Download Interaction between p53 Mutation and a Somatic

Published OnlineFirst February 3, 2015; DOI: 10.1158/0008-5472.CAN-14-2637
Cancer
Research
Molecular and Cellular Pathobiology
Interaction between p53 Mutation and a Somatic
HDMX Biomarker Better Defines Metastatic
Potential in Breast Cancer
Anna M. Grawenda1, Elen K. Møller2,3, Suzanne Lam4, Emmanouela Repapi1,
Amina F.A.S. Teunisse4, Grethe I.G. Alnæs2, Anne-Lise Børresen-Dale2,3,
Vessela N. Kristensen2,3,5, Colin R. Goding1, Aart G. Jochemsen4,
Hege Edvardsen2, and Gareth L. Bond1
Abstract
TP53 gene mutation is associated with poor prognosis in
breast cancer, but additional biomarkers that can further refine
the impact of the p53 pathway are needed to achieve clinical
utility. In this study, we evaluated a role for the HDMX-S/FL
ratio as one such biomarker, based on its association with
other suppressor mutations that confer worse prognosis in
sarcomas, another type of cancer that is surveilled by p53. We
found that HDMX-S/FL ratio interacted with p53 mutational
status to significantly improve prognostic capability in patients
with breast cancer. This biomarker pair offered prognostic
utility that was comparable with a microarray-based prognostic assay. Unexpectedly, the utility tracked independently of
DNA-damaging treatments and instead with different tumor
metastasis potential. Finally, we obtained evidence that this
biomarker pair might identify patients who could benefit from
anti-HDM2 strategies to impede metastatic progression. Taken
together, our work offers a p53 pathway marker, which both
refines our understanding of the impact of p53 activity on
prognosis and harbors potential utility as a clinical tool. Cancer
Introduction
steps of metastatic progression (4, 5). Given p530 s roles in
controlling the key cellular processes associated with the prevention of malignant transformation and tumor progression, it is not
surprising that the loss of p530 s function is common in many
cancers. In approximately 50% of all tumors, wt p53 activity is lost
by mutation of the TP53 gene (6, 7). The majority of these
mutations are point mutations leading to single amino acid
substitutions and resulting in the expression of a mutant p53
protein. Mutant p53 can have dominant-negative effects over wt
p53, but can also acquire oncogenic gain of functions (5, 8).
Many attempts have been made to translate our vast knowledge
of the p53 tumor suppressor into personalization strategies and
targeted therapies to improve patient survival. One such approach
is to use p530 s high rate of mutation as a prognostic biomarker for
overall survival, and a predictive marker for response to DNA
damaging therapies, such as radiotherapy and many standard
chemotherapeutics (8–10). Indeed, multiple studies have demonstrated that patients with mutant p53 in their cancers do have
poorer outcomes (11). To date, breast cancer is one of the cancers
wherein TP53 mutational status demonstrates the most significant prognostic impact (12). For example, in a study of 1,794
European patients with breast cancer, it was noted that the
presence of p53 mutations in tumors conferred an increased
relative risk (RR) of tumor-related death of 2.27 (13). The prognostic value of p53 mutations was shown to be independent of
other known prognostic factors, such as tumor size, node status,
and hormone-receptor status.
Although very significant, the prognostic value of TP53 mutational status in isolation is too small to dramatically affect clinical
decisions for breast cancer or other cancers (14). One key reason is
clearly the fact that there are many ways in which a cancer cell can
The tumor suppressor p53, a central node of the cellular stress
response pathway, regulates transcriptional programs important
in suppressing tumor formation and progression, and the cellular
response to certain therapies. The role of p53 in activating apoptosis, cell-cycle arrest, and the DNA damage response is well
established, associating not only with p530 s tumor-suppressing
properties, but also the cellular response to DNA damage-inducing cancer therapies (1–3). In recent years, however, the less well
understood role of p53 in controlling cell migration and invasion
has emerged, whereby both wild-type (wt) and mutant p53 are
involved in processes of cell migration, cellular adhesion, cytoskeletal organization, and angiogenesis, which are important
1
Ludwig Institute for Cancer Research, University of Oxford, Nuffield
Department of Clinical Medicine, Oxford, United Kingdom. 2Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway. 3KG Jebsen Center for Breast
Cancer Research, Institute for Clinical Medicine, Faculty of Medicine,
University of Oslo, Oslo, Norway. 4Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands.
5
Department of Clinical Molecular Biology (EpiGen), Medical Division,
Akershus University Hospital, Lørenskog, Norway.
Note: Supplementary data for this article are available at Cancer Research
Online (http://cancerres.aacrjournals.org/).
Corresponding Author: Gareth L. Bond, University of Oxford, The Ludwig
Institute for Cancer Research, Old Road Campus Research Building, Roosevelt
Drive, Oxford OX3 7DQ, United Kingdom. Phone: 44-1865-617497; Fax: 44-1865617515; E-mail: [email protected]
doi: 10.1158/0008-5472.CAN-14-2637
2015 American Association for Cancer Research.
698
Res; 75(4); 698–708. 2015 AACR.
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Published OnlineFirst February 3, 2015; DOI: 10.1158/0008-5472.CAN-14-2637
Biomarkers in the p53 Pathway and Metastatic Breast Cancer
inhibit p530 s activity and still retain a wt gene. These include
common mutations of crucial upstream or downstream pathway
genes that also result in attenuation of p53-mediated tumor
suppression (8, 15). Thus, additional biomarkers that can identify
cancers with wt TP53, but attenuated p53 signaling, will be
required to increase the prognostic value and maximize clinical
utility.
