Pathological Society report.
Project title: Plasma signatures in familial breast cancer.
Background
Breast cancer and plasma signatures.
Breast cancer affects one in eight women in the UK (Office of National Statistics, 2012). It is
estimated that 10% of all breast cancers are due to an inherited component (McPherson et al.,
2000). Women with a BRCA gene mutation have a 70%-80% chance of developing breast cancer
throughout their lifetime (McPherson et al., 2000).
At present germ line diagnostic testing and computer modelling are used to predict the risk of breast
cancer and eligibility for imaging, prophylactic surgery and chemoprevention. However, these do not
take into consideration the effect of inherited mutations on the somatic genome in real time.
Our team have previously developed a genetic signature based on Single Nucleotide Polymorphisms
and copy number variants in tumours. We are also developing a cancer profile using a newly
discovered molecule called microRNA which is detectable in the plasma.
MicroRNAs (MiRNAs) are a group of small endogenous RNAs of about 22 nucleotides in length. They
are non-coding RNAs with a regulatory function (Bartel, 2004). Numerous studies found that
different MiRNAs are implicated in regulating gene expression in tumours (Calin et al., 2002; Pallante
et al., 2006; He et al., 2005).
MiRNAs are found in the circulation in a stable form and can be used as a biomarker for cancer using
plasma samples (Hunter et al., 2008; Lodes et al., 2009) . Furthermore, studies identified MiRNAs
specific to breast cancer (Heneghan et al., 2009). Several studies have highlighted their potential for
use in early breast cancer detection (Schrauder et al., 2012).
The development of a plasma signature for unaffected women provides the potential to integrate
somatic biomarkers into risk assessment computer modelling and, in turn, clinical management
decision making.
Computer modelling
BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) is a
risk model for familial breast and ovarian cancer. The model can be used to compute BRCA1 and
BRCA2 mutation carrier probabilities and age specific risks for breast and ovarian cancer. It is the
familial breast cancer computer model that we used in this project
Hypothesis and aims
To test the hypothesis that MiRNA profiles reflect the risk of breast cancer in women at high risk due
to strong family history.
Objectives

Develop a database of clinical information of the cohort.

Risk assess the cohort using BOADICEA, looking at 5 year, 10 year and lifetime breast cancer
risks.

Assess whether these risk assessments reflect the known BRCA status of these patients.

Perform an analysis of the candidate MiRNAs in part of the cohort.

Relate MiRNA data to the breast cancer risk.
Methods
In total, 119 patients were recruited for the study prior to the commencement of this BSc project. All
individuals gave written informed consent prior to participation.
MicroRNA extraction and analysis
MicroRNA was extracted from the blood samples of study participants by staff members at the
Cancer Studies and Molecular Medicine (CSMM) department. This was followed by reverse
transcription and pre-amplification steps on all samples in order to carry out real-time PCR.
MiRNAs were selected using a candidate approach. Candidates were picked based on a threefold
increase in relative expression in breast cancer patients when compared to healthy controls using
commercially available MiRNA assays. The microRNA candidate selection was performed by Dr.
Elshaw as part of other on-going studies in the research group.
Thirteen different Candidate MiRNA assays were picked by CSMM staff. They were kept unidentified
at their request. One set was assigned letters A , B, C, and D respectively and another set was
assigned numbers 1 to 9 respectively. They were all Taqman MiRNA assays (Life Technologies).
Expression of different MiRNA in the samples was measured by real time quantitative PCR by
obtaining the Cycle threshold (Ct) values for each sample. The Ct values were normalised against an
endogenous control (U6).
The Ct value of the U6 reaction was subtracted from the Ct value of each individual MiRNA reaction
to generate a delta Ct value for each MiRNA, which represents the relative expression of that
particular MiRNA.
BOADICEA risk assessment software for breast cancer
All participants were risk assessed using the BOADICEA web application. A family pedigree was
drawn for all the study participants. The 5 year, 10 year and lifetime risks of breast cancer were
calculated for all patients, with the lifetime risk being risk by age of 80.
Data processing
The data produced by the real-time PCR reactions and BOADICEA calculations were analysed using
Microsoft Excel and two other software packages as follows:



