Resistance to chemotherapy is the major cause of relapse and mortality due to cancer.
Darwinian principles of fitness-selected genetic mutations underscored the archetypal
paradigm for acquired resistance to chemotherapy1. For example, mutations leading
to structural changes in drug target proteins, upregulation of drug-efflux proteins
or the activation of alternate survival pathways can all lead to chemotherapy failure2.
However, recent evidences have implicated both intrinsic and adaptive resistance governed
by epigenetic alterations of cancer cells in non-Darwinian relapse3. For example,
cancer cells in patients treated with either cytotoxic or targeted agents, such as
a taxane or imatinib, can exhibit drug resistance, and even grow during treatment,
despite the absence of resistance-conferring genetic alterations4
5. In addition, clinical evidence exists to show that cancer cells can become resensitized
to chemotherapy after a ‘drug holiday’6. Indeed, similar transient adaptive resistance
to antibiotics has been reported in bacteria, leading to the generation of ‘persisters’7.
Improved understanding of intrinsic and adaptive resistance is therefore the key to
a successful chemotherapeutic outcome.
Early explanations of intrinsic resistance emphasized a phenotypically distinct subset
of cancer stem-like cells (CSC)8. However, there is an increasing realization that
a higher degree of intratumoral heterogeneity exists beyond CSCs, as an outcome of
stochastic gene expression9 or due to non-genetic cell state dynamics arising from
spontaneous switching between cell states within a clonal population10. Recent studies
have revealed that phenotypic state transitions could be a consequence of external
cues, including radiation and chemotherapy3. These findings support the hypothesis
that cancer cells could potentially, phenotypically transition to a chemotolerant
state, which can offer an initial survival advantage against chemotherapy in the absence
of Darwinian resistance-conferring mutations. Therapeutic regimens that perturb such
cell state transitions could evolve as important and clinically applicable strategies
to overcome resistance. We tested this hypothesis in the context of the development
of adaptive resistance to docetaxel (DTX) in breast cancer, which remains the second
most common cause of cancer deaths in women11, and is treated with taxane-based chemotherapy12.
We report here that treatment of cancer cells with high concentration of taxanes results
in the generation of ‘persister’ cells that are defined by a transition towards a
CD44HiCD24Hi expression status. Using mathematical modelling and further experimental
validation, we demonstrate that these cells arise as a result of chemotherapy-induced
phenotypic transitions from a non-CSC population, and can confer drug resistance.
This phenotypic shift correlates with the activation of the Src family kinase (SFK)/Hck
pathway, and post-treatment with a SFK/Hck inhibitor within a defined temporal window
enhances cell death. The concept of therapy outcome being dependent on the sequence
of administration of chemotherapy agents is an emerging paradigm13
14. Our results indicate that a drug pair administered in the right temporal sequence
combinations, where the leading drug induces a phenotypic cell state transition thereby
uncapping a vulnerability tractable by the partner agent, could overcome adaptive
resistance and enhance cell death.
Results
Drug-induced phenotypic transition in explants
To elucidate the mechanisms underlying adaptive resistance to anticancer therapy,
we used three-dimensional explants derived from fresh tumour biopsies from patients.
Three-dimensional tumour explants are emerging as powerful models to study tumour
biology, as they preserve the tumour heterogeneity and microenvironment15. In a recent
study, we have observed that culturing the explants in autologous serum and in grade-matched
tumour matrix conserves the parental tumour genotypic and phenotypic characteristics16.
We included breast cancers of different stages and receptor status, including those
that were taxanes-treatment naive (Supplementary Table 1). We used 200 μm tumour explants
in this study as drugs can diffuse through such thickness17 (Fig. 1a). CD44, a membrane
glycoprotein, has been associated with chemorefractory, more mesenchymal stem-like
characteristics8
18. In contrast, CD24-positive breast cancer cells have been reported to be more of
the differentiated luminal and a Her2+ subtype, whereas basal-like tumours were classified
as CD24−/Lo (ref. 19). We observed a significant inter-tumoral heterogeneity in the
baseline expression of CD44 and CD24, and the distribution was normal between tumours
from taxane-treated and taxane-naive patients (Fig. 1b–d). Interestingly, incubating
the explants with high-concentration DTX (3.4 μM)20 for 72 h resulted in an increase
in the median expression of both CD44 and CD24 as compared with vehicle-treated explants
(P<0.01) (Fig. 1b–d), irrespective of the tumour type. In addition, the DTX-induced
increase in expression of CD24 and CD44 was similar in explants generated from tumours
that had progressed clinically on taxanes and those generated from taxanes-treatment
naive patients, indicating that the phenotypic plasticity did not rely on the acquisition
of resistance. The upregulation of CD44 following DTX treatment was correlated with
reduced apoptosis as seen in decreased cleaved caspase-3 levels compared with baseline
(Fig. 1e,f). Treatment with doxorubicin, which is widely used in the adjuvant or metastatic
settings in breast cancer, similarly induced CD44 expression with reduced cleaved
caspase-3 levels. Interestingly, in contrast, treatment with carboplatin (100 μM)
and gemcitabine (100 μM) induced apoptosis without any upregulation of CD44 expression
(Fig. 1e,f). Indeed, in a recent study, a combination of gemcitabine and carboplatin
was found to be effective for pretreated patients with metastatic breast cancer21.
Drug-induced phenotypic plasticity and tolerance
The results from explant studies suggested that taxane and anthracycline chemotherapy
induce a phenotypic transition to a chemotherapy-refractory CD44HiCD24Hi state, rather
than selecting for ‘privileged’ subsets. We recapitulated these findings using an
array of established luminal and basal-like breast cancer cell lines. Although the
IC50 values of DTX in cancer cells typically range between high pM and low nM range20,
a subset of the treatment-naive parent cell population was found to survive at supramaximal
concentrations ≥100 nM of DTX. These persister cells were termed as drug-tolerant
cells (DTCs) (Fig. 1g,h), and were characterized by low baseline apoptosis (Supplementary
Fig. 1a,b). The DTCs showed cross-tolerance to other cytotoxics, including doxorubicin,
vincristine and cabazitaxel (Fig. 1h). Cabazitaxel, a recently approved taxane for
treatment of hormone-resistant prostate cancer, has poor affinity for drug-efflux
p-glycoproteins, suggesting that the resistance of DTCs to the cytotoxics is independent
of drug-efflux22. This was further validated by treating the cells with elacridar,
a p-glycoproteins-transport inhibitor, which failed to reverse the resistance to the
cytotoxics (Supplementary Fig. 1c,d). Furthermore, no changes in MDR1 expression were
noted between parent cells and DTCs (Supplementary Fig. 1e).
