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J. Exp. Med. Vol. 209 No. 4 679-696
Clinically relevant biomarkers used to guide
the use of targeted therapeutics for breast cancer
include the overexpression of HER2 (human
epidermal growth factor receptor 2) and the
expression of the estrogen and progesterone
receptors. For breast tumors that are positive for
these receptors (receptor positive), several targeted
therapeutic strategies have been successfully
developed in the past decades. These include
the use of small molecule kinase inhibitors,
treatment with inhibitory monoclonal anti-
bodies, and antihormonal therapies. Unfortu-
nately, no such biomarker to predict response
to selective therapeutics has been established for
the most challenging receptor triple-negative
subtype of breast cancer (Carey et al., 2006;
Bauer et al., 2007; Liedtke et al., 2008). Clearly,
further investigation of the biology of triple-
negative breast cancer is required if effective
therapies are to be developed (Irvin and Carey,
2008; Schneider et al., 2008).
Gene expression profiling of human pri-
mary breast tumors has identified several distinct
molecular subtypes including luminal A and B,
HER2+, basal-like, and normal-like (Perou et al.,
2000; Srlie et al., 2001). Approximately 70%
of triple-negative tumors belong to the basal
subtype (Bertucci et al., 2008), which often ex-
hibits aggressive characteristics such as poor
differentiation, a higher rate of proliferation, and
increased metastatic capability (Livasy et al., 2006;
Sarrió et al., 2008). In clinical studies, patients
with triple-negative tumors have been found
to respond to neoadjuvant chemotherapy with
equal or better efficacy than those with receptor-
positive tumors (Carey et al., 2007; Liedtke
Abbreviations: CDK, cyclin-
dependent kinase; PD, pharmaco-
dynamic; PK, pharmacokinetic;
RCB, residual cancer burden.
Sanjay Chandriani’s present address is Novartis, Inc.,
Emeryville, CA 94608
MYC pathway activation
in triple-negative breast cancer is synthetic
lethal with CDK inhibition
Dai Horiuchi,1 Leonard Kusdra,1 Noelle E. Huskey,1 Sanjay Chandriani,2
Marc E. Lenburg,6 Ana Maria Gonzalez-Angulo,7 Katelyn J. Creasman,1
Alexey V. Bazarov,1,8 James W. Smyth,4 Sarah E. Davis,3,5 Paul Yaswen,8
Gordon B. Mills,7 Laura J. Esserman,3,5 and Andrei Goga1,5
1Department of Medicine, 2Howard Hughes Medical Institute and GW Hooper Foundation, 3Department of Surgery,
4Cardiovascular Research Institute, and 5Helen Diller Family Comprehensive Cancer Center, University of California,
San Francisco, San Francisco, CA 94143
6Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118
7Department of Breast Medical Oncology and Systems Biology, M.D. Anderson Cancer Center, Houston, TX 77030
8Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
Estrogen, progesterone, and HER2 receptor-negative triple-negative breast cancers
encompass the most clinically challenging subtype for which targeted therapeutics are
lacking. We find that triple-negative tumors exhibit elevated MYC expression, as well as
altered expression of MYC regulatory genes, resulting in increased activity of the MYC
pathway. In primary breast tumors, MYC signaling did not predict response to neoadjuvant
chemotherapy but was associated with poor prognosis. We exploit the increased MYC
expression found in triple-negative breast cancers by using a synthetic-lethal approach
dependent on cyclin-dependent kinase (CDK) inhibition. CDK inhibition effectively induced
tumor regression in triple-negative tumor xenografts. The proapoptotic BCL-2 family
member BIM is up-regulated after CDK inhibition and contributes to this synthetic-lethal
mechanism. These results indicate that aggressive breast tumors with elevated MYC are
uniquely sensitive to CDK inhibitors.
© 2012 Horiuchi et al. This article is distributed under the terms of an Attribution–
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The Journal of Experimental Medicine
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
new targeted therapeutics? In this study, we use several
complementary approaches to address these questions.
Synthetic lethality has been proposed as a treatment
strategy against cancers that lack obvious druggable targets
(Reinhardt et al., 2009). As an example, small molecule in-
hibition of poly (ADP-ribose) polymerase (PARP) in patients
with BRCA1/2 mutations has provided a proof of concept
for exploiting synthetic lethality in the clinic (Fong et al., 2009;
Tutt et al., 2010). Our previous work found a synthetic-lethal
interaction between MYC overexpression and inhibition of
the mitotic cyclin-dependent kinase (CDK) 1 by using trans-
genic MYC-driven models of lymphoma and liver cancer
(Goga et al., 2007). However, whether this synthetic-lethal
approach has utility in treating triple-negative breast cancers
In this study, we investigated whether exploiting a synthetic-
lethal approach dependent on elevated MYC signaling is
effective in treating triple-negative breast cancer. To gain a
comprehensive understanding for the role of MYC in triple-
negative breast cancer, in the current study we combine gene
expression and protein analysis from patient tumors with
functional studies in cell lines and tumor models. Here, we
examine the efficacy of a CDK inhibitor currently in clinical
development against triple-negative breast cancer and investi-
gate the mechanism of synthetic lethality.
MYC expression is disproportionally elevated
in triple-negative breast cancer
We sought to understand whether MYC signaling is an impor-
tant oncogenic event in triple-negative breast tumors. The I-SPY
TRIAL (Investigation of Serial Studies to Predict Your Thera-
peutic Response with Imaging and Molecular Analysis) is an
NCI-sponsored multi-institutional study of Stage II and III
breast cancer patients to identify diagnostic markers, validate
hypothesis, and develop new treatment strategies against breast
cancer (Barker et al., 2009; Jones, 2010; Esserman et al., 2012).
This clinical trial collects extensive clinical annotation and global
mRNA expression from primary tumor samples. Using mRNA
expression data from 146 primary patient tumor samples for
which hormone receptor and HER2 expression were available,
we found that MYC mRNA levels are significantly elevated in
triple-negative tumors (Fig. 1 A). We next examined MYC
protein and phospho-MYC (T58/S62) expression in 208 in-
dependent primary breast tumor samples from a separate cohort
using reverse-phase quantitative protein arrays. The 66 triple-
negative tumors expressed significantly elevated MYC protein
and phospho-MYC (Fig. 1 B) compared with the 142 tumors
that were receptor positive. MYC phosphorylation at these sites
has been shown to enhance MYC transcriptional activity as well
as alter protein stability (Chang et al., 2000; Sears et al., 2000;
Seo et al., 2008). Finally, using previously published mRNA
expression profiling data (Neve et al., 2006), we examined
whether MYC expression in 50 established human breast cell
lines correlated with the receptor status. These 50 human breast
cell lines have been shown to accurately model aspects of
et al., 2008), presumably as a result of the higher mitotic
index observed in triple-negative tumors. However, a complete
pathological response is rarely achieved in patients with triple-
negative tumors, who have a tendency to experience early
relapse and a diminished 5-yr disease-free survival (Bauer
et al., 2007; Dent et al., 2007). The molecular events that occur
in triple-negative breast cancer have not been elucidated
and, therefore, the mechanism for the poor prognosis of this
subtype remains unclear. Thus, there is significant interest in
identifying signaling pathways that distinguish triple-negative
breast cancer from other breast cancer subtypes.
Several converging studies have suggested that the MYC
proto-oncogene may play an important function in aggres-
sive breast cancers. MYC is a basic helix-loop-helix zipper
(bHLHZ) motif–containing transcription factor whose activity
is tightly regulated by its direct binding to another bHLHZ
protein MAX. MYC activation can lead to transcriptional
activation or repression of specific genes (Eilers and Eisenman,
2008). The global transcriptional influence of MYC is also
mediated through a MYC regulatory network whereby MYC
activity is precisely controlled by the activity of multiple com-
peting repressive MAX binding partners (i.e., MAD, MGA,
MXD4, and MNT; Grandori et al., 2000; Cowling and Cole,
2006). MYC plays roles in multiple signaling pathways in-
cluding those involved in cell growth, cell proliferation, me-
tabolism, microRNA regulation, cell death, and cell survival
(Dang, 1999; Eilers and Eisenman, 2008; Meyer and Penn,
2008). Furthermore, MYC signaling has recently been shown
to be up-regulated in high-grade mammary tumors with pre-
sumptive cancer stem cell properties (Ben-Porath et al., 2008;
Wong et al., 2008).
The genomic locus, 8q24, which harbors the MYC on-
cogene, is among the most frequently amplified region in breast
cancers of various subtypes (Jain et al., 2001). The amplified
region, however, contains numerous transcripts and, therefore,
amplification is not strictly correlated with elevated MYC
expression. More recent studies have identified a MYC tran-
scriptional gene signature associated with the basal molecular
subtype (Alles et al., 2009; Chandriani et al., 2009; Gatza et al.,
2010). Other studies have examined staining of primary breast
tumor tissues for MYC protein expression and did not find a
clear connection between MYC overexpression and patient
outcome (Bland et al., 1995; Naidu et al., 2002). Thus, eval-
uating the contribution of MYC signaling to triple-negative
breast cancer would open up new mechanistic insights and
potentially new therapeutic approaches to treat this aggres-
sive tumor type.
Several outstanding questions about the relevance of MYC
signaling in triple-negative breast cancer remain unresolved.
Is MYC expression alone altered in triple-negative tumors or
is the expression of other MYC signaling components (i.e.,
competing MAX binding proteins) also changed? Furthermore,
does MYC signaling alter response to conventional chemo-
therapeutics that are routinely used to treat triple-negative
breast cancer? Finally, if MYC signaling is up-regulated in triple-
negative tumors, can one take advantage of this feature to develop
JEM Vol. 209, No. 4
than luminal A or HER2 subtypes (Fig. 2 B). Although most
luminal B tumors express hormone receptors, this subtype
is often associated with increased tumor proliferation and
worse outcome than luminal A (Voduc et al., 2010). Our
results indicate that along with basal, the luminal B molecular
subtype is also associated with increased MYC signaling,
which may, at least in part, explain the worse outcome for
this tumor type (Cheang et al., 2009).
We next sought to understand the molecular basis for the
increased MYC activity in triple-negative breast cancers.
Activation or repression of MYC target genes involves multiple
signaling complexes comprised of MAX bound to an acti-
vator, such as MYC or MYCN, or bound to a repressor such
as MXD1-4 or MAX (Grandori et al., 2000; Cowling and
Cole, 2006). Thus, MYC/MAX heteromeric complexes can
activate transcription, whereas MXD/MAX or MAX/MAX
complexes can suppress activation of MYC target genes (Eilers
and Eisenman, 2008). Changes in MYC pathway activity
in breast cancer are therefore caused by alterations in the archi-
tecture of the MYC regulatory network through changing the
relative abundance of MAX interacting partners. To our knowl-
edge, the topology of the MYC regulatory network in primary
breast tumors has not been previously described. Therefore,
we sought to determine if, besides MYC, the mRNA expres-
sion of other MAX-interacting genes is also significantly
altered in triple-negative primary tumors. Analyzing the mRNA
array data available from the I-SPY dataset, we found that mul-
tiple MAX-interacting genes are altered in triple-negative
tumors (Fig. 2 C). In addition to MYC, we found that the
activator MYCN is also up-regulated in triple-negative tu-
mors, whereas the repressor molecules MXD4 and MAX were
breast cancer biology (Neve et al., 2006) and have been exten-
sively used for mechanistic studies. Compared with cell lines that
are not triple negative, triple-negative cell lines had substantially
elevated MYC mRNA (Fig. 1 C). Collectively, these three
independent datasets confirm the association between elevated
MYC expression and triple-negative tumors.