A crucial upstream pathway gene is HDMX (MDM4). The
HDMX protein can bind to the N-terminal transactivation
domain of p53 and thereby suppresses its transactivating function (16). In addition, it has been shown that HDMX can
modulate the translocation of p53 by HDM2 from the nucleus
to the cytoplasm (16) and can stimulate HDM2-mediated
ubiquitination and degradation of p53 (17). The crucial role
of HDMX in the regulation of p53 is further highlighted by the
fact that germline inactivation of mdm4 in mice leads to
embryonic lethality through an increased activity of p53 during
the early stages of development (18–21). Importantly, this
phenotype is completely rescued by concomitant inactivation
of the p53 gene (18–21). Recently, it was demonstrated that a
biomarker for HDMX expression (HDMX-S alternatively spliced
transcript levels compared with HDMX full-length transcript
levels, HDMX-S/FL ratio) associates with multiple common
somatic genetic lesions connected with p53 inhibition in cell
line panels and sarcomas. The somatic lesions included TP53
mutation and HDM2 overexpression, a key negative regulator
of p53 (22). Specifically, cancer cell lines and sarcomas with a
high HDMX-S/FL ratio associated with both lower levels of
HDMX protein and an enrichment of cell lines or tumors with
an attenuated p53 pathway, either by direct TP53 gene mutation or overexpression of HDM2, a key inhibitor of p53. A
model was proposed that higher HDMX-S/FL ratios, and therefore lower HDMX protein levels, can arise in cancer cells that
already have inhibited p53 signaling through alterations of
other key p53 pathway genes (22). Consistent with this model,
patients, whose sarcomas contained higher HDMX-S/FL ratios
metastasized faster and had poorer survival rates.
Families who inherit an attenuated p53 stress response by ways
of a mutant TP53 in their germlines, develop tumors at an
alarmingly high rate (23, 24). The two most frequent tumor types
developed in these families are sarcomas and breast cancer. Thus,
a biomarker such as the HDMX-S/FL ratio, which further defines
p53 pathway attenuation and prognoses in sarcoma, is a good
candidate prognostic marker for breast cancer. In this study, we
provide supportive evidence for this hypothesis, and demonstrate
that these two p53 pathway biomarkers could offer similar
prognostic utility for breast cancer survival as microarray-based
molecular subtyping. Unexpectedly, we demonstrate that the
prognostic utility is independent of DNA-damaging treatments,
but due to differential metastatic potentials of patients, thereby
offering further support for an important role of the p53 pathway
in metastasis. Finally, we provide evidence that the p53 pathway
biomarkers identify patients for whom anti-MDM2 agents could
serve as metastatic preventative therapies.
Materials and Methods
Patient material
Clinicopathologic data of 190 patients with breast cancer from
Oslo MicroMetastasis Project (MicMa) and Ulleval University
Hospital (ULL) cohorts are shown in Supplementary Table S5.
www.aacrjournals.org
Tumor material was obtained before adjuvant radio- and/or
chemotherapeutics were administered. The ULL cohort consists
of 78 women with primary breast cancer recruited at the Ulleval
University Hospital between 1990 and 1994, and was first
described by Bukholm and colleagues (25). All patients were
treated according to Norwegian national guidelines at the time of
diagnosis. Patients receiving adjuvant systemic therapy were given
nine courses of CMF (cyclophosphamide, methotrexate, 5-fluorouracil) and/or tamoxifen for 2 years. Dosage of radiation given
as adjuvant treatment was dependent on indication; after
breast conserving therapy, the mammary gland was given 50 Gy
(2 Gy 25). The MicMa breast cancer cohort consists of 112
women with early-stage breast cancer from the Oslo MicroMetastasis Project, and was first described by Wiedswang and colleagues (26). Routine selection of patients to adjuvant treatment
was based upon prevailing National Guidelines, where postmenopausal hormone receptor (HR)–positive patients received
tamoxifen only, postmenopausal HR-negative patients received
CMF and premenopausal patients, if HR positive, received CMF
followed by tamoxifen. Five patients received high-dose chemotherapy and another five, preoperative chemotherapy due to large
tumor size. After completing primary therapy, the patients were
followed at 6 to 12-month intervals.
Gene expression
The RNA from patients with breast cancer was extracted from
primary tumors using the TRIzol reagent (Invitrogen) from
fresh frozen tumor material. The RNA from breast cancer cell
lines was isolated using the SV Total RNA Isolation System
(Promega) according to the manufacturer's protocol. The RNA
extraction was followed by cDNA synthesis following standard protocols. The qRT-PCR amplification was performed
in triplicate duplex reactions according to the manufacturer's
recommendations using FAM-labeled TaqMan probes, MDM4FL (Hs00967241_m1; exon 5–6), and custom designed probes
for MDM4-S, HDM2-P1, and HDM2-P2:
MDM4-S_F: 50 -GCCCTCTCTATGATATGCTAAGAAAGAATC-30 ;
MDM4-S_R: 50 -TTCTGTAGTTCTTTTTCTGGAAGTGGAA-30 ;
MDM4-S_M FAM: 50 -CTGCACTTTGCTGTAGTAGC-30 ),
HDM2-P1_F: 50 -GACTCCAAGCGCGAAAACC-30 ;
HDM2-P1_R: 50 -CACCATCAGTAGGTACAGACATGTT-30 ;
HDM2-P1_M FAM: 50 -CACATTTGCCTGCTCCTC-30 ,
HDM2-P2_F: 50 -GGACGCACGCCACTTTT-30 ;
HDM2-P2_R: 50 -CACCATCAGTAGGTACAGACATGTT-30 ;
HDM2-P2_M FAM: 50 -CTGATCCAGGCAAATGT-30 ).