We next explored whether the DTCs exhibit higher levels of CD44 and CD24. As shown
in Fig. 1i, confocal imaging revealed an enhanced membrane expression of CD44 and
CD24 in the DTCs derived from the basal-like breast cancer cell line MDA-MB-468 as
compared with treatment-naive parent cells, which was validated using fluorescence-activated
cell sorting (FACS) (Fig. 1j). In the context of breast cancer, a CD44HiCD24−/Lo cell
has classically been defined as a belonging to the cancer stem cell population that
confers intrinsic resistance8. Interestingly, consistent with the observation in the
MDA-MB-468s, we observed an increase in the CD44HiCD24Hi population in the DTCs that
were derived from MDA-MB-231 (basal), SUM159 (basal) or 4T1 (basal, murine) cells.
A similar increase in the CD44HiCD24Hi population was also observed in the DTCs generated
from the luminal cell lines, MCF7 and SKBr3, but not in the T47D cell line (Fig. 1j–l).
Additional studies revealed an increase in CD44 expression in the DTCs generated from
melanoma cell line MDA-MB-435 and murine ovarian cell lines, 4306 and 4412 (Supplementary
Fig. 2a), suggesting that this phenomenon is not restricted only to breast cancer.
CD44 was also found to be elevated following treatment with doxorubicin (Supplementary
Fig. 2b). We did not observe an increase in the percentage of CD44HiCD24Lo population
in the DTCs compared with the parent cells (Fig. 1l).
As the high concentration of DTX also induced cell death in parent cells, it was not
evident whether the increase in the CD44HiCD24Hi phenotype was a true induction or
just an enrichment of the subtype as in the case of CSCs. To dissect this, we treated
parent cells acutely (24 h) with low-dose chemotherapy (10 nM for MDA-MB-468 and 25 nM
for MDA-MB-231, respectively) that had no effect on cell viability, that is, did not
select for ‘privileged’ cells (Supplementary Fig. 3a,c). Interestingly, as shown in
Supplementary Fig. 3b,d, this resulted in an increase in the CD44Hi and CD24Hi populations.
Furthermore, we observed a dose-dependent induction towards the CD44HiCD24Hi phenotype
(Supplementary Fig. 3e,f). Taken together, these results suggest that the observed
chemotherapy tolerance could potentially arise from drug-induced phenotypic cell state
transition, distinct from the established models of clonal selections of privileged
subsets.
Quantitative model of phenotypic cell state transitions
To theoretically test the drug-induced phenotypic cell state transition versus clonal
selection, we developed a phenotype switching model consisting of three cellular compartments,
describing the population dynamics of CSCs (CD44HiCD24Lo), the induced cells (CD44HiCD24Hi)
and non-stem cells (CD44LoCD24Hi and CD44LoCD24Lo). Experimental data for the population
dynamics were obtained from FACS data describing the re-equilibration kinetics of
both the parental cells as well as the DTC, generated using the experimental design
shown in Fig. 2a. The obtained parameter sets for the cases of the parental population
and the DTC populations are summarized in Supplementary Table 2, using the methods
described in detail in the Supplementary Information. In addition, Fig. 2b depicts
the curves describing the time-evolution of the system composition from an arbitrary
steady state, and highlights the system dynamics as it reaches equilibrium. In both
cases, the model was able to fit well to the experimental data, implying that the
model is versatile enough to describe the system dynamics of both treatment naive
and post-chemotherapy cases, although, given the phenomenological nature of the model,
we note that the derived parameter sets are useful only in a comparative sense, and
are not necessarily precisely representative of the underlying biology for individual
cases. Interestingly, the parameter values for either system was found to be quantitatively
distinct, giving rise to different system saturations in equilibrium, that is, after
induction of chemotherapy, there is a deterministic shift in the parameters governing
the growth and switching rates of the subpopulations of cells, such that different
steady states are observed. The model predicted that within the DTC population, the
rates of proliferation of CSCs and induced CD44HiCD24Hi cells are significantly increased,
whereas the rate of proliferation of non-stem cells decreases to a negligible value.
The rate of transition from stem to non-stem cells remains the same in both environments,
but the rate of transformation directly from non-stem to stem cells does not occur
to a great degree in the chemoresistant cells. In addition, in the DTC, we observe
high rates of transition between the induced CD44HiCD24Hi cells and non-stem cell
compartments in both directions, indicating high inter-conversion (with no net direction),
whereas in the case of the parental cells, the transition rate between the CD44HiCD24Hi
cells and non-stem cells is highly skewed in the direction of the former, predominantly
switching into the latter and not in the reverse direction. Finally, in the parental
population, the CD44HiCD24Hi cells and CSCs are able to transition between each other.
In contrast, in the DTCs, CSCs do not appear to transition into CD44HiCD24Hi cells,
which are however able to transition into CSCs (Fig. 2c).
Transient drug-tolerant phenotype originates from non-CSCs
To test the theoretical predictions that the CD44HiCD24Hi cells indeed arise from
non-CSCs (that is, CD44Lo cells), we sorted the parental population into four subsets
based on various permutations and combinations of CD44Hi, CD44Lo, CD24Hi and CD24Lo
status (Fig. 2a). These fractionated cellular subsets were then treated with high-concentration
DTX for 48 h, following which the percentage of CD44HiCD24Hi cells in each population
was quantified using FACS. In the absence of DTX, the cells re-equilibrate to a heterogenous
cellular population similar to the treatment-naive parent cells with CD44HiCD24Hi
cells forming ~20% of the total population, which was used as the baseline to elucidate
the effect of DTX on each starting cellular fraction. Interestingly, as shown in Fig.
2d, a statistically significant increase in the CD44HiCD24Hi population was evident
when the starting population consisted of CD44LoCD24Hi or CD44LoCD24Lo subsets.
We next used small molecule salinomycin-selection against CSC23 to significantly deplete
the parent MDA-MB-231 population of CD44HiCD24Lo cells (Fig. 2e, Supplementary Fig.
4a). The parent and salinomycin-selected cells were then treated with DTX to generate
DTCs, that were then FACS analyzed to quantify the percentage of CD44HiCD24Hi cells.
