Morphological predictors of BRCA1 germline mutations in young women with breast cancer.

Abstract

Knowing a young woman with newly diagnosed breast cancer has a germline BRCA1 mutation informs her clinical management and that of her relatives. We sought an optimal strategy for identifying carriers using family history, breast cancer morphology and hormone receptor status data.
We studied a population-based sample of 452 Australian women with invasive breast cancer diagnosed before age 40 years for whom we conducted extensive germline mutation testing (29 carried a BRCA1 mutation) and a systematic pathology review, and collected three-generational family history and tumour ER and PR status. Predictors of mutation status were identified using multiple logistic regression. Areas under receiver operator characteristic (ROC) curves were estimated using five-fold stratified cross-validation.
The probability of being a BRCA1 mutation carrier increased with number of selected histology features even after adjusting for family history and ER and PR status (P<0.0001). From the most parsimonious multivariate model, the odds ratio for being a carrier were: 9.7 (95% confidence interval: 2.6-47.0) for trabecular growth pattern (P=0.001); 7.8 (2.7-25.7) for mitotic index over 50 mitoses per 10 high-powered field (P=0.0003); and 2.7 (1.3-5.9) for each first-degree relative with breast cancer diagnosed before age 60 years (P=0.01).The area under the ROC curve was 0.87 (0.83-0.90).
Pathology review, with attention to a few specific morphological features of invasive breast cancers, can identify almost all BRCA1 germline mutation carriers among women with early-onset breast cancer without taking into account family history.

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Morphological predictors of BRCA1 germline mutations in
young women with breast cancer
MC Southey1, SJ Ramus2, JG Dowty3, LD Smith1, AA Tesoriero1, EEM Wong1, GS Dite3, MA Jenkins3,
GB Byrnes4, I Winship5,6, K-A Phillips7,8, GG Giles1,9and JL Hopper*,3
1Department of Pathology, Genetic Epidemiology Laboratory, Victoria, Carlton, Australia;2Department of Gynaecological Oncology, UCL EGA Institute for
Women’s Health, University College London, London, UK;3Department of Public Health, Centre for Molecular, Environmental, Genetic and Analytic
Epidemiology, School of Population Health, The University of Melbourne, Carlton, Victoria, Australia;4The International Agency for Research on Cancer,
Lyon, France;5Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, Victoria, Australia;6Department of Genetics,
Royal Melbourne Hospital, Parkville, Victoria, Australia;7Division of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Victoria, East
Melbourne, Australia;8Department of Medicine, St Vincent’s Hospital, Victoria, East Melbourne, Australia;9Cancer Epidemiology Centre, The Cancer
Council Victoria, Victoria, Carlton, Australia
BACKGROUND: Knowing a young woman with newly diagnosed breast cancer has a germline BRCA1 mutation informs her clinical
management and that of her relatives. We sought an optimal strategy for identifying carriers using family history, breast cancer
morphology and hormone receptor status data.
METHODS: We studied a population-based sample of 452 Australian women with invasive breast cancer diagnosed before age 40 years
for whom we conducted extensive germline mutation testing (29 carried a BRCA1 mutation) and a systematic pathology review,
and collected three-generational family history and tumour ER and PR status. Predictors of mutation status were identified using
multiple logistic regression. Areas under receiver operator characteristic (ROC) curves were estimated using five-fold stratified
cross-validation.
RESULTS: The probability of being a BRCA1 mutation carrier increased with number of selected histology features even after adjusting
for family history and ER and PR status (Po0.0001). From the most parsimonious multivariate model, the odds ratio for being a
carrier were: 9.7 (95% confidence interval: 2.6–47.0) for trabecular growth pattern (P¼0.001); 7.8 (2.7–25.7) for mitotic index over
50 mitoses per 10 high-powered field (P¼0.0003); and 2.7 (1.3–5.9) for each first-degree relative with breast cancer diagnosed
before age 60 years (P¼0.01).The area under the ROC curve was 0.87 (0.83–0.90).