Relative gene expression was normalized according to expression of GAPDH measured in the same reaction with VIC-labeled
TaqMan probe (4326317E; Applied Biosystems). The qRT-PCR
with breast cancer cell lines cDNA was performed with the Applied
Biosystems 7500 detector. The qRT-PCR with breast cancer
patients' cDNA was performed with the Applied Biosystems
7900HT detector where a Human Breast Total RNA (Ambion)
was used as a reference to generate a standard curve. The Sequence
Detection Systems Software v2.3 was used to calculate the
amount of transcript expressed for each sample from the standard
curve.
Expression profiling of 50 classifier genes and 5 control genes
(PAM50; ref. 27) defined five molecular subtypes in both the
MicMa and ULL cohort. Gene expression was measured, as
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Grawenda et al.
previously reported, using the Stanford 42K cDNA microarray and
Agilent catalog design whole human genome 4 44K one color
oligo array for the ULL and MicMa cohorts, respectively (28, 29).
Cell culture and lentiviral transductions
Breast cancer cell lines were a gift from Mieke Schutte/John
Martens (Erasmus MC, Rotterdam, the Netherlands). The origin
of all breast cancer cell lines and verification of their individual
identity has been described previously, including a full description of the TP53 gene sequencing (30, 31). MCF-10A1, MCF10AT, and MCF-10CA1a were obtained from Dr. F. Miller
(Karmanos Cancer Center, Detroit, MI). These cell lines were
authenticated by STR typing within the last 6 months; STR profile
was found to match the profiles as shown on the ATCC website. All
cell lines were maintained in DMEM/F-12 medium supplemented
with 5% horse serum (HS) and antibiotics (all purchased from
Invitrogen), 20 ng/mL epidermal growth factor (Upstate Biotechnology), 100 mg/mL cholera enterotoxin, 5 mg/mL hydrocortisone, 10 mg/mL insulin (all purchased from Sigma-Aldrich). Cells
were treated with 10 mmol/L Nutlin-3 (Cayman Chemical) where
indicated. The lentiviral shRNA expression vector targeting p53
was described previously (32). For lentiviral transductions, cells
were seeded 24 hours before infection and a multiplicity of
infection of 2.0 was used. Cells were transduced overnight in the
presence of 8 mg/mL polybrene (Sigma-Aldrich). The next day,
medium was replaced with medium containing 0.5 mg/mL puromycin. Transduced cells were seeded for experimental purposes 3
to 4 days after transduction.
Spheroid invasion assay
The spheroid invasion assay was performed as described previously (33). Briefly, spheroids were formed from 1,000 cells
and embedded in a 1:1 mixture of Type I collagen (PureCol;
Advanced BioMatrix) and methylcellulose (Sigma-Aldrich) onto a
collagen-coated 96-well flat-bottom plate. Invasion was monitored for 2 days and images were quantified by measuring the area
occupied by cells using the ImageJ software.
Statistical analysis
Survival analyses were performed using the Kaplan–Meier
analysis with log-rank test and the Cox multivariate proportional hazards regression model with the SPSS 21.0 software
(SPSS Inc.; IBM). The Shapiro–Wilk test was used to assess a
normal distribution of the sample. The ANOVA test with a
Bonferroni correction for multiple comparisons, Kruskal–Wallis
test, Mann–Whitney test, and unpaired t test were applied to
determine the statistical significance in the differences between
the means. The Fisher exact test was used to compare the
differences in frequency distributions. Statistical significance
was regarded as P < 0.05.
Results
The HDMX-S/FL ratio interacts with TP53 mutational status
to further define breast cancer survival
We began to explore whether the HDMX-S/FL ratio can interact
with TP53 mutational status to improve the prognostic value in
two different breast cancer cohorts, namely 78 patients from
Ulleval University Hospital (28) and 112 patients from the Oslo
MicroMetastasis Project (26, 34). TP53 mutational status was
determined in all tumors: 19 (24%) ULL and 42 (37%) MicMa
700 Cancer Res; 75(4) February 15, 2015
tumors were found to have a TP53 mutation. Specifically, 80% of
all TP53 mutations in both cohorts were missense mutations
located in exon 4, 10% frame-shift deletions, 5% nonsense
mutations, 3% splice-site mutations, and 2% were in-frame
insertions.
We first determined the prognostic value of TP53 mutational status in both the ULL and MicMa cohorts, whereby we
compared the breast cancer survival of patients whose tumors
had wt TP53 with those whose tumors had mutant TP53. The
breast cancer survival of the 19 patients from the ULL cohort
whose tumors had mutant TP53 was significantly shorter than
those 59 patients whose tumors had wt TP53 (P ¼ 0.005, logrank test, Fig. 1A). Indeed, mutant TP53 tumors associated with
a 2.8-fold higher RR of tumor-related death, as derived from a
Cox multivariate regression analysis adjusted for known breast
cancer prognostic factors: pathologic node status and adjuvant
systemic therapy (P ¼ 0.015, Table 1). Similarly, the breast
cancer survival of the 42 patients from the MicMa cohort with
mutant TP53 was shorter than those 70 patients whose tumors
had mutant TP53, although this failed to reach statistical
significance (P ¼ 0.111, log-rank test, Fig. 1A, Table 1).
