Louis, MO) was
added to the LCL cells as described in a previous study (12).
Subcloning the PCR Product and Sequence Analysis
PCR fragments containing a common polymorphism and deleterious mutation were
subcloned directly into pCR®4-TOPO vector (Invitrogen, Carlsbad, CA). PCR was then
performed to identify bacterial colonies containing appropriate inserts. Plasmid DNA was
purified using QIAfilter™ Plasmid Maxi Kit (Qiagen Inc., Valencia, CA) and the insert was
sequenced using either the universal M13-primers or the primers for PCR reactions.
RNA Isolation and Reverse Transcription (RT).
Total cellular RNAs were isolated from blood lymphocyte pellets using TRIzol reagent
according to the protocols provided by the manufacturer (Invitrogen Corp., Carlsbad, CA).
Purified RNAs were further processed to remove any contaminating DNA (DNA-free kit,
Ambion, Inc. Houston, TX). After quantification with Bioanalyzer-2100 system using RNA
6000 Nano LabChip kits (Agilent Technologies, Palo Alto, CA), 2 μg of total RNA from each
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sample was used as a template to be reverse-transcribed (RT) in a 20-μl reaction (containing 5
μM random hexamers, 500 μM deoxynucleoside triphosphate mix, 1x RT (reverse transcriptase)
buffer, 5 mM MgCl2, 1.5 units of RNase inhibitor, and 7.5 units of MuLV reverse transcriptase).
All reagents were purchased from Applied Biosystems (Branchburg, NJ). The RT reaction
conditions were 10 min at 25°C, 1 h at 42°C, and 5 min at 94°C.
Haplotype Analysis
Haplotypes were constructed for BRCA1 using 3 polymorphic microsatellite repeat markers
located within (D17S855 and D17S1322) or adjacent (D17S1325) to the BRCA1 locus. The
sequences of the primer pairs were obtained from the Genome Database (http://www.gdb.org)
and PCR reaction was carried out as previously reported (43, 44). PCR products with
fluorescent dye (HEX) labeled primer were mixed with Hi-Di Formamide and a fluorescent
labeled internal size marker. The mixture was subjected to electrophoreseis on an ABI 3100
Automated DNA Sequencer (Applied Biosystems, Foster City, CA) and the data were analyzed
by the GeneScan (Version 3.7) and GeneMapper (Version 4.0) software provided by the
manufacturer.
Statistical Analysis
Allele specific real-time PCR data were analyzed with ABI SDS software v2.2.2 (Applied
Biosystems, Foster City, CA). Statistical analysis was conducted using the SAS System (version
9) developed by the SAS Institute, Inc (Cary, NC). Student’s t-test was employed for continuous
data and results were presented as the mean ± SD. We compared the distribution of under-
expressed alleles in BRCA1 or BRCA2 between cases and controls using chi-squared 95%
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ACKNOWLEDGEMENTS
We would like to acknowledge Dr. S. Litwin for help with statistical analyses and Ms. K.A.
Cattie with haplotype analysis. We also thank the numerous volunteers for providing blood
samples for these studies. This work is supported in part by Cheryl Herman and the Eileen
Stein-Jacoby Fund, grants from the National Cancer Institute, i.e., P50 CA83638 (AKG), U01
CA69631 (MBD), and R25 CA057708 (SW); a fellowship from the Department of Defense,
W81XWH-04-1-0573 (XC); and DOD grants, DAMD17-03-1-0707 (AKG) and DAMD17-03-1-
0312 (AKG); and by an appropriation from the Commonwealth of Pennsylvania.
CONFLICT OF INTEREST STATEMENT
None of the authors has any conflict of interest.
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LEGENDS
Figure 1. Standard curves for BRCA1 and BRCA2 allelic imbalance.
(A) Allele specific real-time PCR amplification plot analyses of BRCA1-c.4308T (VIC) and -
c.4308C (FAM) was performed in cDNAs generated by RT-PCR using RNAs from blood
lymphocytes of two individuals homozygous for either the BRCA1-c.4308T/T or BRCA1-
c.4308C/C. DNA sequencing chromatograms confirming the genotype are shown in the right
panel. (B) Allele specific real-time PCR amplification plot was analyzed in mixed cDNAs of
BRCA1-c.4308T/T (detected by VIC) and BRCA1-c.4308C/C (detected by FAM) at the
following ratios: 8:1, 4:1, 2:1, 1:1, 1:2, 1:4, and 1:8, respectively. (C) The standard curve for
BRCA1 allelic imbalance: Log2 (c.4308T/C) = – 0.0877 + 1.57897 * ΔCT. The Pearson
correlation coefficient (r) between Log2 (c.4308T/c.4308C) and ΔCT was 0.9798 (Data expressed
as Mean ± SD, n=3; the mean value of
Δ
CT for c.4308T/C=1 has been adjusted to zero). (D)
Allele specific real-time PCR amplification plot was analyzed in mixed cDNAs of BRCA2-
c.3396A/A (detected by VIC) and BRCA2-c.3396G/G (detected by FAM) at the following ratios:
8:1, 4:1, 2:1, 1:1, 1:2, 1:4, and 1:8, respectively. (E) The standard curve for BRCA2 allelic
imbalance: Log2 (c.3396A/G) = 0.11726 + 1.26458 * ΔCT. The Pearson correlation coefficient (r)
between Log2 (c.3396A/G) and ΔCT was 0.9868 (Data expressed as Mean ± SD, n=3; the mean
value of
Δ
CT for c.3396A/G=1 has been adjusted to zero).