CONCLUSION: Pathology review, with attention to a few specific morphological features of invasive breast cancers, can identify almost
all BRCA1 germline mutation carriers among women with early-onset breast cancer without taking into account family history.
British Journal of Cancer (2011) 104, 903–909. doi:10.1038/bjc.2011.41
Published online 22 February 2011
& 2011 Cancer Research UK
www.bjcancer.com
Keywords: BRCA1; early-onset breast cancer; tumour morphology
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About 5–10% of young women diagnosed with breast cancer carry
germline mutations in BRCA1 (Newman et al, 1998; Southey et al,
1999; Malone et al, 2000; Ozcelik et al, 2003). Knowing a woman’s
BRCA1 mutation status soon after the time of diagnosis is
important because it could inform her immediate treatment
choices, particularly with regard to breast conservation therapy
vs mastectomy (Pierce et al, 2010), and perhaps in the future, her
use of targeted therapies, such as Poly (ADP-ribose) polymerase
inhibitors (Goodwin et al, 2007; Fong et al, 2009). Her mutation
status is also important for her female relatives because a
substantial proportion of these women will carry the same
mutation and so will therefore be at high risk of breast and
ovarian cancer (Antoniou et al, 2003) and will have different
preventive and screening options than the general population.
What is the best approach to identify the BRCA1 mutation
carriers among young women with newly diagnosed breast cancer,
in terms of timeliness, sensitivity, specificity? Family history is
difficult to collect well, especially in a busy clinical setting, in the
context of a young woman newly diagnosed with breast cancer.
In addition, the woman might not know her family cancer history
details and gathering the relevant data and ensuring its accuracy
might involve other family members, a process that can be time
consuming. Even when accurately reported, well collected and
verified cancer family history is often uninformative, unless
extreme, as it is neither sensitive nor specific to BRCA1 mutation
status. The areas under the receiver operator characteristic (ROC)
curves for algorithms based on family history alone, such as
BRCAPRO and BOADICEA, are at most 0.7–0.8 (e.g., Antoniou
et al, 2008b). The odds of being a carrier increases on average by
only a few fold for each first-degree relative with breast cancer
(Ozcelik et al, 2003; Apicella et al, 2007) and given that the
mutation frequency in the general population is about 1 in 600
(Antoniou et al, 2008a), it can be seen that family history must be
extreme before the probability of being a carrier exceeds a nominal
Received 20 August 2010; revised 4 January 2011; accepted 26 January
2011; published online 22 February 2011
*Correspondence: Professor JL Hopper;
E-mail: j.hopper@unimelb.edu.au
British Journal of Cancer (2011) 104, 903–909
& 2011 Cancer Research UKAll rights reserved 0007– 0920/11
www.bjcancer.com
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and historic testing threshold of 10% (Burke et al, 1997), let alone
the newer UK benchmark of 20% (National Institute for Clinical
Excellence, 2004). Even for the women with apparently strong
family histories being tested by cancer genetics services, the great
majority (470%) are found to be non-carriers (Parmigiani et al,
2007; Antoniou et al, 2008b). Further, large proportions of the
women who have been tested have had low a priori probability of
being a carrier. For example, in a high-risk setting in the United
States 440% of those tested were below the 10% threshold based
on BRCAPRO (Parmigiani et al, 2007), and similar figures have
been observed in the United Kingdom (Antoniou et al, 2008b).
It has been known for some time that there are particular
morphological and histopathological features evident on tumour
review that are more common in cancers arising in BRCA1
mutation carriers (Lakhani et al, 1998). These features have been
identified by studying carriers across a wide range of ages at
diagnosis who were ascertained because of their strong family
cancer history. Lack of ER and PR expression have also been
reported to improve prediction of BRCA1 mutation status based
on family history (Lakhani et al, 2002; James et al, 2006; Mavaddat
et al, 2010). To date, no validated algorithm appears to have been
developed based on all of these predictive features, let alone using
a population-based series of early-onset breast cancers.