To begin to explore a potential interaction with TP53
mutational status and the HDMX-S/FL ratio, we determined
HDMX-FL and HDMX-S expression levels by performing qRTPCR using TaqMan gene expression assays designed to detect
full-length (FL) and splice isoform S transcripts of HDMX. The
HDMX-FL probe detects the exon 5–6 boundary of the HDMX
gene, which is present in HDMX-FL transcript and only one
out of six alternative transcripts, namely HDMX-A. The
HDMX-S assay was designed to recognize the exon 5–7 boundary created by the deletion of exon 6, which is specific to the
HDMX-S transcript. Next, we divided patients from both
cohorts into two groups of high and low HDMX-S/FL ratios.
High HDMX-S/FL ratio was defined as tumors with HDMXS/FL ratios above the mean levels of HDMX-S/FL found in
all 78 and 112 tumors from ULL and MicMa cohorts, respectively. Twenty-two patients from the ULL cohort and 41 patients from the MicMa cohort had tumors with high HDMX-S/FL
ratios, whereas 56 patients from the ULL cohort and 71
patients from MicMa cohort had low HDMX-S/FL ratios.
Interestingly, and in both cohorts, the TP53 mutational status
associated with differential breast cancer survival only in patients
with low HDMX-S/FL ratios. Specifically, in ULL patients with low
HDMX-S/FL ratios, patients with wt p53 tumors associated with
an almost 15-fold better survival rate than patients with p53mutant tumors (P ¼ 0.003 log-rank test, Fig. 1B; RR, 14.8; P ¼ 3.6
104, Cox analysis, Table 1). In the MicMa cohort, patients
with wt TP53 tumors associated with better survival rate than
patients with mutant TP53 tumors (P ¼ 0.027 log-rank test, Fig.
1B; Table 1). However, in both cohorts, there were no significant
differences in survival rates between patients with either wt and
mutant TP53 tumors when the tumors also had high HDMX-S/FL
ratios (Fig. 1C, Table 1).
Another biomarker, estrogen receptor (ER) status, and a
clinical parameter, adjuvant systemic treatment, have been
suggested to interact with TP53 mutational status to increase
its prognostic value (35). Thus, we next compared the effects of
these factors on breast cancer survival in our patients and
explored potential interactions of them with the HDMX-S/FL
ratio. To do this, we first combined all patients from both
cohorts and defined the high and low HDMX-S/FL ratios, as
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Published OnlineFirst February 3, 2015; DOI: 10.1158/0008-5472.CAN-14-2637
Biomarkers in the p53 Pathway and Metastatic Breast Cancer
Figure 1.
The HDMX-S/FL ratio interacts with
TP53 mutational status to further
define breast cancer survival in two
patient cohorts. A, Kaplan–Meier plots
depicting the breast cancer survival of
78 patients from the ULL cohort (left)
and 112 patients from the MicMa cohort
(right), each separated in groups
based on the tumor TP53 mutational
status. B, Kaplan–Meier plots
depicting the breast cancer survival of
56 patients from the ULL cohort (left)
and 71 patients from the MicMa cohort
(right) whose tumors had low HDMXS/FL ratios. C, Kaplan–Meier plots
depicting the breast cancer survival of
22 ULL cohort patients (left) and 41
MicMa patients (right) whose tumors
had high HDMX-S/FL ratios. Also
noted for each plot are the P values
that were derived from a log-rank
test.
described above, whereby 62 tumors were noted to contain high
HDMX-S/FL ratios and 128 tumors low. As expected, the TP53
mutational status associated with differential breast cancer
survival rates in all 190 patients (P ¼ 0.005, log-rank test,
Supplementary Fig. S1, Supplementary Table S1) and this association was greater in those 128 patients whose cancer had low
HDMX-S/FL ratios (P ¼ 1.44 104, log-rank test, Supplementary Fig. S1, Supplementary Table S1) and absent in those 62
patients with cancers with high HDMX-S/FL ratios (P ¼ 0.996,
Supplementary Fig. S1 and Supplementary Table S1). Interestingly, the HDMX-S/FL ratio was the only factor that significantly
interacted with TP53 mutational status to affect breast cancer
survival as measured by the Cox proportional hazards model
(P ¼ 0.031, Table 2) and no additional interactions with the
HDMX-S/FL ratio and these factors were noted.
www.aacrjournals.org
The HDMX-S/FL ratio and TP53 mutational status define breast
cancer survival in a similar manner to microarray-based
molecular subtypes
When the 190 patients are stratified into four groups based
on the two p53 pathway biomarkers (Fig. 2A), it becomes clear
that these four groups fall into three different categories of
prognoses with the wtTP53-lowHDMX-S/FL having good prognoses, wtTP53-highHDMX-S/FL and mutTP53-highHDMX-S/FL
having intermediate prognoses, and mutTP53-lowHDMX-S/FL
having poor prognoses (P ¼ 0.003, log-rank test, Fig. 2B). As
expected, those 102 patients without either biomarker for p53
pathway attenuation (good prognosis, wtTP53-lowHDMX-S/
FL) had the longest survival times compared with the 62
patients from the intermediate group (wtTP53-highHDMX-S/FL
and mutTP53-highHDMX-S/FL; P ¼ 0.039, log-rank test,
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Grawenda et al.