MYC signaling is up-regulated in triple-negative tumors
To evaluate MYC activity in primary tumors, we applied a
previously validated MYC-regulated transcriptional signature
(Chandriani et al., 2009), which is comprised of 352 genes, to
the I-SPY dataset. We ordered the patient tumor samples by
the Pearson correlation to the MYC gene expression signature
(Fig. 2 A), and tumors were divided into high, intermediate,
and low correlation groups (see Materials and methods).
Triple-negative breast tumors were significantly enriched in
the high MYC gene expression group (P < 0.005; Fig. 2 A).
These results indicate that a disproportionate number of pri-
mary triple-negative breast tumors exhibit elevated MYC
function. A MYC gene expression signature has previously
been correlated with a basal molecular subtype of breast
cancer (Alles et al., 2009; Chandriani et al., 2009; Gatza et al.,
2010), which encompasses 70% of triple-negative cancers.
Whether MYC signaling is also increased in other molecular
subtypes of human breast cancer remains unclear. We there-
fore examined the MYC gene expression signature across
different molecular subtypes of breast cancer in the I-SPY
dataset. Consistent with prior studies, we found that a high
MYC gene expression signature correlated most strongly with
the basal subtype; however, we also observed that the luminal
B molecular subtype had significantly higher MYC signature
Figure 1. Elevated MYC expression in human triple-negative cancers. (A) MYC mRNA expression in triple-negative versus receptor-positive
primary breast tumors collected through the I-SPY TRIAL (P < 0.0001). (B) MYC and phospho-MYC (T58/S62) protein expression in triple-negative primary
tumors. Shown is an independent cohort of 208 patients for which quantitative reverse-phase protein arrays were performed. (C) Relative expression of
MYC mRNA in a panel of established human breast cell lines (Neve et al., 2006). The error bars represent means ± SEM. P-values were calculated by
two-tailed Student’s t test.
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
We found that an increased MYC gene signature was associated
with significantly shortened disease-free survival with a median
follow-up of 3.9 yr (P = 0.005; Fig. 3 A). Surprisingly, we
found that the response of primary tumors at the time of surgery,
immediately after the completion of conventional neoadjuvant
chemotherapy, did not differ significantly based on MYC
signature scores (Fig. 3 B). To further examine the association
between MYC signaling and response to neoadjuvant che-
motherapy, we divided the total patient population into two
significantly down-regulated (Fig. 2 C). Thus, the enhanced
MYC activity in triple-negative tumors may be a result not
only of MYC up-regulation but also of alterations in the expres-
sion of other MYC regulatory genes.
Up-regulation of MYC signaling is associated
with poor prognosis
We next examined the clinical outcome of patients based on
their MYC gene signature in the neoadjuvant I-SPY TRIAL.
Figure 2. Elevated MYC signaling in human triple-negative breast cancers. (A) 149 primary tumors ordered by each tumor’s Pearson correlation
to a 352 gene MYC signature centroid (P < 0.005; Fisher’s exact test). Triple-negative tumor samples are indicated with a red dot, whereas molecular
subtypes are indicated with colored bars. (B) Correlation between MYC gene expression signature and breast cancer molecular subtypes. Relative MYC
gene expression was based on each tumor’s Pearson correlation to the MYC gene signature. The error bars represent means ± SEM. P-values were calculated
by two-tailed Student’s t test for each comparison. (C) Expression of multiple genes within the MYC signaling pathway is altered in triple-negative breast
cancers. Schema shows various MAX-interacting genes that are deregulated and can positively or negatively modulate MYC transcriptional activity. Genes
are shaded green if expression is suppressed in triple-negative versus receptor-positive tumors, and red if expression is increased in triple-negative
tumors. MAX, P = 0.02; MYC, P < 0.0001; MYCN, P = 0.06; MXD4, P = 0.03 (two-tailed Student’s t test).
JEM Vol. 209, No. 4
association between receptor status and outcome is consider-
ably diminished (hazard ratio [HR] = 1.6; 95% CI: 0.8–3.0;
P = 0.176; n = 146), whereas MYC pathway activation and
outcome remain significantly associated (HR = 14.2; 95%
CI: 3.1–64.4; P < 0.001; n = 146; Fig. 3 E). These results
suggest that MYC pathway activation contains additional
information about risk of recurrence that is not reflected in
receptor status alone.
Triple-negative breast cells with elevated MYC expression
are sensitive to CDK inhibition
Given the poor outcome of patients that have tumors with
elevated MYC activity, we sought to identify a therapeutic strat-
egy that could target these tumors. Synthetic lethality between
MYC overexpression and the inhibition of the mitotic kinase
CDK1 has previously been observed in engineered cells and
transgenic mouse models (Goga et al., 2007). However, this
synthetic lethality has not been examined in breast cancer. Pur-
valanol A, an experimental small molecule CDK inhibitor that
has higher specificity toward CDK1 (Gray et al., 1998), induced
apoptosis in lymphoma cells and hepatocytes engineered to
overexpress MYC (Goga et al., 2007). Importantly, CDK1
inhibition has been found to have little effect on the viability of
control cells or normal mouse tissues (Goga et al., 2007).
We reasoned that triple-negative breast cancer cells that
have arisen to overexpress MYC might also be sensitive to
a synthetic-lethal interaction with CDK
inhibition. To model our observations from
the I-SPY clinical samples, we tested a
panel of triple-negative cell lines with
elevated MYC expression and a panel of
receptor-positive lines that were expected
to have lower MYC protein expression
categories. One group was composed of those patients who
exhibited either complete response or minimal residual cancer
burden (RCB 0/I; Symmans et al., 2007) after conventional
chemotherapy. The other group was composed of those with
substantial residual disease (RCB II/III). For patients who had
a dramatic response to neoadjuvant chemotherapy (RCB 0/I),
increased MYC signaling did not significantly alter prognosis
(Fig. 3 C). In contrast, for those patients whose tumors had only
minimal response to neoadjuvant chemotherapy (RCB II/III),
an increased MYC signature was associated with early disease
recurrence (Fig. 3 D; P = 0.001).
Although there was a strong association between elevated
MYC expression and triple-negative breast cancer (Figs. 1
and 2), there also appeared to be considerable heterogeneity
with regard to receptor status and MYC pathway activation
(Fig. 2 A), leading us to explore whether the poor outcome
associated with MYC pathway activation (univariate Cox
proportional hazards ratio = 19.0 for MYC pathway activa-
tion as a continuous measure; 95% confidence interval [CI]:
4.4–82.3; P < 0.001; n = 146) reflects the aggressiveness of
triple-negative disease (univariate Cox proportional hazards
ratio = 2.2 for triple-negative tumors relative to receptor-
positive tumors; 95% CI: 1.2–4.0; P = 0.015; n = 146) or
whether MYC pathway activation might be an independent
predictor of poor prognosis. In multivariate models that con-
sider both receptor status and MYC pathway activation, the
Figure 3. Elevated MYC signaling is associ-
ated with poor outcome. (A) Overall risk of
breast cancer recurrence in patients with varying
levels of MYC pathway activation. I-SPY TRIAL
patients (n = 149) were divided into tertiles based
on the relative expression of the genes in the
MYC signature in their pretreatment biopsy
samples. Differences in risk of disease recurrence
between these groups were assessed using a
Cox proportional hazards model and Wald’s test.
(B) Correlation of MYC pathway activation with
tumor burden after neoadjuvant chemotherapy.
The MYC pathway activation tertile for the I-SPY
TRIAL patients with gene expression data, and for
which RCB (0-III; Symmans et al., 2007) was
determined at the time of surgery (n = 133), was
examined. The association between residual
tumor burden and MYC pathway activation tertile
was assessed using Fisher’s exact test. (C) Disease
recurrence by MYC signature in RCB 0/I patients.
(D) Disease recurrence by MYC signature in RCB
II/III patients. (E) Multivariate analysis considering
receptor status and MYC pathway activation as a
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
is currently in phase II clinical trials against various tumor types
(Dickson and Schwartz, 2009). Dinaciclib inhibits CDK1, 2,
5, and 9 at concentrations of 1–5 nM, which can be readily
achieved in vivo, and exhibits improved pharmacokinetic
based on previously published mRNA data (Neve et al.,
2006) for their sensitivity to CDK inhibitors (Fig. 4 A). In
these experiments, we tested two CDK inhibitors: purvalanol A,
and another CDK inhibitor dinaciclib (Parry et al., 2010) that
Figure 4. Elevated MYC expression sensitizes triple-negative cancers to CDK inhibition. (A) A panel of triple-negative as well as receptor-positive
breast cells, together with a matched pair of nontumorigenic model epithelial cells (RPE cells) engineered to overexpress MYC, was treated with CDK
inhibitors purvalanol A (10 µM) or dinaciclib (10 nM) for 72 h and subjected to viability assay. The dashed line indicates the relative starting cell number
at the time of adding CDK inhibitors (time 0). Positive numbers indicate cell growth and negative numbers indicate cell death. The experiment was
independently repeated five times. The error bars represent means ± SEM. P-values were calculated by two-tailed Student’s t test for comparisons of
cell lines treated with each of the two inhibitors. Western blots showing MYC and actin protein expression from the indicated cell lines are shown.
(B) Cell cycle profiles of three triple-negative cell lines and three receptor-positive cell lines after treatment with 10 µM purvalanol A or 10 nM dinaciclib
for 72 h. The percentage of cells in G1 and G2-M phases of the cell cycle, as determined by DNA content based on propidium iodide staining, is indicated.
(C) A panel of triple-negative, as well as receptor-positive, breast cancer cells was treated with siRNA against CDK1 or CDK2 for 72 h and assessed for
cell viability. The experiment was independently repeated three times. The error bars represent means ± SEM. P-values were calculated by two-tailed
Student’s t test. Western blots showing CDK1, CDK2, and actin protein expression are shown.