Our aim was to devise a practical strategy that could be applied at
the time of diagnosis for identifying, with high sensitivity and
specificity, those young women with early-onset breast cancer who
have the highest probability of carrying a germline mutation in
BRCA1. We used morphological and immunohistochemical data that
could be routinely collected at diagnosis, as well as data on family
history of breast cancer in first- and second-degree relatives.
PARTICIPANTS AND METHODS
Australian Breast Cancer Family Registry
The Australian Breast Cancer Family Registry (ABCFR) includes a
population-based, case–control–family study of breast cancer, in
which cases and controls (probands) and their relatives were
administered the same questionnaires, carried out in Melbourne
and Sydney, Australia (Hopper et al, 1994, 1999; McCredie et al, 1998).
It is a component of the international Breast Cancer Family Registry
(John et al, 2004; Neuhausen et al, 2008). The study was approved by
the ethics committees of The University of Melbourne and The Cancer
Councils of Victoria and New South Wales. All participants provided
written informed consent for participation in the study.
Family history data
For each family, a pedigree was constructed by interviewing the
proband and all participating relatives, covering all known first-
and second-degree adult relatives of the proband, their vital
statuses, cancer histories, dates of birth and, if appropriate, dates
of death and/or diagnosis. Consequently, reports of cancer in
relatives came from multiple sources within the family, so that the
pedigree information for each person was based on a self-report or
report(s) from first-degree relatives and rarely was only on a
report from a second-degree relative. Attempts were made to verify
cancer reports using cancer registries, death certificates and other
medical records (Dite et al, 2003). For probands, a strong family
history was defined as having two or more first- or second-degree
relatives diagnosed with breast or ovarian cancer (including male
breast cancer) on the same side of the pedigree.
Tumour retrieval, pathology review and morphology score
We attempted to retrieve archival blocks for the first primary
invasive breast tumours of all 856 probands diagnosed before the
age of 40 years. A total of 452 tumours (53%) were retrieved and
included material from 16 surgical biopsies, 7 spot/needle localisa-
tions, 309 lumpectomy/quadrantectomies, 167 mastectomies and
442 axillary dissections. The mean age at diagnosis for these
retrieved cases was 35 years, as it was for the 856 probands.
Histological type was recorded for 442 (98%) of the reviewed cases,
the majority of the tumours were infiltrating ductal carcinomas
(NOS) (83%). Atypical medullary (5%), classical medullary (1%),
pleomorphic lobular (4%), classical lobular (2%) and other very rare
histological types (tubular, cribiform, mucinous, secretory, meta-
plastic and alveolar lobular, 4%) made up the remainder.
These were scored for morphology features by a pathologist blind
to the mutation status as described by Armes et al (1998) and
Longacre et al (2006). Briefly, the tumours were typed into primary
pattern and secondary pattern using the World Health Organisation
breast carcinoma classification with minor modifications as des-
cribed by Page et al (1987). Tumour grade was scored using the
modified system of Bloom and Richardson by assessing mitotic rate,
tubular differentiation and nuclear pleomorphism (Page et al, 1987).
We then selected nine features that have been reported
to be associated with BRCA1 germline mutation status (Eisinger
et al, 1996; Breast Cancer Linkage Consortium, 1997; Jo ´hannsson
et al, 1997; Armes et al, 1998; Lakhani et al, 1998):
(1) Very high mitotic index defined here (see justification
below) as 450 mitoses per 10 high-powered fields (h.p.f.).
The mitotic index of each tumour was scored as: number of
mitoses per 10 h.p.f.
(2) High nuclear grade (score of 3 for nuclear pleomorphism
according to Elston (1987), which is scored as: (1) bland,
(2) intermediate or (3) malignant (high)).
(3) Little or no tubule formation (score of 3 for tubule formation
(o10%)) according to Elston (1987), which is scored as: (1)
475%, (2) 10–75% and (3) o10%).