The fact that both parent-derived DTCs and DTCs that were generated from salinomycin-selected
cells (Sal-DTCs) exhibited similar percentages of CD44HiCD24Hi cells (Fig. 2f), despite
the salinomycin-treated cells having ~50% less CD44HiCD24Lo cells to start with, indicated
that the CD44HiCD24Hi were indeed originating from a non CD44HiCD24Lo population.
Interestingly, the chemotherapy-induced upregulation of CD44 and CD24 levels were
only transient, and in both DTCs and Sal-DTCs, the cells recalibrated back to parental
CD44 and CD24 basal expression phenotype when expanded (DTC-E or Sal-DTC-E) over 35
days in the absence of chemotherapy pressure (Fig. 2g, Supplementary Fig. 4b shows
schematic for FACS isolation). Although the cells existed in the transient CD44HiCD24Hi
state, they were found to be resistant to high concentration of both DTX and doxorubicin.
The expanded cultures, however, regained drug sensitivity (Fig. 2h), suggesting that
the acquired tolerance to chemotherapy is reversible. Cell cycle analysis of the DTCs
revealed that the cells were primarily in the G2M phase, with a large subset also
undergoing endoreduplication, consistent with previous observations24. Interestingly,
endoreduplication, the replication of DNA without undergoing an intervening mitotic
division, has been reported to result in chemoresistance 25. Cell cycle analysis of
the expanded DTCs revealed reversion to the parental phenotype, although a remnant
tail of endoreduplicating cells was still evident (Supplementary Fig. 4c).
Kinase library screening in DTCs
To identify whether DTCs are therapeutically tractable during this transient phase,
we performed a drug screen with a library of kinase-targeted agents (Fig. 3a). Although
some targeted therapeutics, such as the Akt inhibitor, PI103, or the pan-kinase inhibitor,
sorafenib, were non-selective for DTCs over parent cells (Sensitivity index~1), others
like the EGFR inhibitor, erlotinib, inhibited parent cells, whereas the DTCs continued
to grow (sensitivity index (SI) <1) (Fig. 3b). Interestingly, dasatinib, a dual SFK/BCR-Abl
inhibitor exerted greater cell killing of DTCs compared with the parental fraction
(SI >1) (Fig. 3b–d). In contrast to dasatinib, imatinib, a selective BCR-Abl inhibitor,
had no effect on the DTCs, suggesting that the activity of dasatinib could be attributed
to its SFK-inhibiting property (Fig. 3c,d). Interestingly, RK20449, a selective inhibitor
of the SFK protein Hck26, was found to be ~600% more selective in reducing the viability
of DTCs as compared with parental cells (Fig. 3d). Furthermore, dasatinib was found
to exert a synergistic outcome against DTCs selected with increasing concentrations
of DTX, suggesting that the refractory cancer cells exert a DTX dose-dependent reliance
on the SFK-signalling pathway to persist during chemotherapy (Fig. 3e).
Consistent with the above results, a phosphorylation array revealed a global activation
of the pro-oncogenic and pro-survival SFK family27 in the DTCs as compared with parent
cells, with Hck as the predominant target (Fig. 3f). Western blotting revealed that
the Src-activating residue (Y419) remained unchanged but the inactivating residue
(Y527) was diminished in DTCs, indicating a gain-of-function mechanism underlying
the activation of this pathway (Fig. 3f). Furthermore, Immunoprecipitation (IP) for
phospho-Tyr Hck revealed a DTX concentration-dependent increase in phosphorylated
Hck with maximal expression in the DTCs, which reverted back to parental levels by
day 35 (in the expanded population) (Supplementary Fig. 5a). We next tested the efficacy
of dasatinib and RK20449 against an array of basal and luminal parental breast cancer
cell lines and the corresponding DTCs. As shown in Fig. 3g, the cell lines where treatment
with DTX induced the CD44HiCD24Hi population were significantly more sensitive to
SFK inhibition than the parent population. In contrast, this discrimination was lost
in the luminal cancer cell line, T47D, which did not demonstrate an augmentation of
the CD44HiCD24Hi population in the drug-tolerant subset. The addition of the BCR/ABL
inhibitor, imatinib, with RK20449 did not further augment this sensitivity of DTCs
to the latter, implicating only the SFK pathway in this response (Supplementary Fig.
5b). Furthermore, dasatinib treatment significantly inhibited the CD44HiCD24Hi population
in the DTCs (Supplementary Fig. 5c). Taken together, these results suggested the involvement
of the SFK pathway in mediating the transient chemotherapy tolerance arising owing
to drug-induced phenotypic cell state transitions.
CD44/CD24 clusters in lipid rafts with SFK/Hck
As the DTCs exhibited increased activation of the SFK signalling, we used a short
interfering RNA (siRNA)-based approach to test if the increased expressions of CD44
and CD24 are directly linked with the activation of SFK (knockdown validation can
be found in Supplementary Fig. 5d). A phosphorylation array-based analysis of DTCs
generated from cells treated with DTX following siRNA-knockdown of CD44 revealed a
reduction in the phosphorylation of Hck. SiRNA-mediated knockdown of CD24 also decreased
the phosphorylation of Hck and additionally of Lyn (Fig. 4a). Indeed, previous studies
have implicated SFK proteins in mediating signalling through CD24 (refs 28, 29). Immunoprecipitation
studies validated that both CD44 and CD24 scaffold with Hck (Fig. 4b). A double knockdown
of CD44 and CD24, however, did not exert any additive effect, suggesting that the
both contribute to the activation of SFK but do so through the same machinery (Fig.
4a).
To study the interactions between CD44, CD24 and SFK/Hck, we looked at the role of
caveolins (Cav). Cav are major protein components of lipid rafts, and its upregulation
is associated with poor prognosis in several human cancers30
31. Studies have implicated that engagement of CD44 and CD24 in lipid rafts can result
in the activation of cortex kinases via clustering-mediated autophosphorylation32
33. Furthermore, recent studies have reported the activation of SFK by Cav-1 and vice
versa34. Immunoprecipitation studies revealed an enhanced interaction between Hck
and Cav-1 in the DTCs as compared with in the parents. Consistent with the earlier
observation of the requirement of both CD44 and CD24 for the activation of Hck, we
observed that siRNAs-mediated knockdown of either CD44 or CD24 is sufficient to inhibit
this interaction (Fig. 4c). Interaction between Cav-1 and Hck was further evidenced
by confocal microscopy (Supplementary Fig. 5b). Labelling the lipid rafts using a
cholera toxin-based fluorescent tracer followed by confocal imaging revealed a robust
colocalization of CD44, CD24 and Hck in the lipid rafts (Fig. 4d). Similarly, immunofluorescence
imaging confirmed the colocalization of CD44, CD24 and Hck with Cav-1 in the DTCs.