JEM Vol. 209, No. 4
inhibition, we performed siRNA experiments to knock down
specific CDKs in a panel of triple-negative as well as receptor-
positive cell lines (Fig. 4 C). We found that the treatment of all
three triple-negative cell lines with CDK1 siRNA for 72 h
resulted in a significant amount of cell death, whereas the
viability of receptor-positive lines was only modestly affected
(Fig. 4 C). Interestingly, we found that CDK2 siRNA could
also induce cell death, but to a lesser extent in two of the
triple-negative lines and in one of the receptor-positive lines,
which is in agreement with recent observations that CDK2
function may be essential for the viability of certain cancer types
(Molenaar et al., 2009). The higher potency of cell killing
induced by dinaciclib may be, in part, a result of its ability to
inhibit both CDK1 and CDK2 (Parry et al., 2010). Unexpect-
edly, we found that CDK1 siRNA treatment dramatically
increased CDK2 protein expression in all of the three triple-
negative lines and one of the receptor-positive cell lines, whereas
CDK2 siRNA did not alter CDK1 protein expression (Fig. 4 C).
It remains to be determined whether this CDK2 up-regulation
indicates a compensatory mechanism within the cell cycle
encountering the loss of the only mitotic CDK.
One possible explanation for the sensitivity of breast
tumor cells with high MYC to CDK inhibition is that they
are more proliferative. One would expect these cells to therefore
also be more sensitive to other chemotherapeutics that target
cell proliferation mechanisms. To investigate this possibility,
we tested the sensitivity of the breast lines used in this study
to the chemotherapeutic agents paclitaxel and doxorubicin
(Table 1) that are commonly used in clinical practice to treat
breast cancer, including in the I-SPY TRIAL. The cell lines
used in this study did not show any MYC expression-specific
sensitivity to the chemotherapeutic agents. These cell-based
studies are consistent with the observations from the I-SPY
TRIAL that a MYC signature was not associated with improved
response of primary tumors to conventional chemotherapeutic
agents. It is therefore likely that other functions of MYC,
in addition to its role in cell proliferation, contribute to the
mechanism of synthetic lethality observed.
(PK) and pharmacodynamic (PD) properties, compared with
other CDK inhibitors previously evaluated in clinical trials.
Breast cell lines were treated with 10 µM purvalanol A or
10 nM dinaciclib, respectively, and were subjected to a cell
viability assay. These chosen concentrations induce cell cycle
arrest without causing cell death in normal human epithelial
cells (Fig. 4 A; Goga et al., 2007). Human epithelial cells
engineered to overexpress MYC (RPE-MYC) undergo cell
death when treated with purvalanol A, whereas those with
endogenous levels of MYC expression (RPE-NEO) do not,
serving as positive and negative controls, respectively (Fig. 4 A;
Goga et al., 2007). Dinaciclib exhibited 1,000-fold higher
potency compared with purvalanol A in inducing apoptosis
in epithelial cells engineered to overexpress MYC but not in
control RPE-NEO cells (Fig. 4 A). In breast cell lines, CDK
inhibitor treatment induced substantial cell death in each
of the five triple-negative cell lines tested (range 25–65%),
whereas most of the receptor-positive lines showed resistance
to such treatment (Fig. 4 A). One receptor-positive line,
SKBR3, was sensitive to these inhibitors (eliciting >25% cell
death); however, this may be the result of increased MYC
expression found in these cells (Fig. 4 A), which was not
predicted from previously published mRNA profiling data
(Neve et al., 2006). Cell cycle analysis by flow cytometry
showed that treatment of both triple-negative and receptor-
positive breast cancer cell lines with purvalanol A induced
an increased accumulation of G2-M arrested cells, whereas
treatment with dinaciclib resulted in cells arrested in both
G1-S and G2-M (Fig. 4 B). This is consistent with results
showing that purvalanol A is highly selective for CDK1 (Gray
et al., 1998), whereas dinaciclib targets both CDK1 and CDK2
with similar potency (Parry et al., 2010).
Although these small molecule CDK inhibitors are selective
for either CDK1 (purvalanol A) or for a few CDKs (dinaciclib),
it is possible that other non-CDK kinases may also be inhibited
and thus may contribute to the overall cell death phenotypes.
To address this issue of specificity and further investigate
synthetic lethality between MYC overexpression and CDK
Table 1. Sensitivity (LC50) of human breast cell lines to chemotherapeutic drugs
Cell line Receptor statusElevated MYC Paclitaxel Doxorubicin
MYC protein expression by quantitative Western blotting in triple-negative (TN) and receptor-positive (RP) cell lines is indicated. No correlation between elevated MYC
expression and sensitivity to paclitaxel or doxorubicin was observed (two-tailed Student’s t test).
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
We found that purvalanol A treatment of T47D and HCC1428
cells engineered to overexpress MYC resulted in significantly
increased cell death (Fig. 5 C). Thus, these results suggest that
elevated MYC expression is necessary and it increases the
sensitivity of breast cancer cells to CDK inhibitors. Interest-
ingly, whereas MYC RNAi demonstrated a dramatic decrease
in MYC protein expression (Fig. 5 B), it did not appreciably
alter cell viability (not depicted).
CDK inhibition induces cell death in triple-negative cells
in a three-dimensional (3D) culture system
Although we saw sensitivity of triple-negative cells to CDK
inhibition in monolayer cultures (Fig. 4), there is evidence
We next asked whether the observed CDK inhibition-
induced cell death in cells with elevated MYC expression is
dependent on their MYC status. To address this question, we
first used an RNAi approach to knock down MYC protein
expression. We found that pretreatment of RPE-MYC cells
with MYC-specific siRNA significantly reduced the extent
of CDK inhibition-dependent cell death (Fig. 5 A). Similarly,
the sensitivities of three triple-negative cell lines treated with
MYC siRNA before the addition of purvalanol A were greatly
reduced (Fig. 5 B). We next used a lentiviral transduction
method to overexpress MYC in receptor-positive cells to exam-
ine whether MYC overexpression alone is sufficient to render
these otherwise resistant cells sensitive to CDK inhibition.
Figure 5. CDK inhibitor induced cell death is MYC dependent. (A) MYC dependency of cell death induced by CDK inhibition in RPE cells. RPE-MYC
cells were first treated with either MYC siRNA or control nonspecific siRNA for 24 h, and then treated with purvalanol A for 72 h. The collected cells were
analyzed for cell viability by Guava ViaCount assay and for PARP activation by Western blotting. The experiments were repeated three times in triplicate.
The error bars represent means ± SEM. P-values were calculated by two-tailed Student’s t test. (B) siRNA-mediated MYC knockdown in triple-negative
cells undergoing purvalanol A treatment. The experiment was independently repeated three times. The error bars represent means ± SEM. P-values were
calculated by two-tailed Student’s t test. (C) Receptor-positive cell lines T47D and HCC1428, engineered to overexpress MYC and exposed for 72 h to purvalanol A
treatment. The experiments were repeated three times in triplicate. The error bars represent means ± SEM. P-values were calculated by two-tailed
Student’s t test.
JEM Vol. 209, No. 4
lines (SUM149 and BT549) were seeded onto a solid Matrigel
matrix, allowed to form colonies, and subsequently treated with
10 µM purvalanol or 10 nM dinaciclib and allowed to grow
for 6 d. In this assay, if the cells undergo cell death, the mean col-
ony size decreases between the initial day and the last day of the
treatment incubation time (day 0 and day 6). However, if the cells
are insensitive to CDK inhibition-induced cell death, the size of
the colonies remains the same in the case of cell cycle arrest or
may increase. We found that the mean colony size in the two
triple-negative cell lines significantly decreased over a 6-d pe-
riod after purvalanol A or dinaciclib treatment (Fig. 6, A and B),
that tumor cells can differ in their response to anti-cancer agents
between in vitro and in vivo conditions (Weaver et al., 2002).
As a step toward addressing this question, we initially examined
the effect of CDK inhibition on cell viability in a 3D Matrigel
culture system. 3D epithelial cultures have been used extensively
as an in vitro system to more closely approximate the physio-
logical microenvironment of the epithelial cells (Lee et al., 2007).
Indeed, it has been shown that a 3D gene signature can more
accurately predict outcome in several independent datasets
(Fournier et al., 2006; Martin et al., 2008). Two receptor-positive
cell lines (T47D and HCC1428) and two triple-negative cell
Figure 6. CDK inhibition decreases colony size in triple-negative cancer cells in 3D Matrigel matrices. (A) Low- and high-magnification images
of 3D Matrigel cultures of T47D, HCC1428, BT549, and SUM149 cell lines at day 0 of treatment and day 6 of DMSO-treated, purvalanol A–treated, and
dinaciclib-treated cultures. Nonrefractile dark particles indicated the presence of cell death. Bars, 10 µm. (B) Quantification of colony size of 3D Matrigel
cultures. Low-magnification images of Matrigel cultures were obtained, and the mean size of the colonies in each culture was determined both at the
initial day of treatment (day 0) and at the final day of treatment (day 6). The mean colony size at day 0 was set to 100% for each cell line and the colony
size at day 6 was compared with the day 0 size. The experiment was independently repeated three times. The error bars represent means ± SEM. P-values
were calculated by two-tailed Student’s t test.
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
cell lines with elevated MYC expression, MDA-
MB-231 and HCC3153, formed tumors with high
penetrance and were used for subsequent studies.
Our attempts to grow tumors of receptor-positive
cells were not successful either because of the in-
ability to form tumors or because only small spo-
radic tumors formed, despite our efforts to increase
tumor engraftment by using Matrigel.
The triple-negative tumor-bearing mice were
then treated twice weekly for 2 wk with dinaciclib
(50 mg/kg/dose i.p.) or vehicle control. The mice
treated with diluent experienced a 120–150%
increase in growth of tumors during the experi-
mental period (Fig. 7, A and B). In contrast, dinaciclib-treated
mice had a dramatic response with 50% tumor regression
(Fig. 7, A and B). In independent experiments, we also exam-
ined the effects of dinaciclib on the established MDA-MB-231
and HCC3153 xenografts 24 h after treatment. We found a
dramatic decrease in the overall phosphorylation levels of
presumptive CDK substrates that have the consensus CDK
motif, as well as in the phosphorylation of a validated CDK1
substrate PP1- (protein phosphatase 1–; T320; Dohadwala
et al., 1994; Blethrow et al., 2008) in primary tumors (Fig. 7 C).
This was accompanied by the induction of apoptosis as dem-
onstrated by PARP cleavage in tumor tissues (Fig. 7 C). Collec-
tively, these results demonstrate that small molecule inhibition
of CDKs represents a novel and feasible treatment strategy
against human triple-negative breast cancers.