(4) Trabecular growth pattern. Primary and secondary growth
patterns are scored as: (1) acinar (organoid), (2) lobular,
(3) trabecular and (4) tubular. Primary trabecular growth
pattern was scored according to Ridolfi criteria for medullary
cancers requiring 475% of the tumour to have a trabecular
growth pattern (Ridolfi et al, 1977).
(5) Pushing margin, defined here as a continuous front of cells
observed in 450% of the tumour circumference and scored
as: (1) yes and (2) no.
(6) Circumscribed growth pattern scored as: (1) yes and (2) no.
(7) Syncytial growth pattern 475% scored as: (1) yes or (2) no.
(8) Necrosis scored as: absent or present; and
(9) Moderate or intense lymphocytic infiltrate, scored as:
(1) absent/minimal, (2) moderate or (3) intense.
We generated a morphology score for each tumour by adding up
the number of the following features that were present; high
mitotic index, high nuclear grade, little or no tubule formation,
trabecular growth pattern, pushing margin, circumscribed growth
pattern, syncytial growth pattern, necrosis and a moderate or
intense lymphocytic infiltrate, so that the score ranged from 0 to 9.
ER and PR status had been collected by the ABCFR for 402
(89%) of the 452 reviewed breast cancer cases. This information
was collected from the state cancer registries (58%), diagnostic
pathology reports (29%) and from immunohistochemical staining
of tumour tissues (13%; Armes et al, 1998) as described in
McCredie et al (1998).
BRCA1 mutation screening
For 788 (92%) of the probands, and for all 455 of the reviewed
cases, previous BRCA1 mutation screening included:
(1) Protein truncation testing covering exon 11 of BRCA1
(Hopper et al, 1999).
Morphological predictors of BRCA1 germline mutations
MC Southey et al
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(2) Manual sequencing of the coding and flanking intronic
regions of BRCA1 for: (i) a random sample of 91 probands
(Southey et al, 1999), (ii) 63 probands with a strong family
history of breast and/or ovarian cancer, (iii) 6 probands whose
mothers had two primary breast cancers and (iv) 9 probands
who had at least two or first- or second-degree relatives
(maternal and paternal) with breast or ovarian cancer.
(3) Large genomic alteration screening of BRCA1 using multiplex
ligation-dependent probe amplification (MLPA) as previously
described (Smith et al, 2007) for groups (ii), (iii) and (iv)
in (2) above.
(4) Testing for the two Ashkenazi founder mutations in BRCA1,
185delAG and 5238insC for all (Leong et al, 2000; Dite et al,
2003; Apicella et al, 2007).
(5) Testing for the duplication of exon 13 in BRCA1 for 641 probands
as described previously (Puget et al, 1999; Leong et al, 2000).
Definition of pathogenicity for BRCA1 sequence variants was
consistent with the policy of the Breast Cancer Information Core
database (http://research.nhgri.nih.gov/bic/) and Neuhausen et al
(2008). The above testing identified 39 pathogenic mutation
carriers. The breast cancers arising in 29 (74%) of these carriers
were retrieved and reviewed.
Statistical analysis
Associations between features were assessed by dichotomising
and calculating corresponding odds ratios (ORs). Associations
between the outcome, mutation status and potential predictors
were estimated using simple and multiple logistic regression. The
best fitting model was defined to be the one with the lowest
Bayesian information criterion (BIC) (Schwarz, 1978) using
observations with no missing data for any variable (N¼400).
P-values were based on the likelihood ratio test unless otherwise
indicated and all calculations were performed using R version 2.7.2
(R Development Core Team, 2010).
The performance of the best fitting model was assessed using the
same data set on which the model was fitted using a five-fold stratified
cross-validation approach, as recommended by Kohavi (1995).
