Tissue biomarkers of breast cancer and their association with conventional pathologic features

Abstract

Background:
Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC) diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.

Methods:
To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.

Results:
From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting that it may be associated with a HER2-enriched molecular subtype of breast cancer.

Conclusion:
This independently validated protein panel may be valuable in the classification and prognostication of breast cancer patients.

Full text

Tissue biomarkers of breast cancer and
their association with conventional
pathologic features
L Chung
1,5
, S Shibli
2,5
, K Moore
2
, E E Elder
3
, F M Boyle
4
, D J Marsh
1
and R C Baxter*
,1
1
Hormones and Cancer Division, Kolling Institute of Medical Research, University of Sydney, Royal North Shore Hospital,
St Leonards, New South Wales, Australia;
2
Department of Breast Endocrine Surgery, Royal North Shore Hospital, St Leonards,
New South Wales, Australia;
3
Westmead Breast Cancer Institute, Westmead Hospital, St Leonards, New South Wales, Australia
and
4
Patricia Ritchie Centre for Cancer Care and Research, Mater Hospital, St Leonards, New South Wales, Australia
Background: Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC)
diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from
healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.
Methods: To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange
protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.
Results: From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic
regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin
and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by
immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with
high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting
that it may be associated with a HER2-enriched molecular subtype of breast cancer.
Conclusion: This independently validated protein panel may be valuable in the classification and prognostication of breast cancer
patients.
Breast cancer is the most frequently diagnosed cancer, and the
leading cause of cancer death, in women worldwide (Jemal et al,
2011), with the lifetime risk of developing breast cancer estimated
to be 1 in 8 in Western countries (Feuer et al, 1993). Patient
survival has increased steadily over recent decades, attributable in
part to advances in both mammographic screening (Kopans, 2011)
and adjuvant systemic treatment protocols (Peto et al, 2012).
Whereas pathological features such as tumour size, node positivity,
hormone receptor positivity, and human epidermal growth factor
receptor 2 (HER2) overexpression have been used to guide
clinicians’ prescription of adjuvant therapy, true personalised
medicine requires the development of better biomarkers of risk and
response to therapy.
Gene expression profiling is emerging as a tool for classifying
breast cancers, guiding therapy, and predicting treatment
responses (Cheang et al, 2008; Haas et al, 2011). However, genome
and transcriptome analyses alone provide only a partial picture, as
alternative splicing of mRNA, combined with more than 100
unique post-translational protein modifications, mean that each
gene may give rise to multiple protein species (Banks et al, 2000).
Analysing the proteome may provide a more dynamic reflection
of the impact of the cell’s genetic programme on its immediate
*Correspondence: RC Baxter; E-mail: robert.baxter@sydney.edu.au
5
These authors contributed equally to this work.
Received 12 July 2012; revised 12 November 2012; accepted 19 November 2012; published online 8 January 2013
&2013 Cancer Research UK. All rights reserved 0007 – 0920/13
FULL PAPER
Keywords: breast cancer diagnosis; mass spectrometry; proteomics; tissue biomarkers
British Journal of Cancer (2013) 108, 351–360 | doi: 10.1038/bjc.2012.552
www.bjcancer.com | DOI:10.1038/bjc.2012.552 351

environment (Aebersold et al, 2005). Cancer proteomics encom-
passes the identification and quantitative analysis of differentially
expressed proteins relative to healthy tissue counterparts at
different stages of disease. Proteomic technologies can also be
used to identify markers for cancer diagnosis, to monitor disease
progression and efficacy of therapy, and to identify new therapeutic
targets (Srinivas et al, 2001).
Surface-enhanced laser desorption/ionisation time-of-flight
(SELDI-TOF) mass spectrometry (MS) is a high-throughput
proteomic method that involves solid-phase extraction of subsets
of the proteome before analysis by TOF MS (Callesen et al, 2008).
It has the ability to rapidly analyse hundreds of samples, essential
for obtaining biologically and statistically relevant data in medical
proteomic research. A recent review of protein profiling studies of
breast cancer demonstrates that, despite a considerable diversity
among these studies, there is a pattern of conformity developing,
with increasing numbers of studies reporting similar peaks in
protein profiles (Galvao et al, 2011). This suggests convergence to a
set of common discriminatory peaks for breast cancer, with
reproducibility across different clinical studies.
In this study we have employed SELDI-TOF MS to discover
tissue biomarkers of breast cancer, and validate them on an
independent sample set. We have used two immunological
methods to verify the identified proteins. Finally, the expression
levels of these proteins have been associated with clinical
pathological variables in order to explore their potential value in
breast cancer classification and prognosis.
MATERIALS AND METHODS
Patient samples. The study involved 404 patient samples
comprising 202 pairs: breast tumour tissue (BC) and adjacent
unaffected breast tissue (AT) from each subject. For the discovery
phase, 102 sample pairs were obtained from the Kolling Institute
Breast Tumour Bank at Royal North Shore Hospital, Sydney,
Australia. For independent validation, 100 sample pairs were
provided by the Australian Breast Cancer Tissue Bank, Sydney,
Australia. All breast tissue samples were collected at the day of
surgery with prior informed consent, and the study was approved
by the Human Research Ethics Committee of the Northern Sydney
Central Coast Area Health Service, Sydney, Australia. At the time
of surgical resection, tissues were immediately taken to a
pathologist, who sampled both the tumour itself and adjacent
tissue of normal appearance. Both samples were snap-frozen in
liquid nitrogen within 20 min of resection and stored at !80 1C.