(Fig. 4e). Interestingly, the clustering of Hck with Cav-1 was found to facilitate
a nuclear localization of the complex, which was augmented in the DTC compared with
parent cells (Fig. 4f,g), and was blocked by a siRNA-mediated knockdown of Cav-1 (Fig.
4f). This is consistent with previous observations, where Cav-1 and SFK has been reported
to facilitate stabilization and nuclear translocation of signalling proteins35. Nuclear
translocation of activated Hck is reported to result in the inhibition of p73, resulting
in a survival response via a reduced induction of the Caspase activation and recruitment
domains 12/apoptotic protease-activating factor 1 (CARD-12/APAF1)36. Indeed, the inhibition
of Hck with RK20449 released the suppression of proapoptotic CARD-12/APAF1 (Fig. 4h).
Temporally sequenced SFK inhibitor and taxane in vivo
The in vitro results suggested a novel function of SFK signalling in breast cancer,
driving a transient adaptive resistance during phenotypic cell state transitioning.
We next investigated whether the inhibition of SFK could overcome adaptive resistance
to taxanes in vivo. As the first step, we studied the tumorigenic ability of taxane-‘induced’
CD44HiCD24Hi cells compared with different phenotypic subpopulations of murine mammary
carcinoma drug naive parental cells. The ‘induced’ cells were isolated using FACS
based on the de novo appearance of a previously non-existent population (~2.2%) post
acute cytotoxic pressure (Fig. 5a). We performed a dilution assay, where defined numbers
of parental CD44Hi, CD44Lo or the induced cells were implanted in mice. As shown in
Fig. 5b, all the phenotypes could contribute to tumour progression in 100% of the
animals when implanted in >2,500 cells. However, at the lower dilutions of 1,000 and
100 cells, only 60% and 20% of the CD44Lo cells gave rise to tumours, respectively,
consistent with previous observations that CD44Lo cells are less tumorigenic. In contrast,
both CD44Hi and ‘induced’ CD44HiCD24Hi cells contributed to tumorigenesis in 100%
of the animals even at the lowest dilution (100 cells). Monitoring the rate to tumorigenesis
revealed that the parental CD44Hi cells led to faster tumour development as compared
with the induced cells, which in turn led to tumour development faster than the CD44Lo
cells (Fig. 5c). We next sorted the parental cells into CD44HiCD24Hi, CD44HiCD24Lo,
CD44LoCD24Hi and CD44LoCD24Lo, which were subsequently implanted in mouse at a dilution
of 5,000 cells each. ‘Induced’ CD44HiCD24Hi cells were also injected at the same dilution
(Fig. 5d). As shown in Fig. 5e, tumours derived from CD44HiCD24Hi cells were most
aggressive in terms of tumour growth followed by the ‘induced’ CD44HiCD24Hi cells,
whereas the tumours derived from CD44LoCD24Lo cells were the slowest growing. These
results implicated that phenotypically transitioned chemotherapy-refractory cells
can potentially reinitiate tumour growth.
We next studied whether the temporal induction of phenotypic cell state transition
in response to chemotherapy can be recapitulated in vivo. Treatment-naive cells were
implanted in a syngeneic 4T1 mammary carcinoma mouse model, which were then treated
with a maximum-tolerated dose of docetaxel on days 2 and 5 post implantation. A control
group was treated with the vehicle. As shown in Fig. 6a, a separation of the growth
curves between treated and untreated group was evident by day 6, reaching a cutoff
point by day 9 in the control group. The vehicle-treated animals and a batch of DTX-treated
animals (during growth-plateau phase) were killed on day 9. The remaining drug-treated
animals were subsequently killed on day 19, when the tumour growth rate (the slope
of the curve) had reached the slope of growth observed in the vehicle-treated control
group. Immunohistological and western blot analysis of the tumour tissue revealed
a significant upregulation of CD44, phosphorylated Hck, as well as activated Src (via
ablation of the inactivating residue at Y527) in the day 9 tumours from animals treated
with DTX as compared with vehicle-treated controls. Furthermore, day 19 drug-treated
tumours showed a reversal to the baseline (Fig. 6b, Supplementary Fig. 6a–d). This
was consistent with the earlier in vitro observations, where chemotherapy induced
a phenotypic switch towards a CD44Hi state, with an associated activation of Hck,
resulting in adaptive resistance (that is, generation of DTCs), and the transiently
acquired phenotype recalibrating to the parental state with time.
Based on this understanding of the temporal induction of the phenotypic transition
in vivo, we next explored whether a time-constrained administration of a SFK inhibitor
could potentially reverse the adaptive drug-resistant state. The experimental design
is outlined in Fig. 6c. The animals were treated with vehicle or DTX (at maximum-tolerated
dose) on days 2 and 5 post implantation of tumour cells. The DTX-treated animals were
then randomized into four groups. The first group was treated with four, once daily,
doses of dasatinib, simultaneously administered with DTX between days 2 and 5. The
second group was treated with dasatinib administered between days 8 and 11, that is,
schedule 1, timed to target the induction phase of DTX-induced cell state transition.
The third group was similarly treated with imatinib. The fourth group was administered
with dasatinib between days 14 and 17 (Schedule 2), timed to target SFK during the
reversion phase to parental phenotype. As shown in Fig. 6d (and Supplementary Fig.
6d), the simultaneous administration of DTX and dasatinib only marginally improved
the antitumour efficacy compared with DTX alone treatment, whereas treatment with
dasatinib as per schedule 2 had no statistically significant effect. Interestingly,
dasatinib administered as per schedule 1 synergized with DTX in tumour growth inhibition.
Imatinib, which was included as a negative control, had no effect on DTX-induced tumour
growth inhibition. The Kaplan–Meier curves demonstrate that orthotopic tumour-bearing
mice treated as per schedule 1 exhibited significantly superior survival than schedule
2, simultaneous administration and vehicle-treated controls (Fig. 6e). Consistent
with the in vitro results, study of cross-sections of DTX-treated tumours revealed
the localization of phosphorylated HCK with CD44Hi cells. Furthermore, cells that
had lower CD44 expression also exhibited low phosphorylated Hck (Fig. 6f). Finally,
as shown in Supplementary Fig. 6e, treatment of animals with the Hck inhibitor administered
post treatment with DTX treatment resulted in increased APAF1 expression that overlapped
with TdT-mediated dUTP nick end labelling positivity, validating the role of Hck in
suppression of apoptosis in vivo.