The predicted probability that a woman carries a BRCA1
mutation according to the best fitting model was calculated in the
standard way as f(xTb) where f is the logistic function given by
f(L)¼exp(L)/(1+exp(L)), b is the column matrix of maximum
likelihood estimates, and x is the relevant part of the woman’s
column matrix of covariates. Asymptotic likelihood theory implies
that b is normally distributed and allows estimation of its
variance–covariance matrix S so it follows that xTb is normally
distributed with variance s2¼xTSx. A 95% confidence interval
(CI) for the predicted carrier probability, f(xTb), was obtained by
applying f to the limits, xTb±1.96s, for the predicted log-odds.
RESULTS
A total of 29 (6%) of the 452 probands whose tumours had
undergone pathology review were found to be BRCA1 mutation
carriers. The odds of being a carrier depended strongly on the
morphology score, increasing on average by 80% (95% CI: 40–
240%) with each additional feature after adjusting for family
history and ER and PR status (P for trend o0.0001). Figure 1A
shows that mutation carriers tended to have higher morphology
scores than non-carriers; of the carriers, 27 (93%) had a
morphology score of 5 or more compared with 25% of all
probands. Figure 1B shows that, when restricted to probands with
a strong family history, the distribution of the morphology score
was bimodal. All 10 carriers with a strong family history also had a
morphology score of 5 or more, and these carriers comprised 48%
of all probands with a strong family history and a morphology
score of 5 or more.
Table 1 shows that some characteristics of family history were
associated with mutations status, but no family history feature
alone was highly sensitive (maximum 41%) and all had low
positive predictive values (o0.25%).
Table 1 also shows that, in contrast to family history features,
most morphological features studied were individually predictive
of BRCA1 mutation status, with four having smaller P-values
than even the most significant family history feature. In general,
the morphological features had high sensitivities (all but
three475%) and specificities (all but threeX65%). Moderate or
intense lymphocytic infiltrate, nuclear grade and little or no tubule
formation had high sensitivities but low specificities while
syncytial growth pattern, pushing margins and circumscribed
growth pattern had high specificities but low sensitivities.
The predictive features with crude OR410 were: trabecular growth
pattern, high mitotic index, and necrosis (sensitivityX79%,
specificityX65%, positive predictive value 0.14–0.32).
Of the 452 tumours reviewed, 154 had mitotic indices between
0 and 9/10 h.p.f. and of these 1 (1%) was a BRCA1 mutation
carrier, 115 had mitotic indices between 10 and 19/10 h.p.f and of
these 1 (1%) was a BRCA1 mutation carrier, 111 had indices
between 20 and 49/10 h.p.f. and of these 2 (2%) were BRCA1
mutation carriers and 72 had mitotic indices of 50 or more/10
h.p.f. (range 0–292/10 h.p.f.) and of these 25 (35%) were BRCA1
mutation carriers. A total of 74 tumours were scored to have
primary trabecular growth pattern and 36 were scored to have a
secondary trabecular growth pattern (all these 36 tumours were
scored to have a primary acinar growth pattern).
ER and PR statuses were both individually predictive of
mutation status, but while they had high sensitivities they did
not have high specificities and alone their positive predictive
values were each only 0.13. Being negative for both ER and PR was
120
100
80
Number of tumours
Number of tumours
60
40
20
0
0
1234
Score
5
67
8
9
012345
6
Score
7
89
BRCA1 mutation carrier
27/29 BRCA1 carriers
27carriers/107 tumours
BRCA1 mutation carrier
8/8 BRCA1 carriers
8 carriers /18 tumours
16
14
12
10
8
6
4
2
0
Figure 1
early-onset breast cancer with a strong family history (B).
Morphology scores of all early-onset breast cancer (A) and
Morphological predictors of BRCA1 germline mutations
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associated with an OR of 5.3 (95% CI: 2.3–13.0), sensitivity of 70%,
specificity of 67% and positive and negative predictive values of 13
and 97%, respectively.
Figure 2 shows that the features within each of the three
categories (family history, morphology and immunohistochemis-
try) were strongly associated with each other. The morphology
features and ER and PR status were generally associated with each
other but not with the family history features.