BIM up-regulation mediates CDK
inhibition-dependent cell death
We next sought to understand the mechanism that underlies
the synthetic-lethal interaction between elevated MYC
expression and CDK inhibition in epithelial cells. We previ-
ously reported that CDK inhibition–induced apoptosis was
independent of p53 but required activation of the mitochon-
drial intrinsic apoptotic pathway (Goga et al., 2007). We there-
fore reasoned that inhibition of CDKs might either increase
indicating that the cells in these cultures underwent extensive
cell death. In contrast, the colonies formed by the two receptor-
positive cell lines did not significantly change (Fig. 6, A and B),
suggesting the induction of cell cycle arrest. As positive
controls, DMSO-treated cultures for all the four cell lines
showed robust growth at day 6 (Fig. 6, A and B). Thus,
consistent with what we observed in 2D cultures, receptor-
positive cells expressing low MYC appear to be resistant to
cell death, whereas triple-negative cells with elevated MYC
expression are particularly sensitive to CDK inhibition in
CDK inhibition induces in vivo tumor regression in mouse
xenograft models of triple-negative breast cancer
A stringent test of any therapeutic strategy is the ability to
inhibit or regress in vivo tumor growth. Using xenograft
transplant models of breast cancer, we examined the in vivo
efficacy of inhibiting CDKs in human triple-negative tumors
with elevated MYC expression. We focused on dinaciclib
because of its higher potency and improved PK properties
compared with purvalanol A. We attempted to generate tumor
xenografts of three triple-negative and three receptor-positive
human breast cancer cell lines. Tumor cells were transplanted
into BALB/c Nu/Nu mice and were allowed to form mea-
surable tumors (200–250 mm3 in volume). Two triple-negative
Figure 7. CDK inhibition is effective in treating xeno-
grafted triple-negative tumors in mice. (A) Tumor
growth in mouse xenograft models of triple-negative breast
cancer. Representative photos of tumors after 2 wk of treat-
ment with either vehicle alone or with dinaciclib (50 mg/kg
i.p. twice weekly) are shown. (B) Growth of triple-negative
tumors in nude mice treated with the CDK inhibitor dinaci-
clib (50 mg/kg i.p. twice weekly) for 2 wk. Each treatment
group per tumor cell type contained the indicated number
of mice. The error bars represent means ± SEM. P-values
were calculated by two-tailed Student’s t test. (C) PARP
cleavage and serine phosphorylation of presumptive CDK
substrates occurs in tumors within 24 h of dinaciclib ad-
ministration. This antibody recognizes amino acid sequences
that contain phosphorylated CDK consensus epitope, which
is (K/R)(phosphorylated-S)(PX)(K/R) where X can be any
amino acid. Two independent tumor samples for each
treatment (vehicle or dinaciclib) are shown per cell line.
JEM Vol. 209, No. 4
after purvalanol A treatment (Fig. 8 I). These results indicate
that the mechanism of CDK inhibitor–induced apoptosis in
triple-negative cells with elevated MYC expression involves
In addition to the proapoptotic BCL-2 family member BIM,
we found that all of the three prosurvival BCL-2 family mem-
bers, BCL-2, BCL-xL, and MCL-1, were also up-regulated
in RPE-MYC cells compared with RPE-NEO cells in the
absence of purvalanol A (Fig. 8 A). This is consistent with
the hypothesis that cancers that have evolved to sustain high
MYC expression have reached a balance between pro- and
antiapoptotic factors to limit spontaneous apoptosis (Lowe
et al., 2004; Hemann et al., 2005). This is also consistent with
previous observation that co-overexpression of MYC and
BCL-2 occurs in high-grade human breast tumors (Sierra
et al., 1999). In our study, however, the expression levels of
these prosurvival members did not change appreciably upon
CDK inhibition (Fig. 8 A). Thus, treatment of tumors with
elevated MYC expression, such as triple-negative breast can-
cers, with CDK inhibitors may tip the balance in favor of
apoptosis by increasing BIM expression.
There is an evident and urgent need to develop targeted
therapeutic strategies against triple-negative breast cancer.
To identify these therapeutic targets, a better understanding
of the biology of triple-negative breast cancer is therefore
needed. In this study, we investigated the biology of triple-
negative cancers and identified that MYC signaling is ele-
vated in these tumors. Prior studies have examined MYC
expression in breast cancers; however, we find, for the first
time, that the expression of multiple MAX binding part-
ners, which can regulate MYC activity, is altered in triple-
negative tumors and may therefore contribute to MYC
Several studies have shown that the basal breast cancer
subtype exhibits enrichment for a MYC transcriptional gene
signature (Alles et al., 2009; Chandriani et al., 2009; Gatza
et al., 2010). However, basal breast tumors account for only
70% of triple-negative tumors, and the importance of MYC
signaling in the remaining 30% has previously been unknown.
In clinical practice, tumors are routinely evaluated for estrogen
receptor, progesterone receptor, and HER2 receptor status
and, thus, the pathological determination of the triple-negative
subtype is more clinically relevant for deciding on a course of
treatment. We found that 33 of 36 triple-negative tumors
(92%) in the I-SPY TRIAL had a high or intermediate MYC
gene signature score (Fig. 2 A) that correlated with worse
outcome (Fig. 3, A and D). The present study also demonstrates,
for the first time, that MYC signaling is associated with dimin-
ished disease-free survival in patients whose tumors exhibited
poor response to neoadjuvant chemotherapy (Fig. 3 D).
To determine if elevated MYC signaling can be exploited
to treat triple-negative breast cancer, we assessed the utility of
a synthetic-lethal approach between MYC up-regulation and
CDK inhibition. Small molecule inhibition of CDK activity
the activity of proapoptotic BCL-2 family members or
decrease the activity of prosurvival factors. To test this hy-
pothesis, we examined the protein expression of components
of the mitochondrial intrinsic pathway in matched RPE
cells engineered to overexpress MYC (RPE-MYC) or control
cells (RPE-NEO). RPE cells have modest levels of endoge-
nous MYC expression (Fig. 4 A), which makes them a suit-
able model to study their response to MYC overexpression.
We found that a proapoptotic BH3-only member BIM was
substantially up-regulated in RPE-MYC cells (Fig. 8 A) as
has been previously observed in other cell types engineered
to overexpress MYC (Egle et al., 2004; Hemann et al., 2005).
Surprisingly, BIM was dramatically further up-regulated after
RPE-MYC cells were treated with purvalanol A, whereas
BIM protein levels remained undetectable in RPE-NEO cells
throughout the time course (Fig. 8 A). BIM up-regulation
in RPE-MYC cells coincided with the cleavage of PARP,
an event which indicated induction of apoptosis (Fig. 8 A).
Protein quantification revealed that the extent of BIM pro-
tein up-regulation in this particular cell line was 2.2-fold
(Fig. 8 B). This level of BIM up-regulation is likely to be
significant because BIM up-regulation induces apoptosis
unless its activity is concomitantly suppressed by overexpression
of antiapoptotic BCL-2 family members (O’Connor et al.,
1998). Quantitative PCR (TaqMan) analysis showed up-
regulation of BIM mRNA (2.5-fold) after treatment of
RPE-MYC cells with purvalanol A (Fig. 8 C). These results
suggest that BIM up-regulation after CDK inhibition is, in
part, a result of increased BIM mRNA levels. BIM mRNA
also increased in RPE-NEO cells after purvalanol A treat-
ment but remained substantially lower than the levels ob-
served in RPE-MYC cells (Fig. 8 C). Interestingly, we found
that dinaciclib treatment of RPE-MYC cells resulted in
the up-regulation of not only the BIM-EL isoform found
with purvalanol A treatment but also the shorter isoforms
BIM-L and BIM-S (Fig. 8 D), which have been shown to
be significantly more potent in inducing apoptosis (O’Connor
et al., 1998). This may, at least in part, explain the higher po-
tency associated with dinaciclib compared with that of pur-
valanol A (Fig. 4 A).
We postulated that the cell death observed in MYC-
overexpressing cells after CDK inhibition is dependent on a
threshold level of BIM, which is not reached in RPE-NEO
cells. To test this hypothesis, we asked whether BIM is neces-
sary for cell death induced by CDK inhibition in the context
of MYC overexpression. Pretreatment of RPE-MYC cells
with BIM-specific siRNAs protected the cells from purvala-
nol A–induced cell death (Fig. 8 E). These findings establish
BIM as a major contributor to CDK inhibitor-induced apop-
tosis. We next tested triple-negative breast cell lines with en-
dogenously elevated MYC expression and observed similar
BIM up-regulation upon CDK inhibitor treatment (Figs. 8,
F–G). To determine if BIM was required for the induction of
cell death in these cells after CDK inhibitor treatment, we
generated stable cell lines expressing a control or BIM shRNA
(Fig. 8 H). We found that BIM depletion attenuates cell death
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
Figure 8. BIM contributes to CDK inhibition-induced cell death in cells with elevated MYC expression. (A) RPE cells, with or without constitu-
tive MYC overexpression, were treated with 10 µM purvalanol A for 72 h and were tested for protein expression of multiple pro- and antiapoptotic BCL2
family members, as well as cleaved PARP and loading control -actin. The results shown are representatives of at least five independent experiments.
(B) Relative fold change in BIM protein expression in RPE-MYC cells treated with purvalanol A for 72 h. The purvalanol A time course experiments were
repeated at least five times and BIM protein was quantified as described in Materials and methods. The error bars represent means ± SEM. (C) BIM mRNA
expression after purvalanol A treatment. BIM mRNA levels were quantified by qPCR in RPE cells with or without constitutive MYC overexpression. Mean
of three experiments ± SEM are shown. (D) BIM expression in RPE-MYC cells treated with dinaciclib for 36 h. (E) RPE-MYC cells were transfected with
JEM Vol. 209, No. 4
has also reported that agonist-mediated activation of a TRAIL
(tumor necrosis factor–related apoptosis-inducing ligand) re-
ceptor (DR5) induces MYC-dependent synthetic lethality
(Wang et al., 2004). These distinct forms of synthetic lethality
appear to require fundamentally different cellular mechanisms
of cell death, including different requirements for an intact
p53 tumor suppressor pathway (Wang et al., 2004; Goga et al.,
2007; Molenaar et al., 2009; Yang et al., 2010). For example,
44–82% of primary basal breast tumors either lack p53 or
harbor p53 mutant alleles (Srlie et al., 2001; Carey et al.,
2006). Therefore, CDK1-MYC synthetic lethality, which is
p53 independent (Goga et al., 2007), may be particularly useful
in treating these tumors.
The idea of targeting cell cycle kinases to selectively kill
tumor cells, which often exhibit higher proliferation rates
than nontumorigenic cells, is appealing and has indeed led to
the clinical development of several small-molecule CDK in-
hibitors (Shapiro, 2006; Malumbres et al., 2008). However,
there have been several issues associated with their clinical
development. Previous generations of CDK inhibitors gen-
erally suffered from low in vivo potency and poor PK/PD
properties. More recent third-generation CDK inhibitors,
including dinaciclib, exhibit significantly improved PK/PD
properties and offer greater promise for in vivo use. Indeed,
dinaciclib was well tolerated in phase I trials and is currently
being evaluated in phase II trials against various tumor types
(Dickson and Schwartz, 2009; Parry et al., 2010). Prior clini-
cal studies have also suffered from a lack of understanding of
which tumor types are the most likely to be responsive to
CDK inhibitors (Malumbres et al., 2008). Therefore, selec-
tion of patient cohorts based on molecular targets, such as
MYC overexpression, may improve the therapeutic potential
of CDK inhibitors in clinical trials.