Oestrogen receptor (ER) and progesterone receptor (PR) were
scored as either negative or positive by immunohistochemistry,
using rabbit monoclonal SP1 (Biocare Medical, Concord, CA,
USA) and mouse monoclonal Clone PgR636 (Dako, Carpinteria,
CA, USA), respectively. The HER2 status was defined as positive or
negative by immunohistochemistry using the HercepTest (Dako).
Any equivocal result using this test was confirmed by FISH.
Tissue preparation. Approximately 20 mg of each tissue
sample (BC or AT) was prepared for proteomic analysis by
grinding with a mortar and pestle while frozen in liquid
nitrogen, and then solubilising in 10 volumes of lysis buffer
(9.5 Murea, 2% 3-[(3-cholamidopropyl)dimethylammonio]-1-pro-
panesulfonate (CHAPS), and 1% dithiothreitol). Lysates were
added to a QiaShredder spin column (Qiagen, Hilden, Germany)
and centrifuged (12 000 r.p.m., 5 min) to remove insoluble material.
Samples were applied to weak cation-exchange (CM10) protein
chips (Bio-Rad Laboratories, Hercules, CA, USA) for immediate
analysis as described below, or aliquotted and stored at !80 1C for
future analysis. The protein concentration of each extract was
determined by BCA Protein Assay (Thermo Scientific, Rockford,
IL, USA).
Preparation of protein chips. The CM10 protein chips were pre-
equilibrated twice with 5 ml of binding buffer (50 mMsodium
acetate, pH 6.0) for 5 min. Protein extracts were diluted 1 : 5 with
binding buffer and 5 ml of each diluted extract was pipetted onto
the chips. All samples were run in duplicate. Chips were then
incubated with shaking using a MicroMix 5 (settings: form 20,
amplitude 4; EURO/DPC Instrument Systems, Flanders, NJ, USA)
for 90 min at room temperature. Each spot was treated with
2"1ml of 50% cyano-4-hydroxycinnamic acid in 50% acetonitrile
containing 0.5% trifluoroacetic acid (TFA), and air dried.
Generation of MS profiles. Protein profiles were initially obtained
using a PBSIIc protein chip reader (Bio-Rad Laboratories,
Hercules, CA, USA), and in the latter part of the study, a SELDI
Enterprise Edition protein chip reader (Bio-Rad). Mass spectra
were generated for each sample in the mass/charge (m/z) range of
1000–30 000 with a laser intensity setting of 175 (arbitrary units).
The laser was optimised for data collection between 1000 and
15 000 m/z, with detector sensitivity set at 8. Peaks o1000 m/z
were deflected away from the detector. Data were averaged from
328 spectra evenly distributed across each spot. Mean values from
duplicate spectra for each sample were used in all subsequent
analyses. The m/z value for each peak was determined using
external calibration with protein standards including bovine
insulin (5734.51 Da), equine cytochrome c(12 362 Da), equine
apomyoglobulin (16 952.3 Da), and bovine carbonic anhydrase
(29 023.70 Da; Sigma-Aldrich, St Louis, MO, USA). After calibra-
tion, spectra were baseline-subtracted and normalised using the
total ion current between 1500 and 15 000 m/z. Spectra that
required a normalisation factor of 42 were repeated, and if the
high normalisation factor persisted, these data were discarded.
Peak detection was initially performed using Biomarker Wizard
Version 3.2.2 (Bio-Rad Laboratories) on all peaks with signal/noise
ratio X5 and present in at least 10% of all spectra. Subsequently, all
MS spectra were exported to ProteinChip Data Manger v4.1 used
with the ProteinChip SELDI System Enterprise Edition (Bio-Rad)
to refine the combined data analysis.
MS data analysis. Data analysis was designed in three stages. For
initial discovery, biomarker panels were developed on the training
data set using 102 BC and AT sample pairs. Cluster analysis was
performed using Biomarker Wizard version 3.2.2 (Bio-Rad).
Univariate analysis of individual peaks was performed by Mann–
Whitney U-test using SPSS (Version 18.0, SPSS Inc., Chicago, IL,
USA). All protein peaks that significantly discriminated BC from
AT at Po0.001 were then subjected to multivariate analysis using
forward and reverse binary logistic regression (SPSS) to develop
the training model. The discriminatory power of each putative
marker was further described using receiver operating character-
istic (ROC) area-under-the-curve (AUC) analysis. To test protein
panels that were best able to discriminate BC from AT, 10-fold
internal cross-validation was used as previously described
(Ambroise and McLachlan, 2002; Scarlett et al, 2006). External
validation was carried using an independent set of 100 paired BC
and AT samples.
After external validation, to consolidate and unify the initial
discovery and validation data, a further analysis was performed on
the combined data sets. This coincided with the acquisition of new
peak cluster analysis software, ProteinChip Data Manager Version
4.1 (Bio-Rad). Similar to the initial discovery phase, both univariate
analysis using nonparametric statistics and multivariate analysis
using binary logistic regression were applied, confirming a final
two-protein marker panel and allowing calculation of overall
estimates of sensitivity and specificity, accuracy, and ROC
AUC values. The final stage of data analysis was to re-evaluate
BRITISH JOURNAL OF CANCER Protein biomarkers in breast cancer tissue
352 www.bjcancer.com | DOI:10.1038/bjc.2012.552

the two-protein panel on the separate training and validation sets
to ensure consistency between the findings from the new and
original software packages. In this re-testing, all common peaks
obtained from the combined data set study were used for each
regression analysis to achieve classification of tumour samples
separately in the training and validation sets.
Protein identification. For purification of the putative biomar-
kers, tissue lysates were fractionated using a cation-exchange resin
(Mustang S, Pall Corp., Ann Arbor, MI, USA) with stepwise pH
elution from pH 4 to pH 9 in a 96-well filter plate format
(AcroPrep, Pall) as previously described (Chung et al, 2009).