At last, to study the clinical implications of these findings, we generated explants
from primary tumour biopsies that were clinically resistant to DTX. The explants were
treated with vehicle, DTX or a schedule of DTX followed by dasatinib. As shown in
Fig. 7a, treatment with DTX did not cause a significant change in the percentage of
apoptotic cells as compared with vehicle. In contrast, the sequenced dosing resulted
in a marked increase in apoptosis. Taken together, these results suggest that targeting
SFK/Hck during the chemotherapy-induced phenotypic cell state transition can overcome
adaptive resistance.
Discussion
In this study, we demonstrate that the introduction of temporality in the application
of a chemotherapy drug pair can induce novel biological behaviour and an outcome that
otherwise is not unmasked, if the two drugs are administered simultaneously or in
the incorrect temporal window. We show that the treatment of breast cancer cells with
SFK inhibitors immediately following a taxane-based chemotherapy results in an enhanced
anticancer outcome. The first drug induces a phenotypic cell state transition from
a non-CSC to a preferred CD44HiCD24Hi state, which can activate SFK signalling and
confer adaptive resistance to chemotherapy. Interestingly, it is during this drug-induced
phenotypic transition that the cells are also exquisitely sensitive to inhibition
by a SFK inhibitor such as dasatinib. However, this sensitivity is lost when the cells
recalibrate to the parental phenotype following removal of cytotoxic-chemotherapy
pressure. This is consistent with recent clinical findings, where single-agent dasatinib
did not exhibit significant antitumour activity in patients with heavily pre-treated
metastatic breast cancer37
38, whereas dasatinib administered after cessation of DTX was found to be effective39.
Previous studies had reported CD44HiCD24Lo/− breast cancer cells as chemotolerant
stem-like (CSCs) or basal-like state, whereas CD24Hi cells were considered luminal
and differentiated40. Interestingly, plasticity was reported between CD44HiCD24− cells
and non-CSCs or CSC-depleted fractions, and the former could be enriched by chemotherapy10.
Here we observed an increase in the CD44HiCD24Hi fraction following treatment with
DTX. The increase in the expression of CD44 and CD24 in the explant cultures that
were generated from a broad range of breast tumour types, and not just the basal type,
indicated a chemotherapy-induced expression of CD44Hi and CD24Hi cells rather than
an enriching of the CSCs. A similar increase was observed in both basal and luminal
cell lines in response to DTX treatment in vitro, although it should be noted that
the effects were more pronounced in the basal type cell lines. The use of CSC-depleted
or CD44Lo cells, using non-lethal chemotherapy concentrations, and mathematical modelling
validated that this increase in the CD44HiCD24Hi fraction is indeed a result of de
novo induction rather than selection of chemoresistant cells, and that these cells
could arise from a non-CSC population. Interestingly, a membrane circumference CD24
expression, as observed in these ‘induced’ cells, is implicated in tumour progression
and poor prognosis41. Furthermore, previous studies had shown that whereas CD44Hi
status is associated with increased risk of distant metastasis, the distant metastases
that are frequently detected following chemotherapy are enriched for CD24Hi cells19.
Such observations could be similar to the phenotypic plasticity observed in this study.
Indeed, recent studies have implicated stressor-induced phenotypic plasticity in driving
the emergence of metastable phenotypic variants42.
Mechanistic studies revealed that the induction of CD44 and CD24 in the drug-tolerant
CD44HiCD24Hi cells was associated with a colocalization in the lipid rafts, leading
to an activation of the SFK signalling. CD44 engagement is reported to induce lipid
raft coalescence to facilitate a CD44-Src-integrin-signalling axis, leading to increased
matrix-derived survival33. Similarly, CD24 is known to augment c-src kinase activity
and increase the formation of focal adhesion complexes in intact lipid rafts32. Of
note, the Y419 residue was not differentially enhanced as expected based on previous
reports43. We speculate this may arise as a consequence of phosphatase activity versus
kinase activities within the DTC. Interestingly, in this study, the colocalization
of both CD44 and CD24 was found to be critical for complexing with Cav-1, and the
subsequent activation and subsequent perinuclear localization of SFK/Hck. Cav-1 is
known to be overexpressed in aggressive breast carcinomas, and is also correlated
with multi-drug resistance44. It is possible that increased apoptosis seen with the
SFK and Hck inhibitors is a consequence of blocking Hck-mediated inhibition of p73
function45 and APAF-1 activation36. Interestingly, we did observe that a regimen containing
carboplatin could induce apoptosis in the drug-refractory explants unlike DTX or doxorubicin,
which could be a result of the ability of platinum-based cytotoxics to upregulate
p73 (ref. 46). Platinum-containing regimens are being studied in chemotherapy-refractory
triple-negative breast cancer47, and could potentially be useful in overcoming adaptive
resistance to taxanes. Figure 7b summarizes the mechanisms underlying chemotherapy-induced
phenotypic plasticity-driven adaptive response of the cells.
This study has several translational implications. First, it sheds newer insights
into chemotherapy-induced adaptive resistance in breast cancer, where a taxane induces
phenotypic cell state transition in the cells towards a transient CD44HiCD24Hi state.
The clustering of CD44 and CD24 in the lipid rafts, and the complex with Cav-1 leads
to the activation of SFK/Hck, which can confer adaptive resistance. It is possible
that such transient drug-refractory states can confer an advantage for a fraction
of cells to survive the initial onslaught of chemotherapy in the absence of stable
resistance driven by Darwinian principles. Second, the understanding of the new role
of SFK/Hck in mediating this phenotype also indicates that existing clinically approved
drugs could potentially be repositioned for overcoming taxanes-induced adaptive resistance.
Results from our explant studies, which closely mimic the clinical context, indicate
that dasatinib, for example, can resensitize refractory breast cancer to taxane. Although
the emphasis of this study has been on breast cancer, early data from other cancer
cell lines indicate that this phenomenon could be ubiquitous. Finally, an interesting
point to note is that the above signalling interactions are only triggered owing to
drug-induced phenotypic cell state transition, and SFK inhibition has no effect in
the absence of this phenotypic transition. The possibility of using a drug pair, administered
in the correct temporal sequence, where the leading drug transitions a cancer cell
to a phenotypic state vulnerable to the second agent opens up a new paradigm in the
treatment of cancer.