When models containing multiple features were considered, the
best fitting model (as judged by BIC) included just three features.
These features and their jointly estimated ORs (95% CI, Wald
P-value) were: trabecular growth pattern, OR¼9.7 (2.6–47.0;
P¼0.001); high mitotic index, OR¼7.8 (2.7–25.7; P¼0.0003);
and number of first-degree relatives with breast cancer diagnosed
before the age of 60 years, OR for each relative¼2.7 (1.3–5.9;
P¼0.01). The area under the ROC curve for this model was 0.87
(95% CI: 0.83 to 0.90). The OR estimates for the two morphology
features were negatively correlated with each other (r¼?0.44) but
neither was correlated with the OR estimate for family history
(r¼?0.04 and 0.09).
Table 2 shows cross-validation estimates of the areas under the
ROC curves for models that included the most predictive features
from one or more of the three categories of morphology,
immunohistochemistry (comprising ER and PR receptor status)
and family history. It is apparent from Table 2 that the
morphology features have the greatest impact on the area under
the ROC curve, and that models that include the morphology
features are not improved by the addition of family history
variables or ER and PR status.
Under the best fitting model, the probability that a woman
diagnosed with breast cancer before age 40 years carries a BRCA1
mutation is exp(L)/[1þexp(L)] where the log-odds L is given by
the formula L¼?5.1544þ2.0539 xþ2.2750 yþ0.9784 z and: x is
1 if the woman’s tumour has high mitotic index and 0 otherwise; y
is 1 if the woman’s tumour has a trabecular growth pattern and 0
otherwise; and z is the number of the woman’s first-degree
relatives who have been diagnosed with breast cancer before the
age of 60 years. A list of predicted probabilities for various values
of x, y and z are given in Table 3.
Table 1
OR with 95% CI and corresponding statistical significance (P), and the NPV, PPV, sensitivity (proportion of carriers with the feature) and specificity
(proportion of non-carriers without the feature)
For each feature potentially predictive of BRCA1 mutation status, the number (frequency in brackets) overall and by BRCA1 mutation status, the
Feature
BRCA1 carriers
(sensitivity)
BRCA1 non-carriers
(1-specificity)
OR
(95% CI)P NPVPPV
Family history
One or more first-degree relatives with breast cancer o60years
One or more first- or second-degree relatives with ovarian cancer
Strong family history
One or more first-degree relatives with breast cancer X60years
One or more second-degree relatives with breast cancer
N¼29
12 (41%)
2 (7%)
10 (34%)
2 (7%)
9 (31%)
N¼426
48 (11%)
6 (1%)
61 (14%)
19 (4%)
126 (30%)
5.6 (2.5–12)
5.2 (0.74–24)
3.1 (1.3–7)
1.6 (0.24–5.9)
1.1 (0.45–2.4)
0.0001
0.09
0.009
0.6
0.9
0.96
0.94
0.95
0.94
0.94
0.2
0.25
0.14
0.1
0.07
Morphological features
Trabecular growth pattern
High mitotic index
Necrosis
Circumscribed growth pattern
Moderate or intense lymphocytic infiltrate
Syncytial growth pattern
Malignant nuclear grade
Pushing margins (4 50%)
Little or no tubule formation
N¼29
26 (90%)
23 (79%)
25 (86%)
18 (62%)
28 (97%)
8 (28%)
28 (97%)
2 (7%)
25 (86%)
N¼426
84 (20%)
48 (11%)
148 (35%)
75 (18%)
339 (80%)
28 (7%)
361 (85%)
14 (3%)
319 (75%)
35 (12–150)
30 (12–85)
12 (4.5–40)
7.7 (3.5–17)
7.2 (1.5–130)
5.4 (2.1–13)
5 (1–91)
2.2 (0.33–8.3)
2.1 (0.79–7.2)
o0.0001
o0.0001
o0.0001
o0.0001
0.008
0.0009
0.04
0.4
0.1
0.99
0.98
0.99
0.97
0.99
0.95
0.98
0.94
0.96
0.24
0.32
0.14
0.19
0.08
0.22
0.07
0.12
0.07
ER and PR status
ER negative
PR negative
N¼27
23 (85%)
19 (70%)
N¼375
153 (41%)
125 (33%)
8.3 (3.1–29)
4.7 (2.1–12)
o0.0001
0.0002
0.98
0.97
0.13
0.13
Abbreviations: CI¼confidence interval; ER¼estrogen receptor; NPV¼ negative predictive value; OR¼odds ratio; PPV¼positive predictive value; PR¼progesterone
receptor.