Proteins of interest in the eluates were monitored by SELDI-TOF
MS on normal-phase (NP20) chips. Fractions containing an
B8.5 kDa putative biomarker were further purified using reverse-
phase liquid chromatography (LC) on a 250 "4.6 mm Jupiter 5 mm
300-Å C18 column (Phenomenex, Lane Cove, Australia), eluted
with a 35-min linear gradient from 15% to 60% acetonitrile in 0.1%
TFA at 1.5 ml min
!1
, followed by separation on 12% SDS–PAGE
detected with SYPRO ruby protein stain (Invitrogen, Eugene, OR,
USA). Protein bands of interest were excised from the gel and
analysed using both nanoLC-ESI-MS/MS and MALDI-TOF
peptide mass fingerprinting by the Australian Proteome Analysis
Facility (Macquarie University and University of New South
Wales, Sydney, Australia). The protein peak at 9.2 kDa was purified
and identified in a similar manner.
Immunological validation of protein markers. To detect ubiqui-
tin and S100P by western blotting, BC and AT tissue extracts were
separated by 12% SDS–PAGE and transferred to PVDF mem-
branes (Bio-Rad). Membranes were blocked for 1 h at room
temperature with 5% skim milk. Ubiquitin was detected by
incubating the transferred membranes for 2 h at room temperature
on a shaking platform with anti-human ubiquitin monoclonal
antibody (R&D Systems, Minneapolis, MN, USA) in a 1 : 500
dilution in 5% skim milk. For S100P western blotting, samples
were concentrated five-fold by centrifugal ultrafiltration with
3-kDa MW cutoff (Nanosep 3K Omega, Pall Corp.) before
electrophoresis. This was necessary to increase detection sensitiv-
ity. Concentrated samples were separated and transferred, and
membranes blocked, as described above, and S100P was detected
by incubating overnight at 4 1C with rabbit anti-human antibody
(Invitrogen) in a 1 : 500 dilution in 5% skim milk. Secondary
antibody, peroxidase-linked anti-rabbit IgG (1 : 2000) was added
for 1 h at room temperature and the protein bands were visualised
by enhanced chemiluminescence using the SuperSignal West Pico
Luminol/Enhancer solution (Thermo Scientific). Western blot data
were imaged using the LAS 3000 imaging system (Fujifilm,
Stamford, CT, USA) and the images were analysed with Multi-
Gauge version 3.0 software (Fujifilm). The quantitative data were
normalised to the loading control of b-actin, and analysed using
the Wilcoxon signed-rank test (SPSS).
To confirm the identity of the m/z 8558 protein peak by protein
chip immunocapture, pre-activated RS100 protein chips (Bio-Rad)
were pre-coupled with 2 mg of monoclonal anti-human ubiquitin
antibody (R&D) in 50 mMNaHCO
3
buffer (pH 9.2) at 4 1C. The
spots were washed with 50 mMBSA to block the remaining active
sites. Tissue lysates were diluted 1 : 5 in buffer containing 50%
human serum in 0.1% Triton X-100 in PBS, spotted onto RS100
protein chips, and incubated for 2 h at room temperature on a
shaker to achieve optimal binding. After washing with PBS, all
spots were rinsed by 50 mMTris-HCl, 1 Murea, 0.1% CHAPS, and
0.5 MNaCl, pH 7.2. After further washing in 5 mMHEPES, pH 7.2,
the spots were coated with 2 "1ml of 50% sinapinic acid in 50%
acetonitrile, 0.5% TFA, and air dried. The chips were then analysed
on the SELDI-TOF MS. A His-tagged recombinant ubiquitin
standard (10.6 kDa; R&D) was used as a control. The m/z 9226
protein peak was similarly verified using RS100 protein chips to
confirm its identity as S100P. Before protein chip preparation, all
tissue extracts were pre-concentrated as described above for
western blotting. The RS100 protein chips were pre-coupled with
2mg of rabbit anti-human S100P antibody (Invitrogen) in 50 mM
NaHCO
3
buffer (pH 9.2) at 4 1C. The samples were then treated
and analysed as described above. His-tagged recombinant S100P
(12.6 kDa; Novus Biologicals, Littleton, CO, USA) was used as a
control.
Statistical analysis of clinical features. The association between
levels of the two protein markers, individually and in combination,
and tumour pathologic variables (tumour size, histological grade,
lymphovascular invasion, lymph node involvement, ER and PR
status, and HER2 expression) was examined using the Mann–
Whitney U-test (SPSS). Subgroup analyses were also performed, in
which lymph node-negative (n¼84) or lymph node-positive
(n¼85) groups were analysed separately. Significance was set at
Po0.05.
RESULTS
Patient characteristics. A total of 202 pairs of tissue samples were
used in this study, generating 808 spectra, of which 684 (duplicate
spectra on 171 pairs of samples) were subjected to full analysis. Of
the 102 pairs of samples selected for the training stage, 82 pairs
were fully analysed. Of the remaining 20 pairs, 8 were excluded
on clinicopathologic grounds: 4 had DCIS, 2 had neoadjuvant
treatment, and 2 had recurrent tumours; a further 12 sample pairs
were excluded when their mass spectra did not meet normalisation
criteria. For the validation set of 100 samples pairs, 89 pairs of the
subjects were analysed. Seven sample pairs were excluded on
clinicopathologic grounds: 4 had neo-adjuvant therapy, 1 had
metastatic disease, and 2 had recurrent disease; 3 sample pairs were
lost during preparation; and 1 pair was excluded when the mass
spectra did not meet normalisation criteria. The median age for the
patients included in the training and validation sets was 60 (range
28–92) and 58 (range 27–85), respectively. The clinical pathologic
characteristics of the tumours including histologic type and grade,
size, presence of lymphovascular invasion (LVI), hormone receptor
(ER and PR), HER2 status as well as lymph node status are
presented in Table 1.