Methods
Reagents
Unless noted otherwise, all reagents and drugs were of the highest grade purchased
from Sigma-Aldrich (St Louis, MO, USA). Vincristine was purchased from Tocris biosciences
(Minneapolis, MN, USA). Cabazitaxel, PI103, Dasatinib, Doxorubicin, LY294002 and Erlotinib
were purchased from LC Labs (Woburn, MA, USA).
Cell culture and gene knockdown with siRNA
MCF-7 (American Type Culture Collection; ATCC), MDA-MB-231 (ATCC), SKBR3 (ATCC) and
SUM159 (Asterand, Detroit, MI, USA) were cultured in DMEM containing 10% fetal bovine
serum, MDA-MB-468 (ATCC), T47D (ATCC) and 4T-1 mammary carcinoma cells (ATCC) were
cultured in RPMI containing 10% fetal bovine serum (Invitrogen, Carlsbad CA, USA)
at 37 °C and 5%CO2. In total, 4306 and 4412 ovarian cancer cell lines were a kind
gift from Dr Daniela Dinulescu, BWH. During treatments with chemotherapeutics, cells
were grown to semi-confluence and treated with indicated concentrations of chemotherapy
in serum-containing medium for indicated time points. For siRNA gene knockdown, cells
were plated at a concentration of 5 × 104 cells ml−1. Pre-validated Silencer Select
siRNA targeting (sense sequences) pan-CD44(1) (5′- UAUUCCACGUGGAGAAAAAtt -3′) panCD44(2)
(5′- GCGCAGAUCGAUUUGAAUAtt -3′) panCD24 (5′- GGAGAGGAACAUCCAAAAtt -3′) Cav-1 (5′-
GCUUCCUGAUUGAGAUUCAtt -3′) were purchased from Ambion (Invitrogen, Grand Island, NY,
USA), and were transfected using lipofectamine 2000 (Invitrogen) following manufacturer’s
protocol. Scrambled siRNA was used as a control.
Cell culture and generating DTC
Cancer cells were plated at a density of 0.5–1 × 105 cells ml−1 and allowed to adhere
for 24–48 h. When cells reached ~70% confluency, they were treated with cytotoxic
drugs at indicated concentrations for 4–48 h and utilized for subsequent assays. Following
washes with PBS, adherent cells were trypsinized and re-plated at a density of 1.5–2
× 105cells ml−1 and cultured in serum-containing medium. After 24 h incubation, floating
cells were removed and remaining cells were washed with 1 × PBS and considered as
chemotherapy-tolerant cells. Expander populations were cultured in fresh media replaced
at routine intervals over a 35-day period.
Cytotoxicity and cell viability assays and calculation of drug sensitivity index
Parent cells, DTC or an expanded population of DTC were generated as described and
plated at a concentration of 1.5 × 105 in a clear bottom 96-well plate. Cells were
exposed to treatments for 48 h in serum-containing medium. Following incubation, cells
were washed with PBS and recovered in serum and phenol red-free RPMI or DMEM and subsequently
treated with MTS reagent using manufacturer’s protocol (Promega, Madison, WI, USA).
Drug SI was derived as follows: cell viability was determined as % of vehicle control
for treatment conditions of 10 nM, 100 nM 1,000 nM and 10 μM of indicated drug, and
the values were averaged across these four drug concentrations. SI was calculated
as a ratio of this average value for parent:DTC. SI=1 correlates to parental sensitivity,
SI<1 correlates to resistance compared with parent cell line and SI>1 correlates to
enhanced sensitivity to the indicated drug compared with the parent cell line at the
same concentration average. Validation of cytotoxicity was performed by bright field
microscopy and trypan blue co-stain.
Human explant studies
Human breast cancer biopsy tissues (N=14) from anonymous patients with varying stages
of disease, receptor status and prior treatment history (Supplementary Fig. 1 shows
patient history) were obtained from HCG Bangalore Institute of Oncology, Kidwai Memorial
Institute of Oncology, Mazumdar Shaw Cancer Center under institutional review board
approval. The tumour samples were transported to the laboratory at 4 °C, in appropriate
transfer buffer for ex vivo studies and molecular and pathological evaluation. Tissues
were cut into thin sections and cultured in 96-well plates that were coated with tumour
matrix proteins and media supplemented with 2% autologous serum. Tumours were treated
with taxanes, doxorubicin or platinum-containing regimen for 72 h at concentrations
based on reported clinical Cmax values20. For this study, the concentrations used
were 3.4 μM DTX and 5.6 μM doxorubicin. After treatment, tumour cell viability was
measured by cleaved caspase-3 determined by immunohistochemistry (IHC) score. CD44
(clone IM7) and CD24 (clone ML5) were used at a dilution of 1:50.
In vivo experiments
All in vivo experiments were performed in compliance with Institutional Animal Care
and Use Committee protocol approved by Harvard Medical School and in accordance with
institutional guidelines, supervised on-site by veterinary staff. 4T-1 mouse mammary
carcinoma cells (106 cells) suspended in 100 μl PBS were injected into the flanks
of 5–6-week-old female Balb/C mice (heterotopic) or mammary fat pads (orthotopic)
(Charles River, Wilmington, MA, USA). DTX was dissolved in pure ethanol at a concentration
of 50 mg ml-1 mixed 1:1 with Polysorbate 80 (Tween 80) and brought to a final working
concentration with 5% glucose in PBS. Once tumours became palpable (~100 mm3), DTX
or vehicle treatments were administered at 100 μl volumes. Dasatinib was dissolved
in DMSO to working concentration and delivered as 50 μl injections on indicated days.
RK20449 was dissolved directly in PBS and administered i.p. at 30 mg kg−1, twice daily
for 3 consecutive days. Tumour volumes were measured by a third party unaware of treatment
conditions using digital calipers (Starlett, Athol, MA, USA), and tumour volumes were
calculated by the following formula: (width × width × length)/2 and expressed as mm3
or relative volume increase from day 1. Tumour-specific growth rate48 was calculated
by the algorithm (ln[V
2/V
1]/[t
2−t
1]) where V=volume and t=time in days. At the end of study, tumour cell lysis was
done by homogenization of equal weight tissue sections incubated in 3 × RIPA buffer
containing 2 × protease/phosphatase inhibitor cocktail (Thermo Fisher, Waltham, MA,
USA). CD44 western blotting was performed with a mouse-specific antibody (clone ABIN135065,
Antibodies Online, Atlanta GA, USA, 1:500 dilution) conjugated to Biotin (Thermo Fischer).
All in vivo experiments were performed in compliance with Institutional Animal Care
and Use Committee protocol approved by Harvard Medical School.