Table 1. Cox proportional regression analysis to predict the risk of tumorrelated death for patients from individual MicMa and ULL cohorts
95% CI
df P
RR
Lower Upper
ULL
TP53
1
0.015
2.8
1.226
6.618
pN
1
0.82
1.1
0.411
3.075
Adjuvant systemic therapy 1
0.749
0.8
0.301
2.37
ULL; low HDMX-S/FL ratio
3.372
65.355
TP53
1
3.6 104 14.8
pN
1
0.108
3.403 0.763
15.174
Adjuvant systemic therapy 1
0.367
0.563
0.162
1.962
ULL; high HDMX-S/FL ratio
TP53
1
0.851
0.9
0.224
3.438
pN
1
0.886
1.1
0.195
6.637
Adjuvant systemic therapy 1
0.774
0.8
0.118
4.899
MicMa
TP53
1
0.066
1.8
0.962
3.441
pN
1
0.022
2.8
1.156
6.649
Adjuvant systemic therapy 1
0.586
0.3
0.315
1.922
MicMa; low HDMX-S/FL ratio
TP53
1
0.07
2.2
0.939
5.104
pN
1
0.313
1.7
0.598
4.985
Adjuvant systemic therapy 1
0.484
1.5
0.457
5.216
MicMa; high HDMX-S/FL ratio
TP53
1
0.796
0.8
0.258
2.829
pN
1
0.023
5.2
1.251
22.016
Adjuvant systemic therapy 1
0.055
0.2
0.057
1.029
Abbreviations: CI, confidence interval; df, degrees of freedom; pN, pathologic
node status.
Fig. 2B) and compared with the 26 patients from poor prognosis group (mutTP53-lowHDMX-S/FL; P ¼ 1.44 104 logrank test, Fig. 2B). For example, after 5 years, 13% of good
prognosis (wtTP53-lowHDMX-S/FL) patients had died of breast
cancer, compared with 51% of poor prognosis (mutTP53lowHDMX-S/FL) patients, and 30% of the intermediate
group (wtTP53-highHDMX-S/FL and mutTP53-highHDMXS/FL; Fig. 2B). Indeed, the good prognosis group had a 1.9fold lower RR of tumor-related death compared with the
intermediate group (P ¼ 0.033, Cox analysis, Fig. 2C) and a
4.1-fold lower risk relative to the poor group (P ¼ 4.4 x105,
Cox analysis, Fig. 2C).
To begin to assess the potential prognostic strength of these
two p53 pathway biomarkers, we compared them to the wellutilized breast cancer molecular subtyping. Microarray-based
gene expression profiling has been very successful in developing a molecular classification system for breast cancer and
prognostic multigene classifiers (36, 37). Briefly, expression
profiling of 50 classifier genes and 5 control genes (PAM50)
is able to define minimally five different subtypes of breast
cancer, which are known to have either good (Luminal A),
intermediate (normal-like), or poor prognoses (Luminal B,
Basal, and ERBB2þ; Fig. 2D; ref. 27). We were able to success-
Table 2. The interactions of TP53 and the HDMX-S/FL ratio with other known
prognostic factors are known to alter breast cancer survival
TP53
HDMX-S/FL ratio
ER status
Adjuvant systemic therapy
TP53
NA
0.031
0.870
0.057
702 Cancer Res; 75(4) February 15, 2015
P
HDMX-S/FL ratio
0.031
NA
0.494
0.066
fully determine the molecular subtype for the tumors from
RNA derived from the tumors of 176 patients using the PAM50
assay (27). As expected, the five different breast cancer molecular subtypes fell into three different categories of prognoses
(P ¼ 0.016, log-rank test, Fig. 2E). Interestingly, the differences
in survival times between the different categories were remarkably similar to differences found in the groupings based on
the two p53 pathway biomarkers. For example, the good
prognosis group (Luminal A subtype) had a 3-fold lower RR
of tumor-related death compared with the poor groups (Luminal B, Basal, and ERBB2þ; P ¼ 0.001, Cox analysis, Fig. 2F).
This association is very similar compared with the 4-fold lower
RR noted between the good and poor prognoses groups as
defined by TP53 mutational status and the HDMX-S/FL ratio.
Together, these results suggest that these two p53 pathway
biomarkers could offer a similar prognostic utility for breast
cancer survival as the more complex microarray-based molecular subtyping.
The HDMX-S/FL ratio interacts with TP53 mutational status
to further define metastasis-free survival
Mutant p53 is known to increase a cancer's ability to both
metastasize and become resistant to DNA-damaging therapies
(38). To begin to assess whether the associations of the p53
pathway biomarkers with differential breast cancer survival are
a consequence of differential metastatic progression or differential responses to the chemo- and radiotherapy, we performed
the breast cancer survival analyses on patients who did not
receive any type of adjuvant treatment or radiotherapy. Of the
181 patients from the ULL and MicMa cohorts for whom the
treatment status was known, 110 patients had been treated with
adjuvant- and/or radiotherapies (Fig. 3A). The remaining 71
patients, who did not receive any form of adjuvant- or radiotherapies, were, as expected, enriched for node-negative and
early-stage cases, but did not significantly differ in the p53
biomarker grouping (Fig. 3B). Interestingly, when we separated
the 71 patients into the four groups based on both p53 pathway
biomarkers, they still fell into the same three different categories of prognoses as seen in the breast cancer survival analysis of
all patients. Specifically, the wtTP53-lowHDMX-S/FL group had
the best prognoses, the wtTP53-highHDMX-S/FL and mutTP53highHDMX-S/FL groups had intermediate prognoses, and the
mutTP53-lowHDMX-S/FL group had the poorest prognoses (P ¼
0.017, log-rank test, Fig. 3C, Supplementary Table S2). These results
suggest that the association of these p53 biomarkers with differential times of survival after breast cancer diagnosis is not due to
differential responses to DNA-damaging therapies.