Kruskal-Wallis one-way ANOVA (Analysis of Variance) and Dunn’s tests to compare the
BOADICEA 5 year, 10 year and lifetime risk of breast cancer of study subjects with a known
BRCA mutation status.
Kruskal-Wallis one-way ANOVA and Dunn’s tests to compare delta Ct values of different
categories of patients on two subsets of the cohort.
Partial correlations (correlations controlling for age) between the 5 year, 10 year and
lifetime breast cancer risks and delta Ct values for individual MiRNAs.
Results
The patients were grouped according to their risk of breast cancer due to their BRCA mutation status
and history of breast cancer. Table 1 describes the categories of study participants used in this study.
Category
Description
BRCA1 Affected
Patients carrying a BRCA1 mutation
affected by breast or ovarian cancer, postoperative blood sample obtained
Participant carrying a BRCA1 mutation
not affected by breast cancer
Participant from a known BRCA family,
but does not carry a BRCA mutation.
Does not have breast cancer
Patients carrying a BRCA2 mutation
affected by breast or ovarian cancer
Participant carrying a BRCA2 mutation
not affected by breast cancer
Participant from a known BRCA family,
but does not carry a BRCA mutation.
Does not have breast cancer
Patients affected by breast cancer but
who were found not to have a BRCA1 or
BRCA2 mutation. No family history of
BRCA mutations.
Participant who were found not to have a
pathological BRCA1 or BRCA2. They are
not affected by breast cancer. No family
history of BRCA mutations.
Participants who have not undergone
genetic testing for BRCA mutations.
BRCA1 Carrier
BRCA1 Negative Control
BRCA2 Affected
BRCA2 Carrier
BRCA2 Negative Control
BRCA Uninformative
Cancer Affected
BRCA Uninformative
Cancer Unaffected
Untested
Total
Number of
patients in
category
12
16
12
9
7
7
25
1
30
119
Table 1. Categories of study participants.
The ‘Untested’ category in Table 1 was further divided into the following subcategories (see Table 2).
Subcategory
Description
Untested Family, Cancer
Unaffected.
Participant and their family have not
undergone genetic testing for BRCA
mutations. Patient unaffected by
breast cancer
Patient and their family have not
undergone genetic testing for BRCA
mutations. Patient affected by breast
cancer
Patient has not been tested for BRCA
mutations. One of their relative has
undergone testing with an
uninformative result (see
uninformative category). Patient is
unaffected by cancer
Patient has not been tested for BRCA
mutations. There is a history of a
BRCA1 mutation in the family. Patient
is unaffected by cancer.
Patient has not been tested for BRCA
mutations. There is a history of a
BRCA2 mutation in the family. Patient
is unaffected by cancer.
Untested Family Cancer
Affected
Untested Patient,
Uninformative relative.
Cancer Unaffected
Untested patient, BRCA1
in family. Cancer
unaffected
Untested patient, BRCA2
in family. Cancer
unaffected
Total
Table 2. ‘Untested’ subcategories.
Number of
patients in
subcategory
9
2
16
2
1
30
Summary of results
BOADICEA provides a significantly higher breast cancer risk calculation for individuals with a BRCA
mutation. This was seen in the study cohort across 5 year, 10 year and lifetime risk estimation (see
Figure 1 below).
B O A D IC E A S C O R E S
1 .0
L ife tim e b re a s t c a n c e r r is k
Key
BRCA1 No Ca = BRCA1 Carrier
BRCA1 Ca = BRCA1 Affected
BRCA2 No Ca = BRCA2 Carrier
BRCA2 Ca = BRCA2 Affected
BRCA1 Neg. Cont. = BRCA1 Negative
Control
BRCA2 Neg. Cont. = BRCA2 Negative
Control
Uninformative Ca = BRCA Uninformative
Cancer Unaffected
0 .8
0 .6
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C a te g o ry
Figure 1 Box and whisker plot of Lifetime BOADICEA breast cancer risk of study participants in their
respective categories. Interpreting box and whisker plots: The box extends from the 25th to the 75th
percentile. The whiskers extend from the minimum to the maximum value. The line within the box
represents the median value.
BRCA1 and BRCA2 breast cancer affected individuals, BRCA mutation carriers and negative controls
all have significantly lower Ct values compared to healthy controls. However, there was no statistical
difference between BRCA cancer affected individuals, BRCA carriers, BRCA negative controls and
untested participants unaffected by cancer. This was replicated using MicroRNAs A – D (p-values
<0.01 to <0.001 depending on category). Figure 2 shows the real-time PCR results of the MicroRNA A
assay.
M ic ro R N A 'A '
0
D e lt a C t
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C a te g o ry
Figure 2 Box and whisker plot of delta Ct values of MicroRNA ‘A’ of healthy controls and study
participants within their respective categories.
Nine different MiRNA assays (MicroRNA assays 1-9) failed to find a statistical difference among the
BRCA untested patients and BRCA uninformative individuals. However, there were several outliers
identified in several groups. Figure 3 shows the real-time PCR results of MicroRNA 1 assay.
M iR 1
Key
20
Untested pt. Unif rel Ca unaf = Untested patient,
uninformative relative cancer unaffected
15
Untested Ca unaffected = Untested cancer
unaffected
D e lta C T
10
5
Untested BRCA1 in family.unaff = Untested patient,
BRCA1 in family cancer unaffected
0
Untested BRCA2 in family.unaff = Untested patient,
BRCA2 in family cancer unaffected
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Figure 3 Scatter graph of delta Ct values of MicroRNA ‘1’ of study participants within their respective
categories.
Partial correlations between the same 4 candidate MiRNA assays (assays A-D) and 5 year, 10 year
and lifetime BOADICEA breast cancer risk scores were not found to be of statistical significance (see
Figure 4).
Figure 4 A partial correlation controlling for age between BOADICIA Lifetime breast cancer risk scores
and MicroRNA ‘A’ Delta Ct (Untested fam. Cancer unaf. = untested family, cancer unaffected).