Phosphorylation arrays
The Proteome Profiler (R&D systems, Minneapolis MN, USA) was used to identify phosphorylated
residues correlating to SFK-associated proteins. Following the Bradford protein analysis
assay to normalize total protein content, cell lysate was applied to the phosphorylation
membranes following manufacturer’s protocol. Western blot of total protein (Akt and
Src) was used to confirm equal loading of lysate. Membranes were visualized by chemiluminescence
(Syngene, Cambridge, UK). Optical densities were determined by Image J software (NIH.gov)
and Adobe CS5. Reference spots were used to normalize between array membranes.
FACS analyses and cell cycle
Cells were cultured as indicated and fixed with 4% paraformaldehyde in PBS for 30 min
at room temperature (RT) and blocked in 10% goat serum (v/v), 0.05% saponin was used
to permeabilize cells when necessary. Following PBS washes, cells were incubated with
CD24-PE and CD44-APC (BD biosciences, San Jose, CA, USA) for 60 min at RT or overnight
at 4 °C and analyzed by FACS (Accuri cyomteters Inc. Ann Arbor, MI, USA). Single-stain
controls were used to set gating parameters and any compensations. AnnexinV/PI was
analyzed following manufacturer’s protocol (BD biosciences). All FACS results were
analyzed by FlowJo software following a rigorous doublet discrimination based on FSC:A
versus width as well as FSC:A versus height (Tree Star Inc., Ashland, OR, USA). Analyses
were also performed through Accuri cFlow plus software to obtain and confirm mean
fluorescent intensity (GNU.org). Cell cycle analysis was performed as follows: cells
were generated as described, following two washes with 1 × PBS, cells were trypsinized
and collected. Permeabilization was achieved by incubation in 70% ethanol overnight
at 4 °C. Cells were then washed two times with 1 × PBS and incubated with RNAase A
for 15 min at 37C followed by propidium iodide solution for 30 min at 4C (Genscript
USA Inc. Piscataway, NJ, USA). Cells were read at excitation/emission 594/535 by Flow
cytometry (BD Accuri C6, BD biosciences). All results were analyzed by FlowJo flow
cytometric analysis and cell cycle analysis software following a rigorous doublet
discrimination based on FSC:A versus width as well as FSC:A versus height. Cell sorting
was performed on live cells. In brief, cells were incubated with fluorescent antibody
for 20 min at RT in PBS. Following washes, cells were sorted by FACS (BD FACS Aria
IIU Special Order, BD biosciences). Schematic in Supplementary Fig. 4a shows example
of sorting ‘induced population of DTC’ and schematics in Fig. 5 show example of sorting
a chemotherapy-induced subset for in vivo analyses (defined as a population of cancer
cells, which harbour a CD44+/CD24+ phenotype not originally present in the parent
populations).
Immunohistochemistry
Tumour tissues were fixed in Phos stop (Roche, Basel, Switzerland) containing 4% buffered
formalin and embedded in paraffin. Before immunohistochemical staining of target proteins,
4-μm-thick tissue sections mounted in poly L-lysine-coated glass slides were deparaffinized
and rehydrated. Heat-induced antigen retrieval was achieved using citrate buffer (pH7.8).
The sections were soaked in Antigen Unmasking Solution (Vector Laboratories, Burlingame,
CA, USA) for 10 min followed by retrieval using a microwave for 25 min. Endogenous
hydrogen peroxidase was blocked by incubating the sections with 3% H2O2 (Merck) for
15 min and washed in running tap water for 3 min followed by a wash in 1 × TBS for
7 min. After initial blocking of the slides in 10% normal goat serum (Vector Laboratories)
for h at RT, tissue sections were incubated with primary antibodies for additional
1 h at RT. Following primary antibodies were used: anti human Ki-67 (rabbit polyclonal
from Vector Laboratory, 1:600 dilution), anti-human cleaved caspase3 (rabbit polyclonal,
clone D175, Cell Signaling Technology, Cambridge, MA, USA), Anti-human CD44 (Clone
IM7), P-HckY410 (Cell Signaling Technology). Secondary antibody (Signal Stain Boost
IHC Detection Reagent, horseradish peroxidase, Rabbit, Cell Signaling Technology)
was added to the sections and incubated for 45 min at RT and washed four times in
1 × PBS for 3 min each. Appropriate isotype-matched immunoglobulin G controls were
included for each secondary antibody. Chromogenic development was done by exposure
of tissues to 3,3′-diaminobenzidine substrate (DAB Peroxidase Substrate Kit; Vector
Laboratories). Images of immunostained sections were visualized by Leica DM4000 microscope
at × 200 or × 400 magnifications and images were acquired. Immunoreactivity was scored
by intensity of staining (0, no staining; 1, weak; 2, moderate; 3, strong) and percentage
of positive cells. By multiplying both values, a final score was calculated. Scoring
was performed in a blinded fashion by two experienced pathologists. IHC performed
from frozen sections were fixed with 10% formalin and permeabilized with 0.05% saponin
or fixed and permeabilized with ice-cold methanol. Frozen section IHC was visualized
by confocal microscopy as described below. Immunohistochemical images shown in figures
chosen as representative are derived as examples determined by an experienced pathologist
in each case to reflect overall alterations in tissue staining/architecture of the
respective experiments performed. Tumours from at least four individual mice per group
were used for IHC, and were evaluated from at least 25 individual fields per group
at shown magnifications. Quantification of data for selected IHC can be found in supplementary
information. For human explant IHC, representative images were obtained from 14 individual
patients
Confocal and immunofluorescence microscopy
Cells were generated as described above and plated in four chamber glass slides (BD
Biosciences) at a concentration of 100,000 cells ml−1. Following treatments, cells
were washed in PBS and fixed in 4% paraformaldehyde for 30 min. Permeabilization,
when necessary, was achieved with 10% (v/v) goat serum (Vector Laboratories) and 0.05%
Saponin (w/v) in PBS for 90 min. Blocking was performed in 10% (v/v) goat serum in
PBS. The cells were labelled with the indicated primary antibodies CD44 (Clone IM7
from eBioScience) conjugated to FITC (AnaSpec, Freemont, CA, USA) at 1:100, CD24 (BD
biosciences) conjugated to Fluor 594 (Anaspec), unconjugated antibodies were incubated
and followed by a secondary antibody conjugated with Alexa Fluor 488 or Alexa Fluor
594 (Invitrogen) at 1:250 and masked with DAPI-containing hard-set mounting medium
(Vector Laboratories). Bright field and fluorescent images were obtained using three
channels on a NIKON Eclipse TI-U microscope with a × 20 ELDW, × 10 or × 40 Plan-Apo
objective lens (Nikon, Melville, NY, USA). NIS Elements Viewer version 3.22 (Nikon)
software was used to capture the images to file. Confocal microscopy was performed
with an inverted Nikon Confocal microscope (TE2000) with Auto DeVlur deconvolution
software and fitted with three laser detection (Nikon). Gains were set manually based
on negative control stains (secondary antibody only) and were left unaltered between
treatment groups of similar experiments. Visualization of lipid rafts by confocal
microscopy was achieved using Vybrant Lipid raft staining kit (fluor594) obtained
from Life Technologies according to manufacturer’s protocol. TdT-mediated dUTP nick
end labelling staining was performed to visualize regions of apoptosis using the TUNEL
assay kit and performed as indicated by provider (Roche). When representative images
are shown in figures, these are derived from experiments performed in at least biological
triplicate on independent occasions. In general, images were obtained from >100 cells
per conditions and chosen to represent the overall alterations in each experimental
group.