We next explored potential associations of these p53 pathway biomarkers with differential times to metastasis after
diagnosis. For the 190 patients from the ULL and MicMa
cohorts, we had information on the occurrence of distal metastases after diagnosis for 185 patients. When we separated the
185 patients into the four groups based on both p53 pathway
biomarkers, and compared their metastasis-free survival (MFS),
they still fell into the same three different categories of prognosis as seen in the breast cancer survival analyses. Specifically,
the wtTP53-lowHDMX-S/FL group had the best prognoses, the
wtTP53-highHDMX-S/FL and mutTP53-highHDMX-S/FL groups
had intermediate prognoses, and the mutTP53-lowHDMX-S/FL
group had the poorest prognoses (P ¼ 0.028, log-rank test, Fig.
3D, Supplementary Table S1). These same trends in MFS were
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Biomarkers in the p53 Pathway and Metastatic Breast Cancer
found for all four groups in those patients that did not receive
adjuvant treatments or radiotherapy (P ¼ 0.048, log-rank
test, Fig. 3E, Supplementary Table S2). Together, these observations suggest that the associations of the p53 pathway biomarkers with differential survival are a consequence of differential
metastatic progression, and not differential responses to the
chemo- and radiotherapy.
High HDMX-S/FL ratios associate with p53 inhibition in breast
cancer
As mentioned above, it was previously demonstrated that the
HDMX-S/FL ratio associates with multiple common somatic
genetic lesions connected with p53 inhibition in cell line panels
and sarcomas (22). The somatic lesions included TP53 mutation and HDM2 overexpression, a key negative regulator of p53.
Specifically, cancer cell lines and sarcomas with a high HDMXS/FL ratio associated with both lower levels of HDMX protein
and an enrichment of cell lines or tumors with an attenuated
p53 pathway, either by direct TP53 gene mutation or overexpression of HDM2. To gain a better understanding of the
molecular underpinnings of these highly significant associations of the HDMX-S/FL ratio and TP53 mutational status
in the metastatic progression of breast cancer, we explored
if similar associations could be noted in the breast tumors of
the ULL and MicMa cohorts, as well as in a breast cancer cell
panel consisting of 36 well-characterized cell lines, wherein it
had previously been shown that the HDMX-S/FL ratio was a
biomarker for overall HDMX protein levels (22).
HDM2 levels in tumors were determined through qRT-PCR
measurements of mRNA transcripts from both HDM2 promoters,
namely the P1 promoter and the p53-responsive P2 promoter.
The average levels of HDM2-P1 transcript were 0.062 and ranged
from 0.06 to 1.8, whereby 113 tumors had HDM2-P1 below
average and 76 tumors had HDM2-P1 above. The average levels of
HDM2-P2 transcript in wt TP53 tumors were 1.08 and ranged
from 0.1 to 15.5, whereby 98 tumors had HDM2-P1 below
average and 32 tumors had HDM2-P1 above average. The HDM2
levels of the cell line panel had been previously reported (32).
With these data and together with the TP53 mutational status of
both tumors (Supplementary Table S3) and cell lines (Supplementary Table S4), we were able to determine that like in sarcomas, higher levels of HDMX splicing associate with biomarkers for
p53 pathway attenuation in breast tumors and breast cancer–
derived cell lines. Specifically, we observed that the 62 tumors
with high HDMX-S/FL ratios were significantly enriched for TP53
mutation or high expression of HDM2 in wt TP53 tumors.
Specifically, 81% of tumors with high HDMX-S/FL ratios had
either mutant TP53 or above-average HDM2 transcripts levels,
whereas only 66% of tumors with low HDMX-S/FL ratios had
mutant TP53 or above-average levels of either HDM2 transcripts (P ¼ 0.0359, Fisher exact test, Table 3). Interestingly, we
observed the similar enrichment of p53 pathway attenuation
biomarkers in the 18 breast cancer cell lines with high HDMXS/FL ratios. Specifically, 100% of the cell lines with high HDMXS/FL ratios had either mutant TP53 or wt TP53 and the highest
levels of HDM2 (the top 4), in contrast with only 78% in those
cell lines that had low HDMX-S/FL ratios (P ¼ 0.033, Fisher exact
test, Table 3).
Overexpression of HDM2 and subsequent attenuation of p53
signaling in p53 wt cancers is frequently observed. These
observations have motivated a large effort to develop small
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molecules to inhibit the HDM2-p53 interaction and reactive
the pathway to promote tumor clearance (10, 39, 40). Our data
would suggest that, together with the TP53 mutational status,
the HDMX-S/FL ratio could help identify those patients for
whom anti-HDM2 agents could serve as metastatic preventative
therapies. As a test of this hypothesis, we studied the effects of
HDM2 inhibition by the small molecule Nutlin-3, and its
effects on an invasive phenotype of a breast cancer cell line
with wt TP53 (MCF-10A). The invasive phenotype was measured using a spheroid invasion assay (33). Importantly, reducing p53 levels using short hairpin RNA promoted invasion of
normal (MCF-10A1), premalignant (MCF-10AT), and metastatic (MCF-10CA1a) breast epithelial cells (Fig. 4A and B), thereby
underlining the importance of wt p53 in inhibiting metastatic
progression of breast cancer. In contrast, the stabilization and
activation of p53 by addition of Nutlin-3 inhibited invasion
(Fig. 4C and D). Together, these data lend support to the
hypothesis that anti-HDM2 agents could serve as metastatic
preventative therapies in those patients with breast cancer with
shorter MFS times, such as those defined by a wt TP53 gene and
high HDMX-S/FL ratios.