Selection of protein biomarker panel by MS-based protein
profiling. The training set sample pairs (BC and AT) were
subjected to MS analysis in duplicate to identify putative protein
biomarkers that could distinguish tumour from unaffected tissue.
The 82 sample pairs whose spectra were amenable to normalisation
yielded 328 spectra, from which 53 common peaks were
determined by clustering analysis. Of these, 14 peaks (m/z 1337,
1705, 1842, 2033, 3790, 3804, 8346, 8548, 8599, 9205, 9239, 9292,
9641, and 12 220) were significantly differentially expressed
(Po0.005, Mann–Whitney test). These individual putative
biomarkers had ROC AUC values ranging from 0.70 to 0.84.
The 14 peaks were tested in forward and reverse binary logistic
regression analysis with 10-fold cross-validation. This produced a
final panel of 3 peaks (m/z 1842, 8599, and 9292) that classified BC
and AT, with ROC AUC of 0.87, as shown in Figure 1A (curve Ti).
Independent validation. The three putative biomarkers were
tested using an independent validation set of 100 sample pairs, of
which 89 pairs of spectra (in duplicate, 356 spectra) could be
analysed after normalisation. For the validation set, 57 common
protein peaks were determined by clustering analysis. Testing
the three-protein panel derived from the training set on the
independent sample set of 89 BC and 89 AT samples gave a
ROC AUC of 0.91 (Figure 1A, curve Vi). The sensitivity and
Protein biomarkers in breast cancer tissue BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2012.552 353

specificity were 80.9% and 91%, respectively, and overall accuracy
was 90%.
Re-analysis of combined data sets. To increase the statistical
power of the training and validation analyses and confirm the
results using a newer software version, we combined the data sets
into a single analysis of all 171 breast tissue sample pairs. Using
new clustering analysis software, ProteinChip Data Manager
Version 4, we found 28 peaks common to all spectra in the m/z
range of 2500 to 15 000. Peaks of lower mass were excluded from
this analysis because the putative marker at m/z 1842 had been
determined by LC-MS/MS to be non-peptide in nature (data not
shown). By univariate analysis (Mann–Whitney), the significant
peaks (Po0.001) were selected with the additional criterion that
individual ROC AUC was at least 0.80, as summarised in Table 2.
Multivariate analysis using binary logistic regression again
confirmed the two protein markers at m/z 8558 and 9226. The
difference in m/z values from those determined in the initial
training set analysis (m/z 8599, 9292) is larger than expected and
may be attributable to the fact that they are averaged from 684
spectra (171 sample pairs in duplicate) rather than 328 spectra (82
sample pairs in duplicate), re-calibration of standard curves
between the initial and subsequent analyses, the use of different
analysis software, and the relative mass inaccuracy of this
technique. Both protein peaks were elevated in BC tissue relative
to AT. The sensitivity and specificity for the binary classification
using the combined 2-marker panel were 77.2% and 88.9%,
respectively, with a ROC AUC value of 0.92 (Figure 1B, curve C).
Re-testing of initial training and validation sets. For final
confirmation of the potential two-marker panel, it was re-tested on
the original separate training and validation sets. The sensitivity
and specificity of the classification for breast tissue biopsy samples
were 73.2% and 87.8%, respectively, in the training set, compared
with 80.9% and 91% in the validation set. Their corresponding
ROC AUC values were 0.86 (curve Tr) and 0.91 (curve Vr) for the
training and validation sets, respectively (Figure 1C).
Together, these results suggest that two protein biomarkers in
combination provide efficient discrimination between breast
cancer tissue and healthy tissue. Figures 1D–F demonstrate the
performance of the two protein peaks of m/z 8558 and 9226 alone
and in combination. By paired sample t-test, a significant
difference between BC and AT groups was found for each protein
tested separately (Figures 1D and E, n¼171, Po0.001). For the
two-protein combined panel, the mean value was 3.3-fold
increased in BC compared with AT samples (Figure 1F, n¼171,
Po0.001).
Identification and verification of putative biomarkers. Both
proteins of m/z 8558 and 9226, retained by weak cation-exchange
protein chips, were significantly increased in breast cancer tissue.
For identification, initial purification was carried out using cation-
exchange followed by reversed-phase HPLC. Eluted fractions were
pooled and fractionated by SDS–PAGE, and bands of B8 kDa
were excised for final identification by LC-MS/MS. Ubiquitin was
identified from 6 peptides (two overlapping), giving 72% sequence
coverage. The calculated mass of monomeric ubiquitin (8560 Da)
was in good agreement with the consensus mass obtained
experimentally with SELDI (m/z 8558). Similarly, analysis of the
marker of B9.2 kDa identified it as a fragment or variant of S100P
(10 400 Da) from two peptides, giving 24% sequence coverage
relative to full-length S100P (Supplementary Figure S1). Notably,
the two peptides found in this study were identical to those
previously used to identify S100P in a MALDI-MS study of
proteins upregulated in colorectal cancer (Lam et al, 2010).
Immunological verification of the two protein identities was
performed using both western blotting and protein chip immuno-
capture. For ubiquitin, western blot confirmed differential expres-
sion of this protein between BC and AT tissue extracts. Figure 2A
shows that for BC and AT samples from four randomly selected
patients, relative overexpression of ubiquitin in the cancer samples
was observed. When quantitated and analysed for eight randomly
selected sample pairs, the increase in ubiquitin in BC was
significant (Figure 2B, P¼0.017, Wilcoxon signed-rank test).