Discussion
BOADICEA as a breast cancer risk assessment tool
Based on our cohort, BOADICEA provided significantly higher 5 year, 10 year and lifetime breast
cancer risk estimations for individuals who have a BRCA mutation compared to individuals without a
BRCA mutation. This has been replicated in other studies (Antoniou et al., 2008). However, it could
not distinguish between groups who have different risks of developing breast cancer based on their
family history in the absence of a BRCA mutation.
MicroRNA assays
The delta Ct values based on 4 different assays were statistically significantly lower in participants
who are BRCA1 or BRCA2 cancer affected, BRCA mutation carriers or BRCA negative controls
compared to healthy individuals. This reflects the increased expression of these 4 MiRNA molecules
in the plasma of these patients. These 4 MiRNA molecules were selected as candidate assays for this
study based on the fact that they are raised in breast cancer patients compared to controls. We
show here that they are raised in groups who have not developed cancer but are at an increased risk
of developing breast cancer due to a family history of breast cancer, a personal history of breast
cancer or a BRCA mutation.
However, the relative expression of MicroRNAs A - D did not significantly differ between the BRCA1
affected, BRCA1 mutation carriers and the BRCA1 negative controls and the same applies for the
BRCA2 counterparts of these categories. One could hypothesise that since all the blood samples in
this study were collected post-surgery from breast cancer affected females, the BRCA affected and
the BRCA mutation carriers would not necessarily show any changes as there is increasing evidence
in the literature that the relative expression of certain MicroRNAs is reduced in blood post-surgically
(Tsujiura et al., 2010).
Explaining why the BRCA negative controls had a similar MicroRNA profile to these groups is more
difficult. Two factors can account for the similarity of these profiles. The first one being the fact that
59 out of 119 participants have at least one relative within the study. This was not accounted for in
the methodology of the statistical analyses.
On the other hand, evidence from a recent study suggests that individuals who test negative for a
BRCA mutation present in their family, i.e. BRCA negative controls, may have a significantly higher
risk of breast cancer compared to the general population. So, the similarity of the MicroRNA profiles
of BRCA negative controls to the profiles of BRCA mutation carriers may actually indicate the fact
that the BRCA negative controls in our cohort have a high breast cancer risk profile that is more
similar to BRCA mutation carriers than it is to the general population. (Vos et al., 2013).
The 9 other MicroRNA assays (MicroRNA assays 1-9) did not separate the different groups of BRCA
uninformative and BRCA untested individuals. However, finding a statistically significant difference
between the groups may have been too hard to achieve given the limited number of individuals in
each group. On the other hand, as mentioned earlier, there were a number of ‘outliers’ in several
groups. These outliers may be indicating that these individuals possibly have a higher risk of breast
cancer compared to the rest of their group.
Partial correlations
The age-adjusted correlations carried out between MiRNA profiles and BOADICEA risk scores were
not statistically significant for any of the MiRNA assays. This could be due to the fact that there were
several untested individuals for BRCA mutations. As mentioned earlier, BOADICEA was unable to
distinguish between groups when there are no BRCA mutations present.
Conclusions
The relative expression of MicroRNA molecules A - D were all found to be significantly elevated in
participants with a known BRCA mutation status with a personal or family history of a BRCA
mutation compared to a control group with no personal or family history of cancer or BRCA
mutation. However, no statistically significant difference was found among the categories
mentioned above. The relative expression of other MicroRNA molecules 1-9 did not show any
statistically significant increase in other categories of the cohort compared to healthy controls.
Further studies are required to draw a definitive conclusion on the use of these MicroRNA assays in
breast cancer risk prediction.
The BOADICEA breast cancer risk assessments showed that BOADICEA provided significantly
increased breast cancer risk estimates for individuals with a known BRCA mutation status who have
a personal or family history of a BRCA mutation compared to participants who were untested for
BRCA mutations or had no personal or family history of a BRCA mutation. We can conclude that
BOADICEA is of better value in families where a BRCA mutation has been identified.
Correlations between 5 year, 10 year and lifetime BOADICEA breast cancer risks and 4 different
MicroRNA assays were not statistically significant which lead us to the conclusion that the BOADICEA
breast cancer risk estimates and the relative expression of these 4 MicroRNAs in plasma are not
related. Therefore, the MicroRNA profiles do not reflect the risk of breast cancer in women at high
risk due to strong family history as calculated by the BOADICEA.
The inclusion of more participants in the study would allow more statistically robust tests to be
employed. Follow up of the current cohort is recommended and observing changes within groups
with regards to breast cancer status would be very useful as certain changes could potentially be
linked to certain MiRNA profiles.
Further research into the classification of certain groups of MiRNA assays to ascertain which MiRNA
profiles are associated with the development of new breast cancer in previously cancer free patients
within the cohort.
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