Figure 2. BRCA1 and BRCA2 allelic imbalance caused by nonsense-mediated mRNA decay.
(A) Allele specific real-time PCR amplification plots of BRCA1-c.4308T (VIC) and -c.4308C
(FAM) for non-template control, BRCA1 wild-type lymphoblastoid cells (WT), BRCA1 mutant
(heterozygous BRCA1-c.3671ins4) lymphoblastoid cells without [PC (-)] or with [PC (+)]
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puromycin treatment. (B) Allelic expression ratios of BRCA1-c.4308T to BRCA1-c.4308C (a: vs.
WT; b: vs. PC (+); t-test, p<0.05). (C) Allele specific real-time PCR amplification plots of
BRCA2-c.3396A (VIC) and -c.3396G (FAM) for non-template control, BRCA2 wild-type
lymphoblastoid cells (WT), BRCA2 mutant (heterozygous BRCA2-c.796delT) lymphoblastoid
cells without [PC (-)] or with [PC (+)] puromycin treatment. (D) Allelic expression ratios of
BRCA2-c.3396G to BRCA2-c.3396A (a: vs. WT; b: vs. PC (+); t-test, p<0.05).
Figure 3. BRCA1 allelic expression ratios in cancer-free controls, familial and non-familial
breast cancer patients. The AI assays were performed using specific primer and probe sets
targeting BRCA1-c.4308T/C alleles. Log2 ratios of BRCA1-c.4308T allele to -c.4308C allele
expression were presented in cancer-free controls (A), familial (B) and non-familial breast
cancer patients (C). (Data expressed as Mean ± SD, n=3; the mean value of allelic expression
ratios of total normal samples has been adjusted to zero).
Figure 4. BRCA2 allelic expression ratios in cancer-free controls, familial and non-familial
breast cancer patients. The AI assays were performed using specific primer and probe sets
targeting BRCA2-c.3396A/G. Log2 ratios of BRCA2-c.3396A allele to -c.3396G allele
expression were presented in cancer-free controls (A), familial (B) and non-familial cancer
patients (C). (Data expressed as Mean ± SD, n=3; the mean value of allelic expression ratios of
total normal samples has been adjusted to zero).
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Table 1. Characteristics of the Study Groups
Characters Study Groups
Familial Non-familial Cancer-free
Sample size 85 112 102
Age (median)
At diagnosis 44 49 NA
At sample collection 47 52 51
Family history†
2 or more 85 0 0
1 0 23 25
0 0 89 77
Genotypes
BRCA1-c.4308T/C 37 48 41
BRCA2-c.3396A/G 39 44 36
†: Number of first and/or second-degree relatives affected with either breast and/or ovarian
cancer.
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Table 2. Allelic Imbalance in BRCA1 and BRCA2 Expression
Genes Population Sample AI T-test (p value)
Number (Mean ± SD) vs. Cancer-free
BRCA1
Cancer-free 40 0.211 ± 0.169
Familial 32 0.424 ± 0.157 0.00001
Non-familial 32 0.353 ± 0.209 0.002
BRCA2
Cancer-free 31 0.172 ± 0.123
Familial 37 0.206 ± 0.180 0.38
Non-familial 26 0.267 ± 0.171 0.03
Note: To calculate the mean value of AI, all negative value of Log2 (BRCA1-c.4308T/C) and
Log2 (BRCA2-c.3396A/c.3396G) in Figures 3 and 4 were change to the positive values.
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Table 3. Distribution of Under-expressed Alleles of BRCA1 and BRCA2
Genes Group Under-expressed Alleles O.R. [95% C.I.] p-value†
BRCA1 c.4308T c.4308C
Log
2 [4308T/C] < 0 Log2 [4308T/C] > 0
Cancer-free Controls 21 19
Familial 9 23 2.82 [1.05, 7.60] 0.02
Non-Familial 16 16 1.11 [0.44, 2.80] 0.18
BRCA2 c.3396A c.3396G
Log
2 [3396A/G] < 0 Log2 [3396A/G] > 0
Cancer-free Controls 14 17
Familial 26 11 0.35 [0.13, 0.95] 0.02
Non-Familial 13 13 0.82 [0.29, 2.34] 0.20
†: A chi-squared test was used to assess the 2 by 2 tables.
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Table 4. Allelic Expression and Haplotype Analysis of BRCA1 in Sisters from
Three Breast Cancer-Prone Families
Family Members Allelic Expression Haplotypes
Log2 (BRCA1-c.4308T/C) D17S855-D17S1322-D17S1325
Family A
Proband 0.375 ± 0.060 145/155-121/121-193/193
Sis-01 0.007 ± 0.147 145/153-121/121-195/193
Sis-02 0.382 ± 0.176 145/155-121/121-193/193
Family B
Proband 0.477 ± 0.070 145/151-121/124-193/193
Sis-01 0.232 ± 0.214 145/151-121/124-193/193
Family C
Proband 0.583 ± 0.243 145/153-121/127-189/189
Sis-01 0.522 ± 0.156 145/153-121/127-189/189
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