The identity of this protein as ubiquitin was also verified by
immunocapture on RS100 protein chips (Figure 2C). The m/z 8558
peak, captured by immobilised ubiquitin antibody and displayed
by SELDI-TOF MS, was increased in two BC samples in
Figure 2Cii and iv compared with their corresponding AT samples
in Figure 2Ci and iii, and absent when the capture antibody was
nonimmune IgG (Figure 2Cvi). Figure 2Cv shows His-tagged
recombinant ubiquitin (10.6 kDa) as a control.
Table 1. Patient characteristics
Characteristics Training
set
Validation
set
No. of patients 82 89
Age (median) 60 58
Histologic type
Ductal (IDC) 68 76
Lobular (ILC) 10 10
Other 4 3
Histologic grade
Grade 1 7 11
Grade 2 32 27
Grade 3 43 49
Missing 2
Tumour size
p2 cm 29 28
X2 cm 53 59
Missing 2
Oestrogen receptor
Positive 56 64
Negative 25 23
Missing 1 2
Progesterone receptor
Positive 44 54
Negative 38 33
Missing 0 2
HER2 overexpression
Positive 15 16
Negative 57 63
Missing 10 10
Lymphovascular invasion
Present 34 35
Absent 48 54
Lymph node involvement
Positive 42 43
Negative 40 44
Missing 2
Abbreviations: HER2 ¼human epidermal growth factor receptor 2; IDC ¼invasive ductal
carcinoma; ILC ¼invasive lobular carcinoma.
BRITISH JOURNAL OF CANCER Protein biomarkers in breast cancer tissue
354 www.bjcancer.com | DOI:10.1038/bjc.2012.552

Similarly, the expression of S100P was also examined by western
blot in eight random sets of BC and AT samples. Figure 2D shows
the western blot data for four pairs, indicating variable levels of this
protein between patients, with upregulation in BC samples. When
quantitated and analysed for all eight sample pairs, the increase in
immunoreactive S100P in BC was significant (Figure 2E, P¼0.012,
Wilcoxon signed-rank test). By immunocapture using the same
S100P antibody immobilised on RS100 protein chips, an
apparently truncated form (m/z 9226) of S100P protein was
observed, similar to that found in the discovery programme using
CM10 cation-exchange chips. This peak was more abundant in BC
samples (Figure 2Fii and iv) than in the corresponding AT samples
(Figure 2Fi and iii), and absent when the capture antibody was
nonimmune IgG (Figure 2Fvi). Figure 2Fv shows His-tagged
recombinant S100P (12.6 kDa) as a control.
To further confirm the identity of the 9.22 kDa protein as a
short form of S100P associated with breast cancer, we also isolated
this protein from cell lysates prepared from MCF-7 breast cancer
cells. As shown in Supplementary Figures S2A–C, this protein
could be immunoprecipitated from MCF-7 lysates using three
different S100P antibodies (rabbit monoclonal, mouse polyclonal,
and rabbit polyclonal). Together with the S100P sequence data
(Supplementary Figure S1), this unequivocally confirms its
relationship to S100P. Also visible in the immunoprecipitates
was a smaller peak of 10.48 kDa, presumably representing full-
length S100P. The 9.22 kDa form could be separated from the full-
length protein by further purification on reverse-phase HPLC
(Supplementary Figure S2D).
Association of two protein biomarkers and their combination
with prognostic variables. To investigate the potential prognostic
value of ubiquitin and S100P separately and in combination in
breast cancer, we initially examined the association of each protein
with variables including tumour stage, nodal stage, histologic type
and grade, hormone receptor (ER and PR) and HER2 status, and
LVI. As shown in Table 3, significant positive associations were
seen between expression of the short form of S100P and tumour
size, higher grade, LVI, lymph node involvement, hormone
receptor positive status, and HER2 overexpression, whereas for
ubiquitin a significant association was only seen with tumour size,
grade, and HER2. When analysed together (Table 3), the combined
panel was significantly associated with tumour histologic grade,
size, and LVI, and also with ER-positive (ER þ) and PR-positive
(PR þ) status and HER2 overexpression (Figure 3).
As levels of the short form of S100P showed stronger
associations than ubiquitin with each of the pathological indicators
1-Specificity
Sensitivity
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
Tr
Vr
1-Specificity
Sensitivity
C
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity
Sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
0.0
Ti
Vi
4
3
2
1
0
Normal
Peak intensity m/z 8558
3
2
1
0
Peak intensity m/z 9226
6
5
4
3
2
1
0
Combined marker
Cancer Normal Cancer Normal Cancer
Figure 1. Performance of two protein peaks individually and in combination. (A) The ROC area-under-curve (AUC) after cross-validation was
0.87 (Ti) for the combination of peaks at m/z 1842, 8599 and 9292. For the independent validation sample set, the average value of ROC AUC was
0.91 (Vi). (B) Combination of the discovery and validation sets. The sensitivity and specificity of the combination peaks of m/z 8558 and 9226
were 77.2% and 88.9% with a ROC AUC value of 0.92. (C) Retesting of initial training and validation sets. The ROC AUC values for these tests
were 0.86 (Tr) and 0.91 (Vr) for training and validation sets, respectively. (D) Mean peak intensity values±s.e.m. (normal vs cancer) for the marker
at m/z 8558; (E) mean values±s.e.m. for the marker at m/z 9226, and (F) mean values±s.e.m. for the two markers combined. For the comparisons
in (D–F), n¼171, Po0.001.