Family
history
Family
history
Morphology
Morphology
IHC
IHC
1<OR<2
2<OR<5
5<OR<10
10<OR
Figure 2
Each row and column corresponds to a feature (in the same order as in
Table 1) and the shading represents different levels of odds ratios (ORs), as
indicated in the figure. Odds ratios that were o1 were shaded the same
as their reciprocals.
Levels of association between the features listed in Table 1.
Table 2
logistic regression models whose predictors were restricted to one or
more of the categories of explanatory features given in the table 1
Cross-validation estimates of areas under the ROC curves from
Without
morphology
With
morphology
No family history or ER or PR status
Family history alone
ER and PR receptor status alone
Family history and ER and PR status
— 0.88 (0.85–0.91)
0.87 (0.83–0.90)
0.88 (0.85–0.91)
0.87 (0.83–0.90)
0.65 (0.61–0.70)
0.73 (0.68–0.77)
0.76 (0.71–0.79)
Abbreviations: ER¼estrogen receptor; PR¼progesterone receptor; ROC¼recei-
ver operating characteristic.
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The fit of the best model was not improved by more than could
be attributed to chance by the addition of either ER and/or PR
receptor status. After adjusting for the three variables in the best
fitting model, the ORs (95% CI) associated with negative ER and
PR receptor statuses were 1.8 (0.5–7.1) and 1.5 (0.6–4.4),
respectively (both P¼0.3).
Of the 58 probands whose tumours had both high mitotic index
and trabecular growth pattern, 21 (36%) were BRCA1 mutation
carriers. Of the 332 probands who had neither of these
morphological features, only one (0.3%) was a carrier. Of the
123 who had one or both features, 28 were carriers, and this was
associated with OR¼98 (95% CI: 20–1750), sensitivity¼97%,
specificity¼78% and positive predictive value¼23%.
Of the 156 (34%) probands who had one or more of the three
features high mitotic index, trabecular growth pattern and one or
more first-degree relatives diagnosed with breast cancer before the
age of 60 years, 28 (18%) were carriers. Of the 70 probands who
had two or more of these three features, 24 (34%) were carriers.
DISCUSSION
Our study has shown that, by considering just two tumour
morphological features that could potentially be reported at the
time of diagnostic pathology, and one aspect of family history of
breast cancer, it is feasible to establish a simple way to identify
those young women with breast cancer who are most likely to
carry a BRCA1 germline mutation. Even if not supplemented by
the information on family history, a trabecular growth pattern and
a high mitotic index high were strong indicators of a woman’s
BRCA1 mutation status. Moreover, knowing ER and PR status did
not improve the model predictions once the two morphology
features and a family history variable were taken into account. In
the context of breast cancers arising in young women, we found
that any BRCA1 predictive value of these two measures appears to
be subsumed by the two key morphological features. It would be
of interest to know if this applied to breast cancers in women with
later age at diagnosis.
For our population-based sample of women with early-onset
breast cancer, if BRCA1 screening had been restricted to those with
a strong family history then we would have screened 71 cases and
found 10 carriers (14%). If instead we had screened those with a
high mitotic index, we would still have screened 71 cases but found
23 carriers (32%), more than twice as many. If we also screened
those with a trabecular growth pattern, we would have screened a
further 53 cases and found another 5 carriers (10%). Therefore,
screening those with high mitotic index and/or trabecular growth
pattern would have found 28 BRCA1 mutation carriers in 124
women screened (24%) and missed only one carrier. Screening
only one-quarter of early-onset cases based on just these two
morphology features would have been sufficient to identify almost
all carriers, without any reference to family history.