Discussion
There is great heterogeneity between individuals in their
cancer risk, progression, and responses to therapy. This heterogeneity is a major obstacle in designing uniformly effective
prevention, screening and treatment strategies, and motivates
the large effort to personalize them. The utilization of multiple
pathologic and molecular biomarkers, particularly HR status,
has improved breast cancer treatment management and survival. However, the vast majority of patients have yet to benefit
from the existing biomarkers and still either succumb to their
disease or are overtreated, underlining the need for additional
prognostic and predictive biomarkers (36). A potential prognostic and/or predictive breast cancer biomarker is certainly the
mutational status of the TP53 tumor-suppressor gene. As mentioned above, the prognostic value of TP53 mutational status
alone is too small to dramatically affect clinical decisions for
breast cancer or other cancers (14). Here, we demonstrated in
two different breast cancer cohorts that an additional p53
pathway biomarker, the HDMX-S/FL ratio, can interact with
TP53 mutational status to further identify patients with significantly different prognoses. We provided evidence that the
associations are independent of DNA-damaging treatments,
but due to differential metastatic potentials of patients.
Indeed, there is a growing body of evidence that the p53
pathway can play key roles in suppressing metastatic progression (41–43). For example, p53 can activate E-cadherin, the key
cell adhesion mediator and suppressor of EMT-dependent
cancer cell invasion, through the facilitation of HDM2-mediated ubiquitination and the subsequent degradation of proinvasive Zinc-finger transcription factor SLUG, a transcriptional
suppressor of E-cadherin (41).
It was previously demonstrated that the HDMX-S/FL ratio
associates with multiple common somatic genetic lesions
connected with p53 inhibition in cell line panels and sarcomas (22). The somatic lesions included TP53 mutation and
HDM2 overexpression, a key negative regulator of p53. In this
report, we offer evidence that like in sarcomas, higher HDMXS/FL ratios associate with these biomarkers for p53 pathway
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Biomarkers in the p53 Pathway and Metastatic Breast Cancer
Figure 3.
The HDMX-S/FL ratio interacts with TP53 mutational status to further define MFS. A, pie chart depicting the frequency distribution of adjuvant treatments
and radiotherapy of the 181 patients from MicMa and ULL cohorts. B, bar graphs depicting the frequency distributions of node status, stage, and p53 biomarkers in the
110 patients who received adjuvant treatment and/or radiotherapy, and the 71 patients who did not. C, Kaplan–Meier plot depicting the breast cancer survival
of the 71 patients from the ULL and MicMa cohorts that did not receive any type of adjuvant treatment or radiotherapy and that were stratified into the four groups
based on p53 biomarkers. D and E, Kaplan–Meier plots depicting the MFS of the 185 from the ULL and MicMa cohorts (D), and the 68 patients that did not receive any
type of adjuvant treatment or radiotherapy (E), both stratified into four groups based on the p53 biomarkers. The P values noted are derived from a log-rank test.
attenuation in breast tumors and breast cancer–derived cell
lines, namely TP53 mutation and HDM2 overexpression.
Consistent with this, we observed that patients with wt TP53
and low HDMX-S/FL ratios associated with the longest overall
and MFS times in both cohorts. Together, these data support a
model, whereby tumors with wt TP53 and low HDMX-S/FL
ratios, and therefore lower levels of p53 inhibitors like
HDM2, will have retained greater p53 pathway-dependent
suppression of metastasis, resulting in better outcomes for
the patients.
Intriguingly, our observations suggest that the p53 attenuation
associated with higher HDMX-S/FL ratios could also be relevant to
patients with breast cancer whose tumors contain mutant p53.
Indeed, an even larger body of literature has described a gain-of-
function for mutant p53 that results in more aggressive/metastatic
cancers (4, 44). The most well-described mechanism involves the
inhibition of the p53 family member, p63. Specifically, it is well
known that loss of p63 expression associates with metastatic
phenotypes in a number of tumors, and, indeed, p63 expression
serves as a marker for noninvasive epithelial tumors (45). Subsequently, it has been shown that mutant p53 can directly inhibit
p63, resulting in lower expression levels of p63 target genes,
including antimetastatic genes, such as the miRNA regulator
Dicer, SHARP1, and cyclin G2, which both oppose TGFb–mediated metastasis (46–48). It is therefore reasonable to
hypothesize that our observation that patients with mutant
p53 and low HDMX-S/FL ratios have the worst prognoses is due
to the fact that the gain-of-function activity of mutant p53 is less
Figure 2.
The HDMX-S/FL ratio and TP53 mutational statuses define breast cancer survival in a similar manner to microarray-based molecular subtypes. A, schematic
representation of the combinations of p53 biomarkers. B, Kaplan–Meier plot depicting the breast cancer survival of 190 patients divided into four groups depending
on the TP53 mutational status and the HDMX-S/FL ratios, namely wt TP53 with low HDMX-S/FL ratio (n ¼ 102), wt TP53 with high HDMX-S/FL ratio (n ¼ 27), mutant
TP53 with low HDMX-S/FL ratio (n ¼ 26), and mutant TP53 with high HDMX-S/FL ratio (n ¼ 35). C, predicted survival curves for patients with different prognoses
associated with TP53 mutational status and different HDMX-S/FL ratios derived from a Cox multivariate regression analysis that was adjusted to the known
prognostic factors: pathologic lymph node status and adjuvant therapy treatment. Also noted is the calculated RR. D, schematic representation of the molecular
subtypes and their gene expression signatures. E, Kaplan–Meier plot depicting the breast cancer survival of 176 patients divided into four molecular subtypes
based on microarray analysis using the PAM50 assay. The P value noted is derived from a log-rank test. F, predicted survival curves for the different molecular
subtypes derived from a Cox multivariate regression analysis that was adjusted to the independent prognostic factors: pathologic lymph node status and
adjuvant therapy treatment (n ¼ 156).