Table 2. Summary of data analysis
Data analysis
Stage Training Validation Combination Retesting
training set
Retesting
validation set
No. of patients 82 89 171 82 89
MS profile no. 164 178 342 164 178
ROC AUC 0.87 0.91 0.92 0.86 0.91
Classification Sens 75.6%
Spec 91.5%
Sens 80.9%
Spec 91%
Sens 77.2%
Spec 88.9%
Sens 73.2%
Spec 87.8%
Sens 80.9%
Spec 90.0%
Abbreviations: AUC ¼area under the curve; MS ¼mass spectrometry; ROC ¼receiver operating characteristic; Sens ¼sensitivity; Spec ¼specificity.
Protein biomarkers in breast cancer tissue BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2012.552 355

(except for grade), and appeared to point to an ER/PR þ, HER2-
overexpressing phenotype (possibly corresponding to a ‘HER2-
enriched’ molecular subtype; Reis-Filho and Pusztai, 2011), we
undertook further analysis of its relationship to these prognostic
features. When examined separately for ER !and ER þtumours,
high S100P expression in both groups was equally associated with
tumour size and the presence of LVI (not shown). However,
the association between S100P and lymph node involvement was
only significant for ER !tumours (P¼0.010). In contrast, the
association between S100P and HER2 overexpression was only
significant for ER þtumours (P¼0.004), supporting the concept
that a high S100P level might be associated with a hormone
receptor-positive, HER2-enriched molecular subtype.
When examined separately for lymph node-negative and lymph
node-positive tumours, the positive association between ubiquitin,
the short form of S100P, or the combined panel and LVI, ER þ
status, and PR þstatus was entirely attributable to the lymph
node-positive tumours. A significant relationship between the
combined panel and HER2 overexpression was also confined to the
lymph node-positive tumours (Supplementary Table S1). This
subanalysis again points to a link between high expression
of the short form of S100P in breast tumours, and an ER/PR þ,
HER2-overexpressing phenotype that has been associated with
markers of poor patient outcome without treatment. However,
because sample numbers are low in some subanalyses, these
interpretations should be regarded as preliminary.
5
4
3
2
1
0
Normal Cancer
Relative expression
S100P
!-Actin
Peak intensity
m/z 9226
m/z
(i)
(ii)
(iii)
(iv)
(v)
(vi)
9000 13 00011 000 12 00010 000
40
20
0
40
20
0
40
20
0
40
20
0
40
20
0
40
20
0
Relative expression
Normal Cancer
3
2
1
0
Ubiquitin
!-Actin
N
Patient
Peak intensity
(i)
(ii)
(iii)
(iv)
(v)
m/z
(vi)
m/z 8558
8000
40
20
0
40
20
0
40
20
0
40
20
0
40
20
0
40
20
0
d
c
ba
CNCNCNC
NCNCNCNC
Patient dcba
12 000
11 00010 0009000
8000 12 000
11 00010 0009000
9000 13 00011 000 12 00010 000
Figure 2. Immunological validation of ubiquitin and S100P. (A) For ubiquitin, four BC and corresponding AT extracts were analysed by immuno-
blotting, indicating relative upregulation of ubiquitin in some breast cancer patients. b-Actin is shown as a loading control. (B) Densitometric
analysis of ubiquitin western blots of eight sample pairs. Box plot shows median and upper and lower quartiles; lines show maximum and minimum
values. P¼0.017, Wilcoxon signed-rank test. (C) Mass spectrometry (MS) spectra of proteins bound to immobilised mouse anti-ubiquitin antibody.
Samples were (i) patient 1 normal tissue, (ii) patient 1 cancer tissue, (iii) patient 2 normal tissue, (iv) patient 2 cancer tissue, (v) recombinant
His-tagged ubiquitin, and (vi) patient 2 cancer tissue, mouse IgG control. Arrow indicates the mass of monomeric ubiquitin, m/z 8558. N ¼normal
tissue; C ¼cancer tissue. (D) For S100P, four BC and corresponding AT extracts were analysed by immunoblotting, indicating relative upregulation
of S100P in some breast cancer patients. b-Actin is shown as a loading control. (E) Densitometric analysis of S100P Western blots of 8 sample
pairs. Box plot shows median and upper and lower quartiles; lines show maximum and minimum values. P¼0.012, Wilcoxon signed-rank test.
(F) Mass spectrometry spectra of proteins bound to immobilised rabbit anti-S100P antibody. Samples were (i) patient 3 normal tissue, (ii) patient 3
cancer tissue, (iii) patient 4 normal tissue, (iv) patient 4 cancer tissue, (v) recombinant His-tagged S100P, and (vi) patient 4 cancer tissue, rabbit
IgG control. Arrow indicates the mass of the S100P form of m/z 9226. N ¼normal tissue; C ¼cancer tissue.
BRITISH JOURNAL OF CANCER Protein biomarkers in breast cancer tissue
356 www.bjcancer.com | DOI:10.1038/bjc.2012.552

DISCUSSION
We have used SELDI-TOF MS to discover two proteins that, in
combination, show high discrimination between breast cancer and
healthy breast tissue samples. A limitation of the protocol was that
no microdissection was used, and hence tissue samples could have
contained heterogeneous cell types. Despite this technical limita-
tion, a robust panel of two putative breast cancer biomarkers was
discovered, and verified on an independent sample set. After
purification, the proteins were identified by LC-MS/MS as
ubiquitin and a truncated form of the S100-family member, S100P.
To discover tissue biomarkers in various cancers, SELDI-TOF
MS has been used previously, although the majority of such studies
in breast cancer have examined serum rather than tumour tissue.