In the present study, we found that CDK inhibition in-
creases BIM protein levels not only in the model cells engi-
neered to overexpress MYC but also in a panel of patient-derived
triple-negative breast cancer cell lines with elevated MYC ex-
pression. Elevated BIM plays a direct role in CDK inhibition–
induced cell death. Prior studies have found that BIM isoforms
can be regulated at both the transcriptional and posttransla-
tional levels. Protein expression of the shortest isoform Bim-S
is sufficient to potently induce apoptosis (O’Connor et al., 1998).
In contrast, the activity of the longest isoform BIM-EL can
also be modulated by distinct phosphorylation mechanisms
that can either stabilize the protein or induce its degradation
via the proteasome pathway (Hübner et al., 2008). In our
was effective at inducing significant cell death in triple-negative
cell lines with elevated MYC expression as well as in mouse
xenograft models. We found that the mechanism of such cell
death included the up-regulation of the proapoptotic BCL-2
family member BIM. Thus, this study represents a significant
step forward in identifying apoptotic mechanisms for treating
triple-negative breast cancers.
Although prior studies have focused on the many biological
functions of MYC, how this information could be translated
to develop novel ways to treat breast cancer remains unclear.
In an emerging era of personalized medicine, it is crucial to
identify what specific patient populations would most benefit
from a given treatment strategy. We found that, for patients
who experienced a limited tumor response to conventional
neoadjuvant chemotherapy, disease-free survival was strongly
influenced by MYC expression. Insight into components of
MYC signaling, therefore, will provide potential targets that
could be exploited as new therapies for this subset of breast
cancer patients. However, despite up-regulation of MYC sig-
naling in a variety of human cancers, the potential utility of
direct MYC inhibition remains unclear. For instance, MYC
knockdown via RNAi has not been found to diminish the
viability of cultured tumor-derived cells (Guan et al., 2007),
consistent with our observations in this study with breast
cancer cells. In contrast, inhibition of MYC-MAX dimer-
ization using an experimental compound, 10058-F4, led to
the induction of apoptosis in cultured human leukemia cells
(Huang et al., 2006). Furthermore, in vivo inhibition of MYC
transcriptional activity using a conditional dominant-negative
mutant induced cell death and caused tumor regression in
a KRAS-initiated mouse lung tumor model (Soucek et al.,
2008). Whether the differences among these observations are
a result of the methods used (i.e., knocking down MYC pro-
tein expression versus inhibition of its transcriptional activity)
or to the extent that each cancer type depends on deregulated
MYC activity requires further investigation.
An alternative approach to directly inhibiting MYC is to
use a MYC-dependent synthetic-lethal strategy. We previ-
ously identified a form of synthetic-lethal interaction between
MYC overexpression and CDK1 inhibition using engineered
cell lines as well as in vivo model systems (Goga et al., 2007).
More recent studies have shown that RNAi-mediated or
small molecule inhibition of two additional cell cycle kinases,
CDK2 and aurora kinase B, respectively, has synthetic-lethal
interactions with MYC overexpression in certain cancer cell
types (Molenaar et al., 2009; Yang et al., 2010). Another study
either a pool of specific BIM siRNAs or a pool of control nontargeting siRNAs and cell viability was determined after treatment with 10 µM purvalanol A
for 72 h. The experiment was repeated three times. The error bars represent ± SEM. (F) BIM protein expression in a panel of triple-negative cells. Cell lines
were tested for BIM protein expression after treatment with 10 µM purvalanol A for 72 h. The results shown are the representatives of at least three
independent experiments. (G) Quantification of BIM protein expression. Shown are the means from at least three independent experiments that yielded
the following SEMs, respectively: ±0.2 (HCC3153), ±0.88 (BT549), ±0.48 (MCF10AMYC), and ±0.57 (SUM149PT). (H) shRNA-mediated BIM knockdown in
two triple-negative cell lines, MCF10AMYC and BT549. shRNA against GFP was used as negative control. (I) BIM knockdown effects on cell death of MCF10AMYC
and BT549 cells after purvalanol A treatment. The experiments were repeated at least three times in triplicate. Error bars represent means ± SEM. P-values
were calculated by two-tailed Student’s t test.
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
Lowess-normalized log2 ratio of Cy3 and Cy5 intensity values were calcu-
lated. For MYC mRNA levels, a MYC-specific probe (A_32_P60687) was
compared across the 146 samples for which receptor status was known. Pa-
tients enrolled in the trial received conventional neoadjuvant chemotherapy
that included doxorubicin and cyclophosphamide, and/or paclitaxel per the
I-SPY protocol before definitive surgical resection. The extent of residual
disease was quantified using RCB and reported by RCB class (RCB 0-III;
Symmans et al., 2007).
Bioinformatics and analysis of the I-SPY data. Expression of the MYC
signature (Chandriani et al., 2009) in the I-SPY dataset was examined by first
converting the platform-specific probe identifiers in both studies to UNIGENE
cluster identifiers (Unigene Build 219 Homo sapiens). Data from multiple
probes mapping to the same UNIGENE cluster IDs were averaged. The
MYC pathway activity refers to the MYC gene signature score centroid that
was calculated as previously described (Chandriani et al., 2009). The Pearson
correlation of each tumor’s expression profile of these genes to the MYC
signature centroid was determined. The tumors were then ordered, from
high to low, by the Pearson correlation value. Tumors that lacked conclusive
receptor status information were excluded from analysis. The remaining
tumors were divided into tertiles representing three groups with high, in-
termediate, or low correlation to the MYC signature centroid. Recurrence-
free survival was analyzed using Cox Proportional Hazards models that
were evaluated using Wald’s Test. This analysis was performed using the
survival package in the R Environment for Statistical Computing (http://
www.r-project.org/). Associations between categorical variables were eval-
uated using Fisher’s Exact Test. This analysis was performed using the stats
package in the R Environment for Statistical Computing (http://www
Reverse-phase protein array analysis of MYC and phospho-MYC
expression. Protein was extracted from the human tumors, and reverse-
phase protein lysate microarray was done as described previously (Tibes
et al., 2006; Hennessy et al., 2007; Hu et al., 2007; Liang et al., 2007).
Briefly, lysis buffer was used to lyse frozen tumors by homogenization.
Tumor lysates were normalized to 1 µg/µl concentration with the use of
bicinchoninic acid assay and were boiled with 1% SDS, and the superna-
tants were manually diluted in six or eight twofold serial dilutions with
lysis buffer. An arrayer (2470; Aushon Biosystems) created 1,056 sample
arrays on nitrocellulose-coated FAST slides (Schleicher & Schuell BioScience)
from the serial dilutions. A slide was then probed with a validated primary
MYC and phospho-MYC T58/S62 antibody (Cell Signaling Technology),
and the signals were amplified with a catalyzed system (Dako). A secondary
antibody was used as a starting point for amplification. The slides were
scanned, analyzed, and quantitated with the use of MicroVigene software
(VigeneTech Inc.) to generate serial dilution signal intensity curves for
each sample with the logistic fit model: ln(y) = a + (b a)/(1 + expc*[dln(x)]).
A representative natural logarithmic value of each sample curve on the slide
(curve mean) was then used as a relative quantification of the amount of each
protein in each sample. The levels of unphosphorylated and phosphorylated-
MYC (T58/S62) in each sample was expressed as a log-mean centered
value after correction for protein loading with the use of the mean ex-
pression levels of >150 proteins as previously described (Tibes et al., 2006;
Hennessy et al., 2007; Hu et al., 2007; Liang et al., 2007; Stemke-Hale et al.,
2008; Gonzalez-Angulo et al., 2009).
Cell culture. The propagation of human breast cell lines used in this study
and their global mRNA expression profiling has been previously described
(Neve et al., 2006). Engineered human epithelial cell lines RPE-NEO and
RPE-MYC cells were previously described (Goga et al., 2007). The MYC-
overexpressing versions of the receptor-positive cell lines T47D and HCC1428
were established by infecting the cells with the lentivirus prepared using
pLVX-AcGFP-N1 plasmid with a full-length human MYC cDNA cloned
into the EcoRI–XhoI sites or with recombinant retrovirus expressing MYC
to generate the MCF10A-MYC cells..
studies, we found that both isoforms can be up-regulated in epi-
thelial cells after CDK inhibitor treatment.
Previous studies have shown that BIM expression is ele-
vated upon MYC overexpression (Egle et al., 2004; Hemann
et al., 2005). Having uncovered that CDK inhibition triggers
induction of BIM expression, we became interested in study-
ing the relationship between the protein expression levels of
MYC and BIM in a panel of untreated breast cancer cell
lines. We did not find a correlation between MYC and BIM
expression in these cells regardless of their molecular subtypes
or receptor status (unpublished data). Therefore, in a given
cellular context, it is likely that the relative increase in BIM
activity in response to CDK inhibition, not the absolute basal
level of BIM expression, determines whether or not apoptosis
can be initiated. Thus, the protein stoichiometry among BIM
and antiapoptotic BCL-2 family members such as BCL-2,
BCL-xL, and MCL-1 is likely to dictate if apoptosis is trig-
gered. In this respect, a combinatorial approach of CDK inhi-
bition with inhibition of antiapoptotic BCL-2 family members
would be predicted to have a synergistic effect in inducing
cell death in MYC overexpressing triple-negative tumors. In-
deed, several BH3 mimetics (Chonghaile and Letai, 2008),
namely ABT-737/263 (Oltersdorf et al., 2005) and obato-
clax (Nguyen et al., 2007), are currently under development
for clinical use.
In conclusion, we have shown utility for small molecule
CDK inhibitors in the treatment of triple-negative breast tumors
with elevated MYC expression. It is likely that CDK inhibitors
will be effective not only against MYC-overexpressing triple-
negative tumors but also for aggressive receptor-positive tumors
with elevated MYC expression (i.e., luminal B). Methods for
immunohistochemical analysis on paraffin-embedded primary
tumor biopsies for MYC protein expression have been previ-
ously described (Gurel et al., 2008; Ruzinova et al., 2010). In
addition, gene expression profiling methods on primary breast
tumors are becoming available for use in the clinic (van ’t Veer
et al., 2002). Such technologies could be applied to assess
whether MYC signaling is elevated in patient tumor samples
(Alles et al., 2009; Chandriani et al., 2009; Gatza et al., 2010).
Thus, these detection methods for MYC activity have the po-
tential to be translated for routine use in the clinic. Considering
the lack of established targeted therapeutics against triple-
negative tumors, we propose that MYC status will prove useful
as a predictive biomarker of response to CDK inhibitors for the
treatment of triple-negative breast cancers.