Protein expression and interaction studies
Laemmli sample buffer was prepared as a 5 × solution containing β-mercaptoethanol
as a reducing agent. Immunoprecipitaion was performed using both classic and direct
IP kits purchased from Pierce following manufacturer’s protocols (Thermo Fisher inc.
Rockford, IL, USA). In brief, cell lysates were prepared using IP/Lysis buffer (Thermo
Fisher inc.) in the presence of 2 × HALT protease/phosphatase inhibitor cocktail (Thermo
Fisher inc.). For classic immunoprecipitation, lysates were combined with indicated
antibodies for 48 h at 4 °C and combined with protein A/G agarose beads for 4 h before
elution with 2 × Laemli buffer at 100C. Direct immunoprecipitation was performed following
manufacturer’s protocol. In brief, antibodies were covalently attached to agarose
beads, lysate was combined with antibody-agarose bead conjugates for 24 h before washes
and elution with provided Elution buffer. Protein samples were resolved by SDS–PAGE
and transferred to polyvinylidene difluoride membranes before incubation at 4 °C with
indicated primary antibodies; Hck, p-Tyr, Src pY527, PARP, Hck, cleaved caspase-3
and β-Actin were purchased from cell signaling technology. Cav-1 was purchased from
BD biosciences. Polyvinylidene difluoride membranes with primary antibody were incubated
at RT with horseradish peroxidase-conjugated secondary antibodies (BD Ann Arbor) and
resolved by chemiluminescence using the G-Box and Syngene software (Syngene). When
possible, blots were stripped (Thermo Fischer, Rockford IL) and re-probed with a second
primary antibody. Optical densities of western blots were measured using ImageJ open
source software (National Institutes of Health) and validated using Adobe CS5. Nuclear
and cytoplasmic isolation was performed using the subcellular fraction kit following
manufacturer’s protocol (Thermo Fisher inc.). Total PARP antibody (Cell signaling)
or β-Actin were used to control loading from nuclear and cytoplasmic compartments,
respectively. Western blotting images chosen as representative depictions in the figures
demonstrate equivalent results taken from biological replicates (N>3). Full blot images
in the main and supplemental figures, which were cropped for figure preparation, can
be found in a separate supplemental figure. Molecular weight ladders have been inserted
graphically as shown.
Mathematical modelling
To theoretically test the drug-induced phenotypic plasticity versus clonal selection,
we developed a phenotype switching model consisting of three cellular compartments,
describing the population dynamics of CSCs (S) (CD44HiCD24Lo), the induced (I) cells
(CD44HiCD24Hi), and non-stem (NS) cells (CD44LoCD24Hi and CD44LoCD24Lo). The model
consists of nine parameters, three of which describe the net proliferation rates for
each of the compartments, and the remaining six parameters describe the transition
rates between the compartments (that is, the rates of cells switching from one cellular
subtype to another). We denote the number of CSCs, non-stem cells and induced CD44HiCD24Hi
cells at the time t by S(t), N(t), I(t), respectively. We take ρ
k
to be the (net) reproductive rate of cell compartment k. We let ρ
ij
be the rate of transfer of cells from compartment i to compartment j.
By using FACS data (described above), the experimental data obtained were summarized
into time-dependent curves describing the proportion of each cellular subpopulation
over time. These data were then combined with the model, for both the parental cell
experiments as well as the ‘mimic’ experiments (independently). The model parameters
were then fitted to the experimental data using the PotterWheel toolbox for Matlab,
which uses numerical methods to fit parameter sets, describing data most accurately.
It was noted that the final day experimental values for the parental cells (at FACS
day 3), and for the CD44HiCD24Hi (at FACS day 1) produced values representative of
the steady state cellular proportions for each of the systems, so while fitting for
parental cells gave a best fit value with such a steady state immediately, fitting
with the CD44HICD24HI values did not. Therefore, when fitting to the experimental
data, the steady state was restricted to values near the day 1 values, by adding a
data point to be fitted with the same values as reported on day 1, but at large time.
The results obtained using this additional data point gives the reported values for
the CD44HICD24HI parameters, providing a realistic experimental steady state. The
bounds set on each of the parameter values, when fitting, were restricted between
0 and 0.5 for the net proliferation rates, because experimentally it was observed
that there was a great deal of cell death, thereby necessitating reduced net proliferation
rates. The bounds for the transition rates were restricted to be between 0 and 1,
because these rates represent the proportion of cells undergoing a transition at any
given time, it was felt that there was no experimental observation, supporting the
further restriction of these parameters.
Statistics
Statistical analysis was carried out with Prism software (Graphpad, LaJolla, CA, USA).
Experimental data is expressed as mean±s.e.m., and analyzed using analysis of variance
followed by Bonferroni post test or Student’s t-test.
Author contributions
A.G. and S.S. designed the study, analyzed the data and wrote the manuscript. B.M.
and P.K.M. conducted human explant experiments. A.G. and S.R. performed in vitro and
in vivo studies. D.G. performed pathology analysis. A.D. and M.K. developed the mathematical
model.
Additional information
How to cite this article: Goldman, A. et al. Temporally sequenced anticancer drugs
overcome adaptive resistance by targeting a vulnerable chemotherapy-induced phenotypic
transition. Nat. Commun. 6:6139 doi: 10.1038/ncomms7139 (2015).
Supplementary Material
Supplementary Information
Supplementary Figures 1-7 and Supplementary Tables 1-2,