Included among proteins previously identified from breast tumour
tissue lysates are albumin fragments (Gast et al, 2009) and
complement C3a (Zhang et al, 2012), both presumably derived
from the circulation. Tissue proteomic profiling using SELDI-TOF
MS has also yielded peak clusters that can contribute to the
classification of breast tumours into molecular subtypes (Brozkova
et al, 2008; Goncalves et al, 2008) that resemble the luminal A and
B, basal, and HER2-like subtypes defined by gene expression
analysis (Reis-Filho and Pusztai, 2011).
Of the two breast cancer-associated proteins identified in this
study, ubiquitin is a small protein of 76 amino acids that is
involved in both apoptotic signalling (Vucic et al, 2011) and
transcriptional regulation (Hammond-Martel et al, 2011).
Although monomeric ubiquitin has been identified in several
previous biomarker studies in breast cancer, its exact relationship
to disease status is unclear. In a SELDI-TOF MS study of breast
cancer cell lines, we previously discovered ubiquitin as a strongly
downregulated protein following treatment with chemotherapeutic
drugs (Leong et al, 2007). Another SELDI analysis found the
combination of a high ubiquitin level and low ferritin light chain
level to be a positive prognostic marker in node-negative breast
cancer (Ricolleau et al, 2006). In contrast, SELDI was also used to
show that a protein of similar mass (not identified as ubiquitin)
was a significant predictive factor for axillary lymph node
metastasis (Nakagawa et al, 2006). In a MALDI MS analysis of
microdissected cells from invasive breast cancer and healthy
(reduction mammoplasty) tissue, ubiquitin was one of a cluster of
proteins with increased expression in the cancer tissue (Sanders
et al, 2008).
Several E3 ubiquitin ligases are regarded as tumour suppressors
in breast cancer and are either mutated or downregulated; in
contrast, some others are regarded as oncogenes and are
overexpressed (Chen et al, 2006). Among key downregulated or
mutated E3 ligases are BRCA1 and Siah1, involved in DNA repair
and transcriptional regulation, among other functions. The E3
ligases downregulated in cancer are involved in both monoubi-
quitination (Hahn et al, 2012) and polyubiquitination (Wen et al,
2010), and low expression of the E3 ligase Siah1 is associated with
poorer disease-free survival in women with breast cancer
(Confalonieri et al, 2009). It may be speculated that the increased
level of monomeric ubiquitin that we observed associated
with larger tumours, higher grade, and HER2 overexpression, but
not with other pathological markers (Table 3), reflects a decrease in
the activity of some key ubiquitin ligase complexes. Interestingly,
a component of the Siah1 ubiquitination complex, calcyclin-
binding protein/Siah1-interacting protein (CacyBP/SIP), has
increased expression in breast cancer tissue compared with
14
Grade
P= 0.016
Size (cm)
P= 0.008
LVI
P= 0.044
ER
P= 0.016
PR
P= 0.022
HER2
P= 0.009
12
10
8
6
Peak intensity
4
2
0
14
12
10
8
6
4
2
0
14
12
10
8
6
4
2
0
14
12
10
8
6
4
2
0
14
12
10
8
6
4
2
0
14
12
10
8
6
4
2
0
G1
(18)
G3
(92)
!2
(57)
"2
(112)
Neg
(102)
Pos
(69)
Neg
(48)
Pos
(120)
Neg
(71)
Pos
(98)
Neg
(120)
Pos
(31)
Figure 3. Association of the combined panel with histopathologic variables. Higher expression of the combined panel was significantly associated
with higher histologic grade (P¼0.016) and higher tumour size (P¼0.008), and weakly associated with the presence of LVI (P¼0.044). The panel
was also relatively increased in tumours that were positive for oestrogen receptors (P¼0.016), progesterone receptors (P¼0.022), and HER2
overexpression (P¼0.009). Box plots show median and upper and lower quartiles; lines show maximum and minimum values.
Table 3. Association of two protein markers and their combination with tumour histopathologic variables
Tumour variables P-value, ubiquitin P-value, S100P P-value, combined
Tumour size (Tp2 cm, n¼57 vs T42 cm, n¼112) 0.024 0.009 0.008
Grade (G1, n¼18 vs G3, n¼92) 0.026 0.032 0.016
LVI (present, n¼69 vs absent, n¼102) 0.106 0.011 0.044
ER (positive, n¼120 vs negative, n¼48) 0.059 0.004 0.016
PR (positive, n¼98 vs negative, n¼71) 0.067 0.006 0.022
HER2 (positive, n¼31 vs negative, n¼120) 0.033 0.002 0.009
LN (positive, n¼85 vs negative, n¼84) 0.315 0.027 0.121
Histologic type (IDC, n¼144 vs ILC, n¼20) 0.607 0.765 0.708
Abbreviations: ER ¼oestrogen receptor; HER2 ¼human epidermal growth factor receptor 2; IDC ¼invasive ductal carcinoma; ILC ¼invasive lobular carcinoma; LN ¼lymph node;
LVI ¼lymphovascular invasion; PR ¼progesterone receptor.
Protein biomarkers in breast cancer tissue BRITISH JOURNAL OF CANCER
www.bjcancer.com | DOI:10.1038/bjc.2012.552 357

adjacent unaffected breast tissue, and is associated with
markers of poor prognosis (Wang et al, 2010). CacyBP/SIP is a
documented binding partner of S100P (Filipek et al, 2002),
raising the possibility that the disruption of ubiquitination
pathways in breast cancer might be involved in the increased
levels of both of the cancer-related biomarkers discovered in
our study.