Table 2 shows the area under the ROC curve (AUC), an omnibus
measure of a model’s predictive power. Knowing family history in
addition to the morphology features made no improvement in the
AUC. The AUC based on family history alone was less than that
based on ER and PR, and when used together was still less than the
AUC based on the two key morphology features alone. In terms of
predictive strength, the traditional indicators – measures of family
history – were weaker than most of the morphological features.
Our family history data are likely to be no less accurate and
complete than family history data collected in a clinical setting.
This work has also identified a fascinating group of early-onset
breast cancers that share the morphological features of early-onset
breast cancers that carry identifiable BRCA1 mutations. These are
the cancers, illustrated in Figure 1A, that have a morphology score
of 5 or more but, despite extensive testing, are from women for
whom we have not been able to identify germline BRCA1
mutations. We performed further BRCA1 mutation screening on
DNA samples from the women whose tumours had mitotic indices
over 50 per 10 h.p.f. and a trabecular growth pattern and who
were not already known to carry a mutation in BRCA1 (n¼37).
(The mutation testing that had been performed on DNA from these
women was varied because of the previous mutation testing
strategy described above in the methods). A further two BRCA1
mutations, BRCA1 del1A-23 and BRCA1 delexon20, were identified
by MLPA testing. The woman with the BRCA1 del1A-23 mutation
had breast cancer diagnosed at the age of 33 years and a paternal
grandmother with breast cancer diagnosed at age 71 years
(verified). The women with the BRCA1 delexon20 mutation had a
breast cancer diagnosed at the age of 31 at recruitment, and
subsequently another at age 35 years, and had an unverified report of
breast cancer in a paternal grandmother (age at diagnosis unknown).
Using tumours from multiple-case breast cancer families with on
average later age at onset than in our series, Lakhani et al (1998 and
2002) carried out similar studies to ours to try to identify BRCA1
mutation carriers on the basis of pathology and immunohistochem-
istry. The ORs for prediction they found were somewhat lower than
we observed for early-onset cases. This might be due to a potential
change in morphological features associated with BRCA1 germline
mutations in postmenopausal women that could be due to non-
germline factors, such as oestrogens and methylation (both known to
have a changing role with increasing age).
From our data and model fit, we have calculated the predicted
probability of carrying a germline BRCA1 mutation based on the
two morphology features and family history: see Table 3. It can be
seen that 15% of our sample had predicted probabilities 410%
(68 women in 7 categories). On the other hand, for the group of
women who had none of the three predictive features, comprising
66% of our sample (299 women), the predicted probability was
Table 3The predicted probability that a woman carries a BRCA1 mutation according to the best fitting logistic regression model
Trabecular
growth pattern
High mitotic
index
Number of first-degree relatives diagnosed
with breast cancer before age 60 yearsn (%)
Predicted percent probability of
carrying a BRCA1 mutation (95% CI)
Absent Absent0
1
2
0
1
2
0
1
2
0
1
2
299 (65.7%)
29 (6.4%)
4 (0.9%)
11 (2.4%)
2 (0.4%)
0 (0%)
42 (9.2%)
9 (2%)
1 (0.2%)
43 (9.5%)
11 (2.4%)
2 (0.4%)
1% (0–2%)
2% (0–5%)
4% (1–18%)
4% (1–16%)
11% (2–36%)
24% (4–69%)
5% (2–14%)
13% (5–31%)
28% (8–65%)
30% (19–44%)
54% (36–71%)
76% (44–92%)
Present
PresentAbsent
Present
Abbreviation: CI¼confidence interval.
Morphological predictors of BRCA1 germline mutations
MC Southey et al
907
British Journal of Cancer (2011) 104(6), 903–909
& 2011 Cancer Research UK
Clinical Studies