MATERIALS AMD METHODS
I-SPY Trial. Expression microarray results were available for 149 tumors
before treatment. Three tumors could not be designated as triple negative or
receptor positive, owing to a lack of staining for one or more receptor types,
leaving 146 available for analysis. Details of the I-SPY Trial protocol, patient
array data, and characteristics of enrolled patients are described elsewhere
(Esserman et al., 2012) and are also available at the NCI I-SPY Data Portal
(http://ispy.nci.nih.gov/). All patients included in our analysis had pretreatment
core biopsies for which gene expression measurements had been determined
using 44K microarrays (Agilent Technologies). Microarray hybridization was
performed as per the manufacturer, the background was subtracted, and
JEM Vol. 209, No. 4
the culture images, and the mean area of the colonies within the field was
determined using the particle analysis algorithm within ImageJ. To decrease
the chance of debris being included in the analysis, particles with an area of
5,000 pixels (3.1 µm) or less (the mean size of noncellular debris within the
images as determined empirically) were excluded. The mean surface area of
the day 0 cultures for each cell line was set to 100%, and the mean surface area
of day 6–treated cultures were determined relative to day 0. The Student’s t test
was used to determine statistical significance.
Mouse xenograft studies. For all the breast cancer cell lines used in this
study, 107 cells in 200 µl PBS were subcutaneously injected into immuno-
deficient female mice (BALB/c nude/nude) aged 6–8 wk. The tumors were
allowed to grow for 3–4 wk (with the tumors reaching 200–250 mm3 in
volume) before the animals were treated with either dinaciclib at 50 mg/kg
or vehicle alone (20% HPBCD) via i.p. injection. All animal experiments
were approved by the University of California San Francisco institutional
animal care and use committee.
siRNA/shRNA experiments. Unless otherwise noted, siRNA against
human CDK1, CDK2, MYC, and BIM (BCL2L11), respectively, and a pool
of nontargeting control siRNA (siGENOME SMART pool siRNA; Dhar-
macon) were used according to the manufacturer’s protocol. For the MYC
knockdown experiments described in Fig. 5 A, the liposomal siRNA prepa-
rations against human MYC and luciferase (negative control), respectively,
were provided by Alnylam Pharmaceuticals, Inc. The retroviral shRNA con-
structs used in this study are pMKO shRNA Bim (plasmid 17235; Addgene;
Schmelzle et al., 2007) and pMKO shRNA GFP (plasmid 10675; Addgene;
Masutomi et al., 2005).
Analysis of BIM mRNA levels using quantitative PCR. RPE cells, with
or without constitutive MYC overexpression, were treated with purvalanol
A for 0, 24, 48, and 72 h. Total RNA was isolated from cells using mirVana
(Ambion) and digested with DNaseI to remove contaminating DNA (Ambion).
cDNA was prepared from 1 µg of total RNA using Superscript II reverse
transcription kit (Invitrogen). Real-time PCR was performed using probes
specific for human BIM and -ACTIN (ABI), according to the manufacturer’s
instructions. Samples were run in triplicate on a Real-Time Thermal Cycler
(Bio-Rad Laboratories). Variation of BIM expression was calculated using the
CT method (Livak and Schmittgen, 2001) with -ACTIN mRNA as an
Online supplemental material. A list of 352 genes that comprise the
MYC gene signature used for the analysis in this study is provided in
Table S1. Online supplemental material is available at http://www.jem.org/
We thank the patients, investigators, and institutions that participated in the
I-SPY TRIAL. We are grateful to Drs. Luika Timmerman, Koei Chin, Wen-Lin Kuo, Adi
Gazdar, Stephen Ethier, and Joe Gray for providing cell lines. We thank Drs. Clifford
Hudis and J. Michael Bishop, and Ms. Brittany Anderton for comments and critical
reading of the manuscript.
We acknowledge the following support: California Breast Cancer Research
Program post-doctoral (D. Horiuchi and L. Kusdra) and pre-doctoral (N.E. Huskey)
fellowships, NCI 2T32CA108462 post-doctoral training grant (L. Kusdra), the
Howard Hughes Medical Institute (S. Chandriani), the UC Cancer Coordinating
Committee (N.E. Huskey), AHA Scientist Development Grant SDG3420042
(J.W. Smyth), NCI 1K23CA121994 (A.M. Gonzalez-Angulo), the Susan G. Komen
Foundation (A.M.Gonzalez-Angulo, G.B. Mills, and A. Goga), NCI 1K08CA104032,
1R01CA136717 (A. Goga), a UCSF SPORE 5P50CA058207 Developmental Project
(A. Goga), and V-Foundation Scholar Award (A. Goga). We also acknowledge the
I-SPY program for additional statistical support: NCI SPORE, CA58207; ACRIN, U01
CA079778 and CA080098; and CALGB, CA31964 and CA3360.
The authors do not declare any competing financial interests.
Submitted: 21 July 2011
Accepted: 10 February 2012
Cell viability assays. CellTiter (Promega) cell viability assay shown in Fig. 4 A
was performed in 96-well plates, using a Safire2 plate reader (TECAN) that
runs Magellan software, according to the manufacturer’s instruction. Each cell
line was plated onto 10 wells per experiment and the assay was repeated at
least five times. For the rest of the cell viability experiments described through-
out this manuscript, a flow cytometry–based Guava ViaCount viability assay
(Millipore) was performed according to the manufacturer’s instruction.
Cell cycle analysis. Cell lines were treated with DMSO, 10 µM purvalanol
A, or 10 nM dinaciclib (provided by the Drug Synthesis and Chemistry
Branch, Developmental Therapeutics Program, Division of Cancer Treatment
and Diagnosis, National Cancer Institute [Bethesda, MD] and MERCK)
for 72 h. After treatment, cells were fixed in 70% ethanol and stained with
propidium iodide to measure DNA content. Samples were analyzed on a
LSRII flow cytometer. Cell populations were gated to exclude cell debris
and doublets and cell cycle distribution was determined using FlowJo analysis
software (Tree Star).
Protein lysate preparation and Western blotting analysis. Cultured
cells were washed with ice-cold PBS and harvested directly into radioim-
munoprecipitation assay (RIPA) buffer (50 mM Tris, 150 mM NaCl, 0.5%
sodium-deoxycholate, 1% NP-40, 0.1% SDS, 2 mM EDTA, pH 7.5) con-
taining COMPLETE protease inhibitor cocktail (Roche) and phosphatase
inhibitors (Santa Cruz Biotechnology, Inc.). Isolated tumor tissues were
first washed in ice-cold PBS and homogenized on ice using Tissue Tearor
(Biospec Products, Inc.) in RIPA buffer containing protease inhibitors and
phosphatase inhibitors. Protein concentrations were determined by per-
forming DC Protein Assay (Bio-Rad Laboratories) using BSA as standard.
The following antibodies were used for western analyses: MYC (Epitomics),
-actin (Sigma-Aldrich), PARP (Cell Signaling Technology), BIM (Assay
Designs), Puma (Cell Signaling Technology), Bid (R&D Systems), Bax
(Cell Signaling Technology), Bak (Santa Cruz Biotechnology, Inc.), BCL-2
(Cell Signaling Technology), BCL-xl (Santa Cruz Biotechnology, Inc.),
MCL-1 (Abcam), Phospho-(Ser) CDK substrates (Cell Signaling Technology),
PP1- (Epitomics), PP1- (pT320; Epitomics), CDK1 (Santa Cruz Biotech-
nology, Inc.), and CDK2 (Santa Cruz Biotechnology, Inc.).
Determination of relative protein expression levels. VersaDoc Imaging
System (4000 MP; Bio-Rad Laboratories) was used to quantify protein ex-
pression. BIM up-regulation at the protein level was determined by normal-
izing presaturated BIM signals to those of -actin. To determine the relative
MYC protein expression levels across a panel of breast cell lines, the MYC
signals acquired through anti-MYC Western blotting were normalized
against total fluorescence from the Ponceau S–stained bands as previously
described (Nijjar et al., 2005). This method was necessary as a result of signifi-
cantly different protein expression levels of major house keeping proteins
across breast cell lines used in this study.
Small molecule CDK inhibitors. Purvalanol A (Sigma-Aldrich) was re-
constituted in 100% DMSO. Unless otherwise indicated, it was used at a
concentration (10 µM) previously shown to induce cell cycle arrest in vari-
ous mammalian cell lines (Gray et al., 1998; Goga et al., 2007). Dinaciclib
was reconstituted either in 100% DMSO for cell culture use or in 20% HPBCD
(hydroxypropyl cyclodextrin) for mouse studies.
Matrigel 3D cultures. The establishment of 3D cultures was performed as
previously described (Lee et al., 2007). Briefly, cells were seeded on top of
the solidified Matrigel (BD) layer at 2,000 cells per well in 96-well plates and
incubated for 6 d to allow for multicellular colonies to form. Phase contrast
images were obtained at this point and designated as day 0. Cultures were
treated with DMSO, 10 µM purvalanol A, or 10 nM dinaciclib and incu-
bated for an additional 6 d before obtaining images of treated and control
cultures and designated as day 6. Images were analyzed using ImageJ image
analysis software (National Institutes of Health). The background was sub-
tracted to remove differences in intensity as a result of shadow effects within
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
and inactivation of protein phosphatase 1 by cyclin-dependent kinases.
Proc. Natl. Acad. Sci. USA. 91:6408–6412. http://dx.doi.org/10.1073/
Egle, A., A.W. Harris, P. Bouillet, and S. Cory. 2004. Bim is a suppressor
of Myc-induced mouse B cell leukemia. Proc. Natl. Acad. Sci. USA.
Eilers, M., and R.N. Eisenman. 2008. Myc’s broad reach. Genes Dev.
Esserman, L.J., D.A. Berry, A. DeMichele, L.A. Carey, S.E. Davis, M.
Buxton, C. Hudis, J.W. Gray, C. Perou, C. Yau, et al. 2012. Pathologic
complete response predicts recurrence-free survival more effectively by
cancer subset: results from the I-SPY 1 TRIAL (CALGB 150007/
150012; ACRIN 6657). J. Clin. Oncol. In press.
Fong, P.C., D.S. Boss, T.A. Yap, A. Tutt, P. Wu, M. Mergui-Roelvink, P.
Mortimer, H. Swaisland, A. Lau, M.J. O’Connor, et al. 2009. Inhibition
of poly(ADP-ribose) polymerase in tumors from BRCA mutation
carriers. N. Engl. J. Med. 361:123–134. http://dx.doi.org/10.1056/
Fournier, M.V., K.J. Martin, P.A. Kenny, K. Xhaja, I. Bosch, P. Yaswen,
and M.J. Bissell. 2006. Gene expression signature in organized and
growth-arrested mammary acini predicts good outcome in breast can-
cer. Cancer Res. 66:7095–7102. http://dx.doi.org/10.1158/0008-5472.
Gatza, M.L., J.E. Lucas, W.T. Barry, J.W. Kim, Q. Wang, M.D. Crawford,
M.B. Datto, M. Kelley, B. Mathey-Prevot, A. Potti, and J.R. Nevins.
2010. A pathway-based classification of human breast cancer. Proc.
Natl. Acad. Sci. USA. 107:6994–6999. http://dx.doi.org/10.1073/pnas
Goga, A., D. Yang, A.D. Tward, D.O. Morgan, and J.M. Bishop. 2007.