In contrast to the relatively weak associations observed
between elevated ubiquitin levels and tumour size, higher grade,
and HER2 overexpression, a high level of the novel short form of
S100P was positively associated with larger tumours, higher grade,
LVI, lymph node involvement, ER/PR positivity, and HER2
overexpression. Of the two identified biomarkers, S100P made
the stronger contribution towards the association of the combined
panel towards each of these pathological features apart from
tumour grade. As the association between S100P and HER2
overexpression was exclusive to the ER þtumours (P¼0.004),
and absent in the ER !subgroup, a high tissue S100P level may
point to a group of tumours with high ER/PR þstatus, HER2
overexpression, and – given the association with size, grade, and
LVI – relatively poor outcome, although our study did not include
actual outcome variables. This corresponds most closely to the
‘HER-enriched’ breast cancer subtype (Slamon et al, 1987;
Reis-Filho and Pusztai, 2011), and suggests that S100P might have
potential, both in the classification of breast cancer and possibly as
a target for therapy.
S100P is a member of the calcium-binding S100 protein family
that contain a characteristic structural domain known as the
EF hand motif (Marenholz et al, 2004). There are at least 24
homologous S100 proteins with similar subcellular localisation, but
differing in expression pattern and function (Marenholz et al,
2004). The S100 proteins are low-molecular-weight (10–12 kDa)
acidic proteins that exist as intracellular or secreted homo- or
hetero-dimers with composition depending on the abundance of
individual family members and the cellular context (Santamaria-
Kisiel et al, 2006). Although the factors that regulate S100P have
not been studied extensively, DNA microarray studies have
included S100P among panels of genes upregulated by oestradiol
(Terasaka et al, 2004), progesterone (Bray et al, 2005), and HER2
overexpression (Mackay et al, 2003). These preliminary gene
expression reports are consistent with the clinical associations we
observed between high S100P levels and ER/PR þand HER2-
overexpressing tumours.
Through its effects on tumour growth and metastasis, S100P has
been associated with the progression of several types of cancer
including pancreatic, prostate, colorectal, and breast (Lam et al,
2010; Jiang et al, 2011). At least some of its effects have been shown
to be mediated through extracellular interaction with RAGE
(receptor for activated glycation end products) (Arumugam et al,
2004). Several studies of pancreatic cancer-related molecular
profiles have identified S100P as a significantly elevated gene
(Crnogorac-Jurcevic et al, 2003; Logsdon et al, 2003) whose
upregulation is an early event in the development of pancreatic
cancer (Whiteman et al, 2007). In breast cancer, S100P was linked
to immortalisation of breast epithelial cells in vitro and both
tumour progression (Guerreiro Da Silva et al, 2000; Schor et al,
2006) and early relapse (Barraclough et al, 2010) in patients.
Survival of breast cancer patients with S100P-positive carcinomas
was significantly worse than those negative for S100P (Wang et al,
2006; Barraclough et al, 2009). S100P was also prominent
among genes overexpressed in primary breast cancer cells
from high-grade tumours (Dairkee et al, 2009). In contrast,
gastric cancers that stain positive for S100P are associated with a
better patient outcome than those that are negative for S100P
(Jia et al, 2009).
The S100P form detected in our study by MS on cation-
exchange chips, and confirmed by MS after selective binding to
immobilised S100P antibody, appeared at a m/z value of 9226. This
contrasts with the expected size of mature S100P that contains 95
amino acids and has a molecular mass of 10.4 kDa, suggesting that
the observed S100P species detected by MS is a previously
unreported truncated form of this protein. An amino-terminally
truncated form of S100P, termed migration-inducing gene 9
protein or MIG9, has been reported in GenBank (Protein
Accession No. AAS00487.1), described as an alternatively spliced
product. The predicted protein is identical to S100P[8–95] except
for an isoleucine to methionine substitution at S100P residue 12
(MIG9 residue 5), and has a predicted molecular mass of 9.64 kDa.
If the true translation start site is methionine-5, the predicted
molecular mass would be 9.21 kDa and could explain our observed
peak on SELDI-TOF MS. Importantly, it is unlikely that the many
immunohistochemical studies that have measured S100P distribu-
tion in patient tissues could distinguish between S100P and these
truncated forms. Mass spectrometry would be the optimal method
for this identification. We have therefore identified for the first
time a novel isoform of S100P that is associated with pathologic
markers in breast cancer.
In conclusion, this study has discovered two protein
biomarkers, ubiquitin and S100P – the latter as a novel truncated
isoform – that, in combination, provide high discrimination
between breast cancer tissue and healthy breast tissue. Correlation
with clinical pathologic variables demonstrated that high
values for the two-protein panel were associated with high
histologic grade and tumour size, presence of LVI, ER- and PR-
positive status, and HER2 overexpression. We propose that this
independently validated protein biomarker panel may indicate a
HER2-enriched breast cancer subtype with poor prognosis,
and that measurement of S100P, in particular, may be valuable
both in the classification of breast cancer and as a possible target
for treatment.
ACKNOWLEDGEMENTS
This work was supported by Project Grant 632558 to RCB, KM,
DJM, and FMB from the National Health and Medical Research
Council, Australia. DJM is the recipient of an ARC Future
Fellowship and CINSW Fellowship, Australia. We thank Professor
Ross Smith for helpful discussions. We also thank the Kolling
Institute Breast Tumour Bank at Royal North Shore Hospital, and
Australian Breast Cancer Tumour Bank at Westmead Hospital,
Sydney, Australia, for their support in providing patient informa-
tion and breast tissue samples. This research has been facilitated by
access to the Australia Proteome Analysis Facility (APAF) funded
by the National Collaborative Research Infrastructure Strategy
(NCRIS).
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BRITISH JOURNAL OF CANCER Protein biomarkers in breast cancer tissue
360 www.bjcancer.com | DOI:10.1038/bjc.2012.552