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Table 3. Higher HDMX-S/FL ratios associate with the p53 pathway alterations in breast cancer tumors and cell lines
Breast cancer tumors
Breast cancer cell lines
Low HDMX-S/FL
High HDMX-S/FL
Low HDMX-S/FL
High HDMX-S/FL
ratio (n ¼ 129)
ratio (n ¼ 62)
ratio (n ¼ 18)
ratio (n ¼ 18)
Mutant TP53 or wt TP53-high HDM2, %
66
81
78
100
wt TP53-low HDM2, %
34
19
22
0
Fisher exact test P value
0.0359
0.033
attenuated by the associated higher expression of p53 inhibitors,
like HDM2. Indeed, further support of this hypothesis is provided
by Terzian and colleagues, who clearly demonstrated that a loss of
mdm2 in murine models resulted in the stabilization of both
mutant p53 and a gain-of-function metastatic phenotype (49).
However, we cannot rule out the possibility that the observed
associations are due to the p53-independent effects of higher
levels of HDMX (50).
Clearly, further elucidation of the p53 pathway biomarkers is
needed in additional breast cancer patient cohorts and model
systems to fully understand how they interact with each other to
better predict and create the aggressive phenotype of primary
tumors. However, it is likely that p53 pathway biomarkers could
contribute to better prognostication, and decrease the overtreatment of patients with nonaggressive cancers, such as those that
retain high levels of p53 signaling. Moreover, both wt and mutant
p53 signaling directly affect malignant progression of tumors,
therefore prognostic biomarkers in the pathway will not only
serve to foresee prognoses, but also offer potential nodes of
intervention. For example, our observations lend support to the
hypothesis that anti-HDM2 agents could serve as metastatic
preventative therapies in those patients with breast cancer with
shorter MFS times, such as those defined by a wt TP53 gene and
high HDMX-S/FL ratios.
Figure 4.
Breast cancer cell invasion is inhibited by p53 activation. A and B, MCF-10A1, MCF-10AT, and MCF-10CA1a expressing sh ctrl or sh p53 were harvested for total
protein extraction and p53 expression was analyzed by Western blot using USP7 as loading control (A), or spheroids were formed and embedded
into collagen (B). Invasion was determined by calculating the measured invaded area relative to the area on day 0. Results are expressed as mean SD of
at least three spheroids and are representative of three independent experiments; , P < 0.05 compared with sh ctrl of the same cell line on the same day.
C and D, MCF-10A1, MCF-10AT, and MCF-10CA1a were mock-treated or treated with 10 mmol/L Nutlin-3 and harvested after 24 hours and analyzed for protein
expression by Western blot (C), or spheroids were formed and embedded into collagen for 48 hours (D). Results are expressed as mean SD of at
least three spheroids and are representative of three independent experiments; , P < 0.05 compared with mock-treated cells of the same cell line.
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Biomarkers in the p53 Pathway and Metastatic Breast Cancer
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: A.M. Grawenda, A.-L. Børresen-Dale, A.G. Jochemsen,
H. Edvardsen, G.L. Bond
Development of methodology: A.M. Grawenda, G.L. Bond
Acquisition of data (provided animals, acquired and managed patients,
provided facilities, etc.): A.M. Grawenda, S. Lam, A.-L. Børresen-Dale,
V.N. Kristensen, H. Edvardsen
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.M. Grawenda, E.K. Møller, E. Repapi,
A.-L. Børresen-Dale, V.N. Kristensen, A.G. Jochemsen, H. Edvardsen
Writing, review, and/or revision of the manuscript: A.M. Grawenda, E.K.
Møller, A.-L. Børresen-Dale, V.N. Kristensen, A.G. Jochemsen, H. Edvardsen,
G.L. Bond
Administrative, technical, or material support (i.e., reporting or organizing
data, constructing databases): A.F.A.S. Teunisse, G.I.G. Alnæs, G.L. Bond
Study supervision: C.R. Goding, A.G. Jochemsen, G.L. Bond
Acknowledgments
The authors wish to thank Magali Olivier for helpful discussions, Mieke
Schutte/John Martens for providing the breast cancer cell lines, Fred Miller for
the kind gift of the MCF-10A series of cell lines, Pauline Wijers-Koster for STR
typing, and Drs. Bjørn Naume, Rolf Karesen, and Anita Langerød for providing
and curating the clinical data.
Grant Support
This study was supported by funding from the Ludwig Institute for Cancer
Research to G.L. Bond and the Worldwide Cancer Research (formerly known
as Association for International Cancer Research) to A.G. Jochemsen (grant #090073). Results generated in Oslo were with funding from the K.G. Jebsen Centre
for Breast Cancer Research; E.K. Møller was a PhD fellow of the Norwegian
Cancer Society, project Nr 71248 - PR-2006-0282 to V.N. Kristensen.
The costs of publication of this article were defrayed in part by the payment
of page charges. This article must therefore be hereby marked advertisement
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received September 10, 2014; revised November 14, 2014; accepted
December 2, 2014; published OnlineFirst February 3, 2015.
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Published OnlineFirst February 3, 2015; DOI: 10.1158/0008-5472.CAN-14-2637
Interaction between p53 Mutation and a Somatic HDMX Biomarker
Better Defines Metastatic Potential in Breast Cancer
Anna M. Grawenda, Elen K. Møller, Suzanne Lam, et al.
Cancer Res 2015;75:698-708. Published OnlineFirst February 3, 2015.
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