Inhibition of CDK1 as a potential therapy for tumors over-expressing
MYC. Nat. Med. 13:820–827. http://dx.doi.org/10.1038/nm1606
Gonzalez-Angulo, A.M., K. Stemke-Hale, S.L. Palla, M. Carey, R.
Agarwal, F. Meric-Berstam, T.A. Traina, C. Hudis, G.N. Hortobagyi,
W.L. Gerald, et al. 2009. Androgen receptor levels and association with
PIK3CA mutations and prognosis in breast cancer. Clin. Cancer Res.
Grandori, C., S.M. Cowley, L.P. James, and R.N. Eisenman. 2000. The
Myc/Max/Mad network and the transcriptional control of cell behav-
ior. Annu. Rev. Cell Dev. Biol. 16:653–699. http://dx.doi.org/10.1146/
Gray, N.S., L. Wodicka, A.M. Thunnissen, T.C. Norman, S. Kwon, F.H.
Espinoza, D.O. Morgan, G. Barnes, S. LeClerc, L. Meijer, et al. 1998.
Exploiting chemical libraries, structure, and genomics in the search for
kinase inhibitors. Science. 281:533–538. http://dx.doi.org/10.1126/
Guan, Y., W.L. Kuo, J.L. Stilwell, H. Takano, A.V. Lapuk, J. Fridlyand,
J.H. Mao, M. Yu, M.A. Miller, J.L. Santos, et al. 2007. Amplification
of PVT1 contributes to the pathophysiology of ovarian and breast can-
cer. Clin. Cancer Res. 13:5745–5755. http://dx.doi.org/10.1158/1078-
Gurel, B., T. Iwata, C.M. Koh, R.B. Jenkins, F. Lan, C. Van Dang, J.L. Hicks, J.
Morgan, T.C. Cornish, S. Sutcliffe, et al. 2008. Nuclear MYC protein over-
expression is an early alteration in human prostate carcinogenesis. Mod.
Pathol. 21:1156–1167. http://dx.doi.org/10.1038/modpathol.2008.111
Hemann, M.T., A. Bric, J. Teruya-Feldstein, A. Herbst, J.A. Nilsson, C. Cordon-
Cardo, J.L. Cleveland, W.P. Tansey, and S.W. Lowe. 2005. Evasion of the
p53 tumour surveillance network by tumour-derived MYC mutants.
Nature. 436:807–811. http://dx.doi.org/10.1038/nature03845
Hennessy, B.T., Y. Lu, E. Poradosu, Q. Yu, S. Yu, H. Hall, M.S. Carey, M. Ravoori,
A.M. Gonzalez-Angulo, R. Birch, et al. 2007. Pharmacodynamic mark-
ers of perifosine efficacy. Clin. Cancer Res. 13:7421–7431. http://dx.doi
Hu, J., X. He, K.A. Baggerly, K.R. Coombes, B.T. Hennessy, and G.B. Mills.
2007. Non-parametric quantification of protein lysate arrays. Bioinformatics.
Huang, M.J., Y.C. Cheng, C.R. Liu, S. Lin, and H.E. Liu. 2006. A small-
molecule c-Myc inhibitor, 10058-F4, induces cell-cycle arrest, apoptosis, and
myeloid differentiation of human acute myeloid leukemia. Exp. Hematol.
Alles, M.C., M. Gardiner-Garden, D.J. Nott, Y. Wang, J.A. Foekens, R.L.
Sutherland, E.A. Musgrove, and C.J. Ormandy. 2009. Meta-analysis and
gene set enrichment relative to er status reveal elevated activity of MYC
and E2F in the “basal” breast cancer subgroup. PLoS ONE. 4:e4710.
Barker, A.D., C.C. Sigman, G.J. Kelloff, N.M. Hylton, D.A. Berry, and L.J.
Esserman. 2009. I-SPY 2: an adaptive breast cancer trial design in the
setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86:97–100.
Bauer, K.R., M. Brown, R.D. Cress, C.A. Parise, and V. Caggiano. 2007.
Descriptive analysis of estrogen receptor (ER)-negative, progesterone
receptor (PR)-negative, and HER2-negative invasive breast cancer, the
so-called triple-negative phenotype: a population-based study from the
California cancer Registry. Cancer. 109:1721–1728. http://dx.doi.org/
Ben-Porath, I., M.W. Thomson, V.J. Carey, R. Ge, G.W. Bell, A. Regev,
and R.A. Weinberg. 2008. An embryonic stem cell-like gene expression
signature in poorly differentiated aggressive human tumors. Nat. Genet.
Bertucci, F., P. Finetti, N. Cervera, B. Esterni, F. Hermitte, P. Viens, and
D. Birnbaum. 2008. How basal are triple-negative breast cancers? Int. J.
Cancer. 123:236–240. http://dx.doi.org/10.1002/ijc.23518
Bland, K.I., M.M. Konstadoulakis, M.P. Vezeridis, and H.J. Wanebo. 1995.
Oncogene protein co-expression. Value of Ha-ras, c-myc, c-fos, and p53
as prognostic discriminants for breast carcinoma. Ann. Surg. 221:706–718.
Blethrow, J.D., J.S. Glavy, D.O. Morgan, and K.M. Shokat. 2008. Covalent
capture of kinase-specific phosphopeptides reveals Cdk1-cyclin B sub-
strates. Proc. Natl. Acad. Sci. USA. 105:1442–1447. http://dx.doi.org/
Carey, L.A., C.M. Perou, C.A. Livasy, L.G. Dressler, D. Cowan, K. Conway,
G. Karaca, M.A. Troester, C.K. Tse, S. Edmiston, et al. 2006. Race, breast
cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA.
Carey, L.A., E.C. Dees, L. Sawyer, L. Gatti, D.T. Moore, F. Collichio, D.W.
Ollila, C.I. Sartor, M.L. Graham, and C.M. Perou. 2007. The triple
negative paradox: primary tumor chemosensitivity of breast cancer
subtypes. Clin. Cancer Res. 13:2329–2334. http://dx.doi.org/10.1158/
Chandriani, S., E. Frengen, V.H. Cowling, S.A. Pendergrass, C.M. Perou,
M.L. Whitfield, and M.D. Cole. 2009. A core MYC gene expression
signature is prominent in basal-like breast cancer but only partially over-
laps the core serum response. PLoS ONE. 4:e6693. http://dx.doi.org/
Chang, D.W., G.F. Claassen, S.R. Hann, and M.D. Cole. 2000. The c-Myc
transactivation domain is a direct modulator of apoptotic versus prolifera-
tive signals. Mol. Cell. Biol. 20:4309–4319. http://dx.doi.org/10.1128/
Cheang, M.C., S.K. Chia, D. Voduc, D. Gao, S. Leung, J. Snider, M.
Watson, S. Davies, P.S. Bernard, J.S. Parker, et al. 2009. Ki67 index,
HER2 status, and prognosis of patients with luminal B breast cancer.
J. Natl. Cancer Inst. 101:736–750. http://dx.doi.org/10.1093/jnci/djp082
Chonghaile, T.N., and A. Letai. 2008. Mimicking the BH3 domain to kill cancer
cells. Oncogene. 27:S149–S157. http://dx.doi.org/10.1038/onc.2009.52
Cowling, V.H., and M.D. Cole. 2006. Mechanism of transcriptional activa-
tion by the Myc oncoproteins. Semin. Cancer Biol. 16:242–252. http://
Dang, C.V. 1999. c-Myc target genes involved in cell growth, apoptosis,
and metabolism. Mol. Cell. Biol. 19:1–11.
Dent, R., M. Trudeau, K.I. Pritchard, W.M. Hanna, H.K. Kahn, C.A.
Sawka, L.A. Lickley, E. Rawlinson, P. Sun, and S.A. Narod. 2007.
Triple-negative breast cancer: clinical features and patterns of recurrence.
Clin. Cancer Res. 13:4429–4434. http://dx.doi.org/10.1158/1078-0432
Dickson, M.A., and G.K. Schwartz. 2009. Development of cell-cycle in-
hibitors for cancer therapy. Curr. Oncol. 16:36–43.
Dohadwala, M., E.F. da Cruz e Silva, F.L. Hall, R.T. Williams, D.A. Carbonaro-
Hall, A.C. Nairn, P. Greengard, and N. Berndt. 1994. Phosphorylation
JEM Vol. 209, No. 4
O’Connor, L., A. Strasser, L.A. O’Reilly, G. Hausmann, J.M. Adams, S.
Cory, and D.C. Huang. 1998. Bim: a novel member of the Bcl-2 fam-
ily that promotes apoptosis. EMBO J. 17:384–395. http://dx.doi.org/
Oltersdorf, T., S.W. Elmore, A.R. Shoemaker, R.C. Armstrong, D.J.
Augeri, B.A. Belli, M. Bruncko, T.L. Deckwerth, J. Dinges, P.J. Hajduk,
et al. 2005. An inhibitor of Bcl-2 family proteins induces regression
of solid tumours. Nature. 435:677–681. http://dx.doi.org/10.1038/
Parry, D., T. Guzi, F. Shanahan, N. Davis, D. Prabhavalkar, D. Wiswell,
W. Seghezzi, K. Paruch, M.P. Dwyer, R. Doll, et al. 2010. Dinaciclib
(SCH 727965), a novel and potent cyclin-dependent kinase inhibitor.
Mol. Cancer Ther. 9:2344–2353. http://dx.doi.org/10.1158/1535-7163.
Perou, C.M., T. Sørlie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, C.A.
Rees, J.R. Pollack, D.T. Ross, H. Johnsen, L.A. Akslen, et al. 2000.
Molecular portraits of human breast tumours. Nature. 406:747–752.
Reinhardt, H.C., H. Jiang, M.T. Hemann, and M.B. Yaffe. 2009. Exploiting
synthetic lethal interactions for targeted cancer therapy. Cell Cycle. 8:
Ruzinova, M.B., T. Caron, and S.J. Rodig. 2010. Altered subcellular localiza-
tion of c-Myc protein identifies aggressive B-cell lymphomas harboring
a c-MYC translocation. Am. J. Surg. Pathol. 34:882–891. http://dx.doi
Sarrió, D., S.M. Rodriguez-Pinilla, D. Hardisson, A. Cano, G. Moreno-
Bueno, and J. Palacios. 2008. Epithelial-mesenchymal transition in
breast cancer relates to the basal-like phenotype. Cancer Res. 68:989–
Schmelzle, T., A.A. Mailleux, M. Overholtzer, J.S. Carroll, N.L. Solimini,
E.S. Lightcap, O.P. Veiby, and J.S. Brugge. 2007. Functional role and
oncogene-regulated expression of the BH3-only factor Bmf in mammary
epithelial anoikis and morphogenesis. Proc. Natl. Acad. Sci. USA. 104:
Schneider, B.P., E.P. Winer, W.D. Foulkes, J. Garber, C.M. Perou, A.
Richardson, G.W. Sledge, and L.A. Carey. 2008. Triple-negative breast
cancer: risk factors to potential targets. Clin. Cancer Res. 14:8010–8018.
Sears, R., F. Nuckolls, E. Haura, Y. Taya, K. Tamai, and J.R. Nevins.
2000. Multiple Ras-dependent phosphorylation pathways regulate Myc
protein stability. Genes Dev. 14:2501–2514. http://dx.doi.org/10.1101/
Seo, H.R., J. Kim, S. Bae, J.W. Soh, and Y.S. Lee. 2008. Cdk5-mediated
phosphorylation of c-Myc on Ser-62 is essential in transcriptional
activation of cyclin B1 by cyclin G1. J. Biol. Chem. 283:15601–15610.
Shapiro, G.I. 2006. Cyclin-dependent kinase pathways as targets for cancer
treatment. J. Clin. Oncol. 24:1770–1783. http://dx.doi.org/10.1200/JCO
Sierra, A., X. Castellsague, A. Escobedo, A. Moreno, T. Drudis, and A.
Fabra. 1999. Synergistic cooperation between c-Myc and Bcl-2 in
lymph node progression of T1 human breast carcinomas. Breast Cancer
Res. Treat. 54:39–45. http://dx.doi.org/10.1023/A:1006120006471
Sørlie, T., C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T.
Hastie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, et al. 2001. Gene ex-
pression patterns of breast carcinomas distinguish tumor subclasses with
clinical implications. Proc. Natl. Acad. Sci. USA. 98:10869–10874. http://
Soucek, L., J. Whitfield, C.P. Martins, A.J. Finch, D.J. Murphy, N.M. Sodir,
A.N. Karnezis, L.B. Swigart, S. Nasi, and G.I. Evan. 2008. Modelling Myc
inhibition as a cancer therapy. Nature. 455:679–683. http://dx.doi.org/
Stemke-Hale, K., A.M. Gonzalez-Angulo, A. Lluch, R.M. Neve, W.L. Kuo, M.
Davies, M. Carey, Z. Hu, Y. Guan, A. Sahin, et al. 2008. An integrative ge-
nomic and proteomic analysis of PIK3CA, PTEN, and AKT mutations in
breast cancer. Cancer Res. 68:6084–6091. http://dx.doi.org/10.1158/0008-
Symmans, W.F., F. Peintinger, C. Hatzis, R. Rajan, H. Kuerer, V. Valero, L. Assad,
A. Poniecka, B. Hennessy, M. Green, et al. 2007. Measurement of residual breast
Hübner, A., T. Barrett, R.A. Flavell, and R.J. Davis. 2008. Multisite phos-
phorylation regulates Bim stability and apoptotic activity. Mol. Cell.
Irvin, W.J. Jr., and L.A. Carey. 2008. What is triple-negative breast cancer? Eur.
J. Cancer. 44:2799–2805. http://dx.doi.org/10.1016/j.ejca.2008.09.034
Jain, A.N., K. Chin, A.L. Børresen-Dale, B.K. Erikstein, P. Eynstein Lonning,
R. Kaaresen, and J.W. Gray. 2001. Quantitative analysis of chromo-
somal CGH in human breast tumors associates copy number abnormali-
ties with p53 status and patient survival. Proc. Natl. Acad. Sci. USA. 98:
Jones, D. 2010. Adaptive trials receive boost. Nat. Rev. Drug Discov. 9:345–
Lee, G.Y., P.A. Kenny, E.H. Lee, and M.J. Bissell. 2007. Three-dimensional
culture models of normal and malignant breast epithelial cells. Nat. Methods.
Liang, J., S.H. Shao, Z.X. Xu, B. Hennessy, Z. Ding, M. Larrea, S. Kondo,
D.J. Dumont, J.U. Gutterman, C.L. Walker, et al. 2007. The energy
sensing LKB1-AMPK pathway regulates p27(kip1) phosphorylation me-
diating the decision to enter autophagy or apoptosis. Nat. Cell Biol. 9:
Liedtke, C., C. Mazouni, K.R. Hess, F. André, A. Tordai, J.A. Mejia, W.F.
Symmans, A.M. Gonzalez-Angulo, B. Hennessy, M. Green, et al. 2008.
Response to neoadjuvant therapy and long-term survival in patients
with triple-negative breast cancer. J. Clin. Oncol. 26:1275–1281. http://
Livak, K.J., and T.D. Schmittgen. 2001. Analysis of relative gene expression data
using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.
Methods. 25:402–408. http://dx.doi.org/10.1006/meth.2001.1262
Livasy, C.A., G. Karaca, R. Nanda, M.S. Tretiakova, O.I. Olopade, D.T.
Moore, and C.M. Perou. 2006. Phenotypic evaluation of the basal-like
subtype of invasive breast carcinoma. Mod. Pathol. 19:264–271. http://
Lowe, S.W., E. Cepero, and G. Evan. 2004. Intrinsic tumour suppression.
Nature. 432:307–315. http://dx.doi.org/10.1038/nature03098
Malumbres, M., P. Pevarello, M. Barbacid, and J.R. Bischoff. 2008. CDK
inhibitors in cancer therapy: what is next? Trends Pharmacol. Sci. 29:16–
Martin, K.J., D.R. Patrick, M.J. Bissell, and M.V. Fournier. 2008. Prognostic
breast cancer signature identified from 3D culture model accurately pre-
dicts clinical outcome across independent datasets. PLoS ONE. 3:e2994.
Masutomi, K., R. Possemato, J.M. Wong, J.L. Currier, Z. Tothova, J.B.
Manola, S. Ganesan, P.M. Lansdorp, K. Collins, and W.C. Hahn. 2005. The
telomerase reverse transcriptase regulates chromatin state and DNA dam-
age responses. Proc. Natl. Acad. Sci. USA. 102:8222–8227. http://dx.doi
Meyer, N., and L.Z. Penn. 2008. Reflecting on 25 years with MYC. Nat.
Rev. Cancer. 8:976–990. http://dx.doi.org/10.1038/nrc2231
Molenaar, J.J., M.E. Ebus, D. Geerts, J. Koster, F. Lamers, L.J. Valentijn, E.M.
Westerhout, R. Versteeg, and H.N. Caron. 2009. Inactivation of CDK2 is
synthetically lethal to MYCN over-expressing cancer cells. Proc. Natl. Acad.
Sci. USA. 106:12968–12973. http://dx.doi.org/10.1073/pnas.0901418106
Naidu, R., N.A. Wahab, M. Yadav, and M.K. Kutty. 2002. Protein expres-
sion and molecular analysis of c-myc gene in primary breast carcinomas
using immunohistochemistry and differential polymerase chain reaction.
Int. J. Mol. Med. 9:189–196.
Neve, R.M., K. Chin, J. Fridlyand, J. Yeh, F.L. Baehner, T. Fevr, L. Clark,
N. Bayani, J.P. Coppe, F. Tong, et al. 2006. A collection of breast can-
cer cell lines for the study of functionally distinct cancer subtypes. Cancer
Cell. 10:515–527. http://dx.doi.org/10.1016/j.ccr.2006.10.008
Nguyen, M., R.C. Marcellus, A. Roulston, M. Watson, L. Serfass, S.R. Murthy
Madiraju, D. Goulet, J. Viallet, L. Bélec, X. Billot, et al. 2007. Small
molecule obatoclax (GX15-070) antagonizes MCL-1 and overcomes
MCL-1-mediated resistance to apoptosis. Proc. Natl. Acad. Sci. USA.
Nijjar, T., E. Bassett, J. Garbe, Y. Takenaka, M.R. Stampfer, D. Gilley, and P.
Yaswen. 2005. Accumulation and altered localization of telomere-associated
protein TRF2 in immortally transformed and tumor-derived human breast
cells. Oncogene. 24:3369–3376. http://dx.doi.org/10.1038/sj.onc.1208482
696 Download full-text
CDK inhibitors for therapy of triple negative breast cancer | Horiuchi et al.
cancer burden to predict survival after neoadjuvant chemotherapy.
J. Clin. Oncol. 25:4414–4422. http://dx.doi.org/10.1200/JCO.2007.10.6823
Tibes, R., Y. Qiu, Y. Lu, B. Hennessy, M. Andreeff, G.B. Mills, and S.M. Kornblau.
2006. Reverse phase protein array: validation of a novel proteomic tech-
nology and utility for analysis of primary leukemia specimens and he-
matopoietic stem cells. Mol. Cancer Ther. 5:2512–2521. http://dx.doi.org/
Tutt, A., M. Robson, J.E. Garber, S.M. Domchek, M.W. Audeh, J.N. Weitzel,
M. Friedlander, B. Arun, N. Loman, R.K. Schmutzler, et al. 2010. Oral
poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1
or BRCA2 mutations and advanced breast cancer: a proof-of-concept
trial. Lancet. 376:235–244. http://dx.doi.org/10.1016/S0140-6736(10)
van ’t Veer, L.J., H. Dai, M.J. van de Vijver, Y.D. He, A.A. Hart, M. Mao,
H.L. Peterse, K. van der Kooy, M.J. Marton, A.T. Witteveen, et al.
2002. Gene expression profiling predicts clinical outcome of breast can-
cer. Nature. 415:530–536. http://dx.doi.org/10.1038/415530a
Voduc, K.D., M.C. Cheang, S. Tyldesley, K. Gelmon, T.O. Nielsen, and H.
Kennecke. 2010. Breast cancer subtypes and the risk of local and regional
relapse. J. Clin. Oncol. 28:1684–1691. http://dx.doi.org/10.1200/JCO
Wang, Y., I.H. Engels, D.A. Knee, M. Nasoff, Q.L. Deveraux, and K.C.
Quon. 2004. Synthetic lethal targeting of MYC by activation of the
DR5 death receptor pathway. Cancer Cell. 5:501–512. http://dx.doi
Weaver, V.M., S. Lelièvre, J.N. Lakins, M.A. Chrenek, J.C. Jones, F. Giancotti,
Z. Werb, and M.J. Bissell. 2002. beta4 integrin-dependent formation of
polarized three-dimensional architecture confers resistance to apoptosis
in normal and malignant mammary epithelium. Cancer Cell. 2:205–216.
Wong, D.J., H. Liu, T.W. Ridky, D. Cassarino, E. Segal, and H.Y.
Chang. 2008. Module map of stem cell genes guides creation of epi-
thelial cancer stem cells. Cell Stem Cell. 2:333–344. http://dx.doi.org/
Yang, D., H. Liu, A. Goga, S. Kim, M. Yuneva, and J.M. Bishop. 2010.
Therapeutic potential of a synthetic lethal interaction between the MYC
proto-oncogene and inhibition of aurora-B kinase. Proc. Natl. Acad. Sci.
USA. 107:13836–13841. http://dx.doi.org/10.1073/pnas.1008366107